Path->str in call to expand_prompts

- Previous PR to truncate long filenames won't work on windows due to
lack of support for os.pathconf(). This works around the limitation by
hardcoding the value for PC_NAME_MAX when pathconf is unavailable.
- The `multiprocessing` send() and recv() methods weren't working
properly on Windows due to issues involving `utf-8` encoding and
pickling/unpickling. Changed these calls to `send_bytes()` and
`recv_bytes()` , which seems to fix the issue.

Not fully tested on Windows since I lack a GPU machine to test on, but
is working on CPU.

.coveragerc

@@ -0,0 +1,6 @@
+[run]
+omit='.env/*'
+source='.'
+
+[report]
+show_missing = true

.gitignore

@@ -68,6 +68,7 @@ htmlcov/
 .cache
 nosetests.xml
 coverage.xml
+cov.xml
 *.cover
 *.py,cover
 .hypothesis/

.pytest.ini

@@ -0,0 +1,5 @@
+[pytest]
+DJANGO_SETTINGS_MODULE = webtas.settings
+; python_files = tests.py test_*.py *_tests.py
+
+addopts = --cov=. --cov-config=.coveragerc --cov-report xml:cov.xml

docs/contributing/ARCHITECTURE.md

@@ -0,0 +1,93 @@
+# Invoke.AI Architecture
+
+```mermaid
+flowchart TB
+
+  subgraph apps[Applications]
+    webui[WebUI]
+    cli[CLI]
+
+  subgraph webapi[Web API]
+    api[HTTP API]
+    sio[Socket.IO]
+  end
+
+  end
+
+  subgraph invoke[Invoke]
+    direction LR
+    invoker
+    services
+    sessions
+    invocations
+  end
+
+  subgraph core[AI Core]
+    Generate
+  end
+
+  webui --> webapi
+  webapi --> invoke
+  cli --> invoke
+
+  invoker --> services & sessions
+  invocations --> services
+  sessions --> invocations
+
+  services --> core
+
+  %% Styles
+  classDef sg fill:#5028C8,font-weight:bold,stroke-width:2,color:#fff,stroke:#14141A
+  classDef default stroke-width:2px,stroke:#F6B314,color:#fff,fill:#14141A
+
+  class apps,webapi,invoke,core sg
+
+```
+
+## Applications
+
+Applications are built on top of the invoke framework. They should construct `invoker` and then interact through it. They should avoid interacting directly with core code in order to support a variety of configurations.
+
+### Web UI
+
+The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/frontend` and the backend code is found in `/ldm/invoke/app/api_app.py` and `/ldm/invoke/app/api/`. The code is further organized as such:
+
+| Component | Description |
+| --- | --- |
+| api_app.py | Sets up the API app, annotates the OpenAPI spec with additional data, and runs the API |
+| dependencies | Creates all invoker services and the invoker, and provides them to the API |
+| events | An eventing system that could in the future be adapted to support horizontal scale-out |
+| sockets | The Socket.IO interface - handles listening to and emitting session events (events are defined in the events service module) |
+| routers | API definitions for different areas of API functionality |
+
+### CLI
+
+The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/ldm/invoke/app/cli_app.py`.
+
+## Invoke
+
+The Invoke framework provides the interface to the underlying AI systems and is built with flexibility and extensibility in mind. There are four major concepts: invoker, sessions, invocations, and services.
+
+### Invoker
+
+The invoker (`/ldm/invoke/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
+- **invocation services**, which are used by invocations to interact with core functionality.
+- **invoker services**, which are used by the invoker to manage sessions and manage the invocation queue.
+
+### Sessions
+
+Invocations and links between them form a graph, which is maintained in a session. Sessions can be queued for invocation, which will execute their graph (either the next ready invocation, or all invocations). Sessions also maintain execution history for the graph (including storage of any outputs). An invocation may be added to a session at any time, and there is capability to add and entire graph at once, as well as to automatically link new invocations to previous invocations. Invocations can not be deleted or modified once added.
+
+The session graph does not support looping. This is left as an application problem to prevent additional complexity in the graph.
+
+### Invocations
+
+Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/ldm/invoke/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
+
+### Services
+
+Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/ldm/invoke/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
+
+## AI Core
+
+The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/app/`).

docs/contributing/INVOCATIONS.md

@@ -0,0 +1,105 @@
+# Invocations
+
+Invocations represent a single operation, its inputs, and its outputs. These operations and their outputs can be chained together to generate and modify images.
+
+## Creating a new invocation
+
+To create a new invocation, either find the appropriate module file in `/ldm/invoke/app/invocations` to add your invocation to, or create a new one in that folder. All invocations in that folder will be discovered and made available to the CLI and API automatically. Invocations make use of [typing](https://docs.python.org/3/library/typing.html) and [pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration into the CLI and API.
+
+An invocation looks like this:
+
+```py
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    type: Literal['upscale'] = 'upscale'
+
+    # Inputs
+    image: Union[ImageField,None] = Field(description="The input image")
+    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2,4]           = Field(default=2, description = "The upscale level")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        results = context.services.generate.upscale_and_reconstruct(
+            image_list     = [[image, 0]],
+            upscale        = (self.level, self.strength),
+            strength       = 0.0, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
+        )
+
+        # Results are image and seed, unwrap for now
+        # TODO: can this return multiple results?
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+```
+
+Each portion is important to implement correctly.
+
+### Class definition and type
+```py
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    type: Literal['upscale'] = 'upscale'
+```
+All invocations must derive from `BaseInvocation`. They should have a docstring that declares what they do in a single, short line. They should also have a `type` with a type hint that's `Literal["command_name"]`, where `command_name` is what the user will type on the CLI or use in the API to create this invocation. The `command_name` must be unique. The `type` must be assigned to the value of the literal in the type hint.
+
+### Inputs
+```py
+    # Inputs
+    image: Union[ImageField,None] = Field(description="The input image")
+    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2,4]           = Field(default=2, description="The upscale level")
+```
+Inputs consist of three parts: a name, a type hint, and a `Field` with default, description, and validation information. For example:
+| Part | Value | Description |
+| ---- | ----- | ----------- |
+| Name | `strength` | This field is referred to as `strength` |
+| Type Hint | `float` | This field must be of type `float` |
+| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
+
+Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this field to be parsed with `None` as a value, which enables linking to previous invocations. All fields should either provide a default value or allow `None` as a value, so that they can be overwritten with a linked output from another invocation.
+
+The special type `ImageField` is also used here. All images are passed as `ImageField`, which protects them from pydantic validation errors (since images only ever come from links).
+
+Finally, note that for all linking, the `type` of the linked fields must match. If the `name` also matches, then the field can be **automatically linked** to a previous invocation by name and matching.
+
+### Invoke Function
+```py
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        results = context.services.generate.upscale_and_reconstruct(
+            image_list     = [[image, 0]],
+            upscale        = (self.level, self.strength),
+            strength       = 0.0, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
+        )
+
+        # Results are image and seed, unwrap for now
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+```
+The `invoke` function is the last portion of an invocation. It is provided an `InvocationContext` which contains services to perform work as well as a `session_id` for use as needed. It should return a class with output values that derives from `BaseInvocationOutput`.
+
+Before being called, the invocation will have all of its fields set from defaults, inputs, and finally links (overriding in that order).
+
+Assume that this invocation may be running simultaneously with other invocations, may be running on another machine, or in other interesting scenarios. If you need functionality, please provide it as a service in the `InvocationServices` class, and make sure it can be overridden.
+
+### Outputs
+```py
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+    type: Literal['image'] = 'image'
+
+    image: ImageField = Field(default=None, description="The output image")
+```
+Output classes look like an invocation class without the invoke method. Prefer to use an existing output class if available, and prefer to name inputs the same as outputs when possible, to promote automatic invocation linking.

docs/features/CONCEPTS.md

@@ -25,10 +25,14 @@ library which downloads and merges TI files automatically upon request. You can
 also install your own or others' TI files by placing them in a designated
 directory.
 
+You may also be interested in using [LoRA Models](LORAS.md) to
+generate images with specialized styles and subjects.
+
 ### An Example
 
-Here are a few examples to illustrate how it works. All these images were
-generated using the command-line client and the Stable Diffusion 1.5 model:
+Here are a few examples to illustrate how Textual Inversion works. All
+these images were generated using the command-line client and the
+Stable Diffusion 1.5 model:
 
 |         Japanese gardener          | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
 | :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
@@ -147,6 +151,13 @@ angle brackets. In the example above `<easynegative`> is such a file
 (the filename was `easynegative.safetensors`). In such cases, you can
 change the trigger term simply by renaming the file.
 
+## Training your own Textual Inversion models
+
+InvokeAI provides a script that lets you train your own Textual
+Inversion embeddings using a small number (about a half-dozen) images
+of your desired style or subject. Please see [Textual
+Inversion](TEXTUAL_INVERSION.md) for details.
+
 ## Further Reading
 
 Please see [the repository](https://github.com/rinongal/textual_inversion) and

docs/features/LORAS.md

@@ -0,0 +1,100 @@
+---
+title: Low-Rank Adaptation (LoRA) Models
+---
+
+# :material-library-shelves: Using Low-Rank Adaptation (LoRA) Models
+
+## Introduction
+
+LoRA is a technique for fine-tuning Stable Diffusion models using much
+less time and memory than traditional training techniques. The
+resulting model files are much smaller than full model files, and can
+be used to generate specialized styles and subjects.
+
+LoRAs are built on top of Stable Diffusion v1.x or 2.x checkpoint or
+diffusers models. To load a LoRA, you include its name in the text
+prompt using a simple syntax described below. While you will generally
+get the best results when you use the same model the LoRA was trained
+on, they will work to a greater or lesser extent with other models.
+The major caveat is that a LoRA built on top of a SD v1.x model cannot
+be used with a v2.x model, and vice-versa. If you try, you will get an
+error! You may refer to multiple LoRAs in your prompt.
+
+When you apply a LoRA in a prompt you can specify a weight. The higher
+the weight, the more influence it will have on the image. Useful
+ranges for weights are usually in the 0.0 to 1.0 range (with ranges
+between 0.5 and 1.0 being most typical). However you can specify a
+higher weight if you wish. Like models, each LoRA has a slightly
+different useful weight range and will interact with other generation
+parameters such as the CFG, step count and sampler. The author of the
+LoRA will often provide guidance on the best settings, but feel free
+to experiment. Be aware that it often helps to reduce the CFG value
+when using LoRAs.
+
+## Installing LoRAs
+
+This is very easy! Download a LoRA model file from your favorite site
+(e.g. [CIVITAI](https://civitai.com) and place it in the `loras`
+folder in the InvokeAI root directory (usually `~invokeai/loras` on
+Linux/Macintosh machines, and `C:\Users\your-name\invokeai/loras` on
+Windows systems). If the `loras` folder does not already exist, just
+create it. The vast majority of LoRA models use the Kohya file format,
+which is a type of `.safetensors` file.
+
+You may change where InvokeAI looks for the `loras` folder by passing the
+`--lora_directory` option to the `invoke.sh`/`invoke.bat` launcher, or
+by placing the option in `invokeai.init`. For example:
+
+```
+invoke.sh --lora_directory=C:\Users\your-name\SDModels\lora
+```
+
+## Using a LoRA in your prompt
+
+To activate a LoRA use the syntax `withLora(my-lora-name,weight)`
+somewhere in the text of the prompt. The position doesn't matter; use
+whatever is most comfortable for you.
+
+For example, if you have a LoRA named `parchment_people.safetensors`
+in your `loras` directory, you can load it with a weight of 0.9 with a
+prompt like this one:
+
+```
+family sitting at dinner table withLora(parchment_people,0.9)
+```
+
+Add additional `withLora()` phrases to load more LoRAs.
+
+You may omit the weight entirely to default to a weight of 1.0:
+
+```
+family sitting at dinner table withLora(parchment_people)
+```
+
+If you watch the console as your prompt executes, you will see
+messages relating to the loading and execution of the LoRA. If things
+don't work as expected, note down the console messages and report them
+on the InvokeAI Issues pages or Discord channel.
+
+That's pretty much all you need to know!
+
+## Training Kohya Models
+
+InvokeAI cannot currently train LoRA models, but it can load and use
+existing LoRA ones to generate images. While there are several LoRA
+model file formats, the predominant one is ["Kohya"
+format](https://github.com/kohya-ss/sd-scripts), written by [Kohya
+S.](https://github.com/kohya-ss). InvokeAI provides support for this
+format. For creating your own Kohya models, we recommend the Windows
+GUI written by former InvokeAI-team member
+[bmaltais](https://github.com/bmaltais), which can be found at
+[kohya_ss](https://github.com/bmaltais/kohya_ss).
+
+We can also recommend the [HuggingFace DreamBooth Training
+UI](https://huggingface.co/spaces/lora-library/LoRA-DreamBooth-Training-UI),
+a paid service that supports both Textual Inversion and LoRA training.
+
+You may also be interested in [Textual
+Inversion](TEXTUAL_INVERSION.md) training, which is supported by
+InvokeAI as a text console and command-line tool.
+

docs/index.md

@@ -159,6 +159,7 @@ This method is recommended for those familiar with running Docker containers
 -   [Inpainting](features/INPAINTING.md)
 -   [Outpainting](features/OUTPAINTING.md)
 -   [Adding custom styles and subjects](features/CONCEPTS.md)
+-   [Using LoRA models](features/LORAS.md)
 -   [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
 -   [Embiggen upscaling](features/EMBIGGEN.md)
 -   [Other Features](features/OTHER.md)

installer/lib/installer.py

@@ -144,8 +144,8 @@ class Installer:
 
             from plumbum import FG, local
 
-            pip = local[get_pip_from_venv(venv_dir)]
-            pip[ "install", "--upgrade", "pip"] & FG
+            python = local[get_python_from_venv(venv_dir)]
+            python[ "-m", "pip", "install", "--upgrade", "pip"] & FG
 
         return venv_dir
 
@@ -412,6 +412,22 @@ def get_pip_from_venv(venv_path: Path) -> str:
     return str(venv_path.expanduser().resolve() / pip)
 
 
+def get_python_from_venv(venv_path: Path) -> str:
+    """
+    Given a path to a virtual environment, get the absolute path to the `python` executable
+    in a cross-platform fashion. Does not validate that the python executable
+    actually exists in the virtualenv.
+
+    :param venv_path: Path to the virtual environment
+    :type venv_path: Path
+    :return: Absolute path to the python executable
+    :rtype: str
+    """
+
+    python = "Scripts\python.exe" if OS == "Windows" else "bin/python"
+    return str(venv_path.expanduser().resolve() / python)
+
+
 def set_sys_path(venv_path: Path) -> None:
     """
     Given a path to a virtual environment, set the sys.path, in a cross-platform fashion,

invokeai/backend/invoke_ai_web_server.py

@@ -25,14 +25,23 @@ from invokeai.backend.modules.parameters import parameters_to_command
 import invokeai.frontend.dist as frontend
 from ldm.generate import Generate
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
-from ldm.invoke.conditioning import get_tokens_for_prompt_object, get_prompt_structure, split_weighted_subprompts, \
-    get_tokenizer
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+from ldm.invoke.conditioning import (
+    get_tokens_for_prompt_object,
+    get_prompt_structure,
+    split_weighted_subprompts,
+    get_tokenizer,
+)
 from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
 from ldm.invoke.generator.inpaint import infill_methods
-from ldm.invoke.globals import Globals, global_converted_ckpts_dir
+from ldm.invoke.globals import (
+    Globals,
+    global_converted_ckpts_dir,
+    global_models_dir,
+    global_lora_models_dir,
+)
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
 from compel.prompt_parser import Blend
-from ldm.invoke.globals import global_models_dir
 from ldm.invoke.merge_diffusers import merge_diffusion_models
 
 # Loading Arguments
@@ -193,8 +202,7 @@ class InvokeAIWebServer:
                 (width, height) = pil_image.size
 
                 thumbnail_path = save_thumbnail(
-                    pil_image, os.path.basename(
-                        file_path), self.thumbnail_image_path
+                    pil_image, os.path.basename(file_path), self.thumbnail_image_path
                 )
 
                 response = {
@@ -224,7 +232,7 @@ class InvokeAIWebServer:
                     server="flask_socketio",
                     width=1600,
                     height=1000,
-                    port=self.port
+                    port=self.port,
                 ).run()
             except KeyboardInterrupt:
                 import sys
@@ -265,16 +273,14 @@ class InvokeAIWebServer:
         # location for "finished" images
         self.result_path = args.outdir
         # temporary path for intermediates
-        self.intermediate_path = os.path.join(
-            self.result_path, "intermediates/")
+        self.intermediate_path = os.path.join(self.result_path, "intermediates/")
         # path for user-uploaded init images and masks
         self.init_image_path = os.path.join(self.result_path, "init-images/")
         self.mask_image_path = os.path.join(self.result_path, "mask-images/")
         # path for temp images e.g. gallery generations which are not committed
         self.temp_image_path = os.path.join(self.result_path, "temp-images/")
         # path for thumbnail images
-        self.thumbnail_image_path = os.path.join(
-            self.result_path, "thumbnails/")
+        self.thumbnail_image_path = os.path.join(self.result_path, "thumbnails/")
         # txt log
         self.log_path = os.path.join(self.result_path, "invoke_log.txt")
         # make all output paths
@@ -299,21 +305,22 @@ class InvokeAIWebServer:
             config["infill_methods"] = infill_methods()
             socketio.emit("systemConfig", config)
 
-        @socketio.on('searchForModels')
+        @socketio.on("searchForModels")
         def handle_search_models(search_folder: str):
             try:
                 if not search_folder:
                     socketio.emit(
                         "foundModels",
-                        {'search_folder': None, 'found_models': None},
+                        {"search_folder": None, "found_models": None},
                     )
                 else:
-                    search_folder, found_models = self.generate.model_manager.search_models(
-                        search_folder)
+                    (
+                        search_folder,
+                        found_models,
+                    ) = self.generate.model_manager.search_models(search_folder)
                     socketio.emit(
                         "foundModels",
-                        {'search_folder': search_folder,
-                            'found_models': found_models},
+                        {"search_folder": search_folder, "found_models": found_models},
                     )
             except Exception as e:
                 self.handle_exceptions(e)
@@ -322,11 +329,11 @@ class InvokeAIWebServer:
         @socketio.on("addNewModel")
         def handle_add_model(new_model_config: dict):
             try:
-                model_name = new_model_config['name']
-                del new_model_config['name']
+                model_name = new_model_config["name"]
+                del new_model_config["name"]
                 model_attributes = new_model_config
-                if len(model_attributes['vae']) == 0:
-                    del model_attributes['vae']
+                if len(model_attributes["vae"]) == 0:
+                    del model_attributes["vae"]
                 update = False
                 current_model_list = self.generate.model_manager.list_models()
                 if model_name in current_model_list:
@@ -335,14 +342,20 @@ class InvokeAIWebServer:
                 print(f">> Adding New Model: {model_name}")
 
                 self.generate.model_manager.add_model(
-                    model_name=model_name, model_attributes=model_attributes, clobber=True)
+                    model_name=model_name,
+                    model_attributes=model_attributes,
+                    clobber=True,
+                )
                 self.generate.model_manager.commit(opt.conf)
 
                 new_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "newModelAdded",
-                    {"new_model_name": model_name,
-                     "model_list": new_model_list, 'update': update},
+                    {
+                        "new_model_name": model_name,
+                        "model_list": new_model_list,
+                        "update": update,
+                    },
                 )
                 print(f">> New Model Added: {model_name}")
             except Exception as e:
@@ -357,8 +370,10 @@ class InvokeAIWebServer:
                 updated_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "modelDeleted",
-                    {"deleted_model_name": model_name,
-                     "model_list": updated_model_list},
+                    {
+                        "deleted_model_name": model_name,
+                        "model_list": updated_model_list,
+                    },
                 )
                 print(f">> Model Deleted: {model_name}")
             except Exception as e:
@@ -383,41 +398,48 @@ class InvokeAIWebServer:
             except Exception as e:
                 self.handle_exceptions(e)
 
-        @socketio.on('convertToDiffusers')
+        @socketio.on("convertToDiffusers")
         def convert_to_diffusers(model_to_convert: dict):
             try:
-                if (model_info := self.generate.model_manager.model_info(model_name=model_to_convert['model_name'])):
-                    if 'weights' in model_info:
-                        ckpt_path = Path(model_info['weights'])
-                        original_config_file = Path(model_info['config'])
-                        model_name = model_to_convert['model_name']
-                        model_description = model_info['description']
+                if model_info := self.generate.model_manager.model_info(
+                    model_name=model_to_convert["model_name"]
+                ):
+                    if "weights" in model_info:
+                        ckpt_path = Path(model_info["weights"])
+                        original_config_file = Path(model_info["config"])
+                        model_name = model_to_convert["model_name"]
+                        model_description = model_info["description"]
                     else:
                         self.socketio.emit(
-                            "error", {"message": "Model is not a valid checkpoint file"})
+                            "error", {"message": "Model is not a valid checkpoint file"}
+                        )
                 else:
                     self.socketio.emit(
-                        "error", {"message": "Could not retrieve model info."})
+                        "error", {"message": "Could not retrieve model info."}
+                    )
 
                 if not ckpt_path.is_absolute():
                     ckpt_path = Path(Globals.root, ckpt_path)
 
                 if original_config_file and not original_config_file.is_absolute():
-                    original_config_file = Path(
-                        Globals.root, original_config_file)
+                    original_config_file = Path(Globals.root, original_config_file)
 
                 diffusers_path = Path(
-                    ckpt_path.parent.absolute(),
-                    f'{model_name}_diffusers'
+                    ckpt_path.parent.absolute(), f"{model_name}_diffusers"
                 )
 
-                if model_to_convert['save_location'] == 'root':
+                if model_to_convert["save_location"] == "root":
                     diffusers_path = Path(
-                        global_converted_ckpts_dir(), f'{model_name}_diffusers')
+                        global_converted_ckpts_dir(), f"{model_name}_diffusers"
+                    )
 
-                if model_to_convert['save_location'] == 'custom' and model_to_convert['custom_location'] is not None:
+                if (
+                    model_to_convert["save_location"] == "custom"
+                    and model_to_convert["custom_location"] is not None
+                ):
                     diffusers_path = Path(
-                        model_to_convert['custom_location'], f'{model_name}_diffusers')
+                        model_to_convert["custom_location"], f"{model_name}_diffusers"
+                    )
 
                 if diffusers_path.exists():
                     shutil.rmtree(diffusers_path)
@@ -435,54 +457,99 @@ class InvokeAIWebServer:
                 new_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "modelConverted",
-                    {"new_model_name": model_name,
-                     "model_list": new_model_list, 'update': True},
+                    {
+                        "new_model_name": model_name,
+                        "model_list": new_model_list,
+                        "update": True,
+                    },
                 )
                 print(f">> Model Converted: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
-        @socketio.on('mergeDiffusersModels')
+        @socketio.on("mergeDiffusersModels")
         def merge_diffusers_models(model_merge_info: dict):
             try:
-                models_to_merge = model_merge_info['models_to_merge']
+                models_to_merge = model_merge_info["models_to_merge"]
                 model_ids_or_paths = [
-                    self.generate.model_manager.model_name_or_path(x) for x in models_to_merge]
+                    self.generate.model_manager.model_name_or_path(x)
+                    for x in models_to_merge
+                ]
                 merged_pipe = merge_diffusion_models(
-                    model_ids_or_paths, model_merge_info['alpha'], model_merge_info['interp'], model_merge_info['force'])
+                    model_ids_or_paths,
+                    model_merge_info["alpha"],
+                    model_merge_info["interp"],
+                    model_merge_info["force"],
+                )
 
-                dump_path = global_models_dir() / 'merged_models'
-                if model_merge_info['model_merge_save_path'] is not None:
-                    dump_path = Path(model_merge_info['model_merge_save_path'])
+                dump_path = global_models_dir() / "merged_models"
+                if model_merge_info["model_merge_save_path"] is not None:
+                    dump_path = Path(model_merge_info["model_merge_save_path"])
 
                 os.makedirs(dump_path, exist_ok=True)
-                dump_path = dump_path / model_merge_info['merged_model_name']
+                dump_path = dump_path / model_merge_info["merged_model_name"]
                 merged_pipe.save_pretrained(dump_path, safe_serialization=1)
 
                 merged_model_config = dict(
-                    model_name=model_merge_info['merged_model_name'],
+                    model_name=model_merge_info["merged_model_name"],
                     description=f'Merge of models {", ".join(models_to_merge)}',
-                    commit_to_conf=opt.conf
+                    commit_to_conf=opt.conf,
                 )
 
-                if vae := self.generate.model_manager.config[models_to_merge[0]].get("vae", None):
-                    print(
-                        f">> Using configured VAE assigned to {models_to_merge[0]}")
+                if vae := self.generate.model_manager.config[models_to_merge[0]].get(
+                    "vae", None
+                ):
+                    print(f">> Using configured VAE assigned to {models_to_merge[0]}")
                     merged_model_config.update(vae=vae)
 
                 self.generate.model_manager.import_diffuser_model(
-                    dump_path, **merged_model_config)
+                    dump_path, **merged_model_config
+                )
                 new_model_list = self.generate.model_manager.list_models()
 
                 socketio.emit(
                     "modelsMerged",
-                    {"merged_models": models_to_merge,
-                     "merged_model_name": model_merge_info['merged_model_name'],
-                     "model_list": new_model_list, 'update': True},
+                    {
+                        "merged_models": models_to_merge,
+                        "merged_model_name": model_merge_info["merged_model_name"],
+                        "model_list": new_model_list,
+                        "update": True,
+                    },
                 )
                 print(f">> Models Merged: {models_to_merge}")
-                print(
-                    f">> New Model Added: {model_merge_info['merged_model_name']}")
+                print(f">> New Model Added: {model_merge_info['merged_model_name']}")
+            except Exception as e:
+                self.handle_exceptions(e)
+
+        @socketio.on("getLoraModels")
+        def get_lora_models():
+            try:
+                lora_path = global_lora_models_dir()
+                loras = []
+                for root, _, files in os.walk(lora_path):
+                    models = [
+                        Path(root, x)
+                        for x in files
+                        if Path(x).suffix in [".ckpt", ".pt", ".safetensors"]
+                    ]
+                    loras = loras + models
+
+                found_loras = []
+                for lora in sorted(loras, key=lambda s: s.stem.lower()):
+                    location = str(lora.resolve()).replace("\\", "/")
+                    found_loras.append({"name": lora.stem, "location": location})
+                socketio.emit("foundLoras", found_loras)
+            except Exception as e:
+                self.handle_exceptions(e)
+
+        @socketio.on("getTextualInversionTriggers")
+        def get_ti_triggers():
+            try:
+                local_triggers = self.generate.model.textual_inversion_manager.get_all_trigger_strings()
+                locals = [{'name': x} for x in sorted(local_triggers, key=str.casefold)]
+                concepts = HuggingFaceConceptsLibrary().list_concepts(minimum_likes=5)
+                concepts = [{'name': f'<{x}>'} for x in sorted(concepts, key=str.casefold) if f'<{x}>' not in local_triggers]
+                socketio.emit("foundTextualInversionTriggers", {'local_triggers': locals, 'huggingface_concepts': concepts})
             except Exception as e:
                 self.handle_exceptions(e)
 
@@ -500,7 +567,8 @@ class InvokeAIWebServer:
                         os.remove(thumbnail_path)
                     except Exception as e:
                         socketio.emit(
-                            "error", {"message": f"Unable to delete {f}: {str(e)}"})
+                            "error", {"message": f"Unable to delete {f}: {str(e)}"}
+                        )
                         pass
 
                 socketio.emit("tempFolderEmptied")
@@ -511,8 +579,7 @@ class InvokeAIWebServer:
         def save_temp_image_to_gallery(url):
             try:
                 image_path = self.get_image_path_from_url(url)
-                new_path = os.path.join(
-                    self.result_path, os.path.basename(image_path))
+                new_path = os.path.join(self.result_path, os.path.basename(image_path))
                 shutil.copy2(image_path, new_path)
 
                 if os.path.splitext(new_path)[1] == ".png":
@@ -525,8 +592,7 @@ class InvokeAIWebServer:
                 (width, height) = pil_image.size
 
                 thumbnail_path = save_thumbnail(
-                    pil_image, os.path.basename(
-                        new_path), self.thumbnail_image_path
+                    pil_image, os.path.basename(new_path), self.thumbnail_image_path
                 )
 
                 image_array = [
@@ -585,8 +651,7 @@ class InvokeAIWebServer:
                         (width, height) = pil_image.size
 
                         thumbnail_path = save_thumbnail(
-                            pil_image, os.path.basename(
-                                path), self.thumbnail_image_path
+                            pil_image, os.path.basename(path), self.thumbnail_image_path
                         )
 
                         image_array.append(
@@ -605,7 +670,8 @@ class InvokeAIWebServer:
                         )
                     except Exception as e:
                         socketio.emit(
-                            "error", {"message": f"Unable to load {path}: {str(e)}"})
+                            "error", {"message": f"Unable to load {path}: {str(e)}"}
+                        )
                         pass
 
                 socketio.emit(
@@ -655,8 +721,7 @@ class InvokeAIWebServer:
                         (width, height) = pil_image.size
 
                         thumbnail_path = save_thumbnail(
-                            pil_image, os.path.basename(
-                                path), self.thumbnail_image_path
+                            pil_image, os.path.basename(path), self.thumbnail_image_path
                         )
 
                         image_array.append(
@@ -676,7 +741,8 @@ class InvokeAIWebServer:
                     except Exception as e:
                         print(f">> Unable to load {path}")
                         socketio.emit(
-                            "error", {"message": f"Unable to load {path}: {str(e)}"})
+                            "error", {"message": f"Unable to load {path}: {str(e)}"}
+                        )
                         pass
 
                 socketio.emit(
@@ -710,10 +776,9 @@ class InvokeAIWebServer:
                         printable_parameters["init_mask"][:64] + "..."
                     )
 
-                print(
-                    f'\n>> Image Generation Parameters:\n\n{printable_parameters}\n')
-                print(f'>> ESRGAN Parameters: {esrgan_parameters}')
-                print(f'>> Facetool Parameters: {facetool_parameters}')
+                print(f"\n>> Image Generation Parameters:\n\n{printable_parameters}\n")
+                print(f">> ESRGAN Parameters: {esrgan_parameters}")
+                print(f">> Facetool Parameters: {facetool_parameters}")
 
                 self.generate_images(
                     generation_parameters,
@@ -750,11 +815,9 @@ class InvokeAIWebServer:
                 if postprocessing_parameters["type"] == "esrgan":
                     progress.set_current_status("common.statusUpscalingESRGAN")
                 elif postprocessing_parameters["type"] == "gfpgan":
-                    progress.set_current_status(
-                        "common.statusRestoringFacesGFPGAN")
+                    progress.set_current_status("common.statusRestoringFacesGFPGAN")
                 elif postprocessing_parameters["type"] == "codeformer":
-                    progress.set_current_status(
-                        "common.statusRestoringFacesCodeFormer")
+                    progress.set_current_status("common.statusRestoringFacesCodeFormer")
 
                 socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
@@ -919,8 +982,7 @@ class InvokeAIWebServer:
 
                 init_img_url = generation_parameters["init_img"]
 
-                original_bounding_box = generation_parameters["bounding_box"].copy(
-                )
+                original_bounding_box = generation_parameters["bounding_box"].copy()
 
                 initial_image = dataURL_to_image(
                     generation_parameters["init_img"]
@@ -997,8 +1059,9 @@ class InvokeAIWebServer:
             elif generation_parameters["generation_mode"] == "img2img":
                 init_img_url = generation_parameters["init_img"]
                 init_img_path = self.get_image_path_from_url(init_img_url)
-                generation_parameters["init_img"] = Image.open(
-                    init_img_path).convert('RGB')
+                generation_parameters["init_img"] = Image.open(init_img_path).convert(
+                    "RGB"
+                )
 
             def image_progress(sample, step):
                 if self.canceled.is_set():
@@ -1058,8 +1121,7 @@ class InvokeAIWebServer:
                     )
 
                 if generation_parameters["progress_latents"]:
-                    image = self.generate.sample_to_lowres_estimated_image(
-                        sample)
+                    image = self.generate.sample_to_lowres_estimated_image(sample)
                     (width, height) = image.size
                     width *= 8
                     height *= 8
@@ -1078,8 +1140,7 @@ class InvokeAIWebServer:
                         },
                     )
 
-                self.socketio.emit(
-                    "progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
             def image_done(image, seed, first_seed, attention_maps_image=None):
@@ -1106,8 +1167,7 @@ class InvokeAIWebServer:
 
                 progress.set_current_status("common.statusGenerationComplete")
 
-                self.socketio.emit(
-                    "progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
                 all_parameters = generation_parameters
@@ -1118,8 +1178,7 @@ class InvokeAIWebServer:
                     and all_parameters["variation_amount"] > 0
                 ):
                     first_seed = first_seed or seed
-                    this_variation = [
-                        [seed, all_parameters["variation_amount"]]]
+                    this_variation = [[seed, all_parameters["variation_amount"]]]
                     all_parameters["with_variations"] = (
                         prior_variations + this_variation
                     )
@@ -1135,14 +1194,13 @@ class InvokeAIWebServer:
                 if esrgan_parameters:
                     progress.set_current_status("common.statusUpscaling")
                     progress.set_current_status_has_steps(False)
-                    self.socketio.emit(
-                        "progressUpdate", progress.to_formatted_dict())
+                    self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                     eventlet.sleep(0)
 
                     image = self.esrgan.process(
                         image=image,
                         upsampler_scale=esrgan_parameters["level"],
-                        denoise_str=esrgan_parameters['denoise_str'],
+                        denoise_str=esrgan_parameters["denoise_str"],
                         strength=esrgan_parameters["strength"],
                         seed=seed,
                     )
@@ -1150,7 +1208,7 @@ class InvokeAIWebServer:
                     postprocessing = True
                     all_parameters["upscale"] = [
                         esrgan_parameters["level"],
-                        esrgan_parameters['denoise_str'],
+                        esrgan_parameters["denoise_str"],
                         esrgan_parameters["strength"],
                     ]
 
@@ -1159,15 +1217,14 @@ class InvokeAIWebServer:
 
                 if facetool_parameters:
                     if facetool_parameters["type"] == "gfpgan":
-                        progress.set_current_status(
-                            "common.statusRestoringFacesGFPGAN")
+                        progress.set_current_status("common.statusRestoringFacesGFPGAN")
                     elif facetool_parameters["type"] == "codeformer":
                         progress.set_current_status(
-                            "common.statusRestoringFacesCodeFormer")
+                            "common.statusRestoringFacesCodeFormer"
+                        )
 
                     progress.set_current_status_has_steps(False)
-                    self.socketio.emit(
-                        "progressUpdate", progress.to_formatted_dict())
+                    self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                     eventlet.sleep(0)
 
                     if facetool_parameters["type"] == "gfpgan":
@@ -1197,8 +1254,7 @@ class InvokeAIWebServer:
                     all_parameters["facetool_type"] = facetool_parameters["type"]
 
                 progress.set_current_status("common.statusSavingImage")
-                self.socketio.emit(
-                    "progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
                 # restore the stashed URLS and discard the paths, we are about to send the result to client
@@ -1215,8 +1271,7 @@ class InvokeAIWebServer:
                 if generation_parameters["generation_mode"] == "unifiedCanvas":
                     all_parameters["bounding_box"] = original_bounding_box
 
-                metadata = self.parameters_to_generated_image_metadata(
-                    all_parameters)
+                metadata = self.parameters_to_generated_image_metadata(all_parameters)
 
                 command = parameters_to_command(all_parameters)
 
@@ -1246,22 +1301,27 @@ class InvokeAIWebServer:
 
                 if progress.total_iterations > progress.current_iteration:
                     progress.set_current_step(1)
-                    progress.set_current_status(
-                        "common.statusIterationComplete")
+                    progress.set_current_status("common.statusIterationComplete")
                     progress.set_current_status_has_steps(False)
                 else:
                     progress.mark_complete()
 
-                self.socketio.emit(
-                    "progressUpdate", progress.to_formatted_dict())
+                self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 
-                parsed_prompt, _ = get_prompt_structure(
-                    generation_parameters["prompt"])
-                tokens = None if type(parsed_prompt) is Blend else \
-                    get_tokens_for_prompt_object(get_tokenizer(self.generate.model), parsed_prompt)
-                attention_maps_image_base64_url = None if attention_maps_image is None \
+                parsed_prompt, _ = get_prompt_structure(generation_parameters["prompt"])
+                tokens = (
+                    None
+                    if type(parsed_prompt) is Blend
+                    else get_tokens_for_prompt_object(
+                        get_tokenizer(self.generate.model), parsed_prompt
+                    )
+                )
+                attention_maps_image_base64_url = (
+                    None
+                    if attention_maps_image is None
                     else image_to_dataURL(attention_maps_image)
+                )
 
                 self.socketio.emit(
                     "generationResult",
@@ -1293,7 +1353,7 @@ class InvokeAIWebServer:
             self.generate.prompt2image(
                 **generation_parameters,
                 step_callback=diffusers_step_callback_adapter,
-                image_callback=image_done
+                image_callback=image_done,
             )
 
         except KeyboardInterrupt:
@@ -1416,8 +1476,7 @@ class InvokeAIWebServer:
         self, parameters, original_image_path
     ):
         try:
-            current_metadata = retrieve_metadata(
-                original_image_path)["sd-metadata"]
+            current_metadata = retrieve_metadata(original_image_path)["sd-metadata"]
             postprocessing_metadata = {}
 
             """
@@ -1457,8 +1516,7 @@ class InvokeAIWebServer:
                     postprocessing_metadata
                 )
             else:
-                current_metadata["image"]["postprocessing"] = [
-                    postprocessing_metadata]
+                current_metadata["image"]["postprocessing"] = [postprocessing_metadata]
 
             return current_metadata
 
@@ -1554,8 +1612,7 @@ class InvokeAIWebServer:
                 )
             elif "thumbnails" in url:
                 return os.path.abspath(
-                    os.path.join(self.thumbnail_image_path,
-                                 os.path.basename(url))
+                    os.path.join(self.thumbnail_image_path, os.path.basename(url))
                 )
             else:
                 return os.path.abspath(
@@ -1601,7 +1658,7 @@ class InvokeAIWebServer:
         except Exception as e:
             self.handle_exceptions(e)
 
-    def handle_exceptions(self, exception, emit_key: str = 'error'):
+    def handle_exceptions(self, exception, emit_key: str = "error"):
         self.socketio.emit(emit_key, {"message": (str(exception))})
         print("\n")
         traceback.print_exc()

invokeai/frontend/dist/index.html

@@ -5,8 +5,8 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>InvokeAI - A Stable Diffusion Toolkit</title>
     <link rel="shortcut icon" type="icon" href="./assets/favicon-0d253ced.ico" />
-    <script type="module" crossorigin src="./assets/index-c09cf9ca.js"></script>
-    <link rel="stylesheet" href="./assets/index-14cb2922.css">
+    <script type="module" crossorigin src="./assets/index-f56b39bc.js"></script>
+    <link rel="stylesheet" href="./assets/index-2ab0eb58.css">
   </head>
 
   <body>

invokeai/frontend/dist/locales/en.json

@@ -327,6 +327,13 @@
         "addModel": "Add Model",
         "updateModel": "Update Model",
         "availableModels": "Available Models",
+        "addLora": "Add Lora",
+        "clearLoras": "Clear Loras",
+        "noLoraModels": "No Loras Found",
+        "addTextualInversionTrigger": "Add Textual Inversion",
+        "addTIToNegative": "Add To Negative",
+        "clearTextualInversions": "Clear Textual Inversions",
+        "noTextualInversionTriggers": "No Textual Inversions Found",
         "search": "Search",
         "load": "Load",
         "active": "active",
@@ -483,6 +490,7 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
+        "showHuggingFaceConcepts": "Show Textual Inversions from HF Concepts Library",
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",

invokeai/frontend/package.json

@@ -15,8 +15,8 @@
     "postinstall": "patch-package"
   },
   "dependencies": {
-    "@chakra-ui/icons": "^2.0.17",
-    "@chakra-ui/react": "^2.5.1",
+    "@chakra-ui/icons": "^2.0.18",
+    "@chakra-ui/react": "^2.5.5",
     "@emotion/cache": "^11.10.5",
     "@emotion/react": "^11.10.6",
     "@emotion/styled": "^11.10.6",
@@ -52,6 +52,7 @@
     "redux-persist": "^6.0.0",
     "socket.io": "^4.6.0",
     "socket.io-client": "^4.6.0",
+    "typescript": "^5.0.3",
     "use-image": "^1.1.0",
     "uuid": "^9.0.0",
     "yarn": "^1.22.19"
@@ -61,8 +62,8 @@
     "@types/react": "^18.0.28",
     "@types/react-dom": "^18.0.11",
     "@types/react-transition-group": "^4.4.5",
-    "@typescript-eslint/eslint-plugin": "^5.52.0",
-    "@typescript-eslint/parser": "^5.52.0",
+    "@typescript-eslint/eslint-plugin": "^5.57.0",
+    "@typescript-eslint/parser": "^5.57.0",
     "babel-plugin-transform-imports": "^2.0.0",
     "eslint": "^8.34.0",
     "eslint-config-prettier": "^8.6.0",

invokeai/frontend/public/locales/en.json

@@ -327,6 +327,13 @@
         "addModel": "Add Model",
         "updateModel": "Update Model",
         "availableModels": "Available Models",
+        "addLora": "Add Lora",
+        "clearLoras": "Clear Loras",
+        "noLoraModels": "No Loras Found",
+        "addTextualInversionTrigger": "Add Textual Inversion",
+        "addTIToNegative": "Add To Negative",
+        "clearTextualInversions": "Clear Textual Inversions",
+        "noTextualInversionTriggers": "No Textual Inversions Found",
         "search": "Search",
         "load": "Load",
         "active": "active",
@@ -483,6 +490,7 @@
         "useCanvasBeta": "Use Canvas Beta Layout",
         "enableImageDebugging": "Enable Image Debugging",
         "useSlidersForAll": "Use Sliders For All Options",
+        "showHuggingFaceConcepts": "Show Textual Inversions from HF Concepts Library",
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",

invokeai/frontend/src/app/invokeai.d.ts

@@ -271,6 +271,23 @@ export declare type FoundModelResponse = {
   found_models: FoundModel[];
 };
 
+export declare type FoundLora = {
+  name: string;
+  location: string;
+};
+
+export declare type FoundTextualInversionTriggers = {
+  name: string;
+  location: string;
+};
+
+export declare type FoundLorasRsponse = FoundLora[];
+
+export declare type FoundTextualInversionTriggersResponse = {
+  local_triggers: FoundTextualInversionTriggers[];
+  huggingface_concepts: FoundTextualInversionTriggers[];
+};
+
 export declare type SystemStatusResponse = SystemStatus;
 
 export declare type SystemConfigResponse = SystemConfig;

invokeai/frontend/src/app/socketio/actions.ts

@@ -52,6 +52,12 @@ export const requestModelChange = createAction<string>(
   'socketio/requestModelChange'
 );
 
+export const getLoraModels = createAction<undefined>('socketio/getLoraModels');
+
+export const getTextualInversionTriggers = createAction<undefined>(
+  'socketio/getTextualInversionTriggers'
+);
+
 export const saveStagingAreaImageToGallery = createAction<string>(
   'socketio/saveStagingAreaImageToGallery'
 );

invokeai/frontend/src/app/socketio/emitters.ts

@@ -196,6 +196,12 @@ const makeSocketIOEmitters = (
       dispatch(modelChangeRequested());
       socketio.emit('requestModelChange', modelName);
     },
+    emitGetLoraModels: () => {
+      socketio.emit('getLoraModels');
+    },
+    emitGetTextualInversionTriggers: () => {
+      socketio.emit('getTextualInversionTriggers');
+    },
     emitSaveStagingAreaImageToGallery: (url: string) => {
       socketio.emit('requestSaveStagingAreaImageToGallery', url);
     },

invokeai/frontend/src/app/socketio/listeners.ts

@@ -11,6 +11,7 @@ import {
   errorOccurred,
   processingCanceled,
   setCurrentStatus,
+  setFoundLoras,
   setFoundModels,
   setIsCancelable,
   setIsConnected,
@@ -19,6 +20,8 @@ import {
   setSearchFolder,
   setSystemConfig,
   setSystemStatus,
+  setFoundLocalTextualInversionTriggers,
+  setFoundHuggingFaceTextualInversionTriggers,
 } from 'features/system/store/systemSlice';
 
 import {
@@ -34,8 +37,10 @@ import type { RootState } from 'app/store';
 import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
 import {
   clearInitialImage,
+  setHuggingFaceTextualInversionConcepts,
   setInfillMethod,
   setInitialImage,
+  setLocalTextualInversionTriggers,
   setMaskPath,
 } from 'features/parameters/store/generationSlice';
 import { tabMap } from 'features/ui/store/tabMap';
@@ -482,6 +487,37 @@ const makeSocketIOListeners = (
         })
       );
     },
+    onFoundLoras: (data: InvokeAI.FoundLorasRsponse) => {
+      dispatch(setFoundLoras(data));
+    },
+    onFoundTextualInversionTriggers: (
+      data: InvokeAI.FoundTextualInversionTriggersResponse
+    ) => {
+      const localTriggers = data.local_triggers;
+      const huggingFaceConcepts = data.huggingface_concepts;
+
+      dispatch(setFoundLocalTextualInversionTriggers(localTriggers));
+      dispatch(
+        setFoundHuggingFaceTextualInversionTriggers(huggingFaceConcepts)
+      );
+
+      // Assign Local TI's
+      const foundLocalTINames: string[] = [];
+      localTriggers.forEach((textualInversion) => {
+        foundLocalTINames.push(textualInversion.name);
+      });
+      dispatch(setLocalTextualInversionTriggers(foundLocalTINames));
+
+      // Assign HuggingFace Concepts
+      const foundHuggingFaceConceptNames: string[] = [];
+      huggingFaceConcepts.forEach((concept) => {
+        foundHuggingFaceConceptNames.push(concept.name);
+      });
+      dispatch(
+        setHuggingFaceTextualInversionConcepts(foundHuggingFaceConceptNames)
+      );
+    },
+
     onTempFolderEmptied: () => {
       dispatch(
         addToast({

invokeai/frontend/src/app/socketio/middleware.ts

@@ -51,6 +51,8 @@ export const socketioMiddleware = () => {
       onModelConverted,
       onModelsMerged,
       onModelChangeFailed,
+      onFoundLoras,
+      onFoundTextualInversionTriggers,
       onTempFolderEmptied,
     } = makeSocketIOListeners(store);
 
@@ -69,6 +71,8 @@ export const socketioMiddleware = () => {
       emitConvertToDiffusers,
       emitMergeDiffusersModels,
       emitRequestModelChange,
+      emitGetLoraModels,
+      emitGetTextualInversionTriggers,
       emitSaveStagingAreaImageToGallery,
       emitRequestEmptyTempFolder,
     } = makeSocketIOEmitters(store, socketio);
@@ -145,6 +149,17 @@ export const socketioMiddleware = () => {
         onModelChangeFailed(data);
       });
 
+      socketio.on('foundLoras', (data: InvokeAI.FoundLorasRsponse) => {
+        onFoundLoras(data);
+      });
+
+      socketio.on(
+        'foundTextualInversionTriggers',
+        (data: InvokeAI.FoundTextualInversionTriggersResponse) => {
+          onFoundTextualInversionTriggers(data);
+        }
+      );
+
       socketio.on('tempFolderEmptied', () => {
         onTempFolderEmptied();
       });
@@ -226,6 +241,16 @@ export const socketioMiddleware = () => {
         break;
       }
 
+      case 'socketio/getLoraModels': {
+        emitGetLoraModels();
+        break;
+      }
+
+      case 'socketio/getTextualInversionTriggers': {
+        emitGetTextualInversionTriggers();
+        break;
+      }
+
       case 'socketio/saveStagingAreaImageToGallery': {
         emitSaveStagingAreaImageToGallery(action.payload);
         break;

invokeai/frontend/src/common/components/IAISimpleMenu.tsx

@@ -7,13 +7,14 @@ import {
   MenuButtonProps,
   MenuListProps,
   MenuItemProps,
+  Text,
 } from '@chakra-ui/react';
 import { MouseEventHandler, ReactNode } from 'react';
 import { MdArrowDropDown, MdArrowDropUp } from 'react-icons/md';
 import IAIButton from './IAIButton';
 import IAIIconButton from './IAIIconButton';
 
-interface IAIMenuItem {
+export interface IAIMenuItem {
   item: ReactNode | string;
   onClick: MouseEventHandler<HTMLButtonElement> | undefined;
 }
@@ -43,6 +44,7 @@ export default function IAISimpleMenu(props: IAIMenuProps) {
 
   const renderMenuItems = () => {
     const menuItemsToRender: ReactNode[] = [];
+
     menuItems.forEach((menuItem, index) => {
       menuItemsToRender.push(
         <MenuItem
@@ -82,12 +84,17 @@ export default function IAISimpleMenu(props: IAIMenuProps) {
             fontSize="1.5rem"
             {...menuButtonProps}
           >
-            {menuType === 'regular' && buttonText}
+            {menuType === 'regular' && (
+              <Text fontSize="0.9rem">{buttonText}</Text>
+            )}
           </MenuButton>
           <MenuList
             zIndex={15}
             padding={0}
             borderRadius="0.5rem"
+            overflow="scroll"
+            maxWidth={'22.5rem'}
+            maxHeight={500}
             backgroundColor="var(--background-color-secondary)"
             color="var(--text-color-secondary)"
             borderColor="var(--border-color)"

invokeai/frontend/src/features/parameters/components/MainParameters/MainWidth.tsx

@@ -34,7 +34,6 @@ export default function MainWidth() {
       withSliderMarks
       sliderMarkRightOffset={-8}
       inputWidth="6.2rem"
-      inputReadOnly
       sliderNumberInputProps={{ max: 15360 }}
     />
   ) : (

invokeai/frontend/src/features/parameters/components/PromptInput/Extras/LoraManager/LoraManager.tsx

@@ -0,0 +1,86 @@
+import { Box, Flex } from '@chakra-ui/react';
+import { getLoraModels } from 'app/socketio/actions';
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import IAISimpleMenu, { IAIMenuItem } from 'common/components/IAISimpleMenu';
+import {
+  setClearLoras,
+  setLorasInUse,
+} from 'features/parameters/store/generationSlice';
+import { useEffect } from 'react';
+import { useTranslation } from 'react-i18next';
+import { MdClear } from 'react-icons/md';
+
+export default function LoraManager() {
+  const dispatch = useAppDispatch();
+  const foundLoras = useAppSelector((state) => state.system.foundLoras);
+  const lorasInUse = useAppSelector((state) => state.generation.lorasInUse);
+
+  const { t } = useTranslation();
+
+  const handleLora = (lora: string) => {
+    dispatch(setLorasInUse(lora));
+  };
+
+  useEffect(() => {
+    dispatch(getLoraModels());
+  }, [dispatch]);
+
+  const renderLoraOption = (lora: string) => {
+    const thisloraExists = lorasInUse.includes(lora);
+    const loraExistsStyle = {
+      fontWeight: 'bold',
+      color: 'var(--context-menu-active-item)',
+    };
+    return <Box style={thisloraExists ? loraExistsStyle : {}}>{lora}</Box>;
+  };
+
+  const numOfActiveLoras = () => {
+    const foundLoraNames: string[] = [];
+    foundLoras?.forEach((lora) => {
+      foundLoraNames.push(lora.name);
+    });
+    return foundLoraNames.filter((lora) => lorasInUse.includes(lora)).length;
+  };
+
+  const makeLoraItems = () => {
+    const lorasFound: IAIMenuItem[] = [];
+    foundLoras?.forEach((lora) => {
+      if (lora.name !== ' ') {
+        const newLoraItem: IAIMenuItem = {
+          item: renderLoraOption(lora.name),
+          onClick: () => handleLora(lora.name),
+        };
+        lorasFound.push(newLoraItem);
+      }
+    });
+    return lorasFound;
+  };
+
+  return foundLoras && foundLoras?.length > 0 ? (
+    <Flex columnGap={2}>
+      <IAISimpleMenu
+        menuItems={makeLoraItems()}
+        menuType="regular"
+        buttonText={`${t('modelManager.addLora')} (${numOfActiveLoras()})`}
+        menuButtonProps={{ width: '100%', padding: '0 1rem' }}
+      />
+      <IAIIconButton
+        icon={<MdClear />}
+        tooltip={t('modelManager.clearLoras')}
+        aria-label={t('modelManager.clearLoras')}
+        onClick={() => dispatch(setClearLoras())}
+      />
+    </Flex>
+  ) : (
+    <Box
+      background="var(--btn-base-color)"
+      padding={2}
+      textAlign="center"
+      borderRadius={4}
+      fontWeight="bold"
+    >
+      {t('modelManager.noLoraModels')}
+    </Box>
+  );
+}

invokeai/frontend/src/features/parameters/components/PromptInput/Extras/PromptExtras.tsx

@@ -0,0 +1,12 @@
+import { Flex } from '@chakra-ui/react';
+import LoraManager from './LoraManager/LoraManager';
+import TextualInversionManager from './TextualInversionManager/TextualInversionManager';
+
+export default function PromptExtras() {
+  return (
+    <Flex flexDir="column" rowGap={2}>
+      <LoraManager />
+      <TextualInversionManager />
+    </Flex>
+  );
+}

invokeai/frontend/src/features/parameters/components/PromptInput/Extras/TextualInversionManager/TextualInversionManager.tsx

@@ -0,0 +1,163 @@
+import { Box, Flex } from '@chakra-ui/react';
+import { getTextualInversionTriggers } from 'app/socketio/actions';
+import { RootState } from 'app/store';
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import IAISimpleMenu, { IAIMenuItem } from 'common/components/IAISimpleMenu';
+import {
+  setAddTIToNegative,
+  setClearTextualInversions,
+  setTextualInversionsInUse,
+} from 'features/parameters/store/generationSlice';
+import { useEffect } from 'react';
+import { useTranslation } from 'react-i18next';
+import { MdArrowDownward, MdClear } from 'react-icons/md';
+
+export default function TextualInversionManager() {
+  const dispatch = useAppDispatch();
+  const textualInversionsInUse = useAppSelector(
+    (state: RootState) => state.generation.textualInversionsInUse
+  );
+
+  const negativeTextualInversionsInUse = useAppSelector(
+    (state: RootState) => state.generation.negativeTextualInversionsInUse
+  );
+
+  const foundLocalTextualInversionTriggers = useAppSelector(
+    (state) => state.system.foundLocalTextualInversionTriggers
+  );
+  const foundHuggingFaceTextualInversionTriggers = useAppSelector(
+    (state) => state.system.foundHuggingFaceTextualInversionTriggers
+  );
+
+  const localTextualInversionTriggers = useAppSelector(
+    (state) => state.generation.localTextualInversionTriggers
+  );
+
+  const huggingFaceTextualInversionConcepts = useAppSelector(
+    (state) => state.generation.huggingFaceTextualInversionConcepts
+  );
+
+  const shouldShowHuggingFaceConcepts = useAppSelector(
+    (state) => state.ui.shouldShowHuggingFaceConcepts
+  );
+
+  const addTIToNegative = useAppSelector(
+    (state) => state.generation.addTIToNegative
+  );
+
+  const { t } = useTranslation();
+
+  useEffect(() => {
+    dispatch(getTextualInversionTriggers());
+  }, [dispatch]);
+
+  const handleTextualInversion = (textual_inversion: string) => {
+    dispatch(setTextualInversionsInUse(textual_inversion));
+  };
+
+  const TIPip = ({ color }: { color: string }) => {
+    return (
+      <Box width={2} height={2} borderRadius={9999} backgroundColor={color}>
+        {' '}
+      </Box>
+    );
+  };
+
+  const renderTextualInversionOption = (textual_inversion: string) => {
+    return (
+      <Flex alignItems="center" columnGap={1}>
+        {textual_inversion}
+        {textualInversionsInUse.includes(textual_inversion) && (
+          <TIPip color="var(--context-menu-active-item)" />
+        )}
+        {negativeTextualInversionsInUse.includes(textual_inversion) && (
+          <TIPip color="var(--status-bad-color)" />
+        )}
+      </Flex>
+    );
+  };
+
+  const numOfActiveTextualInversions = () => {
+    const allTextualInversions = localTextualInversionTriggers.concat(
+      huggingFaceTextualInversionConcepts
+    );
+    return allTextualInversions.filter(
+      (ti) =>
+        textualInversionsInUse.includes(ti) ||
+        negativeTextualInversionsInUse.includes(ti)
+    ).length;
+  };
+
+  const makeTextualInversionItems = () => {
+    const textualInversionsFound: IAIMenuItem[] = [];
+    foundLocalTextualInversionTriggers?.forEach((textualInversion) => {
+      if (textualInversion.name !== ' ') {
+        const newTextualInversionItem: IAIMenuItem = {
+          item: renderTextualInversionOption(textualInversion.name),
+          onClick: () => handleTextualInversion(textualInversion.name),
+        };
+        textualInversionsFound.push(newTextualInversionItem);
+      }
+    });
+
+    if (shouldShowHuggingFaceConcepts) {
+      foundHuggingFaceTextualInversionTriggers?.forEach((textualInversion) => {
+        if (textualInversion.name !== ' ') {
+          const newTextualInversionItem: IAIMenuItem = {
+            item: renderTextualInversionOption(textualInversion.name),
+            onClick: () => handleTextualInversion(textualInversion.name),
+          };
+          textualInversionsFound.push(newTextualInversionItem);
+        }
+      });
+    }
+
+    return textualInversionsFound;
+  };
+
+  return foundLocalTextualInversionTriggers &&
+    (foundLocalTextualInversionTriggers?.length > 0 ||
+      (foundHuggingFaceTextualInversionTriggers &&
+        foundHuggingFaceTextualInversionTriggers?.length > 0 &&
+        shouldShowHuggingFaceConcepts)) ? (
+    <Flex columnGap={2}>
+      <IAISimpleMenu
+        menuItems={makeTextualInversionItems()}
+        menuType="regular"
+        buttonText={`${t(
+          'modelManager.addTextualInversionTrigger'
+        )} (${numOfActiveTextualInversions()})`}
+        menuButtonProps={{
+          width: '100%',
+          padding: '0 1rem',
+        }}
+      />
+      <IAIIconButton
+        icon={<MdArrowDownward />}
+        style={{
+          backgroundColor: addTIToNegative ? 'var(--btn-delete-image)' : '',
+        }}
+        tooltip={t('modelManager.addTIToNegative')}
+        aria-label={t('modelManager.addTIToNegative')}
+        onClick={() => dispatch(setAddTIToNegative(!addTIToNegative))}
+      />
+      <IAIIconButton
+        icon={<MdClear />}
+        tooltip={t('modelManager.clearTextualInversions')}
+        aria-label={t('modelManager.clearTextualInversions')}
+        onClick={() => dispatch(setClearTextualInversions())}
+      />
+    </Flex>
+  ) : (
+    <Box
+      background="var(--btn-base-color)"
+      padding={2}
+      textAlign="center"
+      borderRadius={4}
+      fontWeight="bold"
+    >
+      {t('modelManager.noTextualInversionTriggers')}
+    </Box>
+  );
+}

invokeai/frontend/src/features/parameters/components/PromptInput/NegativePromptInput.tsx

@@ -1,24 +1,43 @@
 import { FormControl, Textarea } from '@chakra-ui/react';
 import type { RootState } from 'app/store';
 import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import { setNegativePrompt } from 'features/parameters/store/generationSlice';
+import {
+  handlePromptCheckers,
+  setNegativePrompt,
+} from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
+import { ChangeEvent, useState } from 'react';
 
 const NegativePromptInput = () => {
   const negativePrompt = useAppSelector(
     (state: RootState) => state.generation.negativePrompt
   );
 
+  const [promptTimer, setPromptTimer] = useState<number | undefined>(undefined);
+
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
 
+  const handleNegativeChangePrompt = (e: ChangeEvent<HTMLTextAreaElement>) => {
+    dispatch(setNegativePrompt(e.target.value));
+
+    // Debounce Prompt UI Checking
+    clearTimeout(promptTimer);
+    const newPromptTimer = window.setTimeout(() => {
+      dispatch(
+        handlePromptCheckers({ prompt: e.target.value, toNegative: true })
+      );
+    }, 500);
+    setPromptTimer(newPromptTimer);
+  };
+
   return (
     <FormControl>
       <Textarea
         id="negativePrompt"
         name="negativePrompt"
         value={negativePrompt}
-        onChange={(e) => dispatch(setNegativePrompt(e.target.value))}
+        onChange={handleNegativeChangePrompt}
         background="var(--prompt-bg-color)"
         placeholder={t('parameters.negativePrompts')}
         _placeholder={{ fontSize: '0.8rem' }}

invokeai/frontend/src/features/parameters/components/PromptInput/PromptInput.tsx

@@ -2,12 +2,13 @@ import { FormControl, Textarea } from '@chakra-ui/react';
 import { generateImage } from 'app/socketio/actions';
 import { RootState } from 'app/store';
 import { useAppDispatch, useAppSelector } from 'app/storeHooks';
-import { ChangeEvent, KeyboardEvent, useRef } from 'react';
+import { ChangeEvent, KeyboardEvent, useRef, useState } from 'react';
 
 import { createSelector } from '@reduxjs/toolkit';
 import { readinessSelector } from 'app/selectors/readinessSelector';
 import {
   GenerationState,
+  handlePromptCheckers,
   setPrompt,
 } from 'features/parameters/store/generationSlice';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
@@ -40,11 +41,21 @@ const PromptInput = () => {
   const { isReady } = useAppSelector(readinessSelector);
 
   const promptRef = useRef<HTMLTextAreaElement>(null);
+  const [promptTimer, setPromptTimer] = useState<number | undefined>(undefined);
 
   const { t } = useTranslation();
 
   const handleChangePrompt = (e: ChangeEvent<HTMLTextAreaElement>) => {
     dispatch(setPrompt(e.target.value));
+
+    // Debounce Prompt UI Checking
+    clearTimeout(promptTimer);
+    const newPromptTimer = window.setTimeout(() => {
+      dispatch(
+        handlePromptCheckers({ prompt: e.target.value, toNegative: false })
+      );
+    }, 500);
+    setPromptTimer(newPromptTimer);
   };
 
   useHotkeys(

invokeai/frontend/src/features/parameters/hooks/usePrompt.ts

@@ -3,7 +3,11 @@ import { getPromptAndNegative } from 'common/util/getPromptAndNegative';
 import * as InvokeAI from 'app/invokeai';
 import promptToString from 'common/util/promptToString';
 import { useAppDispatch } from 'app/storeHooks';
-import { setNegativePrompt, setPrompt } from '../store/generationSlice';
+import {
+  handlePromptCheckers,
+  setNegativePrompt,
+  setPrompt,
+} from '../store/generationSlice';
 
 // TECHDEBT: We have two metadata prompt formats and need to handle recalling either of them.
 // This hook provides a function to do that.
@@ -20,6 +24,10 @@ const useSetBothPrompts = () => {
 
     dispatch(setPrompt(prompt));
     dispatch(setNegativePrompt(negativePrompt));
+    dispatch(handlePromptCheckers({ prompt: prompt, toNegative: false }));
+    dispatch(
+      handlePromptCheckers({ prompt: negativePrompt, toNegative: true })
+    );
   };
 };
 

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

@@ -1,4 +1,4 @@
-import type { PayloadAction } from '@reduxjs/toolkit';
+import { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import * as InvokeAI from 'app/invokeai';
 import { getPromptAndNegative } from 'common/util/getPromptAndNegative';
@@ -17,6 +17,12 @@ export interface GenerationState {
   perlin: number;
   prompt: string;
   negativePrompt: string;
+  lorasInUse: string[];
+  huggingFaceTextualInversionConcepts: string[];
+  localTextualInversionTriggers: string[];
+  textualInversionsInUse: string[];
+  negativeTextualInversionsInUse: string[];
+  addTIToNegative: boolean;
   sampler: string;
   seamBlur: number;
   seamless: boolean;
@@ -48,6 +54,12 @@ const initialGenerationState: GenerationState = {
   perlin: 0,
   prompt: '',
   negativePrompt: '',
+  lorasInUse: [],
+  huggingFaceTextualInversionConcepts: [],
+  localTextualInversionTriggers: [],
+  textualInversionsInUse: [],
+  negativeTextualInversionsInUse: [],
+  addTIToNegative: false,
   sampler: 'k_lms',
   seamBlur: 16,
   seamless: false,
@@ -71,12 +83,99 @@ const initialGenerationState: GenerationState = {
 
 const initialState: GenerationState = initialGenerationState;
 
+const loraExists = (state: GenerationState, lora: string) => {
+  const loraRegex = new RegExp(`withLora\\(${lora},?\\s*([^\\)]+)?\\)`);
+  if (state.prompt.match(loraRegex)) return true;
+  return false;
+};
+
+const getTIRegex = (textualInversion: string) => {
+  if (textualInversion.includes('<' || '>')) {
+    return new RegExp(`${textualInversion}`);
+  } else {
+    return new RegExp(`\\b${textualInversion}\\b`);
+  }
+};
+
+const textualInversionExists = (
+  state: GenerationState,
+  textualInversion: string
+) => {
+  const textualInversionRegex = getTIRegex(textualInversion);
+
+  if (!state.addTIToNegative) {
+    if (state.prompt.match(textualInversionRegex)) return true;
+  } else {
+    if (state.negativePrompt.match(textualInversionRegex)) return true;
+  }
+  return false;
+};
+
+const handleTypedTICheck = (
+  state: GenerationState,
+  newPrompt: string,
+  toNegative: boolean
+) => {
+  let textualInversionsInUse = !toNegative
+    ? [...state.textualInversionsInUse]
+    : [...state.negativeTextualInversionsInUse]; // Get Words In Prompt
+
+  const textualInversionRegex = /([\w<>!@%&*_-]+)/g; // Scan For Each Word
+
+  const textualInversionMatches = [
+    ...newPrompt.matchAll(textualInversionRegex),
+  ]; // Match All Words
+
+  if (textualInversionMatches.length > 0) {
+    textualInversionsInUse = []; // Reset Textual Inversions In Use
+
+    textualInversionMatches.forEach((textualInversionMatch) => {
+      const textualInversionName = textualInversionMatch[0];
+      if (
+        (!textualInversionsInUse.includes(textualInversionName) &&
+          state.localTextualInversionTriggers.includes(textualInversionName)) ||
+        state.huggingFaceTextualInversionConcepts.includes(textualInversionName)
+      ) {
+        textualInversionsInUse.push(textualInversionName); // Add Textual Inversions In Prompt
+      }
+    });
+  } else {
+    textualInversionsInUse = []; // If No Matches, Remove Textual Inversions In Use
+  }
+
+  if (!toNegative) {
+    state.textualInversionsInUse = textualInversionsInUse;
+  } else {
+    state.negativeTextualInversionsInUse = textualInversionsInUse;
+  }
+};
+
+const handleTypedLoraCheck = (state: GenerationState, newPrompt: string) => {
+  let lorasInUse = [...state.lorasInUse]; // Get Loras In Prompt
+
+  const loraRegex = /withLora\(([^\\)]+)\)/g; // Scan For Lora Syntax
+  const loraMatches = [...newPrompt.matchAll(loraRegex)]; // Match All Lora Syntaxes
+
+  if (loraMatches.length > 0) {
+    lorasInUse = []; // Reset Loras In Use
+    loraMatches.forEach((loraMatch) => {
+      const loraName = loraMatch[1].split(',')[0];
+      if (!lorasInUse.includes(loraName)) lorasInUse.push(loraName); // Add Loras In Prompt
+    });
+  } else {
+    lorasInUse = []; // If No Matches, Remove Loras In Use
+  }
+
+  state.lorasInUse = lorasInUse;
+};
+
 export const generationSlice = createSlice({
   name: 'generation',
   initialState,
   reducers: {
     setPrompt: (state, action: PayloadAction<string | InvokeAI.Prompt>) => {
       const newPrompt = action.payload;
+
       if (typeof newPrompt === 'string') {
         state.prompt = newPrompt;
       } else {
@@ -94,6 +193,136 @@ export const generationSlice = createSlice({
         state.negativePrompt = promptToString(newPrompt);
       }
     },
+    handlePromptCheckers: (
+      state,
+      action: PayloadAction<{
+        prompt: string | InvokeAI.Prompt;
+        toNegative: boolean;
+      }>
+    ) => {
+      const newPrompt = action.payload.prompt;
+
+      if (typeof newPrompt === 'string') {
+        if (!action.payload.toNegative) handleTypedLoraCheck(state, newPrompt);
+        handleTypedTICheck(state, newPrompt, action.payload.toNegative);
+      }
+    },
+    setLorasInUse: (state, action: PayloadAction<string>) => {
+      const newLora = action.payload;
+      const loras = [...state.lorasInUse];
+
+      if (loraExists(state, newLora)) {
+        const loraRegex = new RegExp(
+          `withLora\\(${newLora},?\\s*([^\\)]+)?\\)`,
+          'g'
+        );
+        const newPrompt = state.prompt.replaceAll(loraRegex, '');
+        state.prompt = newPrompt.trim();
+
+        if (loras.includes(newLora)) {
+          const newLoraIndex = loras.indexOf(newLora);
+          if (newLoraIndex > -1) loras.splice(newLoraIndex, 1);
+        }
+      } else {
+        state.prompt = `${state.prompt.trim()} withLora(${newLora},0.75)`;
+        if (!loras.includes(newLora)) loras.push(newLora);
+      }
+      state.lorasInUse = loras;
+    },
+    setClearLoras: (state) => {
+      const lorasInUse = [...state.lorasInUse];
+
+      lorasInUse.forEach((lora) => {
+        const loraRegex = new RegExp(
+          `withLora\\(${lora},?\\s*([^\\)]+)?\\)`,
+          'g'
+        );
+        const newPrompt = state.prompt.replaceAll(loraRegex, '');
+        state.prompt = newPrompt.trim();
+      });
+
+      state.lorasInUse = [];
+    },
+    setTextualInversionsInUse: (state, action: PayloadAction<string>) => {
+      const newTextualInversion = action.payload;
+
+      const textualInversions = [...state.textualInversionsInUse];
+      const negativeTextualInversions = [
+        ...state.negativeTextualInversionsInUse,
+      ];
+
+      if (textualInversionExists(state, newTextualInversion)) {
+        const textualInversionRegex = getTIRegex(newTextualInversion);
+
+        if (!state.addTIToNegative) {
+          const newPrompt = state.prompt.replace(textualInversionRegex, '');
+          state.prompt = newPrompt.trim();
+
+          const newTIIndex = textualInversions.indexOf(newTextualInversion);
+          if (newTIIndex > -1) textualInversions.splice(newTIIndex, 1);
+        } else {
+          const newPrompt = state.negativePrompt.replace(
+            textualInversionRegex,
+            ''
+          );
+          state.negativePrompt = newPrompt.trim();
+
+          const newTIIndex =
+            negativeTextualInversions.indexOf(newTextualInversion);
+          if (newTIIndex > -1) negativeTextualInversions.splice(newTIIndex, 1);
+        }
+      } else {
+        if (!state.addTIToNegative) {
+          state.prompt = `${state.prompt.trim()} ${newTextualInversion}`;
+          textualInversions.push(newTextualInversion);
+        } else {
+          state.negativePrompt = `${state.negativePrompt.trim()} ${newTextualInversion}`;
+          negativeTextualInversions.push(newTextualInversion);
+        }
+      }
+
+      state.textualInversionsInUse = textualInversions;
+      state.negativeTextualInversionsInUse = negativeTextualInversions;
+    },
+    setClearTextualInversions: (state) => {
+      const textualInversions = [...state.textualInversionsInUse];
+      const negativeTextualInversions = [
+        ...state.negativeTextualInversionsInUse,
+      ];
+
+      textualInversions.forEach((ti) => {
+        const textualInversionRegex = getTIRegex(ti);
+        const newPrompt = state.prompt.replace(textualInversionRegex, '');
+        state.prompt = newPrompt.trim();
+      });
+
+      negativeTextualInversions.forEach((ti) => {
+        const textualInversionRegex = getTIRegex(ti);
+        const newPrompt = state.negativePrompt.replace(
+          textualInversionRegex,
+          ''
+        );
+        state.negativePrompt = newPrompt.trim();
+      });
+
+      state.textualInversionsInUse = [];
+      state.negativeTextualInversionsInUse = [];
+    },
+    setAddTIToNegative: (state, action: PayloadAction<boolean>) => {
+      state.addTIToNegative = action.payload;
+    },
+    setLocalTextualInversionTriggers: (
+      state,
+      action: PayloadAction<string[]>
+    ) => {
+      state.localTextualInversionTriggers = action.payload;
+    },
+    setHuggingFaceTextualInversionConcepts: (
+      state,
+      action: PayloadAction<string[]>
+    ) => {
+      state.huggingFaceTextualInversionConcepts = action.payload;
+    },
     setIterations: (state, action: PayloadAction<number>) => {
       state.iterations = action.payload;
     },
@@ -374,6 +603,14 @@ export const {
   setPerlin,
   setPrompt,
   setNegativePrompt,
+  handlePromptCheckers,
+  setLorasInUse,
+  setClearLoras,
+  setHuggingFaceTextualInversionConcepts,
+  setLocalTextualInversionTriggers,
+  setTextualInversionsInUse,
+  setAddTIToNegative,
+  setClearTextualInversions,
   setSampler,
   setSeamBlur,
   setSeamless,

invokeai/frontend/src/features/system/components/SettingsModal/SettingsModal.tsx

@@ -31,6 +31,7 @@ import {
 } from 'features/system/store/systemSlice';
 import { uiSelector } from 'features/ui/store/uiSelectors';
 import {
+  setShouldShowHuggingFaceConcepts,
   setShouldUseCanvasBetaLayout,
   setShouldUseSliders,
 } from 'features/ui/store/uiSlice';
@@ -52,7 +53,11 @@ const selector = createSelector(
       enableImageDebugging,
     } = system;
 
-    const { shouldUseCanvasBetaLayout, shouldUseSliders } = ui;
+    const {
+      shouldUseCanvasBetaLayout,
+      shouldUseSliders,
+      shouldShowHuggingFaceConcepts,
+    } = ui;
 
     return {
       shouldDisplayInProgressType,
@@ -63,6 +68,7 @@ const selector = createSelector(
       enableImageDebugging,
       shouldUseCanvasBetaLayout,
       shouldUseSliders,
+      shouldShowHuggingFaceConcepts,
     };
   },
   {
@@ -107,6 +113,7 @@ const SettingsModal = ({ children }: SettingsModalProps) => {
     enableImageDebugging,
     shouldUseCanvasBetaLayout,
     shouldUseSliders,
+    shouldShowHuggingFaceConcepts,
   } = useAppSelector(selector);
 
   /**
@@ -206,6 +213,14 @@ const SettingsModal = ({ children }: SettingsModalProps) => {
                   dispatch(setShouldUseSliders(e.target.checked))
                 }
               />
+              <IAISwitch
+                styleClass="settings-modal-item"
+                label={t('settings.showHuggingFaceConcepts')}
+                isChecked={shouldShowHuggingFaceConcepts}
+                onChange={(e: ChangeEvent<HTMLInputElement>) =>
+                  dispatch(setShouldShowHuggingFaceConcepts(e.target.checked))
+                }
+              />
             </div>
 
             <div className="settings-modal-items">

invokeai/frontend/src/features/system/store/systemSlice.ts

@@ -51,6 +51,13 @@ export interface SystemState
   toastQueue: UseToastOptions[];
   searchFolder: string | null;
   foundModels: InvokeAI.FoundModel[] | null;
+  foundLoras: InvokeAI.FoundLora[] | null;
+  foundLocalTextualInversionTriggers:
+    | InvokeAI.FoundTextualInversionTriggers[]
+    | null;
+  foundHuggingFaceTextualInversionTriggers:
+    | InvokeAI.FoundTextualInversionTriggers[]
+    | null;
   openModel: string | null;
   cancelOptions: {
     cancelType: CancelType;
@@ -93,6 +100,9 @@ const initialSystemState: SystemState = {
   toastQueue: [],
   searchFolder: null,
   foundModels: null,
+  foundLoras: null,
+  foundLocalTextualInversionTriggers: null,
+  foundHuggingFaceTextualInversionTriggers: null,
   openModel: null,
   cancelOptions: {
     cancelType: 'immediate',
@@ -262,6 +272,24 @@ export const systemSlice = createSlice({
     ) => {
       state.foundModels = action.payload;
     },
+    setFoundLoras: (
+      state,
+      action: PayloadAction<InvokeAI.FoundLora[] | null>
+    ) => {
+      state.foundLoras = action.payload;
+    },
+    setFoundLocalTextualInversionTriggers: (
+      state,
+      action: PayloadAction<InvokeAI.FoundTextualInversionTriggers[] | null>
+    ) => {
+      state.foundLocalTextualInversionTriggers = action.payload;
+    },
+    setFoundHuggingFaceTextualInversionTriggers: (
+      state,
+      action: PayloadAction<InvokeAI.FoundTextualInversionTriggers[] | null>
+    ) => {
+      state.foundHuggingFaceTextualInversionTriggers = action.payload;
+    },
     setOpenModel: (state, action: PayloadAction<string | null>) => {
       state.openModel = action.payload;
     },
@@ -303,6 +331,9 @@ export const {
   setProcessingIndeterminateTask,
   setSearchFolder,
   setFoundModels,
+  setFoundLoras,
+  setFoundLocalTextualInversionTriggers,
+  setFoundHuggingFaceTextualInversionTriggers,
   setOpenModel,
   setCancelType,
   setCancelAfter,

invokeai/frontend/src/features/ui/components/ImageToImage/ImageToImagePanel.tsx

@@ -18,6 +18,7 @@ import PromptInput from 'features/parameters/components/PromptInput/PromptInput'
 import InvokeOptionsPanel from 'features/ui/components/InvokeParametersPanel';
 import { useTranslation } from 'react-i18next';
 import ImageToImageOptions from './ImageToImageOptions';
+import PromptExtras from 'features/parameters/components/PromptInput/Extras/PromptExtras';
 
 export default function ImageToImagePanel() {
   const { t } = useTranslation();
@@ -63,6 +64,7 @@ export default function ImageToImagePanel() {
       <Flex flexDir="column" rowGap="0.5rem">
         <PromptInput />
         <NegativePromptInput />
+        <PromptExtras />
       </Flex>
       <ProcessButtons />
       <MainSettings />

invokeai/frontend/src/features/ui/components/TextToImage/TextToImagePanel.tsx

@@ -17,6 +17,7 @@ import NegativePromptInput from 'features/parameters/components/PromptInput/Nega
 import PromptInput from 'features/parameters/components/PromptInput/PromptInput';
 import InvokeOptionsPanel from 'features/ui/components/InvokeParametersPanel';
 import { useTranslation } from 'react-i18next';
+import PromptExtras from 'features/parameters/components/PromptInput/Extras/PromptExtras';
 
 export default function TextToImagePanel() {
   const { t } = useTranslation();
@@ -62,6 +63,7 @@ export default function TextToImagePanel() {
       <Flex flexDir="column" rowGap="0.5rem">
         <PromptInput />
         <NegativePromptInput />
+        <PromptExtras />
       </Flex>
       <ProcessButtons />
       <MainSettings />

invokeai/frontend/src/features/ui/components/UnifiedCanvas/UnifiedCanvasPanel.tsx

@@ -17,6 +17,7 @@ import NegativePromptInput from 'features/parameters/components/PromptInput/Nega
 import PromptInput from 'features/parameters/components/PromptInput/PromptInput';
 import InvokeOptionsPanel from 'features/ui/components/InvokeParametersPanel';
 import { useTranslation } from 'react-i18next';
+import PromptExtras from 'features/parameters/components/PromptInput/Extras/PromptExtras';
 
 export default function UnifiedCanvasPanel() {
   const { t } = useTranslation();
@@ -73,6 +74,7 @@ export default function UnifiedCanvasPanel() {
       <Flex flexDir="column" rowGap="0.5rem">
         <PromptInput />
         <NegativePromptInput />
+        <PromptExtras />
       </Flex>
       <ProcessButtons />
       <MainSettings />

invokeai/frontend/src/features/ui/store/uiSlice.ts

@@ -15,6 +15,7 @@ const initialtabsState: UIState = {
   shouldUseCanvasBetaLayout: false,
   shouldShowExistingModelsInSearch: false,
   shouldUseSliders: false,
+  shouldShowHuggingFaceConcepts: false,
   addNewModelUIOption: null,
 };
 
@@ -70,6 +71,12 @@ export const uiSlice = createSlice({
     setShouldUseSliders: (state, action: PayloadAction<boolean>) => {
       state.shouldUseSliders = action.payload;
     },
+    setShouldShowHuggingFaceConcepts: (
+      state,
+      action: PayloadAction<boolean>
+    ) => {
+      state.shouldShowHuggingFaceConcepts = action.payload;
+    },
     setAddNewModelUIOption: (state, action: PayloadAction<AddNewModelType>) => {
       state.addNewModelUIOption = action.payload;
     },
@@ -88,6 +95,7 @@ export const {
   setShouldUseCanvasBetaLayout,
   setShouldShowExistingModelsInSearch,
   setShouldUseSliders,
+  setShouldShowHuggingFaceConcepts,
   setAddNewModelUIOption,
 } = uiSlice.actions;
 

invokeai/frontend/src/features/ui/store/uiTypes.ts

@@ -12,5 +12,6 @@ export interface UIState {
   shouldUseCanvasBetaLayout: boolean;
   shouldShowExistingModelsInSearch: boolean;
   shouldUseSliders: boolean;
+  shouldShowHuggingFaceConcepts: boolean;
   addNewModelUIOption: AddNewModelType;
 }

invokeai/frontend/src/styles/Themes/_Colors_Dark.scss

@@ -8,7 +8,6 @@
   --accent-color-bright: rgb(104, 60, 230);
   --accent-color-hover: var(--accent-color-bright);
 
-  // App Colors
   --root-bg-color: rgb(10, 10, 10);
   --background-color: rgb(26, 26, 32);
   --background-color-light: rgb(40, 44, 48);
@@ -119,6 +118,7 @@
   --context-menu-bg-color: rgb(46, 48, 58);
   --context-menu-box-shadow: none;
   --context-menu-bg-color-hover: rgb(30, 32, 42);
+  --context-menu-active-item: var(--accent-color-bright);
 
   // Shadows
   --floating-button-drop-shadow-color: var(--accent-color);

invokeai/frontend/src/styles/Themes/_Colors_Green.scss

@@ -117,6 +117,7 @@
   --context-menu-bg-color: rgb(46, 48, 58);
   --context-menu-box-shadow: none;
   --context-menu-bg-color-hover: rgb(30, 32, 42);
+  --context-menu-active-item: var(--accent-color-bright);
 
   // Shadows
   --floating-button-drop-shadow-color: var(--accent-color);

invokeai/frontend/src/styles/Themes/_Colors_Light.scss

@@ -114,6 +114,7 @@
   --context-menu-box-shadow: 0px 10px 38px -10px rgba(22, 23, 24, 0.35),
     0px 10px 20px -15px rgba(22, 23, 24, 0.2);
   --context-menu-bg-color-hover: var(--background-color-secondary);
+  --context-menu-active-item: rgb(0, 0, 0);
 
   // Shadows
   --floating-button-drop-shadow-color: rgba(0, 0, 0, 0.7);

invokeai/frontend/yarn.lock

@@ -56,26 +56,26 @@
     "@babel/helper-validator-identifier" "^7.19.1"
     to-fast-properties "^2.0.0"
 
-"@chakra-ui/accordion@2.1.9":
-  version "2.1.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.1.9.tgz#20fa86d94dc034251df2f7c8595ae4dd541a29d9"
-  integrity sha512-a9CKIAUHezc0f5FR/SQ4GVxnWuIb2HbDTxTEKTp58w/J9pecIbJaNrJ5TUZ0MVbDU9jkgO9RsZ29jkja8PomAw==
+"@chakra-ui/accordion@2.1.11":
+  version "2.1.11"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/accordion/-/accordion-2.1.11.tgz#c6df0100c543645d0631df3aefde2ea2b8ed6313"
+  integrity sha512-mfVPmqETp9pyRDHJ33AdF19oHv/LyxVzQJtlxUByuvs8Cj9QQZ2LQLg5kejm+b3mj03A7A6yfbuo3RNaI4Bhsg==
   dependencies:
-    "@chakra-ui/descendant" "3.0.13"
+    "@chakra-ui/descendant" "3.0.14"
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.15"
+    "@chakra-ui/transition" "2.0.16"
 
-"@chakra-ui/alert@2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/alert/-/alert-2.0.17.tgz#b129732ec308db6a6a1afa7c06a6595ad853c967"
-  integrity sha512-0Y5vw+HkeXpwbL1roVpSSNM6luMRmUbwduUSHEA4OnX1ismvsDb1ZBfpi4Vxp6w8euJ2Uj6df3krbd5tbCP6tg==
+"@chakra-ui/alert@2.1.0":
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/alert/-/alert-2.1.0.tgz#7a234ac6426231b39243088648455cbcf1cbdf24"
+  integrity sha512-OcfHwoXI5VrmM+tHJTHT62Bx6TfyfCxSa0PWUOueJzSyhlUOKBND5we6UtrOB7D0jwX45qKKEDJOLG5yCG21jQ==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/spinner" "2.0.13"
 
@@ -84,23 +84,23 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/anatomy/-/anatomy-2.1.2.tgz#ea66b1841e7195da08ddc862daaa3f3e56e565f5"
   integrity sha512-pKfOS/mztc4sUXHNc8ypJ1gPWSolWT770jrgVRfolVbYlki8y5Y+As996zMF6k5lewTu6j9DQequ7Cc9a69IVQ==
 
-"@chakra-ui/avatar@2.2.5":
-  version "2.2.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/avatar/-/avatar-2.2.5.tgz#50eb7cc5a172d394b301fa0abd5f607b7f5d3563"
-  integrity sha512-TEHXuGE79+fEn61qJ7J/A0Ec+WjyNwobrDTATcLg9Zx2/WEMmZNfrWIAlI5ANQAwVbdSWeGVbyoLAK5mbcrE0A==
+"@chakra-ui/avatar@2.2.8":
+  version "2.2.8"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/avatar/-/avatar-2.2.8.tgz#a6e16accb2bb9c879f197090ccc9df1ff42992a6"
+  integrity sha512-uBs9PMrqyK111tPIYIKnOM4n3mwgKqGpvYmtwBnnbQLTNLg4gtiWWVbpTuNMpyu1av0xQYomjUt8Doed8w6p8g==
   dependencies:
     "@chakra-ui/image" "2.0.15"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/breadcrumb@2.1.4":
-  version "2.1.4"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/breadcrumb/-/breadcrumb-2.1.4.tgz#0d249dc2a92639bd2bf46d097dd5445112bd2367"
-  integrity sha512-vyBx5TAxPnHhb0b8nyRGfqyjleD//9mySFhk96c9GL+T6YDO4swHw5y/kvDv3Ngc/iRwJ9hdI49PZKwPxLqsEg==
+"@chakra-ui/breadcrumb@2.1.5":
+  version "2.1.5"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/breadcrumb/-/breadcrumb-2.1.5.tgz#a43b22cc8005291a615696a8c88efc37064562f3"
+  integrity sha512-p3eQQrHQBkRB69xOmNyBJqEdfCrMt+e0eOH+Pm/DjFWfIVIbnIaFbmDCeWClqlLa21Ypc6h1hR9jEmvg8kmOog==
   dependencies:
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/breakpoint-utils@2.0.8":
@@ -110,12 +110,12 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/button@2.0.16":
-  version "2.0.16"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/button/-/button-2.0.16.tgz#ff315b57ee47c3511a6507fcfb6f00bb93e2ac7d"
-  integrity sha512-NjuTKa7gNhnGSUutKuTc8HoAOe9WWIigpciBG7yj3ok67kg8bXtSzPyQFZlgTY6XGdAckWTT+Do4tvhwa5LA+g==
+"@chakra-ui/button@2.0.18":
+  version "2.0.18"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/button/-/button-2.0.18.tgz#c13d2e404e22a9873ba5373fde494bedafe32fdd"
+  integrity sha512-E3c99+lOm6ou4nQVOTLkG+IdOPMjsQK+Qe7VyP8A/xeAMFONuibrWPRPpprr4ZkB4kEoLMfNuyH2+aEza3ScUA==
   dependencies:
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/spinner" "2.0.13"
@@ -127,13 +127,13 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/checkbox@2.2.10":
-  version "2.2.10"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/checkbox/-/checkbox-2.2.10.tgz#e4f773e7d2464f1d6e9d18dd88b679290cb33171"
-  integrity sha512-vzxEjw99qj7loxAdP1WuHNt4EAvj/t6cc8oxyOB2mEvkAzhxI34rLR+3zWDuHWsmhyUO+XEDh4FiWdR+DK5Siw==
+"@chakra-ui/checkbox@2.2.14":
+  version "2.2.14"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/checkbox/-/checkbox-2.2.14.tgz#902acc99a9a80c1c304788a230cf36f8116e8260"
+  integrity sha512-uqo6lFWLqYBujPglrvRhTAErtuIXpmdpc5w0W4bjK7kyvLhxOpUh1hlDb2WoqlNpfRn/OaNeF6VinPnf9BJL8w==
   dependencies:
-    "@chakra-ui/form-control" "2.0.17"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
@@ -142,7 +142,7 @@
     "@chakra-ui/react-use-update-effect" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/visually-hidden" "2.0.15"
-    "@zag-js/focus-visible" "0.2.1"
+    "@zag-js/focus-visible" "0.2.2"
 
 "@chakra-ui/clickable@2.0.14":
   version "2.0.14"
@@ -180,17 +180,17 @@
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/css-reset@2.0.12":
-  version "2.0.12"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/css-reset/-/css-reset-2.0.12.tgz#6eebcbe9e971facd215e174e063ace29f647a045"
-  integrity sha512-Q5OYIMvqTl2vZ947kIYxcS5DhQXeStB84BzzBd6C10wOx1gFUu9pL+jLpOnHR3hhpWRMdX5o7eT+gMJWIYUZ0Q==
+"@chakra-ui/css-reset@2.1.1":
+  version "2.1.1"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/css-reset/-/css-reset-2.1.1.tgz#c61f3d2103c13e62a86fd2d359682092e961852c"
+  integrity sha512-jwEOfIAWmQsnChHQTW/eRE+dfE4MjmhvSvoUug5nkV1pI7veC/20noFlIZxzi82EbiQI8Fs0+Jnusgxr2yaOHA==
 
-"@chakra-ui/descendant@3.0.13":
-  version "3.0.13"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/descendant/-/descendant-3.0.13.tgz#e883a2233ee07fe1ae6c014567824c0f79df11cf"
-  integrity sha512-9nzxZVxUSMc4xPL5fSaRkEOQjDQWUGjGvrZI7VzWk9eq63cojOtIxtWMSW383G9148PzWJjJYt30Eud5tdZzlg==
+"@chakra-ui/descendant@3.0.14":
+  version "3.0.14"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/descendant/-/descendant-3.0.14.tgz#fe8bac3f0e1ffe562e3e73eac393dbf222d57e13"
+  integrity sha512-+Ahvp9H4HMpfScIv9w1vaecGz7qWAaK1YFHHolz/SIsGLaLGlbdp+5UNabQC7L6TUnzzJDQDxzwif78rTD7ang==
   dependencies:
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
 
 "@chakra-ui/dom-utils@2.0.6":
@@ -198,12 +198,12 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/dom-utils/-/dom-utils-2.0.6.tgz#68f49f3b4a0bdebd5e416d6fd2c012c9ad64b76a"
   integrity sha512-PVtDkPrDD5b8aoL6Atg7SLjkwhWb7BwMcLOF1L449L3nZN+DAO3nyAh6iUhZVJyunELj9d0r65CDlnMREyJZmA==
 
-"@chakra-ui/editable@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/editable/-/editable-2.0.19.tgz#1af2fe3c215111f61f7872fb5f599f4d8da24e7d"
-  integrity sha512-YxRJsJ2JQd42zfPBgTKzIhg1HugT+gfQz1ZosmUN+IZT9YZXL2yodHTUz6Lee04Vc/CdEqgBFLuREXEUNBfGtA==
+"@chakra-ui/editable@2.0.21":
+  version "2.0.21"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/editable/-/editable-2.0.21.tgz#bc74510470d6d455844438e540851896d3879132"
+  integrity sha512-oYuXbHnggxSYJN7P9Pn0Scs9tPC91no4z1y58Oe+ILoJKZ+bFAEHtL7FEISDNJxw++MEukeFu7GU1hVqmdLsKQ==
   dependencies:
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
@@ -226,13 +226,13 @@
     "@chakra-ui/dom-utils" "2.0.6"
     react-focus-lock "^2.9.2"
 
-"@chakra-ui/form-control@2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/form-control/-/form-control-2.0.17.tgz#2f710325e77ce35067337616d440f903b137bdd5"
-  integrity sha512-34ptCaJ2LNvQNOlB6MAKsmH1AkT1xo7E+3Vw10Urr81yTOjDTM/iU6vG3JKPfRDMyXeowPjXmutlnuk72SSjRg==
+"@chakra-ui/form-control@2.0.18":
+  version "2.0.18"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/form-control/-/form-control-2.0.18.tgz#1923f293afde70b2b07ca731d98fef3660098c56"
+  integrity sha512-I0a0jG01IAtRPccOXSNugyRdUAe8Dy40ctqedZvznMweOXzbMCF1m+sHPLdWeWC/VI13VoAispdPY0/zHOdjsQ==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
@@ -254,10 +254,10 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/icons@^2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/icons/-/icons-2.0.17.tgz#625a46d169707aad36d65c04a4626a422f92e5ae"
-  integrity sha512-HMJP0WrJgAmFR9+Xh/CBH0nVnGMsJ4ZC8MK6tMgxPKd9/muvn0I4hsicHqdPlLpmB0TlxlhkBAKaVMtOdz6F0w==
+"@chakra-ui/icons@^2.0.18":
+  version "2.0.18"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/icons/-/icons-2.0.18.tgz#6f859d2e0d8f31fea9cb2e6507d65eb65cb95cf5"
+  integrity sha512-E/+DF/jw7kdN4/XxCZRnr4FdMXhkl50Q34MVwN9rADWMwPK9uSZPGyC7HOx6rilo7q4bFjYDH3yRj9g+VfbVkg==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
 
@@ -269,27 +269,27 @@
     "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/input@2.0.20":
-  version "2.0.20"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/input/-/input-2.0.20.tgz#8db3ec46b52be901c94599b3659a9003bdb2dd07"
-  integrity sha512-ypmsy4n4uNBVgn6Gd24Zrpi+qRf/T9WEzWkysuYC9Qfxo+i7yuf3snp7XmBy8KSGVSiXE11eO8ZN5oCg6Xg0jg==
+"@chakra-ui/input@2.0.21":
+  version "2.0.21"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/input/-/input-2.0.21.tgz#a7e55ea6fa32ae39c0f6ec44ca2189933fda9eb5"
+  integrity sha512-AIWjjg6MgcOtlvKmVoZfPPfgF+sBSWL3Zq2HSCAMvS6h7jfxz/Xv0UTFGPk5F4Wt0YHT7qMySg0Jsm0b78HZJg==
   dependencies:
-    "@chakra-ui/form-control" "2.0.17"
+    "@chakra-ui/form-control" "2.0.18"
     "@chakra-ui/object-utils" "2.0.8"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/layout@2.1.16":
-  version "2.1.16"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/layout/-/layout-2.1.16.tgz#9d90f25cf9f0537d19cd36a417f7ddc1461e8591"
-  integrity sha512-QFS3feozIGsvB0H74lUocev55aRF26eNrdmhfJifwikZAiq+zzZAMdBdNU9UJhHClnMOU8/iGZ0MF7ti4zQS1A==
+"@chakra-ui/layout@2.1.18":
+  version "2.1.18"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/layout/-/layout-2.1.18.tgz#f5dba687dfced9145d495f3a21edb5672df6bb73"
+  integrity sha512-F4Gh2e+DGdaWdWT5NZduIFD9NM7Bnuh8sXARFHWPvIu7yvAwZ3ddqC9GK4F3qUngdmkJxDLWQqRSwSh96Lxbhw==
   dependencies:
     "@chakra-ui/breakpoint-utils" "2.0.8"
     "@chakra-ui/icon" "3.0.16"
     "@chakra-ui/object-utils" "2.0.8"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/lazy-utils@2.0.5":
@@ -311,17 +311,17 @@
     "@chakra-ui/react-env" "3.0.0"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/menu@2.1.9":
-  version "2.1.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/menu/-/menu-2.1.9.tgz#2f3239a9b2855fd77fc317d9e6b904c1ad50d7c6"
-  integrity sha512-ue5nD4QJcl3H3UwN0zZNJmH89XUebnvEdW6THAUL41hDjJ0J/Fjpg9Sgzwug2aBbBXBNbVMsUuhcCj6x91d+IQ==
+"@chakra-ui/menu@2.1.12":
+  version "2.1.12"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/menu/-/menu-2.1.12.tgz#ab83b7a5165bd31a6c68328d7f65a79e3412c48d"
+  integrity sha512-ylNK1VJlr/3/EGg9dLPZ87cBJJjeiYXeU/gOAphsKXMnByrXWhbp4YVnyyyha2KZ0zEw0aPU4nCZ+A69aT9wrg==
   dependencies:
     "@chakra-ui/clickable" "2.0.14"
-    "@chakra-ui/descendant" "3.0.13"
+    "@chakra-ui/descendant" "3.0.14"
     "@chakra-ui/lazy-utils" "2.0.5"
     "@chakra-ui/popper" "3.0.13"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-use-animation-state" "2.0.8"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-disclosure" "2.0.8"
@@ -330,33 +330,33 @@
     "@chakra-ui/react-use-outside-click" "2.0.7"
     "@chakra-ui/react-use-update-effect" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.15"
+    "@chakra-ui/transition" "2.0.16"
 
-"@chakra-ui/modal@2.2.9":
-  version "2.2.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/modal/-/modal-2.2.9.tgz#aad65a2c60aa974e023f8b3facc0e79eb742e006"
-  integrity sha512-nTfNp7XsVwn5+xJOtstoFA8j0kq/9sJj7KesyYzjEDaMKvCZvIOntRYowoydho43jb4+YC7ebKhp0KOIINS0gg==
+"@chakra-ui/modal@2.2.11":
+  version "2.2.11"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/modal/-/modal-2.2.11.tgz#8a964288759f3d681e23bfc3a837a3e2c7523f8e"
+  integrity sha512-2J0ZUV5tEzkPiawdkgPz6bmex7NXAde1VXooMwdvK+vuT8PV3U61yorTJOZVLdw7TjjI1Yo94mzsp6UwBud43Q==
   dependencies:
     "@chakra-ui/close-button" "2.0.17"
     "@chakra-ui/focus-lock" "2.0.16"
-    "@chakra-ui/portal" "2.0.15"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/portal" "2.0.16"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/transition" "2.0.15"
+    "@chakra-ui/transition" "2.0.16"
     aria-hidden "^1.2.2"
     react-remove-scroll "^2.5.5"
 
-"@chakra-ui/number-input@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/number-input/-/number-input-2.0.18.tgz#072a00ef869ebafa4960cfdee8caae8208864289"
-  integrity sha512-cPkyAFFHHzeFBselrT1BtjlzMkJ6TKrTDUnHFlzqXy6aqeXuhrjFhMfXucjedSpOqedsP9ZbKFTdIAhu9DdL/A==
+"@chakra-ui/number-input@2.0.19":
+  version "2.0.19"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/number-input/-/number-input-2.0.19.tgz#82d4522036904c04d07e7050822fc522f9b32233"
+  integrity sha512-HDaITvtMEqOauOrCPsARDxKD9PSHmhWywpcyCSOX0lMe4xx2aaGhU0QQFhsJsykj8Er6pytMv6t0KZksdDv3YA==
   dependencies:
     "@chakra-ui/counter" "2.0.14"
-    "@chakra-ui/form-control" "2.0.17"
+    "@chakra-ui/form-control" "2.0.18"
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/react-use-event-listener" "2.0.7"
@@ -376,27 +376,27 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/object-utils/-/object-utils-2.0.8.tgz#307f927f6434f99feb32ba92bdf451a6b59a6199"
   integrity sha512-2upjT2JgRuiupdrtBWklKBS6tqeGMA77Nh6Q0JaoQuH/8yq+15CGckqn3IUWkWoGI0Fg3bK9LDlbbD+9DLw95Q==
 
-"@chakra-ui/pin-input@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/pin-input/-/pin-input-2.0.19.tgz#f9b196174f0518feec5c1ee3fcaf2134c301148a"
-  integrity sha512-6O7s4vWz4cqQ6zvMov9sYj6ZqWAsTxR/MNGe3DNgu1zWQg8veNCYtj1rNGhNS3eZNUMAa8uM2dXIphGTP53Xow==
+"@chakra-ui/pin-input@2.0.20":
+  version "2.0.20"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/pin-input/-/pin-input-2.0.20.tgz#5bf115bf4282b69fc6532a9c542cbf41f815d200"
+  integrity sha512-IHVmerrtHN8F+jRB3W1HnMir1S1TUCWhI7qDInxqPtoRffHt6mzZgLZ0izx8p1fD4HkW4c1d4/ZLEz9uH9bBRg==
   dependencies:
-    "@chakra-ui/descendant" "3.0.13"
+    "@chakra-ui/descendant" "3.0.14"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/popover@2.1.8":
-  version "2.1.8"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/popover/-/popover-2.1.8.tgz#e906ce0533693d735b6e13a3a6ffe16d8e0a9ab4"
-  integrity sha512-ob7fAz+WWmXIq7iGHVB3wDKzZTj+T+noYBT/U1Q+jIf+jMr2WOpJLTfb0HTZcfhvn4EBFlfBg7Wk5qbXNaOn7g==
+"@chakra-ui/popover@2.1.9":
+  version "2.1.9"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/popover/-/popover-2.1.9.tgz#890cc0dfc5022757715ccf772ec194e7a409275f"
+  integrity sha512-OMJ12VVs9N32tFaZSOqikkKPtwAVwXYsES/D1pff/amBrE3ngCrpxJSIp4uvTdORfIYDojJqrR52ZplDKS9hRQ==
   dependencies:
     "@chakra-ui/close-button" "2.0.17"
     "@chakra-ui/lazy-utils" "2.0.5"
     "@chakra-ui/popper" "3.0.13"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-animation-state" "2.0.8"
     "@chakra-ui/react-use-disclosure" "2.0.8"
@@ -414,53 +414,53 @@
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@popperjs/core" "^2.9.3"
 
-"@chakra-ui/portal@2.0.15":
-  version "2.0.15"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/portal/-/portal-2.0.15.tgz#21e1f97c4407fc15df8c365cb5cf799dac73ce41"
-  integrity sha512-z8v7K3j1/nMuBzp2+wRIIw7s/eipVtnXLdjK5yqbMxMRa44E8Mu5VNJLz3aQFLHXEUST+ifqrjImQeli9do6LQ==
+"@chakra-ui/portal@2.0.16":
+  version "2.0.16"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/portal/-/portal-2.0.16.tgz#e5ce3f9d9e559f17a95276e0c006d0e9b7703442"
+  integrity sha512-bVID0qbQ0l4xq38LdqAN4EKD4/uFkDnXzFwOlviC9sl0dNhzICDb1ltuH/Adl1d2HTMqyN60O3GO58eHy7plnQ==
   dependencies:
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
 
-"@chakra-ui/progress@2.1.5":
-  version "2.1.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/progress/-/progress-2.1.5.tgz#eb6a47adf2bff93971262d163461d390782a04ff"
-  integrity sha512-jj5Vp4lxUchuwp4RPCepM0yAyKi344bgsOd3Apd+ldxclDcewPc82fbwDu7g/Xv27LqJkT+7E/SlQy04wGrk0g==
+"@chakra-ui/progress@2.1.6":
+  version "2.1.6"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/progress/-/progress-2.1.6.tgz#398db20440979c37adb0a34821f805ae3471873b"
+  integrity sha512-hHh5Ysv4z6bK+j2GJbi/FT9CVyto2PtNUNwBmr3oNMVsoOUMoRjczfXvvYqp0EHr9PCpxqrq7sRwgQXUzhbDSw==
   dependencies:
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
 
-"@chakra-ui/provider@2.1.2":
-  version "2.1.2"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/provider/-/provider-2.1.2.tgz#b025cb718826b003b3c9535b6961e8f3be70ebd5"
-  integrity sha512-4lLlz8QuJv00BhfyKzWpzfoti9MDOdJ/MqXixJV/EZ02RMBOdE9qy9bSz/WckPC2MVhtRUuwMkxH+0QY21PXuw==
+"@chakra-ui/provider@2.2.2":
+  version "2.2.2"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/provider/-/provider-2.2.2.tgz#a798d1c243f33e00c85763834a7350e0d1c643ad"
+  integrity sha512-UVwnIDnAWq1aKroN5AF+OpNpUqLVeIUk7tKvX3z4CY9FsPFFi6LTEhRHdhpwaU1Tau3Tf9agEu5URegpY7S8BA==
   dependencies:
-    "@chakra-ui/css-reset" "2.0.12"
-    "@chakra-ui/portal" "2.0.15"
+    "@chakra-ui/css-reset" "2.1.1"
+    "@chakra-ui/portal" "2.0.16"
     "@chakra-ui/react-env" "3.0.0"
-    "@chakra-ui/system" "2.5.1"
+    "@chakra-ui/system" "2.5.5"
     "@chakra-ui/utils" "2.0.15"
 
-"@chakra-ui/radio@2.0.19":
-  version "2.0.19"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/radio/-/radio-2.0.19.tgz#8d5c02eae8eddbced4476b1b50921ade62f0a744"
-  integrity sha512-PlJiV59eGSmeKP4v/4+ccQUWGRd0cjPKkj/p3L+UbOf8pl9dWm8y9kIeL5TYbghQSDv0nzkrH4+yMnnDTZjdMQ==
+"@chakra-ui/radio@2.0.22":
+  version "2.0.22"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/radio/-/radio-2.0.22.tgz#fad0ce7c9ba4051991ed517cac4cfe526d6d47d9"
+  integrity sha512-GsQ5WAnLwivWl6gPk8P1x+tCcpVakCt5R5T0HumF7DGPXKdJbjS+RaFySrbETmyTJsKY4QrfXn+g8CWVrMjPjw==
   dependencies:
-    "@chakra-ui/form-control" "2.0.17"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/form-control" "2.0.18"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@zag-js/focus-visible" "0.2.1"
+    "@zag-js/focus-visible" "0.2.2"
 
 "@chakra-ui/react-children-utils@2.0.6":
   version "2.0.6"
   resolved "https://registry.yarnpkg.com/@chakra-ui/react-children-utils/-/react-children-utils-2.0.6.tgz#6c480c6a60678fcb75cb7d57107c7a79e5179b92"
   integrity sha512-QVR2RC7QsOsbWwEnq9YduhpqSFnZGvjjGREV8ygKi8ADhXh93C8azLECCUVgRJF2Wc+So1fgxmjLcbZfY2VmBA==
 
-"@chakra-ui/react-context@2.0.7":
-  version "2.0.7"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-context/-/react-context-2.0.7.tgz#f79a2b072d04d4280ec8799dc03a8a1af521ca2e"
-  integrity sha512-i7EGmSU+h2GB30cwrKB4t1R5BMHyGoJM5L2Zz7b+ZUX4aAqyPcfe97wPiQB6Rgr1ImGXrUeov4CDVrRZ2FPgLQ==
+"@chakra-ui/react-context@2.0.8":
+  version "2.0.8"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-context/-/react-context-2.0.8.tgz#5e0ed33ac3995875a21dea0e12b0ee5fc4c2e3cc"
+  integrity sha512-tRTKdn6lCTXM6WPjSokAAKCw2ioih7Eg8cNgaYRSwKBck8nkz9YqxgIIEj3dJD7MGtpl24S/SNI98iRWkRwR/A==
 
 "@chakra-ui/react-env@3.0.0":
   version "3.0.0"
@@ -568,12 +568,12 @@
   resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-safe-layout-effect/-/react-use-safe-layout-effect-2.0.5.tgz#6cf388c37fd2a42b5295a292e149b32f860a00a7"
   integrity sha512-MwAQBz3VxoeFLaesaSEN87reVNVbjcQBDex2WGexAg6hUB6n4gc1OWYH/iXp4tzp4kuggBNhEHkk9BMYXWfhJQ==
 
-"@chakra-ui/react-use-size@2.0.9":
-  version "2.0.9"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-size/-/react-use-size-2.0.9.tgz#00717867b98a24c3bdcfaa0c3e70732404193486"
-  integrity sha512-Jce7QmO1jlQZq+Y77VKckWzroRnajChzUQ8xhLQZO6VbYvrpg3cu+X2QCz3G+MZzB+1/hnvvAqmZ+uJLd8rEJg==
+"@chakra-ui/react-use-size@2.0.10":
+  version "2.0.10"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react-use-size/-/react-use-size-2.0.10.tgz#6131950852490c06e5fb3760bf64097c8057391f"
+  integrity sha512-fdIkH14GDnKQrtQfxX8N3gxbXRPXEl67Y3zeD9z4bKKcQUAYIMqs0MsPZY+FMpGQw8QqafM44nXfL038aIrC5w==
   dependencies:
-    "@zag-js/element-size" "0.3.1"
+    "@zag-js/element-size" "0.3.2"
 
 "@chakra-ui/react-use-timeout@2.0.5":
   version "2.0.5"
@@ -594,69 +594,69 @@
   dependencies:
     "@chakra-ui/utils" "2.0.15"
 
-"@chakra-ui/react@^2.5.1":
-  version "2.5.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/react/-/react-2.5.1.tgz#05414db2b512bd4402e42eecc6b915d85102c576"
-  integrity sha512-ugkaqfcNMb9L4TkalWiF3rnqfr0TlUUD46JZaDIZiORVisaSwXTZTQrVfG40VghhaJT28rnC5WtiE8kd567ZBQ==
+"@chakra-ui/react@^2.5.5":
+  version "2.5.5"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/react/-/react-2.5.5.tgz#5ae2450ec0d10d63e1314747466f21cf542032ff"
+  integrity sha512-aBVMUtdWv2MrptD/tKSqICPsuJ+I+jvauegffO1qPUDlK3RrXIDeOHkLGWohgXNcjY5bGVWguFEzJm97//0ooQ==
   dependencies:
-    "@chakra-ui/accordion" "2.1.9"
-    "@chakra-ui/alert" "2.0.17"
-    "@chakra-ui/avatar" "2.2.5"
-    "@chakra-ui/breadcrumb" "2.1.4"
-    "@chakra-ui/button" "2.0.16"
+    "@chakra-ui/accordion" "2.1.11"
+    "@chakra-ui/alert" "2.1.0"
+    "@chakra-ui/avatar" "2.2.8"
+    "@chakra-ui/breadcrumb" "2.1.5"
+    "@chakra-ui/button" "2.0.18"
     "@chakra-ui/card" "2.1.6"
-    "@chakra-ui/checkbox" "2.2.10"
+    "@chakra-ui/checkbox" "2.2.14"
     "@chakra-ui/close-button" "2.0.17"
     "@chakra-ui/control-box" "2.0.13"
     "@chakra-ui/counter" "2.0.14"
-    "@chakra-ui/css-reset" "2.0.12"
-    "@chakra-ui/editable" "2.0.19"
+    "@chakra-ui/css-reset" "2.1.1"
+    "@chakra-ui/editable" "2.0.21"
     "@chakra-ui/focus-lock" "2.0.16"
-    "@chakra-ui/form-control" "2.0.17"
+    "@chakra-ui/form-control" "2.0.18"
     "@chakra-ui/hooks" "2.1.6"
     "@chakra-ui/icon" "3.0.16"
     "@chakra-ui/image" "2.0.15"
-    "@chakra-ui/input" "2.0.20"
-    "@chakra-ui/layout" "2.1.16"
+    "@chakra-ui/input" "2.0.21"
+    "@chakra-ui/layout" "2.1.18"
     "@chakra-ui/live-region" "2.0.13"
     "@chakra-ui/media-query" "3.2.12"
-    "@chakra-ui/menu" "2.1.9"
-    "@chakra-ui/modal" "2.2.9"
-    "@chakra-ui/number-input" "2.0.18"
-    "@chakra-ui/pin-input" "2.0.19"
-    "@chakra-ui/popover" "2.1.8"
+    "@chakra-ui/menu" "2.1.12"
+    "@chakra-ui/modal" "2.2.11"
+    "@chakra-ui/number-input" "2.0.19"
+    "@chakra-ui/pin-input" "2.0.20"
+    "@chakra-ui/popover" "2.1.9"
     "@chakra-ui/popper" "3.0.13"
-    "@chakra-ui/portal" "2.0.15"
-    "@chakra-ui/progress" "2.1.5"
-    "@chakra-ui/provider" "2.1.2"
-    "@chakra-ui/radio" "2.0.19"
+    "@chakra-ui/portal" "2.0.16"
+    "@chakra-ui/progress" "2.1.6"
+    "@chakra-ui/provider" "2.2.2"
+    "@chakra-ui/radio" "2.0.22"
     "@chakra-ui/react-env" "3.0.0"
-    "@chakra-ui/select" "2.0.18"
+    "@chakra-ui/select" "2.0.19"
     "@chakra-ui/skeleton" "2.0.24"
-    "@chakra-ui/slider" "2.0.21"
+    "@chakra-ui/slider" "2.0.23"
     "@chakra-ui/spinner" "2.0.13"
-    "@chakra-ui/stat" "2.0.17"
-    "@chakra-ui/styled-system" "2.6.1"
-    "@chakra-ui/switch" "2.0.22"
-    "@chakra-ui/system" "2.5.1"
-    "@chakra-ui/table" "2.0.16"
-    "@chakra-ui/tabs" "2.1.8"
-    "@chakra-ui/tag" "2.0.17"
-    "@chakra-ui/textarea" "2.0.18"
-    "@chakra-ui/theme" "2.2.5"
-    "@chakra-ui/theme-utils" "2.0.11"
-    "@chakra-ui/toast" "6.0.1"
-    "@chakra-ui/tooltip" "2.2.6"
-    "@chakra-ui/transition" "2.0.15"
+    "@chakra-ui/stat" "2.0.18"
+    "@chakra-ui/styled-system" "2.8.0"
+    "@chakra-ui/switch" "2.0.26"
+    "@chakra-ui/system" "2.5.5"
+    "@chakra-ui/table" "2.0.17"
+    "@chakra-ui/tabs" "2.1.9"
+    "@chakra-ui/tag" "3.0.0"
+    "@chakra-ui/textarea" "2.0.19"
+    "@chakra-ui/theme" "3.0.1"
+    "@chakra-ui/theme-utils" "2.0.15"
+    "@chakra-ui/toast" "6.1.1"
+    "@chakra-ui/tooltip" "2.2.7"
+    "@chakra-ui/transition" "2.0.16"
     "@chakra-ui/utils" "2.0.15"
     "@chakra-ui/visually-hidden" "2.0.15"
 
-"@chakra-ui/select@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/select/-/select-2.0.18.tgz#4eb6092610067c1b4131353fe39b4466e251395b"
-  integrity sha512-1d2lUT5LM6oOs5x4lzBh4GFDuXX62+lr+sgV7099g951/5UNbb0CS2hSZHsO7yZThLNbr7QTWZvAOAayVcGzdw==
+"@chakra-ui/select@2.0.19":
+  version "2.0.19"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/select/-/select-2.0.19.tgz#957e95a17a890d8c0a851e2f00a8d8dd17932d66"
+  integrity sha512-eAlFh+JhwtJ17OrB6fO6gEAGOMH18ERNrXLqWbYLrs674Le7xuREgtuAYDoxUzvYXYYTTdOJtVbcHGriI3o6rA==
   dependencies:
-    "@chakra-ui/form-control" "2.0.17"
+    "@chakra-ui/form-control" "2.0.18"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/shared-utils@2.0.5":
@@ -673,20 +673,20 @@
     "@chakra-ui/react-use-previous" "2.0.5"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/slider@2.0.21":
-  version "2.0.21"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/slider/-/slider-2.0.21.tgz#f65b15bf0d5f827699ff9a2d6faee35006e2bfce"
-  integrity sha512-Mm76yJxEqJl21+3waEcKg3tM8Y4elJ7mcViN6Brj35PTfzUJfSJxeBGo1nLPJ+X5jLj7o/L4kfBmUk3lY4QYEQ==
+"@chakra-ui/slider@2.0.23":
+  version "2.0.23"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/slider/-/slider-2.0.23.tgz#9130c7aee8ca876be64d1aeba6b84fe421c8207b"
+  integrity sha512-/eyRUXLla+ZdBUPXpakE3SAS2JS8mIJR6qcUYiPVKSpRAi6tMyYeQijAXn2QC1AUVd2JrG8Pz+1Jy7Po3uA7cA==
   dependencies:
     "@chakra-ui/number-utils" "2.0.7"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-callback-ref" "2.0.7"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-latest-ref" "2.0.5"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/react-use-pan-event" "2.0.9"
-    "@chakra-ui/react-use-size" "2.0.9"
+    "@chakra-ui/react-use-size" "2.0.10"
     "@chakra-ui/react-use-update-effect" "2.0.7"
 
 "@chakra-ui/spinner@2.0.13":
@@ -696,82 +696,82 @@
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/stat@2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/stat/-/stat-2.0.17.tgz#2cd712cc7e0d58d9cbd542deea911f1b0925074f"
-  integrity sha512-PhD+5oVLWjQmGLfeZSmexp3AtLcaggWBwoMZ4z8QMZIQzf/fJJWMk0bMqxlpTv8ORDkfY/4ImuFB/RJHvcqlcA==
+"@chakra-ui/stat@2.0.18":
+  version "2.0.18"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/stat/-/stat-2.0.18.tgz#9e5d21d162b7cf2cf92065c19291ead2d4660772"
+  integrity sha512-wKyfBqhVlIs9bkSerUc6F9KJMw0yTIEKArW7dejWwzToCLPr47u+CtYO6jlJHV6lRvkhi4K4Qc6pyvtJxZ3VpA==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/styled-system@2.6.1":
-  version "2.6.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/styled-system/-/styled-system-2.6.1.tgz#302d496d34c0b7b30c646a7e3c9b113a2f4588da"
-  integrity sha512-jy/1dVi1LxjoRCm+Eo5mqBgvPy5SCWMlIcz6GbIZBDpkGeKZwtqrZLjekxxLBCy8ORY+kJlUB0FT6AzVR/1tjw==
+"@chakra-ui/styled-system@2.8.0":
+  version "2.8.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/styled-system/-/styled-system-2.8.0.tgz#c02aa7b4a15bd826c19d055cd226bd44f7470f26"
+  integrity sha512-bmRv/8ACJGGKGx84U1npiUddwdNifJ+/ETklGwooS5APM0ymwUtBYZpFxjYNJrqvVYpg3mVY6HhMyBVptLS7iA==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
     csstype "^3.0.11"
     lodash.mergewith "4.6.2"
 
-"@chakra-ui/switch@2.0.22":
-  version "2.0.22"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/switch/-/switch-2.0.22.tgz#7b35e2b10ea4cf91fb49f5175b4335c61dcd25b3"
-  integrity sha512-+/Yy6y7VFD91uSPruF8ZvePi3tl5D8UNVATtWEQ+QBI92DLSM+PtgJ2F0Y9GMZ9NzMxpZ80DqwY7/kqcPCfLvw==
+"@chakra-ui/switch@2.0.26":
+  version "2.0.26"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/switch/-/switch-2.0.26.tgz#b93eeafd788e47c21222524adceffe9ef62602d6"
+  integrity sha512-x62lF6VazSZJQuVxosChVR6+0lIJe8Pxgkl/C9vxjhp2yVYb3mew5tcX/sDOu0dYZy8ro/9hMfGkdN4r9xEU8A==
   dependencies:
-    "@chakra-ui/checkbox" "2.2.10"
+    "@chakra-ui/checkbox" "2.2.14"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/system@2.5.1":
-  version "2.5.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/system/-/system-2.5.1.tgz#bc03a11ae31e795966c7618280548d5cd866f47e"
-  integrity sha512-4+86OrcSoq7lGkm5fh+sJ3IWXSTzjz+HOllRbCW2Rtnmcg7ritiXVNV2VygEg2DrCcx5+tNqRHDM764zW+AEug==
+"@chakra-ui/system@2.5.5":
+  version "2.5.5"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/system/-/system-2.5.5.tgz#b8b070d07ca9b0190363100396eea02cca754cec"
+  integrity sha512-52BIp/Zyvefgxn5RTByfkTeG4J+y81LWEjWm8jCaRFsLVm8IFgqIrngtcq4I7gD5n/UKbneHlb4eLHo4uc5yDQ==
   dependencies:
     "@chakra-ui/color-mode" "2.1.12"
     "@chakra-ui/object-utils" "2.0.8"
     "@chakra-ui/react-utils" "2.0.12"
-    "@chakra-ui/styled-system" "2.6.1"
-    "@chakra-ui/theme-utils" "2.0.11"
+    "@chakra-ui/styled-system" "2.8.0"
+    "@chakra-ui/theme-utils" "2.0.15"
     "@chakra-ui/utils" "2.0.15"
-    react-fast-compare "3.2.0"
+    react-fast-compare "3.2.1"
 
-"@chakra-ui/table@2.0.16":
-  version "2.0.16"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/table/-/table-2.0.16.tgz#e69736cba5cfb218c5e40592ad9280c6e32f6fe7"
-  integrity sha512-vWDXZ6Ad3Aj66curp1tZBHvCfQHX2FJ4ijLiqGgQszWFIchfhJ5vMgEBJaFMZ+BN1draAjuRTZqaQefOApzvRg==
+"@chakra-ui/table@2.0.17":
+  version "2.0.17"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/table/-/table-2.0.17.tgz#ad394dc6dcbe5a8a9e6d899997ecca3471603977"
+  integrity sha512-OScheTEp1LOYvTki2NFwnAYvac8siAhW9BI5RKm5f5ORL2gVJo4I72RUqE0aKe1oboxgm7CYt5afT5PS5cG61A==
   dependencies:
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/tabs@2.1.8":
-  version "2.1.8"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tabs/-/tabs-2.1.8.tgz#e83071380f9a3633810308d45de51be7a74f5eb9"
-  integrity sha512-B7LeFN04Ny2jsSy5TFOQxnbZ6ITxGxLxsB2PE0vvQjMSblBrUryOxdjw80HZhfiw6od0ikK9CeKQOIt9QCguSw==
+"@chakra-ui/tabs@2.1.9":
+  version "2.1.9"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tabs/-/tabs-2.1.9.tgz#2e5214cb453c6cc0c240e82bd88af1042fc6fe0e"
+  integrity sha512-Yf8e0kRvaGM6jfkJum0aInQ0U3ZlCafmrYYni2lqjcTtThqu+Yosmo3iYlnullXxCw5MVznfrkb9ySvgQowuYg==
   dependencies:
     "@chakra-ui/clickable" "2.0.14"
-    "@chakra-ui/descendant" "3.0.13"
+    "@chakra-ui/descendant" "3.0.14"
     "@chakra-ui/lazy-utils" "2.0.5"
     "@chakra-ui/react-children-utils" "2.0.6"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-use-controllable-state" "2.0.8"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/react-use-safe-layout-effect" "2.0.5"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/tag@2.0.17":
-  version "2.0.17"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tag/-/tag-2.0.17.tgz#97adb86db190ddb3526060b78c590392e0ac8b4c"
-  integrity sha512-A47zE9Ft9qxOJ+5r1cUseKRCoEdqCRzFm0pOtZgRcckqavglk75Xjgz8HbBpUO2zqqd49MlqdOwR8o87fXS1vg==
+"@chakra-ui/tag@3.0.0":
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tag/-/tag-3.0.0.tgz#d86cdab59bb3ff7fc628c2dbe7a5ff1b36bd3e96"
+  integrity sha512-YWdMmw/1OWRwNkG9pX+wVtZio+B89odaPj6XeMn5nfNN8+jyhIEpouWv34+CO9G0m1lupJTxPSfgLAd7cqXZMA==
   dependencies:
     "@chakra-ui/icon" "3.0.16"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/react-context" "2.0.8"
 
-"@chakra-ui/textarea@2.0.18":
-  version "2.0.18"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/textarea/-/textarea-2.0.18.tgz#da6d629b465f65bbc7b48039c2e48a4ae1d853b4"
-  integrity sha512-aGHHb29vVifO0OtcK/k8cMykzjOKo/coDTU0NJqz7OOLAWIMNV2eGenvmO1n9tTZbmbqHiX+Sa1nPRX+pd14lg==
+"@chakra-ui/textarea@2.0.19":
+  version "2.0.19"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/textarea/-/textarea-2.0.19.tgz#470b459f9cb3255d2abbe07d46b0a5b60a6a32c5"
+  integrity sha512-adJk+qVGsFeJDvfn56CcJKKse8k7oMGlODrmpnpTdF+xvlsiTM+1GfaJvgNSpHHuQFdz/A0z1uJtfGefk0G2ZA==
   dependencies:
-    "@chakra-ui/form-control" "2.0.17"
+    "@chakra-ui/form-control" "2.0.18"
     "@chakra-ui/shared-utils" "2.0.5"
 
 "@chakra-ui/theme-tools@2.0.17":
@@ -783,57 +783,57 @@
     "@chakra-ui/shared-utils" "2.0.5"
     color2k "^2.0.0"
 
-"@chakra-ui/theme-utils@2.0.11":
-  version "2.0.11"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/theme-utils/-/theme-utils-2.0.11.tgz#c01b1d14fdd63326d1ad11fd8f0872921ea43872"
-  integrity sha512-lBAay6Sq3/fl7exd3mFxWAbzgdQowytor0fnlHrpNStn1HgFjXukwsf6356XQOie2Vd8qaMM7qZtMh4AiC0dcg==
+"@chakra-ui/theme-utils@2.0.15":
+  version "2.0.15"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/theme-utils/-/theme-utils-2.0.15.tgz#968a5e8c47bb403323fe67049c7b751a6e47f069"
+  integrity sha512-UuxtEgE7gwMTGDXtUpTOI7F5X0iHB9ekEOG5PWPn2wWBL7rlk2JtPI7UP5Um5Yg6vvBfXYGK1ySahxqsgf+87g==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/styled-system" "2.6.1"
-    "@chakra-ui/theme" "2.2.5"
+    "@chakra-ui/styled-system" "2.8.0"
+    "@chakra-ui/theme" "3.0.1"
     lodash.mergewith "4.6.2"
 
-"@chakra-ui/theme@2.2.5":
-  version "2.2.5"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/theme/-/theme-2.2.5.tgz#18ed1755ff27c1ff1f1a77083ffc546c361c926e"
-  integrity sha512-hYASZMwu0NqEv6PPydu+F3I+kMNd44yR4TwjR/lXBz/LEh64L6UPY6kQjebCfgdVtsGdl3HKg+eLlfa7SvfRgw==
+"@chakra-ui/theme@3.0.1":
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/theme/-/theme-3.0.1.tgz#151fc5d1e23d0fd0cd29d28acf8f6017269c13fc"
+  integrity sha512-92kDm/Ux/51uJqhRKevQo/O/rdwucDYcpHg2QuwzdAxISCeYvgtl2TtgOOl5EnqEP0j3IEAvZHZUlv8TTbawaw==
   dependencies:
     "@chakra-ui/anatomy" "2.1.2"
     "@chakra-ui/shared-utils" "2.0.5"
     "@chakra-ui/theme-tools" "2.0.17"
 
-"@chakra-ui/toast@6.0.1":
-  version "6.0.1"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/toast/-/toast-6.0.1.tgz#726b67a57cdd592320bb3f450c66d007a2a1d902"
-  integrity sha512-ej2kJXvu/d2h6qnXU5D8XTyw0qpsfmbiU7hUffo/sPxkz89AUOQ08RUuUmB1ssW/FZcQvNMJ5WgzCTKHGBxtxw==
+"@chakra-ui/toast@6.1.1":
+  version "6.1.1"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/toast/-/toast-6.1.1.tgz#7ca78f38069bc87fa75b64de76c8fc2758bdf419"
+  integrity sha512-JtjIKkPVjEu8okGGCipCxNVgK/15h5AicTATZ6RbG2MsHmr4GfKG3fUCvpbuZseArqmLqGLQZQJjVE9vJzaSkQ==
   dependencies:
-    "@chakra-ui/alert" "2.0.17"
+    "@chakra-ui/alert" "2.1.0"
     "@chakra-ui/close-button" "2.0.17"
-    "@chakra-ui/portal" "2.0.15"
-    "@chakra-ui/react-context" "2.0.7"
+    "@chakra-ui/portal" "2.0.16"
+    "@chakra-ui/react-context" "2.0.8"
     "@chakra-ui/react-use-timeout" "2.0.5"
     "@chakra-ui/react-use-update-effect" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
-    "@chakra-ui/styled-system" "2.6.1"
-    "@chakra-ui/theme" "2.2.5"
+    "@chakra-ui/styled-system" "2.8.0"
+    "@chakra-ui/theme" "3.0.1"
 
-"@chakra-ui/tooltip@2.2.6":
-  version "2.2.6"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/tooltip/-/tooltip-2.2.6.tgz#a38f9ff2dd8a574c8cf49526c3846533455f8ddd"
-  integrity sha512-4cbneidZ5+HCWge3OZzewRQieIvhDjSsl+scrl4Scx7E0z3OmqlTIESU5nGIZDBLYqKn/UirEZhqaQ33FOS2fw==
+"@chakra-ui/tooltip@2.2.7":
+  version "2.2.7"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/tooltip/-/tooltip-2.2.7.tgz#7c305efb057a5fe4694b1b8d82395aec776d8f57"
+  integrity sha512-ImUJ6NnVqARaYqpgtO+kzucDRmxo8AF3jMjARw0bx2LxUkKwgRCOEaaRK5p5dHc0Kr6t5/XqjDeUNa19/sLauA==
   dependencies:
     "@chakra-ui/popper" "3.0.13"
-    "@chakra-ui/portal" "2.0.15"
+    "@chakra-ui/portal" "2.0.16"
     "@chakra-ui/react-types" "2.0.7"
     "@chakra-ui/react-use-disclosure" "2.0.8"
     "@chakra-ui/react-use-event-listener" "2.0.7"
     "@chakra-ui/react-use-merge-refs" "2.0.7"
     "@chakra-ui/shared-utils" "2.0.5"
 
-"@chakra-ui/transition@2.0.15":
-  version "2.0.15"
-  resolved "https://registry.yarnpkg.com/@chakra-ui/transition/-/transition-2.0.15.tgz#c640df2ea82f5ad58c55a6e1a7c338f377cb96d8"
-  integrity sha512-o9LBK/llQfUDHF/Ty3cQ6nShpekKTqHUoJlUOzNKhoTsNpoRerr9v0jwojrX1YI02KtVjfhFU6PiqXlDfREoNw==
+"@chakra-ui/transition@2.0.16":
+  version "2.0.16"
+  resolved "https://registry.yarnpkg.com/@chakra-ui/transition/-/transition-2.0.16.tgz#498c91e6835bb5d950fd1d1402f483b85f7dcd87"
+  integrity sha512-E+RkwlPc3H7P1crEXmXwDXMB2lqY2LLia2P5siQ4IEnRWIgZXlIw+8Em+NtHNgusel2N+9yuB0wT9SeZZeZ3CQ==
   dependencies:
     "@chakra-ui/shared-utils" "2.0.5"
 
@@ -1081,6 +1081,18 @@
   resolved "https://registry.yarnpkg.com/@esbuild/win32-x64/-/win32-x64-0.16.17.tgz#c5a1a4bfe1b57f0c3e61b29883525c6da3e5c091"
   integrity sha512-y+EHuSchhL7FjHgvQL/0fnnFmO4T1bhvWANX6gcnqTjtnKWbTvUMCpGnv2+t+31d7RzyEAYAd4u2fnIhHL6N/Q==
 
+"@eslint-community/eslint-utils@^4.2.0":
+  version "4.4.0"
+  resolved "https://registry.yarnpkg.com/@eslint-community/eslint-utils/-/eslint-utils-4.4.0.tgz#a23514e8fb9af1269d5f7788aa556798d61c6b59"
+  integrity sha512-1/sA4dwrzBAyeUoQ6oxahHKmrZvsnLCg4RfxW3ZFGGmQkSNQPFNLV9CUEFQP1x9EYXHTo5p6xdhZM1Ne9p/AfA==
+  dependencies:
+    eslint-visitor-keys "^3.3.0"
+
+"@eslint-community/regexpp@^4.4.0":
+  version "4.5.0"
+  resolved "https://registry.yarnpkg.com/@eslint-community/regexpp/-/regexpp-4.5.0.tgz#f6f729b02feee2c749f57e334b7a1b5f40a81724"
+  integrity sha512-vITaYzIcNmjn5tF5uxcZ/ft7/RXGrMUIS9HalWckEOF6ESiwXKoMzAQf2UW0aVd6rnOeExTJVd5hmWXucBKGXQ==
+
 "@eslint/eslintrc@^1.4.1":
   version "1.4.1"
   resolved "https://registry.yarnpkg.com/@eslint/eslintrc/-/eslintrc-1.4.1.tgz#af58772019a2d271b7e2d4c23ff4ddcba3ccfb3e"
@@ -1768,47 +1780,47 @@
   resolved "https://registry.yarnpkg.com/@types/uuid/-/uuid-9.0.0.tgz#53ef263e5239728b56096b0a869595135b7952d2"
   integrity sha512-kr90f+ERiQtKWMz5rP32ltJ/BtULDI5RVO0uavn1HQUOwjx0R1h0rnDYNL0CepF1zL5bSY6FISAfd9tOdDhU5Q==
 
-"@typescript-eslint/eslint-plugin@^5.52.0":
-  version "5.52.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-5.52.0.tgz#5fb0d43574c2411f16ea80f5fc335b8eaa7b28a8"
-  integrity sha512-lHazYdvYVsBokwCdKOppvYJKaJ4S41CgKBcPvyd0xjZNbvQdhn/pnJlGtQksQ/NhInzdaeaSarlBjDXHuclEbg==
+"@typescript-eslint/eslint-plugin@^5.57.0":
+  version "5.57.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-5.57.0.tgz#52c8a7a4512f10e7249ca1e2e61f81c62c34365c"
+  integrity sha512-itag0qpN6q2UMM6Xgk6xoHa0D0/P+M17THnr4SVgqn9Rgam5k/He33MA7/D7QoJcdMxHFyX7U9imaBonAX/6qA==
   dependencies:
-    "@typescript-eslint/scope-manager" "5.52.0"
-    "@typescript-eslint/type-utils" "5.52.0"
-    "@typescript-eslint/utils" "5.52.0"
+    "@eslint-community/regexpp" "^4.4.0"
+    "@typescript-eslint/scope-manager" "5.57.0"
+    "@typescript-eslint/type-utils" "5.57.0"
+    "@typescript-eslint/utils" "5.57.0"
     debug "^4.3.4"
     grapheme-splitter "^1.0.4"
     ignore "^5.2.0"
     natural-compare-lite "^1.4.0"
-    regexpp "^3.2.0"
     semver "^7.3.7"
     tsutils "^3.21.0"
 
-"@typescript-eslint/parser@^5.52.0":
-  version "5.52.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/parser/-/parser-5.52.0.tgz#73c136df6c0133f1d7870de7131ccf356f5be5a4"
-  integrity sha512-e2KiLQOZRo4Y0D/b+3y08i3jsekoSkOYStROYmPUnGMEoA0h+k2qOH5H6tcjIc68WDvGwH+PaOrP1XRzLJ6QlA==
+"@typescript-eslint/parser@^5.57.0":
+  version "5.57.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/parser/-/parser-5.57.0.tgz#f675bf2cd1a838949fd0de5683834417b757e4fa"
+  integrity sha512-orrduvpWYkgLCyAdNtR1QIWovcNZlEm6yL8nwH/eTxWLd8gsP+25pdLHYzL2QdkqrieaDwLpytHqycncv0woUQ==
   dependencies:
-    "@typescript-eslint/scope-manager" "5.52.0"
-    "@typescript-eslint/types" "5.52.0"
-    "@typescript-eslint/typescript-estree" "5.52.0"
+    "@typescript-eslint/scope-manager" "5.57.0"
+    "@typescript-eslint/types" "5.57.0"
+    "@typescript-eslint/typescript-estree" "5.57.0"
     debug "^4.3.4"
 
-"@typescript-eslint/scope-manager@5.52.0":
-  version "5.52.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/scope-manager/-/scope-manager-5.52.0.tgz#a993d89a0556ea16811db48eabd7c5b72dcb83d1"
-  integrity sha512-AR7sxxfBKiNV0FWBSARxM8DmNxrwgnYMPwmpkC1Pl1n+eT8/I2NAUPuwDy/FmDcC6F8pBfmOcaxcxRHspgOBMw==
+"@typescript-eslint/scope-manager@5.57.0":
+  version "5.57.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/scope-manager/-/scope-manager-5.57.0.tgz#79ccd3fa7bde0758059172d44239e871e087ea36"
+  integrity sha512-NANBNOQvllPlizl9LatX8+MHi7bx7WGIWYjPHDmQe5Si/0YEYfxSljJpoTyTWFTgRy3X8gLYSE4xQ2U+aCozSw==
   dependencies:
-    "@typescript-eslint/types" "5.52.0"
-    "@typescript-eslint/visitor-keys" "5.52.0"
+    "@typescript-eslint/types" "5.57.0"
+    "@typescript-eslint/visitor-keys" "5.57.0"
 
-"@typescript-eslint/type-utils@5.52.0":
-  version "5.52.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/type-utils/-/type-utils-5.52.0.tgz#9fd28cd02e6f21f5109e35496df41893f33167aa"
-  integrity sha512-tEKuUHfDOv852QGlpPtB3lHOoig5pyFQN/cUiZtpw99D93nEBjexRLre5sQZlkMoHry/lZr8qDAt2oAHLKA6Jw==
+"@typescript-eslint/type-utils@5.57.0":
+  version "5.57.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/type-utils/-/type-utils-5.57.0.tgz#98e7531c4e927855d45bd362de922a619b4319f2"
+  integrity sha512-kxXoq9zOTbvqzLbdNKy1yFrxLC6GDJFE2Yuo3KqSwTmDOFjUGeWSakgoXT864WcK5/NAJkkONCiKb1ddsqhLXQ==
   dependencies:
-    "@typescript-eslint/typescript-estree" "5.52.0"
-    "@typescript-eslint/utils" "5.52.0"
+    "@typescript-eslint/typescript-estree" "5.57.0"
+    "@typescript-eslint/utils" "5.57.0"
     debug "^4.3.4"
     tsutils "^3.21.0"
 
@@ -1822,13 +1834,18 @@
   resolved "https://registry.yarnpkg.com/@typescript-eslint/types/-/types-5.52.0.tgz#19e9abc6afb5bd37a1a9bea877a1a836c0b3241b"
   integrity sha512-oV7XU4CHYfBhk78fS7tkum+/Dpgsfi91IIDy7fjCyq2k6KB63M6gMC0YIvy+iABzmXThCRI6xpCEyVObBdWSDQ==
 
-"@typescript-eslint/typescript-estree@5.52.0", "@typescript-eslint/typescript-estree@^5.13.0":
-  version "5.52.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-5.52.0.tgz#6408cb3c2ccc01c03c278cb201cf07e73347dfca"
-  integrity sha512-WeWnjanyEwt6+fVrSR0MYgEpUAuROxuAH516WPjUblIrClzYJj0kBbjdnbQXLpgAN8qbEuGywiQsXUVDiAoEuQ==
+"@typescript-eslint/types@5.57.0":
+  version "5.57.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/types/-/types-5.57.0.tgz#727bfa2b64c73a4376264379cf1f447998eaa132"
+  integrity sha512-mxsod+aZRSyLT+jiqHw1KK6xrANm19/+VFALVFP5qa/aiJnlP38qpyaTd0fEKhWvQk6YeNZ5LGwI1pDpBRBhtQ==
+
+"@typescript-eslint/typescript-estree@5.57.0":
+  version "5.57.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-5.57.0.tgz#ebcd0ee3e1d6230e888d88cddf654252d41e2e40"
+  integrity sha512-LTzQ23TV82KpO8HPnWuxM2V7ieXW8O142I7hQTxWIHDcCEIjtkat6H96PFkYBQqGFLW/G/eVVOB9Z8rcvdY/Vw==
   dependencies:
-    "@typescript-eslint/types" "5.52.0"
-    "@typescript-eslint/visitor-keys" "5.52.0"
+    "@typescript-eslint/types" "5.57.0"
+    "@typescript-eslint/visitor-keys" "5.57.0"
     debug "^4.3.4"
     globby "^11.1.0"
     is-glob "^4.0.3"
@@ -1848,18 +1865,31 @@
     semver "^7.3.5"
     tsutils "^3.21.0"
 
-"@typescript-eslint/utils@5.52.0":
+"@typescript-eslint/typescript-estree@^5.13.0":
   version "5.52.0"
-  resolved "https://registry.yarnpkg.com/@typescript-eslint/utils/-/utils-5.52.0.tgz#b260bb5a8f6b00a0ed51db66bdba4ed5e4845a72"
-  integrity sha512-As3lChhrbwWQLNk2HC8Ree96hldKIqk98EYvypd3It8Q1f8d5zWyIoaZEp2va5667M4ZyE7X8UUR+azXrFl+NA==
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/typescript-estree/-/typescript-estree-5.52.0.tgz#6408cb3c2ccc01c03c278cb201cf07e73347dfca"
+  integrity sha512-WeWnjanyEwt6+fVrSR0MYgEpUAuROxuAH516WPjUblIrClzYJj0kBbjdnbQXLpgAN8qbEuGywiQsXUVDiAoEuQ==
   dependencies:
+    "@typescript-eslint/types" "5.52.0"
+    "@typescript-eslint/visitor-keys" "5.52.0"
+    debug "^4.3.4"
+    globby "^11.1.0"
+    is-glob "^4.0.3"
+    semver "^7.3.7"
+    tsutils "^3.21.0"
+
+"@typescript-eslint/utils@5.57.0":
+  version "5.57.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/utils/-/utils-5.57.0.tgz#eab8f6563a2ac31f60f3e7024b91bf75f43ecef6"
+  integrity sha512-ps/4WohXV7C+LTSgAL5CApxvxbMkl9B9AUZRtnEFonpIxZDIT7wC1xfvuJONMidrkB9scs4zhtRyIwHh4+18kw==
+  dependencies:
+    "@eslint-community/eslint-utils" "^4.2.0"
     "@types/json-schema" "^7.0.9"
     "@types/semver" "^7.3.12"
-    "@typescript-eslint/scope-manager" "5.52.0"
-    "@typescript-eslint/types" "5.52.0"
-    "@typescript-eslint/typescript-estree" "5.52.0"
+    "@typescript-eslint/scope-manager" "5.57.0"
+    "@typescript-eslint/types" "5.57.0"
+    "@typescript-eslint/typescript-estree" "5.57.0"
     eslint-scope "^5.1.1"
-    eslint-utils "^3.0.0"
     semver "^7.3.7"
 
 "@typescript-eslint/visitor-keys@4.33.0":
@@ -1878,6 +1908,14 @@
     "@typescript-eslint/types" "5.52.0"
     eslint-visitor-keys "^3.3.0"
 
+"@typescript-eslint/visitor-keys@5.57.0":
+  version "5.57.0"
+  resolved "https://registry.yarnpkg.com/@typescript-eslint/visitor-keys/-/visitor-keys-5.57.0.tgz#e2b2f4174aff1d15eef887ce3d019ecc2d7a8ac1"
+  integrity sha512-ery2g3k0hv5BLiKpPuwYt9KBkAp2ugT6VvyShXdLOkax895EC55sP0Tx5L0fZaQueiK3fBLvHVvEl3jFS5ia+g==
+  dependencies:
+    "@typescript-eslint/types" "5.57.0"
+    eslint-visitor-keys "^3.3.0"
+
 "@vitejs/plugin-react-swc@^3.2.0":
   version "3.2.0"
   resolved "https://registry.yarnpkg.com/@vitejs/plugin-react-swc/-/plugin-react-swc-3.2.0.tgz#7c4f6e116a296c27f680d05750f9dbf798cf7709"
@@ -1890,15 +1928,15 @@
   resolved "https://registry.yarnpkg.com/@yarnpkg/lockfile/-/lockfile-1.1.0.tgz#e77a97fbd345b76d83245edcd17d393b1b41fb31"
   integrity sha512-GpSwvyXOcOOlV70vbnzjj4fW5xW/FdUF6nQEt1ENy7m4ZCczi1+/buVUPAqmGfqznsORNFzUMjctTIp8a9tuCQ==
 
-"@zag-js/element-size@0.3.1":
-  version "0.3.1"
-  resolved "https://registry.yarnpkg.com/@zag-js/element-size/-/element-size-0.3.1.tgz#f9f6ae98355e2250d18d0f6e2f1134a0ae4c6a2f"
-  integrity sha512-jR5j4G//bRzcxwAACWi9EfITnwjNmn10LxF4NmALrdZU7/PNWP3uUCdhCxd/0SCyeiJXUl0yvD57rWAbKPs1nw==
+"@zag-js/element-size@0.3.2":
+  version "0.3.2"
+  resolved "https://registry.yarnpkg.com/@zag-js/element-size/-/element-size-0.3.2.tgz#ebb76af2a024230482406db41344598d1a9f54f4"
+  integrity sha512-bVvvigUGvAuj7PCkE5AbzvTJDTw5f3bg9nQdv+ErhVN8SfPPppLJEmmWdxqsRzrHXgx8ypJt/+Ty0kjtISVDsQ==
 
-"@zag-js/focus-visible@0.2.1":
-  version "0.2.1"
-  resolved "https://registry.yarnpkg.com/@zag-js/focus-visible/-/focus-visible-0.2.1.tgz#bf4f1009f4fd35a9728dfaa9214d8cb318fe8b1e"
-  integrity sha512-19uTjoZGP4/Ax7kSNhhay9JA83BirKzpqLkeEAilrpdI1hE5xuq6q+tzJOsrMOOqJrm7LkmZp5lbsTQzvK2pYg==
+"@zag-js/focus-visible@0.2.2":
+  version "0.2.2"
+  resolved "https://registry.yarnpkg.com/@zag-js/focus-visible/-/focus-visible-0.2.2.tgz#56233480ca1275d3218fb2e10696a33d1a6b9e64"
+  integrity sha512-0j2gZq8HiZ51z4zNnSkF1iSkqlwRDvdH+son3wHdoz+7IUdMN/5Exd4TxMJ+gq2Of1DiXReYLL9qqh2PdQ4wgA==
 
 accepts@~1.3.4:
   version "1.3.8"
@@ -4510,10 +4548,10 @@ react-dropzone@^14.2.3:
     file-selector "^0.6.0"
     prop-types "^15.8.1"
 
-react-fast-compare@3.2.0:
-  version "3.2.0"
-  resolved "https://registry.yarnpkg.com/react-fast-compare/-/react-fast-compare-3.2.0.tgz#641a9da81b6a6320f270e89724fb45a0b39e43bb"
-  integrity sha512-rtGImPZ0YyLrscKI9xTpV8psd6I8VAtjKCzQDlzyDvqJA8XOW78TXYQwNRNd8g8JZnDu8q9Fu/1v4HPAVwVdHA==
+react-fast-compare@3.2.1:
+  version "3.2.1"
+  resolved "https://registry.yarnpkg.com/react-fast-compare/-/react-fast-compare-3.2.1.tgz#53933d9e14f364281d6cba24bfed7a4afb808b5f"
+  integrity sha512-xTYf9zFim2pEif/Fw16dBiXpe0hoy5PxcD8+OwBnTtNLfIm3g6WxhKNurY+6OmdH1u6Ta/W/Vl6vjbYP1MFnDg==
 
 react-fast-compare@^2.0.1:
   version "2.0.4"
@@ -5311,6 +5349,11 @@ typescript@^4.0.0, typescript@^4.5.5:
   resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.9.5.tgz#095979f9bcc0d09da324d58d03ce8f8374cbe65a"
   integrity sha512-1FXk9E2Hm+QzZQ7z+McJiHL4NW1F2EzMu9Nq9i3zAaGqibafqYwCVU6WyWAuyQRRzOlxou8xZSyXLEN8oKj24g==
 
+typescript@^5.0.3:
+  version "5.0.3"
+  resolved "https://registry.yarnpkg.com/typescript/-/typescript-5.0.3.tgz#fe976f0c826a88d0a382007681cbb2da44afdedf"
+  integrity sha512-xv8mOEDnigb/tN9PSMTwSEqAnUvkoXMQlicOb0IUVDBSQCgBSaAAROUZYy2IcUy5qU6XajK5jjjO7TMWqBTKZA==
+
 unbox-primitive@^1.0.2:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/unbox-primitive/-/unbox-primitive-1.0.2.tgz#29032021057d5e6cdbd08c5129c226dff8ed6f9e"

ldm/generate.py

@@ -22,6 +22,7 @@ import transformers
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.utils.import_utils import is_xformers_available
 from omegaconf import OmegaConf
+from pathlib import Path
 from PIL import Image, ImageOps
 from pytorch_lightning import logging, seed_everything
 
@@ -528,6 +529,11 @@ class Generate:
                 log_tokens=self.log_tokenization,
             )
 
+
+            # untested code, so commented out for now
+            # if self.model.peft_manager:
+            #     self.model = self.model.peft_manager.load(self.model, self.model.unet.dtype)
+
             init_image, mask_image = self._make_images(
                 init_img,
                 init_mask,
@@ -909,12 +915,8 @@ class Generate:
         return self._load_generator(".omnibus", "Omnibus")
 
     def _load_generator(self, module, class_name):
-        if self.is_legacy_model(self.model_name):
-            mn = f"ldm.invoke.ckpt_generator{module}"
-            cn = f"Ckpt{class_name}"
-        else:
-            mn = f"ldm.invoke.generator{module}"
-            cn = class_name
+        mn = f"ldm.invoke.generator{module}"
+        cn = class_name
         module = importlib.import_module(mn)
         constructor = getattr(module, cn)
         return constructor(self.model, self.precision)
@@ -991,8 +993,17 @@ class Generate:
 
         self.model_name = model_name
         self._set_sampler()  # requires self.model_name to be set first
+        self._save_last_used_model(model_name)
         return self.model
 
+    def _save_last_used_model(self,model_name:str):
+        """
+        Save name of the last model used.
+        """
+        model_file_path = Path(Globals.root,'.last_model')
+        with open(model_file_path,'w') as f:
+            f.write(model_name)
+        
     def load_huggingface_concepts(self, concepts: list[str]):
         self.model.textual_inversion_manager.load_huggingface_concepts(concepts)
 
@@ -1033,6 +1044,8 @@ class Generate:
         image_callback=None,
         prefix=None,
     ):
+
+        results = []
         for r in image_list:
             image, seed = r
             try:
@@ -1086,6 +1099,10 @@ class Generate:
             else:
                 r[0] = image
 
+            results.append([image, seed])
+
+        return results
+
     def apply_textmask(
         self, image_path: str, prompt: str, callback, threshold: float = 0.5
     ):
@@ -1114,9 +1131,6 @@ class Generate:
     def sample_to_lowres_estimated_image(self, samples):
         return self._make_base().sample_to_lowres_estimated_image(samples)
 
-    def is_legacy_model(self, model_name) -> bool:
-        return self.model_manager.is_legacy(model_name)
-
     def _set_sampler(self):
         if isinstance(self.model, DiffusionPipeline):
             return self._set_scheduler()

ldm/invoke/CLI.py

@@ -17,6 +17,8 @@ if sys.platform == "darwin":
 
 import pyparsing  # type: ignore
 
+print(f'DEBUG: [1] All system modules imported', file=sys.stderr)
+
 import ldm.invoke
 
 from ..generate import Generate
@@ -31,13 +33,21 @@ from .pngwriter import PngWriter, retrieve_metadata, write_metadata
 from .readline import Completer, get_completer
 from ..util import url_attachment_name
 
+print(f'DEBUG: [2] All invokeai modules imported', file=sys.stderr)
+
 # global used in multiple functions (fix)
 infile = None
 
 def main():
     """Initialize command-line parsers and the diffusion model"""
     global infile
-
+    
+    print('DEBUG: [3] Entered main()', file=sys.stderr)
+    print('DEBUG: INVOKEAI ENVIRONMENT:')
+    print('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')
+    print("\n".join([f'{x}:{os.environ[x]}' for x in os.environ.keys()]))
+    print('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')
+    
     opt = Args()
     args = opt.parse_args()
     if not args:
@@ -64,11 +74,15 @@ def main():
     Globals.internet_available = args.internet_available and check_internet()
     Globals.disable_xformers = not args.xformers
     Globals.sequential_guidance = args.sequential_guidance
-    Globals.ckpt_convert = args.ckpt_convert
+    Globals.ckpt_convert = True  # always true as of 2.3.4 for LoRA support
+
+    print(f'DEBUG: [4] Globals initialized', file=sys.stderr)
 
     # run any post-install patches needed
     run_patches()
 
+    print(f'DEBUG: [5] Patches run', file=sys.stderr)
+
     print(f">> Internet connectivity is {Globals.internet_available}")
 
     if not args.conf:
@@ -84,8 +98,9 @@ def main():
     # loading here to avoid long delays on startup
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
+    print(f'DEBUG: [6] Importing torch modules', file=sys.stderr)
+    
     import transformers  # type: ignore
-
     from ldm.generate import Generate
 
     transformers.logging.set_verbosity_error()
@@ -93,6 +108,7 @@ def main():
 
     diffusers.logging.set_verbosity_error()
 
+    print(f'DEBUG: [7] loading restoration models', file=sys.stderr)
     # Loading Face Restoration and ESRGAN Modules
     gfpgan, codeformer, esrgan = load_face_restoration(opt)
 
@@ -110,7 +126,11 @@ def main():
     else:
         embedding_path = None
 
+    if opt.lora_path:
+        Globals.lora_models_dir = opt.lora_path
+
     # migrate legacy models
+    print(f'DEBUG: [8] migrating  models', file=sys.stderr)
     ModelManager.migrate_models()
 
     # load the infile as a list of lines
@@ -128,6 +148,7 @@ def main():
 
     model = opt.model or retrieve_last_used_model()
 
+    print(f'DEBUG: [9] Creating generate object', file=sys.stderr)
     # creating a Generate object:
     try:
         gen = Generate(
@@ -154,6 +175,7 @@ def main():
         print(">> changed to seamless tiling mode")
 
     # preload the model
+    print(f'DEBUG: [10] Loading default model', file=sys.stderr)
     try:
         gen.load_model()
     except KeyError:
@@ -168,20 +190,18 @@ def main():
     if path := opt.autoimport:
         gen.model_manager.heuristic_import(
             str(path),
-            convert=False,
             commit_to_conf=opt.conf,
             config_file_callback=lambda x: _pick_configuration_file(completer,x),
         )
 
     if path := opt.autoconvert:
         gen.model_manager.heuristic_import(
-            str(path), convert=True, commit_to_conf=opt.conf
+            str(path), commit_to_conf=opt.conf
         )
 
     # web server loops forever
     if opt.web or opt.gui:
         invoke_ai_web_server_loop(gen, gfpgan, codeformer, esrgan)
-        save_last_used_model(gen.model_name)
         sys.exit(0)
 
     if not infile:
@@ -203,6 +223,7 @@ def main():
 # TODO: main_loop() has gotten busy. Needs to be refactored.
 def main_loop(gen, opt, completer):
     """prompt/read/execute loop"""
+    print(f'DEBUG: [11] In main loop', file=sys.stderr)
     global infile
     done = False
     doneAfterInFile = infile is not None
@@ -502,7 +523,6 @@ def main_loop(gen, opt, completer):
     print(
         f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}'
     )
-    save_last_used_model(gen.model_name)
 
 
 # TO DO: remove repetitive code and the awkward command.replace() trope
@@ -639,7 +659,7 @@ def set_default_output_dir(opt: Args, completer: Completer):
     completer.set_default_dir(opt.outdir)
 
 
-def import_model(model_path: str, gen, opt, completer, convert=False):
+def import_model(model_path: str, gen, opt, completer):
     """
     model_path can be (1) a URL to a .ckpt file; (2) a local .ckpt file path;
     (3) a huggingface repository id; or (4) a local directory containing a
@@ -670,7 +690,6 @@ def import_model(model_path: str, gen, opt, completer, convert=False):
         model_path,
         model_name=model_name,
         description=model_desc,
-        convert=convert,
         config_file_callback=lambda x: _pick_configuration_file(completer,x),
     )
     if not imported_name:
@@ -792,7 +811,7 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
         )
     else:
         try:
-            import_model(model_name_or_path, gen, opt, completer, convert=True)
+            import_model(model_name_or_path, gen, opt, completer)
         except KeyboardInterrupt:
             return
 
@@ -1300,14 +1319,6 @@ def retrieve_last_used_model()->str:
     with open(model_file_path,'r') as f:
         return f.readline()
 
-def save_last_used_model(model_name:str):
-    """
-    Save name of the last model used.
-    """
-    model_file_path = Path(Globals.root,'.last_model')
-    with open(model_file_path,'w') as f:
-        f.write(model_name)
-
 # This routine performs any patch-ups needed after installation
 def run_patches():
     install_missing_config_files()
@@ -1331,15 +1342,16 @@ def install_missing_config_files():
     install ckpt configuration files that may have been added to the
     distro after original root directory configuration
     """
-    import invokeai.configs as conf
-    from shutil import copyfile
+    pass
+    # import invokeai.configs as conf
+    # from shutil import copyfile
     
-    root_configs = Path(global_config_dir(), 'stable-diffusion')
-    repo_configs = Path(conf.__path__[0], 'stable-diffusion')
-    for src in repo_configs.iterdir():
-        dest = root_configs / src.name
-        if not dest.exists():
-            copyfile(src,dest)
+    # root_configs = Path(global_config_dir(), 'stable-diffusion')
+    # repo_configs = Path(conf.__path__[0], 'stable-diffusion')
+    # for src in repo_configs.iterdir():
+    #     dest = root_configs / src.name
+    #     if not dest.exists():
+    #         copyfile(src,dest)
     
 def do_version_update(root_version: version.Version, app_version: Union[str, version.Version]):
     """
@@ -1347,6 +1359,9 @@ def do_version_update(root_version: version.Version, app_version: Union[str, ver
     from release to release. This is not an elegant solution. Instead, the 
     launcher should be moved into the source tree and installed using pip.
     """
+    if root_version < version.Version('v2.3.4'):
+        dest = Path(Globals.root,'loras')
+        dest.mkdir(exist_ok=True)
     if root_version < version.Version('v2.3.3'):
         if sys.platform == "linux":
             print('>> Downloading new version of launcher script and its config file')

ldm/invoke/_version.py

@@ -1,2 +1 @@
-
-__version__='2.3.3'
+__version__='2.3.4'

ldm/invoke/args.py

@@ -522,8 +522,8 @@ class Args(object):
             '--ckpt_convert',
             action=argparse.BooleanOptionalAction,
             dest='ckpt_convert',
-            default=False,
-            help='Load legacy ckpt files as diffusers. Pass --no-ckpt-convert to inhibit this behavior',
+            default=True,
+            help='Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.'
         )
         model_group.add_argument(
             '--internet',
@@ -654,6 +654,13 @@ class Args(object):
             type=str,
             help='Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)'
         )
+        render_group.add_argument(
+            '--lora_directory',
+            dest='lora_path',
+            default='loras',
+            type=str,
+            help='Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)'
+        )
         render_group.add_argument(
             '--embeddings',
             action=argparse.BooleanOptionalAction,
@@ -791,12 +798,12 @@ class Args(object):
             *Model manipulation*
             !models                                   -- list models in configs/models.yaml
             !switch <model_name>                      -- switch to model named <model_name>
-            !import_model /path/to/weights/file.ckpt  -- adds a .ckpt model to your config
+            !import_model /path/to/weights/file       -- imports a model from a ckpt or safetensors file
             !import_model /path/to/weights/           -- interactively import models from a directory
-            !import_model http://path_to_model.ckpt   -- downloads and adds a .ckpt model to your config
-            !import_model hakurei/waifu-diffusion     -- downloads and adds a diffusers model to your config
-            !optimize_model <model_name>              -- converts a .ckpt model to a diffusers model
-            !convert_model /path/to/weights/file.ckpt -- converts a .ckpt file path to a diffusers model
+            !import_model http://path_to_model        -- downloads and adds a ckpt or safetensors model to your config
+            !import_model hakurei/waifu-diffusion     -- downloads and adds a diffusers model to your config using its repo_id
+            !optimize_model <model_name>              -- converts a .ckpt/.safetensors model to a diffusers model
+            !convert_model /path/to/weights/file      -- converts a .ckpt file path to a diffusers model and adds to your config
             !edit_model <model_name>                  -- edit a model's description
             !del_model <model_name>                   -- delete a model
             """

ldm/invoke/ckpt_generator/__init__.py

@@ -1,4 +0,0 @@
-'''
-Initialization file for the ldm.invoke.generator package
-'''
-from .base import CkptGenerator

ldm/invoke/ckpt_generator/base.py

@@ -1,335 +0,0 @@
-'''
-Base class for ldm.invoke.ckpt_generator.*
-including img2img, txt2img, and inpaint
-
-THESE MODULES ARE TRANSITIONAL AND WILL BE REMOVED AT A FUTURE DATE
-WHEN LEGACY CKPT MODEL SUPPORT IS DISCONTINUED.
-'''
-import torch
-import numpy as  np
-import random
-import os
-import os.path as osp
-import traceback
-from tqdm import tqdm, trange
-from PIL import Image, ImageFilter, ImageChops
-import cv2 as cv
-from einops import rearrange, repeat
-from pathlib import Path
-from pytorch_lightning import seed_everything
-import invokeai.assets.web as web_assets
-from ldm.invoke.devices import choose_autocast
-from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
-from ldm.util import rand_perlin_2d
-
-downsampling = 8
-CAUTION_IMG = 'caution.png'
-
-class CkptGenerator():
-    def __init__(self, model, precision):
-        self.model = model
-        self.precision = precision
-        self.seed = None
-        self.latent_channels = model.channels
-        self.downsampling_factor = downsampling   # BUG: should come from model or config
-        self.safety_checker = None
-        self.perlin = 0.0
-        self.threshold = 0
-        self.variation_amount = 0
-        self.with_variations = []
-        self.use_mps_noise = False
-        self.free_gpu_mem = None
-        self.caution_img = None
-
-    # this is going to be overridden in img2img.py, txt2img.py and inpaint.py
-    def get_make_image(self,prompt,**kwargs):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it
-        """
-        raise NotImplementedError("image_iterator() must be implemented in a descendent class")
-
-    def set_variation(self, seed, variation_amount, with_variations):
-        self.seed             = seed
-        self.variation_amount = variation_amount
-        self.with_variations  = with_variations
-
-    def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
-                 image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
-                 safety_checker:dict=None,
-                 attention_maps_callback = None,
-                 free_gpu_mem: bool=False,
-                 **kwargs):
-        scope = choose_autocast(self.precision)
-        self.safety_checker = safety_checker
-        self.free_gpu_mem = free_gpu_mem
-        attention_maps_images = []
-        attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
-        make_image = self.get_make_image(
-            prompt,
-            sampler = sampler,
-            init_image    = init_image,
-            width         = width,
-            height        = height,
-            step_callback = step_callback,
-            threshold     = threshold,
-            perlin        = perlin,
-            attention_maps_callback = attention_maps_callback,
-            **kwargs
-        )
-        results             = []
-        seed                = seed if seed is not None and seed >= 0 else self.new_seed()
-        first_seed          = seed
-        seed, initial_noise = self.generate_initial_noise(seed, width, height)
-
-        # There used to be an additional self.model.ema_scope() here, but it breaks
-        # the inpaint-1.5 model. Not sure what it did.... ?
-        with scope(self.model.device.type):
-            for n in trange(iterations, desc='Generating'):
-                x_T = None
-                if self.variation_amount > 0:
-                    seed_everything(seed)
-                    target_noise = self.get_noise(width,height)
-                    x_T = self.slerp(self.variation_amount, initial_noise, target_noise)
-                elif initial_noise is not None:
-                    # i.e. we specified particular variations
-                    x_T = initial_noise
-                else:
-                    seed_everything(seed)
-                    try:
-                        x_T = self.get_noise(width,height)
-                    except:
-                        print('** An error occurred while getting initial noise **')
-                        print(traceback.format_exc())
-
-                image = make_image(x_T)
-
-                if self.safety_checker is not None:
-                    image = self.safety_check(image)
-
-                results.append([image, seed])
-
-                if image_callback is not None:
-                    attention_maps_image = None if len(attention_maps_images)==0 else attention_maps_images[-1]
-                    image_callback(image, seed, first_seed=first_seed, attention_maps_image=attention_maps_image)
-
-                seed = self.new_seed()
-
-        return results
-
-    def sample_to_image(self,samples)->Image.Image:
-        """
-        Given samples returned from a sampler, converts
-        it into a PIL Image
-        """
-        x_samples = self.model.decode_first_stage(samples)
-        x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-        if len(x_samples) != 1:
-            raise Exception(
-                f'>> expected to get a single image, but got {len(x_samples)}')
-        x_sample = 255.0 * rearrange(
-            x_samples[0].cpu().numpy(), 'c h w -> h w c'
-        )
-        return Image.fromarray(x_sample.astype(np.uint8))
-
-        # write an approximate RGB image from latent samples for a single step to PNG
-
-    def repaste_and_color_correct(self, result: Image.Image, init_image: Image.Image, init_mask: Image.Image, mask_blur_radius: int = 8) -> Image.Image:
-        if init_image is None or init_mask is None:
-            return result
-
-        # Get the original alpha channel of the mask if there is one.
-        # Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
-        pil_init_mask = init_mask.getchannel('A') if init_mask.mode == 'RGBA' else init_mask.convert('L')
-        pil_init_image = init_image.convert('RGBA') # Add an alpha channel if one doesn't exist
-
-        # Build an image with only visible pixels from source to use as reference for color-matching.
-        init_rgb_pixels = np.asarray(init_image.convert('RGB'), dtype=np.uint8)
-        init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8)
-        init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8)
-
-        # Get numpy version of result
-        np_image = np.asarray(result, dtype=np.uint8)
-
-        # Mask and calculate mean and standard deviation
-        mask_pixels = init_a_pixels * init_mask_pixels > 0
-        np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :]
-        np_image_masked = np_image[mask_pixels, :]
-
-        if np_init_rgb_pixels_masked.size > 0:
-            init_means = np_init_rgb_pixels_masked.mean(axis=0)
-            init_std = np_init_rgb_pixels_masked.std(axis=0)
-            gen_means = np_image_masked.mean(axis=0)
-            gen_std = np_image_masked.std(axis=0)
-
-            # Color correct
-            np_matched_result = np_image.copy()
-            np_matched_result[:,:,:] = (((np_matched_result[:,:,:].astype(np.float32) - gen_means[None,None,:]) / gen_std[None,None,:]) * init_std[None,None,:] + init_means[None,None,:]).clip(0, 255).astype(np.uint8)
-            matched_result = Image.fromarray(np_matched_result, mode='RGB')
-        else:
-            matched_result = Image.fromarray(np_image, mode='RGB')
-
-        # Blur the mask out (into init image) by specified amount
-        if mask_blur_radius > 0:
-            nm = np.asarray(pil_init_mask, dtype=np.uint8)
-            nmd = cv.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
-            pmd = Image.fromarray(nmd, mode='L')
-            blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
-        else:
-            blurred_init_mask = pil_init_mask
-
-        multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
-
-        # Paste original on color-corrected generation (using blurred mask)
-        matched_result.paste(init_image, (0,0), mask = multiplied_blurred_init_mask)
-        return matched_result
-
-
-
-    def sample_to_lowres_estimated_image(self,samples):
-        # origingally adapted from code by @erucipe and @keturn here:
-        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
-
-        # these updated numbers for v1.5 are from @torridgristle
-        v1_5_latent_rgb_factors = torch.tensor([
-            #    R        G        B
-            [ 0.3444,  0.1385,  0.0670], # L1
-            [ 0.1247,  0.4027,  0.1494], # L2
-            [-0.3192,  0.2513,  0.2103], # L3
-            [-0.1307, -0.1874, -0.7445]  # L4
-        ], 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)
-                         .clamp(0, 1)  # change scale from -1..1 to 0..1
-                         .mul(0xFF)  # to 0..255
-                         .byte()).cpu()
-
-        return Image.fromarray(latents_ubyte.numpy())
-
-    def generate_initial_noise(self, seed, width, height):
-        initial_noise = None
-        if self.variation_amount > 0 or len(self.with_variations) > 0:
-            # use fixed initial noise plus random noise per iteration
-            seed_everything(seed)
-            initial_noise = self.get_noise(width,height)
-            for v_seed, v_weight in self.with_variations:
-                seed = v_seed
-                seed_everything(seed)
-                next_noise = self.get_noise(width,height)
-                initial_noise = self.slerp(v_weight, initial_noise, next_noise)
-            if self.variation_amount > 0:
-                random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
-                seed = random.randrange(0,np.iinfo(np.uint32).max)
-            return (seed, initial_noise)
-        else:
-            return (seed, None)
-
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self,width,height):
-        """
-        Returns a tensor filled with random numbers, either form a normal distribution
-        (txt2img) or from the latent image (img2img, inpaint)
-        """
-        raise NotImplementedError("get_noise() must be implemented in a descendent class")
-
-    def get_perlin_noise(self,width,height):
-        fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
-        return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
-
-    def new_seed(self):
-        self.seed = random.randrange(0, np.iinfo(np.uint32).max)
-        return self.seed
-
-    def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995):
-        '''
-        Spherical linear interpolation
-        Args:
-            t (float/np.ndarray): Float value between 0.0 and 1.0
-            v0 (np.ndarray): Starting vector
-            v1 (np.ndarray): Final vector
-            DOT_THRESHOLD (float): Threshold for considering the two vectors as
-                                colineal. Not recommended to alter this.
-        Returns:
-            v2 (np.ndarray): Interpolation vector between v0 and v1
-        '''
-        inputs_are_torch = False
-        if not isinstance(v0, np.ndarray):
-            inputs_are_torch = True
-            v0 = v0.detach().cpu().numpy()
-        if not isinstance(v1, np.ndarray):
-            inputs_are_torch = True
-            v1 = v1.detach().cpu().numpy()
-
-        dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
-        if np.abs(dot) > DOT_THRESHOLD:
-            v2 = (1 - t) * v0 + t * v1
-        else:
-            theta_0 = np.arccos(dot)
-            sin_theta_0 = np.sin(theta_0)
-            theta_t = theta_0 * t
-            sin_theta_t = np.sin(theta_t)
-            s0 = np.sin(theta_0 - theta_t) / sin_theta_0
-            s1 = sin_theta_t / sin_theta_0
-            v2 = s0 * v0 + s1 * v1
-
-        if inputs_are_torch:
-            v2 = torch.from_numpy(v2).to(self.model.device)
-
-        return v2
-
-    def safety_check(self,image:Image.Image):
-        '''
-        If the CompViz safety checker flags an NSFW image, we
-        blur it out.
-        '''
-        import diffusers
-
-        checker = self.safety_checker['checker']
-        extractor = self.safety_checker['extractor']
-        features = extractor([image], return_tensors="pt")
-        features.to(self.model.device)
-
-        # unfortunately checker requires the numpy version, so we have to convert back
-        x_image = np.array(image).astype(np.float32) / 255.0
-        x_image = x_image[None].transpose(0, 3, 1, 2)
-
-        diffusers.logging.set_verbosity_error()
-        checked_image, has_nsfw_concept = checker(images=x_image, clip_input=features.pixel_values)
-        if has_nsfw_concept[0]:
-            print('** An image with potential non-safe content has been detected. A blurred image will be returned. **')
-            return self.blur(image)
-        else:
-            return image
-
-    def blur(self,input):
-        blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
-        try:
-            caution = self.get_caution_img()
-            if caution:
-                blurry.paste(caution,(0,0),caution)
-        except FileNotFoundError:
-            pass
-        return blurry
-
-    def get_caution_img(self):
-        path = None
-        if self.caution_img:
-            return self.caution_img
-        path = Path(web_assets.__path__[0]) / CAUTION_IMG
-        caution = Image.open(path)
-        self.caution_img = caution.resize((caution.width // 2, caution.height //2))
-        return self.caution_img
-
-    # this is a handy routine for debugging use. Given a generated sample,
-    # convert it into a PNG image and store it at the indicated path
-    def save_sample(self, sample, filepath):
-        image = self.sample_to_image(sample)
-        dirname = os.path.dirname(filepath) or '.'
-        if not os.path.exists(dirname):
-            print(f'** creating directory {dirname}')
-            os.makedirs(dirname, exist_ok=True)
-        image.save(filepath,'PNG')
-
-    def torch_dtype(self)->torch.dtype:
-        return torch.float16 if self.precision == 'float16' else torch.float32

ldm/invoke/ckpt_generator/embiggen.py

@@ -1,501 +0,0 @@
-'''
-ldm.invoke.ckpt_generator.embiggen descends from ldm.invoke.ckpt_generator
-and generates with ldm.invoke.ckpt_generator.img2img
-'''
-
-import torch
-import numpy as  np
-from tqdm import trange
-from PIL               import Image
-from ldm.invoke.ckpt_generator.base      import CkptGenerator
-from ldm.invoke.ckpt_generator.img2img   import CkptImg2Img
-from ldm.invoke.devices import choose_autocast
-from ldm.models.diffusion.ddim     import DDIMSampler
-
-class CkptEmbiggen(CkptGenerator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent         = None
-
-    # Replace generate because Embiggen doesn't need/use most of what it does normallly
-    def generate(self,prompt,iterations=1,seed=None,
-                 image_callback=None, step_callback=None,
-                 **kwargs):
-
-        scope      = choose_autocast(self.precision)
-        make_image = self.get_make_image(
-            prompt,
-            step_callback = step_callback,
-            **kwargs
-        )
-        results             = []
-        seed                = seed if seed else self.new_seed()
-
-        # Noise will be generated by the Img2Img generator when called
-        with scope(self.model.device.type), self.model.ema_scope():
-            for n in trange(iterations, desc='Generating'):
-                # make_image will call Img2Img which will do the equivalent of get_noise itself
-                image = make_image()
-                results.append([image, seed])
-                if image_callback is not None:
-                    image_callback(image, seed, prompt_in=prompt)
-                seed = self.new_seed()
-        return results
-
-    @torch.no_grad()
-    def get_make_image(
-        self,
-        prompt,
-        sampler,
-        steps,
-        cfg_scale,
-        ddim_eta,
-        conditioning,
-        init_img,
-        strength,
-        width,
-        height,
-        embiggen,
-        embiggen_tiles,
-        step_callback=None,
-        **kwargs
-    ):
-        """
-        Returns a function returning an image derived from the prompt and multi-stage twice-baked potato layering over the img2img on the initial image
-        Return value depends on the seed at the time you call it
-        """
-        assert not sampler.uses_inpainting_model(), "--embiggen is not supported by inpainting models"
-
-        # Construct embiggen arg array, and sanity check arguments
-        if embiggen == None:  # embiggen can also be called with just embiggen_tiles
-            embiggen = [1.0]  # If not specified, assume no scaling
-        elif embiggen[0] < 0:
-            embiggen[0] = 1.0
-            print(
-                '>> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !')
-        if len(embiggen) < 2:
-            embiggen.append(0.75)
-        elif embiggen[1] > 1.0 or embiggen[1] < 0:
-            embiggen[1] = 0.75
-            print('>> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !')
-        if len(embiggen) < 3:
-            embiggen.append(0.25)
-        elif embiggen[2] < 0:
-            embiggen[2] = 0.25
-            print('>> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !')
-
-        # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
-        # and then sort them, because... people.
-        if embiggen_tiles:
-            embiggen_tiles = list(map(lambda n: n-1, embiggen_tiles))
-            embiggen_tiles.sort()
-
-        if strength >= 0.5:
-            print(f'* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45.')
-
-        # Prep img2img generator, since we wrap over it
-        gen_img2img = CkptImg2Img(self.model,self.precision)
-
-        # Open original init image (not a tensor) to manipulate
-        initsuperimage = Image.open(init_img)
-
-        with Image.open(init_img) as img:
-            initsuperimage = img.convert('RGB')
-
-        # Size of the target super init image in pixels
-        initsuperwidth, initsuperheight = initsuperimage.size
-
-        # Increase by scaling factor if not already resized, using ESRGAN as able
-        if embiggen[0] != 1.0:
-            initsuperwidth = round(initsuperwidth*embiggen[0])
-            initsuperheight = round(initsuperheight*embiggen[0])
-            if embiggen[1] > 0:  # No point in ESRGAN upscaling if strength is set zero
-                from ldm.invoke.restoration.realesrgan import ESRGAN
-                esrgan = ESRGAN()
-                print(
-                    f'>> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}')
-                if embiggen[0] > 2:
-                    initsuperimage = esrgan.process(
-                        initsuperimage,
-                        embiggen[1],  # upscale strength
-                        self.seed,
-                        4,  # upscale scale
-                    )
-                else:
-                    initsuperimage = esrgan.process(
-                        initsuperimage,
-                        embiggen[1],  # upscale strength
-                        self.seed,
-                        2,  # upscale scale
-                    )
-            # We could keep recursively re-running ESRGAN for a requested embiggen[0] larger than 4x
-            #   but from personal experiance it doesn't greatly improve anything after 4x
-            # Resize to target scaling factor resolution
-            initsuperimage = initsuperimage.resize(
-                (initsuperwidth, initsuperheight), Image.Resampling.LANCZOS)
-
-        # Use width and height as tile widths and height
-        # Determine buffer size in pixels
-        if embiggen[2] < 1:
-            if embiggen[2] < 0:
-                embiggen[2] = 0
-            overlap_size_x = round(embiggen[2] * width)
-            overlap_size_y = round(embiggen[2] * height)
-        else:
-            overlap_size_x = round(embiggen[2])
-            overlap_size_y = round(embiggen[2])
-
-        # With overall image width and height known, determine how many tiles we need
-        def ceildiv(a, b):
-            return -1 * (-a // b)
-
-        # X and Y needs to be determined independantly (we may have savings on one based on the buffer pixel count)
-        # (initsuperwidth - width) is the area remaining to the right that we need to layers tiles to fill
-        # (width - overlap_size_x) is how much new we can fill with a single tile
-        emb_tiles_x = 1
-        emb_tiles_y = 1
-        if (initsuperwidth - width) > 0:
-            emb_tiles_x = ceildiv(initsuperwidth - width,
-                                  width - overlap_size_x) + 1
-        if (initsuperheight - height) > 0:
-            emb_tiles_y = ceildiv(initsuperheight - height,
-                                  height - overlap_size_y) + 1
-        # Sanity
-        assert emb_tiles_x > 1 or emb_tiles_y > 1, f'ERROR: Based on the requested dimensions of {initsuperwidth}x{initsuperheight} and tiles of {width}x{height} you don\'t need to Embiggen! Check your arguments.'
-
-        # Prep alpha layers --------------
-        # https://stackoverflow.com/questions/69321734/how-to-create-different-transparency-like-gradient-with-python-pil
-        # agradientL is Left-side transparent
-        agradientL = Image.linear_gradient('L').rotate(
-            90).resize((overlap_size_x, height))
-        # agradientT is Top-side transparent
-        agradientT = Image.linear_gradient('L').resize((width, overlap_size_y))
-        # radial corner is the left-top corner, made full circle then cut to just the left-top quadrant
-        agradientC = Image.new('L', (256, 256))
-        for y in range(256):
-            for x in range(256):
-                # Find distance to lower right corner (numpy takes arrays)
-                distanceToLR = np.sqrt([(255 - x) ** 2 + (255 - y) ** 2])[0]
-                # Clamp values to max 255
-                if distanceToLR > 255:
-                    distanceToLR = 255
-                #Place the pixel as invert of distance
-                agradientC.putpixel((x, y), round(255 - distanceToLR))
-
-        # Create alternative asymmetric diagonal corner to use on "tailing" intersections to prevent hard edges
-        # Fits for a left-fading gradient on the bottom side and full opacity on the right side.
-        agradientAsymC = Image.new('L', (256, 256))
-        for y in range(256):
-            for x in range(256):
-                value = round(max(0, x-(255-y)) * (255 / max(1,y)))
-                #Clamp values
-                value = max(0, value)
-                value = min(255, value)
-                agradientAsymC.putpixel((x, y), value)
-
-        # Create alpha layers default fully white
-        alphaLayerL = Image.new("L", (width, height), 255)
-        alphaLayerT = Image.new("L", (width, height), 255)
-        alphaLayerLTC = Image.new("L", (width, height), 255)
-        # Paste gradients into alpha layers
-        alphaLayerL.paste(agradientL, (0, 0))
-        alphaLayerT.paste(agradientT, (0, 0))
-        alphaLayerLTC.paste(agradientL, (0, 0))
-        alphaLayerLTC.paste(agradientT, (0, 0))
-        alphaLayerLTC.paste(agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0))
-        # make masks with an asymmetric upper-right corner so when the curved transparent corner of the next tile
-        # to its right is placed it doesn't reveal a hard trailing semi-transparent edge in the overlapping space
-        alphaLayerTaC = alphaLayerT.copy()
-        alphaLayerTaC.paste(agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
-        alphaLayerLTaC = alphaLayerLTC.copy()
-        alphaLayerLTaC.paste(agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
-
-        if embiggen_tiles:
-            # Individual unconnected sides
-            alphaLayerR = Image.new("L", (width, height), 255)
-            alphaLayerR.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
-            alphaLayerB = Image.new("L", (width, height), 255)
-            alphaLayerB.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
-            alphaLayerTB = Image.new("L", (width, height), 255)
-            alphaLayerTB.paste(agradientT, (0, 0))
-            alphaLayerTB.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
-            alphaLayerLR = Image.new("L", (width, height), 255)
-            alphaLayerLR.paste(agradientL, (0, 0))
-            alphaLayerLR.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
-
-            # Sides and corner Layers
-            alphaLayerRBC = Image.new("L", (width, height), 255)
-            alphaLayerRBC.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
-            alphaLayerRBC.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
-            alphaLayerRBC.paste(agradientC.rotate(180).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
-            alphaLayerLBC = Image.new("L", (width, height), 255)
-            alphaLayerLBC.paste(agradientL, (0, 0))
-            alphaLayerLBC.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
-            alphaLayerLBC.paste(agradientC.rotate(90).resize(
-                (overlap_size_x, overlap_size_y)), (0, height - overlap_size_y))
-            alphaLayerRTC = Image.new("L", (width, height), 255)
-            alphaLayerRTC.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
-            alphaLayerRTC.paste(agradientT, (0, 0))
-            alphaLayerRTC.paste(agradientC.rotate(270).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
-
-            # All but X layers
-            alphaLayerABT = Image.new("L", (width, height), 255)
-            alphaLayerABT.paste(alphaLayerLBC, (0, 0))
-            alphaLayerABT.paste(agradientL.rotate(
-                180), (width - overlap_size_x, 0))
-            alphaLayerABT.paste(agradientC.rotate(180).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
-            alphaLayerABL = Image.new("L", (width, height), 255)
-            alphaLayerABL.paste(alphaLayerRTC, (0, 0))
-            alphaLayerABL.paste(agradientT.rotate(
-                180), (0, height - overlap_size_y))
-            alphaLayerABL.paste(agradientC.rotate(180).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
-            alphaLayerABR = Image.new("L", (width, height), 255)
-            alphaLayerABR.paste(alphaLayerLBC, (0, 0))
-            alphaLayerABR.paste(agradientT, (0, 0))
-            alphaLayerABR.paste(agradientC.resize(
-                (overlap_size_x, overlap_size_y)), (0, 0))
-            alphaLayerABB = Image.new("L", (width, height), 255)
-            alphaLayerABB.paste(alphaLayerRTC, (0, 0))
-            alphaLayerABB.paste(agradientL, (0, 0))
-            alphaLayerABB.paste(agradientC.resize(
-                (overlap_size_x, overlap_size_y)), (0, 0))
-
-            # All-around layer
-            alphaLayerAA = Image.new("L", (width, height), 255)
-            alphaLayerAA.paste(alphaLayerABT, (0, 0))
-            alphaLayerAA.paste(agradientT, (0, 0))
-            alphaLayerAA.paste(agradientC.resize(
-                (overlap_size_x, overlap_size_y)), (0, 0))
-            alphaLayerAA.paste(agradientC.rotate(270).resize(
-                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
-
-        # Clean up temporary gradients
-        del agradientL
-        del agradientT
-        del agradientC
-
-        def make_image():
-            # Make main tiles -------------------------------------------------
-            if embiggen_tiles:
-                print(f'>> Making {len(embiggen_tiles)} Embiggen tiles...')
-            else:
-                print(
-                    f'>> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})...')
-
-            emb_tile_store = []
-            # Although we could use the same seed for every tile for determinism, at higher strengths this may
-            # produce duplicated structures for each tile and make the tiling effect more obvious
-            # instead track and iterate a local seed we pass to Img2Img
-            seed = self.seed
-            seedintlimit = np.iinfo(np.uint32).max - 1 # only retreive this one from numpy
-
-            for tile in range(emb_tiles_x * emb_tiles_y):
-                # Don't iterate on first tile
-                if tile != 0:
-                    if seed < seedintlimit:
-                        seed += 1
-                    else:
-                        seed = 0
-
-                # Determine if this is a re-run and replace
-                if embiggen_tiles and not tile in embiggen_tiles:
-                    continue
-                # Get row and column entries
-                emb_row_i = tile // emb_tiles_x
-                emb_column_i = tile % emb_tiles_x
-                # Determine bounds to cut up the init image
-                # Determine upper-left point
-                if emb_column_i + 1 == emb_tiles_x:
-                    left = initsuperwidth - width
-                else:
-                    left = round(emb_column_i * (width - overlap_size_x))
-                if emb_row_i + 1 == emb_tiles_y:
-                    top = initsuperheight - height
-                else:
-                    top = round(emb_row_i * (height - overlap_size_y))
-                right = left + width
-                bottom = top + height
-
-                # Cropped image of above dimension (does not modify the original)
-                newinitimage = initsuperimage.crop((left, top, right, bottom))
-                # DEBUG:
-                # newinitimagepath = init_img[0:-4] + f'_emb_Ti{tile}.png'
-                # newinitimage.save(newinitimagepath)
-
-                if embiggen_tiles:
-                    print(
-                        f'Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)')
-                else:
-                    print(
-                        f'Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles')
-
-                # create a torch tensor from an Image
-                newinitimage = np.array(
-                    newinitimage).astype(np.float32) / 255.0
-                newinitimage = newinitimage[None].transpose(0, 3, 1, 2)
-                newinitimage = torch.from_numpy(newinitimage)
-                newinitimage = 2.0 * newinitimage - 1.0
-                newinitimage = newinitimage.to(self.model.device)
-
-                tile_results = gen_img2img.generate(
-                    prompt,
-                    iterations     = 1,
-                    seed           = seed,
-                    sampler        = DDIMSampler(self.model, device=self.model.device),
-                    steps          = steps,
-                    cfg_scale      = cfg_scale,
-                    conditioning   = conditioning,
-                    ddim_eta       = ddim_eta,
-                    image_callback = None,  # called only after the final image is generated
-                    step_callback  = step_callback,   # called after each intermediate image is generated
-                    width          = width,
-                    height         = height,
-                    init_image     = newinitimage,    # notice that init_image is different from init_img
-                    mask_image     = None,
-                    strength       = strength,
-                )
-
-                emb_tile_store.append(tile_results[0][0])
-                # DEBUG (but, also has other uses), worth saving if you want tiles without a transparency overlap to manually composite
-                # emb_tile_store[-1].save(init_img[0:-4] + f'_emb_To{tile}.png')
-                del newinitimage
-
-            # Sanity check we have them all
-            if len(emb_tile_store) == (emb_tiles_x * emb_tiles_y) or (embiggen_tiles != [] and len(emb_tile_store) == len(embiggen_tiles)):
-                outputsuperimage = Image.new(
-                    "RGBA", (initsuperwidth, initsuperheight))
-                if embiggen_tiles:
-                    outputsuperimage.alpha_composite(
-                        initsuperimage.convert('RGBA'), (0, 0))
-                for tile in range(emb_tiles_x * emb_tiles_y):
-                    if embiggen_tiles:
-                        if tile in embiggen_tiles:
-                            intileimage = emb_tile_store.pop(0)
-                        else:
-                            continue
-                    else:
-                        intileimage = emb_tile_store[tile]
-                    intileimage = intileimage.convert('RGBA')
-                    # Get row and column entries
-                    emb_row_i = tile // emb_tiles_x
-                    emb_column_i = tile % emb_tiles_x
-                    if emb_row_i == 0 and emb_column_i == 0 and not embiggen_tiles:
-                        left = 0
-                        top = 0
-                    else:
-                        # Determine upper-left point
-                        if emb_column_i + 1 == emb_tiles_x:
-                            left = initsuperwidth - width
-                        else:
-                            left = round(emb_column_i *
-                                         (width - overlap_size_x))
-                        if emb_row_i + 1 == emb_tiles_y:
-                            top = initsuperheight - height
-                        else:
-                            top = round(emb_row_i * (height - overlap_size_y))
-                        # Handle gradients for various conditions
-                        # Handle emb_rerun case
-                        if embiggen_tiles:
-                            # top of image
-                            if emb_row_i == 0:
-                                if emb_column_i == 0:
-                                    if (tile+1) in embiggen_tiles:  # Look-ahead right
-                                        if (tile+emb_tiles_x) not in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerB)
-                                        # Otherwise do nothing on this tile
-                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerR)
-                                    else:
-                                        intileimage.putalpha(alphaLayerRBC)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
-                                        intileimage.putalpha(alphaLayerL)
-                                    else:
-                                        intileimage.putalpha(alphaLayerLBC)
-                                else:
-                                    if (tile+1) in embiggen_tiles:  # Look-ahead right
-                                        if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
-                                            intileimage.putalpha(alphaLayerL)
-                                        else:
-                                            intileimage.putalpha(alphaLayerLBC)
-                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerLR)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABT)
-                            # bottom of image
-                            elif emb_row_i == emb_tiles_y - 1:
-                                if emb_column_i == 0:
-                                    if (tile+1) in embiggen_tiles: # Look-ahead right
-                                        intileimage.putalpha(alphaLayerTaC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerRTC)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    # No tiles to look ahead to
-                                    intileimage.putalpha(alphaLayerLTC)
-                                else:
-                                    if (tile+1) in embiggen_tiles: # Look-ahead right
-                                        intileimage.putalpha(alphaLayerLTaC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABB)
-                            # vertical middle of image
-                            else:
-                                if emb_column_i == 0:
-                                    if (tile+1) in embiggen_tiles: # Look-ahead right
-                                        if (tile+emb_tiles_x) in embiggen_tiles: # Look-ahead down
-                                            intileimage.putalpha(alphaLayerTaC)
-                                        else:
-                                            intileimage.putalpha(alphaLayerTB)
-                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerRTC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABL)
-                                elif emb_column_i == emb_tiles_x - 1:
-                                    if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
-                                        intileimage.putalpha(alphaLayerLTC)
-                                    else:
-                                        intileimage.putalpha(alphaLayerABR)
-                                else:
-                                    if (tile+1) in embiggen_tiles: # Look-ahead right
-                                        if (tile+emb_tiles_x) in embiggen_tiles: # Look-ahead down
-                                            intileimage.putalpha(alphaLayerLTaC)
-                                        else:
-                                            intileimage.putalpha(alphaLayerABR)
-                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
-                                        intileimage.putalpha(alphaLayerABB)
-                                    else:
-                                        intileimage.putalpha(alphaLayerAA)
-                        # Handle normal tiling case (much simpler - since we tile left to right, top to bottom)
-                        else:
-                            if emb_row_i == 0 and emb_column_i >= 1:
-                                intileimage.putalpha(alphaLayerL)
-                            elif emb_row_i >= 1 and emb_column_i == 0:
-                                if emb_column_i + 1 == emb_tiles_x: # If we don't have anything that can be placed to the right
-                                    intileimage.putalpha(alphaLayerT)
-                                else:
-                                    intileimage.putalpha(alphaLayerTaC)
-                            else:
-                                if emb_column_i + 1 == emb_tiles_x: # If we don't have anything that can be placed to the right
-                                    intileimage.putalpha(alphaLayerLTC)
-                                else:
-                                    intileimage.putalpha(alphaLayerLTaC)
-                    # Layer tile onto final image
-                    outputsuperimage.alpha_composite(intileimage, (left, top))
-            else:
-                print(f'Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation.')
-
-            # after internal loops and patching up return Embiggen image
-            return outputsuperimage
-        # end of function declaration
-        return make_image

ldm/invoke/ckpt_generator/img2img.py

@@ -1,97 +0,0 @@
-'''
-ldm.invoke.ckpt_generator.img2img descends from ldm.invoke.generator
-'''
-
-import torch
-import numpy as  np
-import PIL
-from torch import Tensor
-from PIL import Image
-from ldm.invoke.devices import choose_autocast
-from ldm.invoke.ckpt_generator.base import CkptGenerator
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-
-class CkptImg2Img(CkptGenerator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent = None    # by get_noise()
-
-    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,init_image,strength,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it.
-        """
-        self.perlin = perlin
-
-        sampler.make_schedule(
-            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
-        )
-
-        if isinstance(init_image, PIL.Image.Image):
-            init_image = self._image_to_tensor(init_image.convert('RGB'))
-
-        scope = choose_autocast(self.precision)
-        with scope(self.model.device.type):
-            self.init_latent = self.model.get_first_stage_encoding(
-                self.model.encode_first_stage(init_image)
-            ) # move to latent space
-
-        t_enc = int(strength * steps)
-        uc, c, extra_conditioning_info   = conditioning
-
-        def make_image(x_T):
-            # encode (scaled latent)
-            z_enc = sampler.stochastic_encode(
-                self.init_latent,
-                torch.tensor([t_enc - 1]).to(self.model.device),
-                noise=x_T
-            )
-
-            if self.free_gpu_mem and self.model.model.device != self.model.device:
-                self.model.model.to(self.model.device)
-
-            # decode it
-            samples = sampler.decode(
-                z_enc,
-                c,
-                t_enc,
-                img_callback = step_callback,
-                unconditional_guidance_scale=cfg_scale,
-                unconditional_conditioning=uc,
-                init_latent = self.init_latent, # changes how noising is performed in ksampler
-                extra_conditioning_info = extra_conditioning_info,
-                all_timesteps_count = steps
-            )
-
-            if self.free_gpu_mem:
-                self.model.model.to("cpu")
-
-            return self.sample_to_image(samples)
-
-        return make_image
-
-    def get_noise(self,width,height):
-        device      = self.model.device
-        init_latent = self.init_latent
-        assert init_latent is not None,'call to get_noise() when init_latent not set'
-        if device.type == 'mps':
-            x = torch.randn_like(init_latent, device='cpu').to(device)
-        else:
-            x = torch.randn_like(init_latent, device=device)
-        if self.perlin > 0.0:
-            shape = init_latent.shape
-            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
-        return x
-
-    def _image_to_tensor(self, image:Image, normalize:bool=True)->Tensor:
-        image = np.array(image).astype(np.float32) / 255.0
-        if len(image.shape) == 2:  # 'L' image, as in a mask
-            image = image[None,None]
-        else:                      # 'RGB' image
-            image = image[None].transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image)
-        if normalize:
-            image = 2.0 * image - 1.0
-        return image.to(self.model.device)

ldm/invoke/ckpt_generator/inpaint.py

@@ -1,358 +0,0 @@
-'''
-ldm.invoke.ckpt_generator.inpaint descends from ldm.invoke.ckpt_generator
-'''
-
-import math
-import torch
-import torchvision.transforms as T
-import numpy as  np
-import cv2 as cv
-import PIL
-from PIL import Image, ImageFilter, ImageOps, ImageChops
-from skimage.exposure.histogram_matching import match_histograms
-from einops import rearrange, repeat
-from ldm.invoke.devices             import choose_autocast
-from ldm.invoke.ckpt_generator.img2img   import CkptImg2Img
-from ldm.models.diffusion.ddim     import DDIMSampler
-from ldm.models.diffusion.ksampler import KSampler
-from ldm.invoke.generator.base import downsampling
-from ldm.util import debug_image
-from ldm.invoke.patchmatch import PatchMatch 
-from ldm.invoke.globals import Globals
-
-def infill_methods()->list[str]:
-    methods = list()
-    if PatchMatch.patchmatch_available():
-        methods.append('patchmatch')
-    methods.append('tile')
-    return methods
-
-class CkptInpaint(CkptImg2Img):
-    def __init__(self, model, precision):
-        self.init_latent = None
-        self.pil_image = None
-        self.pil_mask = None
-        self.mask_blur_radius = 0
-        self.infill_method = None
-        super().__init__(model, precision)
-
-    # Outpaint support code
-    def get_tile_images(self, image: np.ndarray, width=8, height=8):
-        _nrows, _ncols, depth = image.shape
-        _strides = image.strides
-
-        nrows, _m = divmod(_nrows, height)
-        ncols, _n = divmod(_ncols, width)
-        if _m != 0 or _n != 0:
-            return None
-
-        return np.lib.stride_tricks.as_strided(
-            np.ravel(image),
-            shape=(nrows, ncols, height, width, depth),
-            strides=(height * _strides[0], width * _strides[1], *_strides),
-            writeable=False
-        )
-
-    def infill_patchmatch(self, im: Image.Image) -> Image:
-        if im.mode != 'RGBA':
-            return im
-
-        # Skip patchmatch if patchmatch isn't available
-        if not PatchMatch.patchmatch_available():
-            return im
-
-        # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
-        im_patched_np = PatchMatch.inpaint(im.convert('RGB'), ImageOps.invert(im.split()[-1]), patch_size = 3)
-        im_patched = Image.fromarray(im_patched_np, mode = 'RGB')
-        return im_patched
-
-    def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: int = None) -> Image:
-        # Only fill if there's an alpha layer
-        if im.mode != 'RGBA':
-            return im
-
-        a = np.asarray(im, dtype=np.uint8)
-
-        tile_size = (tile_size, tile_size)
-
-        # Get the image as tiles of a specified size
-        tiles = self.get_tile_images(a,*tile_size).copy()
-
-        # Get the mask as tiles
-        tiles_mask = tiles[:,:,:,:,3]
-
-        # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
-        tmask_shape = tiles_mask.shape
-        tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
-        n,ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
-        tiles_mask = (tiles_mask > 0)
-        tiles_mask = tiles_mask.reshape((n,ny)).all(axis = 1)
-
-        # Get RGB tiles in single array and filter by the mask
-        tshape = tiles.shape
-        tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), * tiles.shape[2:]))
-        filtered_tiles = tiles_all[tiles_mask]
-
-        if len(filtered_tiles) == 0:
-            return im
-
-        # Find all invalid tiles and replace with a random valid tile
-        replace_count = (tiles_mask == False).sum()
-        rng = np.random.default_rng(seed = seed)
-        tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count),:,:,:]
-
-        # Convert back to an image
-        tiles_all = tiles_all.reshape(tshape)
-        tiles_all = tiles_all.swapaxes(1,2)
-        st = tiles_all.reshape((math.prod(tiles_all.shape[0:2]), math.prod(tiles_all.shape[2:4]), tiles_all.shape[4]))
-        si = Image.fromarray(st, mode='RGBA')
-
-        return si
-
-
-    def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
-        npimg = np.asarray(mask, dtype=np.uint8)
-
-        # Detect any partially transparent regions
-        npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
-
-        # Detect hard edges
-        npedge = cv.Canny(npimg, threshold1=100, threshold2=200)
-
-        # Combine
-        npmask = npgradient + npedge
-
-        # Expand
-        npmask = cv.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
-
-        new_mask = Image.fromarray(npmask)
-
-        if edge_blur > 0:
-            new_mask = new_mask.filter(ImageFilter.BoxBlur(edge_blur))
-
-        return ImageOps.invert(new_mask)
-
-
-    def seam_paint(self,
-        im: Image.Image,
-        seam_size: int,
-        seam_blur: int,
-        prompt,sampler,steps,cfg_scale,ddim_eta,
-        conditioning,strength,
-        noise,
-        step_callback
-    ) -> Image.Image:
-        hard_mask = self.pil_image.split()[-1].copy()
-        mask = self.mask_edge(hard_mask, seam_size, seam_blur)
-
-        make_image = self.get_make_image(
-            prompt,
-            sampler,
-            steps,
-            cfg_scale,
-            ddim_eta,
-            conditioning,
-            init_image = im.copy().convert('RGBA'),
-            mask_image = mask.convert('RGB'), # Code currently requires an RGB mask
-            strength = strength,
-            mask_blur_radius = 0,
-            seam_size = 0,
-            step_callback = step_callback,
-            inpaint_width = im.width,
-            inpaint_height = im.height
-        )
-
-        seam_noise = self.get_noise(im.width, im.height)
-
-        result = make_image(seam_noise)
-
-        return result
-
-
-    @torch.no_grad()
-    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,init_image,mask_image,strength,
-                       mask_blur_radius: int = 8,
-                       # Seam settings - when 0, doesn't fill seam
-                       seam_size: int = 0,
-                       seam_blur: int = 0,
-                       seam_strength: float = 0.7,
-                       seam_steps: int = 10,
-                       tile_size: int = 32,
-                       step_callback=None,
-                       inpaint_replace=False, enable_image_debugging=False,
-                       infill_method = None,
-                       inpaint_width=None,
-                       inpaint_height=None,
-                       **kwargs):
-        """
-        Returns a function returning an image derived from the prompt and
-        the initial image + mask.  Return value depends on the seed at
-        the time you call it.  kwargs are 'init_latent' and 'strength'
-        """
-
-        self.enable_image_debugging = enable_image_debugging
-        self.infill_method = infill_method or infill_methods()[0], # The infill method to use
-        
-        self.inpaint_width = inpaint_width
-        self.inpaint_height = inpaint_height
-
-        if isinstance(init_image, PIL.Image.Image):
-            self.pil_image = init_image.copy()
-
-            # Do infill
-            if infill_method == 'patchmatch' and PatchMatch.patchmatch_available():
-                init_filled = self.infill_patchmatch(self.pil_image.copy())
-            else: # if infill_method == 'tile': # Only two methods right now, so always use 'tile' if not patchmatch
-                init_filled = self.tile_fill_missing(
-                    self.pil_image.copy(),
-                    seed = self.seed,
-                    tile_size = tile_size
-                )
-            init_filled.paste(init_image, (0,0), init_image.split()[-1])
-
-            # Resize if requested for inpainting
-            if inpaint_width and inpaint_height:
-                init_filled = init_filled.resize((inpaint_width, inpaint_height))
-
-            debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
-
-            # Create init tensor
-            init_image = self._image_to_tensor(init_filled.convert('RGB'))
-
-        if isinstance(mask_image, PIL.Image.Image):
-            self.pil_mask = mask_image.copy()
-            debug_image(mask_image, "mask_image BEFORE multiply with pil_image", debug_status=self.enable_image_debugging)
-
-            mask_image = ImageChops.multiply(mask_image, self.pil_image.split()[-1].convert('RGB'))
-            self.pil_mask = mask_image
-
-            # Resize if requested for inpainting
-            if inpaint_width and inpaint_height:
-                mask_image = mask_image.resize((inpaint_width, inpaint_height))
-
-            debug_image(mask_image, "mask_image AFTER multiply with pil_image", debug_status=self.enable_image_debugging)
-            mask_image = mask_image.resize(
-                (
-                    mask_image.width // downsampling,
-                    mask_image.height // downsampling
-                ),
-                resample=Image.Resampling.NEAREST
-            )
-            mask_image = self._image_to_tensor(mask_image,normalize=False)
-
-        self.mask_blur_radius = mask_blur_radius
-
-        # klms samplers not supported yet, so ignore previous sampler
-        if isinstance(sampler,KSampler):
-            print(
-                f">> Using recommended DDIM sampler for inpainting."
-            )
-            sampler = DDIMSampler(self.model, device=self.model.device)
-
-        sampler.make_schedule(
-            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
-        )
-
-        mask_image = mask_image[0][0].unsqueeze(0).repeat(4,1,1).unsqueeze(0)
-        mask_image = repeat(mask_image, '1 ... -> b ...', b=1)
-
-        scope = choose_autocast(self.precision)
-        with scope(self.model.device.type):
-            self.init_latent = self.model.get_first_stage_encoding(
-                self.model.encode_first_stage(init_image)
-            ) # move to latent space
-
-        t_enc   = int(strength * steps)
-        # todo: support cross-attention control
-        uc, c, _ = conditioning
-
-        print(f">> target t_enc is {t_enc} steps")
-
-        @torch.no_grad()
-        def make_image(x_T):
-            # encode (scaled latent)
-            z_enc = sampler.stochastic_encode(
-                self.init_latent,
-                torch.tensor([t_enc - 1]).to(self.model.device),
-                noise=x_T
-            )
-
-            # to replace masked area with latent noise, weighted by inpaint_replace strength
-            if inpaint_replace > 0.0:
-                print(f'>> inpaint will replace what was under the mask with a strength of {inpaint_replace}')
-                l_noise = self.get_noise(kwargs['width'],kwargs['height'])
-                inverted_mask = 1.0-mask_image  # there will be 1s where the mask is
-                masked_region = (1.0-inpaint_replace) * inverted_mask * z_enc + inpaint_replace * inverted_mask * l_noise
-                z_enc   = z_enc * mask_image + masked_region
-
-            if self.free_gpu_mem and self.model.model.device != self.model.device:
-                self.model.model.to(self.model.device)
-
-            # decode it
-            samples = sampler.decode(
-                z_enc,
-                c,
-                t_enc,
-                img_callback                 = step_callback,
-                unconditional_guidance_scale = cfg_scale,
-                unconditional_conditioning = uc,
-                mask                       = mask_image,
-                init_latent                = self.init_latent
-            )
-
-            result = self.sample_to_image(samples)
-
-            # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
-            if seam_size > 0:
-                old_image = self.pil_image or init_image
-                old_mask = self.pil_mask or mask_image
-
-                result = self.seam_paint(
-                    result,
-                    seam_size,
-                    seam_blur,
-                    prompt,
-                    sampler,
-                    seam_steps,
-                    cfg_scale,
-                    ddim_eta,
-                    conditioning,
-                    seam_strength,
-                    x_T,
-                    step_callback)
-
-                # Restore original settings
-                self.get_make_image(prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,
-                       old_image,
-                       old_mask,
-                       strength,
-                       mask_blur_radius, seam_size, seam_blur, seam_strength,
-                       seam_steps, tile_size, step_callback,
-                       inpaint_replace, enable_image_debugging,
-                       inpaint_width = inpaint_width,
-                       inpaint_height = inpaint_height,
-                       infill_method = infill_method,
-                       **kwargs)
-
-            return result
-
-        return make_image
-
-
-    def sample_to_image(self, samples)->Image.Image:
-        gen_result = super().sample_to_image(samples).convert('RGB')
-        debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging)
-
-        # Resize if necessary
-        if self.inpaint_width and self.inpaint_height:
-            gen_result = gen_result.resize(self.pil_image.size)
-
-        if self.pil_image is None or self.pil_mask is None:
-            return gen_result
-
-        corrected_result = super().repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
-        debug_image(corrected_result, "corrected_result", debug_status=self.enable_image_debugging)
-
-        return corrected_result

ldm/invoke/ckpt_generator/omnibus.py

@@ -1,175 +0,0 @@
-"""omnibus module to be used with the runwayml 9-channel custom inpainting model"""
-
-import torch
-import numpy as  np
-from einops import repeat
-from PIL import Image, ImageOps, ImageChops
-from ldm.invoke.devices import choose_autocast
-from ldm.invoke.ckpt_generator.base import downsampling
-from ldm.invoke.ckpt_generator.img2img import CkptImg2Img
-from ldm.invoke.ckpt_generator.txt2img import CkptTxt2Img
-
-class CkptOmnibus(CkptImg2Img,CkptTxt2Img):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.pil_mask = None
-        self.pil_image = None
-
-    def get_make_image(
-            self,
-            prompt,
-            sampler,
-            steps,
-            cfg_scale,
-            ddim_eta,
-            conditioning,
-            width,
-            height,
-            init_image = None,
-            mask_image = None,
-            strength = None,
-            step_callback=None,
-            threshold=0.0,
-            perlin=0.0,
-            mask_blur_radius: int = 8,
-            **kwargs):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it.
-        """
-        self.perlin = perlin
-        num_samples = 1
-
-        sampler.make_schedule(
-            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
-        )
-
-        if isinstance(init_image, Image.Image):
-            self.pil_image = init_image
-            if init_image.mode != 'RGB':
-                init_image = init_image.convert('RGB')
-            init_image = self._image_to_tensor(init_image)
-
-        if isinstance(mask_image, Image.Image):
-            self.pil_mask = mask_image
-
-            mask_image = ImageChops.multiply(mask_image.convert('L'), self.pil_image.split()[-1])
-            mask_image = self._image_to_tensor(ImageOps.invert(mask_image), normalize=False)
-
-        self.mask_blur_radius = mask_blur_radius
-
-        t_enc = steps
-
-        if init_image is not None and mask_image is not None: # inpainting
-            masked_image = init_image * (1 - mask_image)  # masked image is the image masked by mask - masked regions zero
-
-        elif init_image is not None: # img2img
-            scope = choose_autocast(self.precision)
-
-            with scope(self.model.device.type):
-                self.init_latent = self.model.get_first_stage_encoding(
-                    self.model.encode_first_stage(init_image)
-                ) # move to latent space
-
-            # create a completely black mask  (1s)
-            mask_image = torch.ones(1, 1, init_image.shape[2], init_image.shape[3], device=self.model.device)
-            # and the masked image is just a copy of the original
-            masked_image = init_image
-
-        else: # txt2img
-            init_image = torch.zeros(1, 3, height, width, device=self.model.device)
-            mask_image = torch.ones(1, 1, height, width, device=self.model.device)
-            masked_image = init_image
-
-        self.init_latent = init_image
-        height = init_image.shape[2]
-        width = init_image.shape[3]
-        model = self.model
-
-        def make_image(x_T):
-            with torch.no_grad():
-                scope = choose_autocast(self.precision)
-                with scope(self.model.device.type):
-
-                    batch = self.make_batch_sd(
-                        init_image,
-                        mask_image,
-                        masked_image,
-                        prompt=prompt,
-                        device=model.device,
-                        num_samples=num_samples,
-                    )
-
-                    c = model.cond_stage_model.encode(batch["txt"])
-                    c_cat = list()
-                    for ck in model.concat_keys:
-                        cc = batch[ck].float()
-                        if ck != model.masked_image_key:
-                            bchw = [num_samples, 4, height//8, width//8]
-                            cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
-                        else:
-                            cc = model.get_first_stage_encoding(model.encode_first_stage(cc))
-                        c_cat.append(cc)
-                    c_cat = torch.cat(c_cat, dim=1)
-
-                    # cond
-                    cond={"c_concat": [c_cat], "c_crossattn": [c]}
-
-                    # uncond cond
-                    uc_cross = model.get_unconditional_conditioning(num_samples, "")
-                    uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
-                    shape = [model.channels, height//8, width//8]
-
-                    samples, _ = sampler.sample(
-                        batch_size = 1,
-                        S = steps,
-                        x_T = x_T,
-                        conditioning = cond,
-                        shape = shape,
-                        verbose = False,
-                        unconditional_guidance_scale = cfg_scale,
-                        unconditional_conditioning = uc_full,
-                        eta = 1.0,
-                        img_callback = step_callback,
-                        threshold = threshold,
-                    )
-                    if self.free_gpu_mem:
-                        self.model.model.to("cpu")
-            return self.sample_to_image(samples)
-
-        return make_image
-
-    def make_batch_sd(
-            self,
-            image,
-            mask,
-            masked_image,
-            prompt,
-            device,
-            num_samples=1):
-        batch = {
-                "image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples),
-                "txt": num_samples * [prompt],
-                "mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples),
-                "masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples),
-                }
-        return batch
-
-    def get_noise(self, width:int, height:int):
-        if self.init_latent is not None:
-            height = self.init_latent.shape[2]
-            width = self.init_latent.shape[3]
-        return CkptTxt2Img.get_noise(self,width,height)
-
-
-    def sample_to_image(self, samples)->Image.Image:
-        gen_result = super().sample_to_image(samples).convert('RGB')
-
-        if self.pil_image is None or self.pil_mask is None:
-            return gen_result
-        if self.pil_image.size != self.pil_mask.size:
-            return gen_result
-
-        corrected_result = super(CkptImg2Img, self).repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
-
-        return corrected_result

ldm/invoke/ckpt_generator/txt2img.py

@@ -1,90 +0,0 @@
-'''
-ldm.invoke.ckpt_generator.txt2img inherits from ldm.invoke.ckpt_generator
-'''
-
-import torch
-import numpy as  np
-from ldm.invoke.ckpt_generator.base import CkptGenerator
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-import gc
-
-
-class CkptTxt2Img(CkptGenerator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-
-    @torch.no_grad()
-    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,
-                       attention_maps_callback=None,
-                       **kwargs):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it
-        kwargs are 'width' and 'height'
-        """
-        self.perlin = perlin
-        uc, c, extra_conditioning_info   = conditioning
-
-        @torch.no_grad()
-        def make_image(x_T):
-            shape = [
-                self.latent_channels,
-                height // self.downsampling_factor,
-                width  // self.downsampling_factor,
-            ]
-
-            if self.free_gpu_mem and self.model.model.device != self.model.device:
-                self.model.model.to(self.model.device)
-
-            sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
-
-            samples, _ = sampler.sample(
-                batch_size                   = 1,
-                S                            = steps,
-                x_T                          = x_T,
-                conditioning                 = c,
-                shape                        = shape,
-                verbose                      = False,
-                unconditional_guidance_scale = cfg_scale,
-                unconditional_conditioning   = uc,
-                extra_conditioning_info      = extra_conditioning_info,
-                eta                          = ddim_eta,
-                img_callback                 = step_callback,
-                threshold                    = threshold,
-                attention_maps_callback      = attention_maps_callback,
-            )
-
-            if self.free_gpu_mem:
-                self.model.model.to('cpu')
-                self.model.cond_stage_model.device = 'cpu'
-                self.model.cond_stage_model.to('cpu')
-                gc.collect()
-                torch.cuda.empty_cache()
-
-            return self.sample_to_image(samples)
-
-        return make_image
-
-
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self,width,height):
-        device         = self.model.device
-        if self.use_mps_noise or device.type == 'mps':
-            x = torch.randn([1,
-                             self.latent_channels,
-                             height // self.downsampling_factor,
-                             width  // self.downsampling_factor],
-                            dtype=self.torch_dtype(),
-                            device='cpu').to(device)
-        else:
-            x = torch.randn([1,
-                             self.latent_channels,
-                             height // self.downsampling_factor,
-                             width  // self.downsampling_factor],
-                            dtype=self.torch_dtype(),
-                            device=device)
-        if self.perlin > 0.0:
-            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
-        return x
-

ldm/invoke/ckpt_generator/txt2img2img.py

@@ -1,182 +0,0 @@
-'''
-ldm.invoke.ckpt_generator.txt2img inherits from ldm.invoke.ckpt_generator
-'''
-
-import torch
-import numpy as  np
-import math
-import gc
-from ldm.invoke.ckpt_generator.base import CkptGenerator
-from ldm.invoke.ckpt_generator.omnibus import CkptOmnibus
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from PIL import Image
-
-class CkptTxt2Img2Img(CkptGenerator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent = None    # for get_noise()
-
-    @torch.no_grad()
-    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,width,height,strength,step_callback=None,**kwargs):
-        """
-        Returns a function returning an image derived from the prompt and the initial image
-        Return value depends on the seed at the time you call it
-        kwargs are 'width' and 'height'
-        """
-        uc, c, extra_conditioning_info = conditioning
-        scale_dim = min(width, height)
-        scale = 512 / scale_dim
-
-        init_width = math.ceil(scale * width / 64) * 64
-        init_height = math.ceil(scale * height / 64) * 64
-
-        @torch.no_grad()
-        def make_image(x_T):
-
-            shape = [
-                self.latent_channels,
-                init_height // self.downsampling_factor,
-                init_width // self.downsampling_factor,
-            ]
-
-            sampler.make_schedule(
-                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
-            )
-
-            #x = self.get_noise(init_width, init_height)
-            x = x_T
-
-            if self.free_gpu_mem and self.model.model.device != self.model.device:
-                self.model.model.to(self.model.device)
-
-            samples, _ = sampler.sample(
-                batch_size                   = 1,
-                S                            = steps,
-                x_T                          = x,
-                conditioning                 = c,
-                shape                        = shape,
-                verbose                      = False,
-                unconditional_guidance_scale = cfg_scale,
-                unconditional_conditioning   = uc,
-                eta                          = ddim_eta,
-                img_callback                 = step_callback,
-                extra_conditioning_info      = extra_conditioning_info
-            )
-
-            print(
-                  f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
-                 )
-
-            # resizing
-            samples = torch.nn.functional.interpolate(
-                samples,
-                size=(height // self.downsampling_factor, width // self.downsampling_factor),
-                mode="bilinear"
-            )
-
-            t_enc = int(strength * steps)
-            ddim_sampler = DDIMSampler(self.model, device=self.model.device)
-            ddim_sampler.make_schedule(
-                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
-            )
-
-            z_enc = ddim_sampler.stochastic_encode(
-                samples,
-                torch.tensor([t_enc-1]).to(self.model.device),
-                noise=self.get_noise(width,height,False)
-            )
-
-            # decode it
-            samples = ddim_sampler.decode(
-                z_enc,
-                c,
-                t_enc,
-                img_callback = step_callback,
-                unconditional_guidance_scale=cfg_scale,
-                unconditional_conditioning=uc,
-                extra_conditioning_info=extra_conditioning_info,
-                all_timesteps_count=steps
-            )
-
-            if self.free_gpu_mem:
-                self.model.model.to('cpu')
-                self.model.cond_stage_model.device = 'cpu'
-                self.model.cond_stage_model.to('cpu')
-                gc.collect()
-                torch.cuda.empty_cache()
-
-            return self.sample_to_image(samples)
-
-        # in the case of the inpainting model being loaded, the trick of
-        # providing an interpolated latent doesn't work, so we transiently
-        # create a 512x512 PIL image, upscale it, and run the inpainting
-        # over it in img2img mode. Because the inpaing model is so conservative
-        # it doesn't change the image (much)
-        def inpaint_make_image(x_T):
-            omnibus = CkptOmnibus(self.model,self.precision)
-            result = omnibus.generate(
-                prompt,
-                sampler=sampler,
-                width=init_width,
-                height=init_height,
-                step_callback=step_callback,
-                steps = steps,
-                cfg_scale = cfg_scale,
-                ddim_eta = ddim_eta,
-                conditioning = conditioning,
-                **kwargs
-            )
-            assert result is not None and len(result)>0,'** txt2img failed **'
-            image = result[0][0]
-            interpolated_image = image.resize((width,height),resample=Image.Resampling.LANCZOS)
-            print(kwargs.pop('init_image',None))
-            result = omnibus.generate(
-                prompt,
-                sampler=sampler,
-                init_image=interpolated_image,
-                width=width,
-                height=height,
-                seed=result[0][1],
-                step_callback=step_callback,
-                steps = steps,
-                cfg_scale = cfg_scale,
-                ddim_eta = ddim_eta,
-                conditioning = conditioning,
-                **kwargs
-                )
-            return result[0][0]
-
-        if sampler.uses_inpainting_model():
-            return inpaint_make_image
-        else:
-            return make_image
-
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self,width,height,scale = True):
-        # print(f"Get noise: {width}x{height}")
-        if scale:
-            trained_square = 512 * 512
-            actual_square = width * height
-            scale = math.sqrt(trained_square / actual_square)
-            scaled_width = math.ceil(scale * width / 64) * 64
-            scaled_height = math.ceil(scale * height / 64) * 64
-        else:
-            scaled_width = width
-            scaled_height = height
-
-        device      = self.model.device
-        if self.use_mps_noise or device.type == 'mps':
-            return torch.randn([1,
-                                self.latent_channels,
-                                scaled_height // self.downsampling_factor,
-                                scaled_width  // self.downsampling_factor],
-                                device='cpu').to(device)
-        else:
-            return torch.randn([1,
-                                self.latent_channels,
-                                scaled_height // self.downsampling_factor,
-                                scaled_width  // self.downsampling_factor],
-                                device=device)
-

ldm/invoke/concepts_lib.py

@@ -24,13 +24,15 @@ class HuggingFaceConceptsLibrary(object):
         self.concepts_loaded = dict()
         self.triggers = dict()            # concept name to trigger phrase
         self.concept_names = dict()       # trigger phrase to concept name
-        self.match_trigger = re.compile('(<[\w\- >]+>)') # trigger is slightly less restrictive than HF concept name
-        self.match_concept = re.compile('<([\w\-]+)>') # HF concept name can only contain A-Za-z0-9_-
+        self.match_trigger = re.compile('(<[a-zA-Z0-9_\- >]+>)') # trigger is slightly less restrictive than HF concept name
+        self.match_concept = re.compile('<([a-zA-Z0-9_\-]+)>') # HF concept name can only contain A-Za-z0-9_-
 
-    def list_concepts(self)->list:
+    def list_concepts(self, minimum_likes: int=0)->list:
         '''
         Return a list of all the concepts by name, without the 'sd-concepts-library' part.
         Also adds local concepts in invokeai/embeddings folder.
+        If minimum_likes is provided, then only concepts that have received at least that
+        many "likes" will be returned.
         '''
         local_concepts_now = self.get_local_concepts(os.path.join(self.root, 'embeddings'))
         local_concepts_to_add = set(local_concepts_now).difference(set(self.local_concepts))
@@ -44,7 +46,7 @@ class HuggingFaceConceptsLibrary(object):
         else:
             try:
                 models = self.hf_api.list_models(filter=ModelFilter(model_name='sd-concepts-library/'))
-                self.concept_list = [a.id.split('/')[1] for a in models]
+                self.concept_list = [a.id.split('/')[1] for a in models if a.likes>=minimum_likes]
                 # when init, add all in dir. when not init, add only concepts added between init and now
                 self.concept_list.extend(list(local_concepts_to_add))
             except Exception as e:
@@ -181,7 +183,7 @@ class HuggingFaceConceptsLibrary(object):
 
         print(f'>> Downloading {repo_id}...',end='')
         try:
-            for file in ('README.md','learned_embeds.bin','token_identifier.txt','type_of_concept.txt'):
+            for file in ('learned_embeds.bin','token_identifier.txt'):
                 url = hf_hub_url(repo_id, file)
                 request.urlretrieve(url, os.path.join(dest,file),reporthook=tally_download_size)
         except ul_error.HTTPError as e:

ldm/invoke/conditioning.py

@@ -12,7 +12,8 @@ from typing import Union, Optional, Any
 from transformers import CLIPTokenizer
 
 from compel import Compel
-from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser
+from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser, \
+    Conjunction
 from .devices import torch_dtype
 from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ldm.invoke.globals import Globals
@@ -55,13 +56,27 @@ def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_l
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
+
     legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_prompt: FlattenedPrompt|Blend
+    positive_conjunction: Conjunction
     if legacy_blend is not None:
-        positive_prompt = legacy_blend
+        positive_conjunction = legacy_blend
     else:
-        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: FlattenedPrompt|Blend = Compel.parse_prompt_string(negative_prompt_string)
+        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
+    positive_prompt = positive_conjunction.prompts[0]
+
+    should_use_lora_manager = True
+    lora_weights = positive_conjunction.lora_weights
+    lora_conditions = None
+    if model.peft_manager:
+        should_use_lora_manager = model.peft_manager.should_use(lora_weights)
+        if not should_use_lora_manager:
+            model.peft_manager.set_loras(lora_weights)
+    if model.lora_manager and should_use_lora_manager:
+        lora_conditions = model.lora_manager.set_loras_conditions(lora_weights)
+
+    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
 
     if log_tokens or getattr(Globals, "log_tokenization", False):
         log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
@@ -73,7 +88,8 @@ def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_l
 
     ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
                                                          cross_attention_control_args=options.get(
-                                                             'cross_attention_control', None))
+                                                             'cross_attention_control', None),
+                                                         lora_conditions=lora_conditions)
     return uc, c, ec
 
 
@@ -81,12 +97,14 @@ def get_prompt_structure(prompt_string, skip_normalize_legacy_blend: bool = Fals
     Union[FlattenedPrompt, Blend], FlattenedPrompt):
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
     legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_prompt: FlattenedPrompt|Blend
+    positive_conjunction: Conjunction
     if legacy_blend is not None:
-        positive_prompt = legacy_blend
+        positive_conjunction = legacy_blend
     else:
-        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
-    negative_prompt: FlattenedPrompt|Blend = Compel.parse_prompt_string(negative_prompt_string)
+        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
+    positive_prompt = positive_conjunction.prompts[0]
+    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
+    negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
 
     return positive_prompt, negative_prompt
 
@@ -203,18 +221,26 @@ def log_tokenization_for_text(text, tokenizer, display_label=None):
         print(f'{discarded}\x1b[0m')
 
 
-def try_parse_legacy_blend(text: str, skip_normalize: bool=False) -> Optional[Blend]:
+def try_parse_legacy_blend(text: str, skip_normalize: bool=False) -> Optional[Conjunction]:
     weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
     if len(weighted_subprompts) <= 1:
         return None
     strings = [x[0] for x in weighted_subprompts]
-    weights = [x[1] for x in weighted_subprompts]
 
     pp = PromptParser()
     parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
-    flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
+    flattened_prompts = []
+    weights = []
+    loras = []
+    for i, x in enumerate(parsed_conjunctions):
+        if len(x.prompts)>0:
+            flattened_prompts.append(x.prompts[0])
+            weights.append(weighted_subprompts[i][1])
+        if len(x.lora_weights)>0:
+            loras.extend(x.lora_weights)
 
-    return Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)
+    return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)],
+                       lora_weights = loras)
 
 
 def split_weighted_subprompts(text, skip_normalize=False)->list:

ldm/invoke/config/invokeai_configure.py

@@ -655,6 +655,7 @@ def initialize_rootdir(root: str, yes_to_all: bool = False):
         "models",
         "configs",
         "embeddings",
+        "loras",
         "text-inversion-output",
         "text-inversion-training-data",
     ):

ldm/invoke/config/invokeai_update.py

@@ -4,14 +4,13 @@ pip install <path_to_git_source>.
 '''
 import os
 import platform
+import psutil
 import requests
 from rich import box, print
-from rich.console import Console, Group, group
+from rich.console import Console, group
 from rich.panel import Panel
 from rich.prompt import Prompt
 from rich.style import Style
-from rich.syntax import Syntax
-from rich.text import Text
 
 from ldm.invoke import __version__
 
@@ -32,6 +31,19 @@ else:
 def get_versions()->dict:
     return requests.get(url=INVOKE_AI_REL).json()
 
+def invokeai_is_running()->bool:
+    for p in psutil.process_iter():
+        try:
+            cmdline = p.cmdline()
+            matches = [x for x in cmdline if x.endswith(('invokeai','invokeai.exe'))]
+            if matches:
+                print(f':exclamation: [bold red]An InvokeAI instance appears to be running as process {p.pid}[/red bold]')
+                return True
+        except psutil.AccessDenied:
+            continue
+    return False
+        
+    
 def welcome(versions: dict):
     
     @group()
@@ -62,6 +74,10 @@ def welcome(versions: dict):
 
 def main():
     versions = get_versions()
+    if invokeai_is_running():
+        print(f':exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]')
+        return
+
     welcome(versions)
 
     tag = None

ldm/invoke/dynamic_prompts.py

@@ -32,7 +32,8 @@ def expand_prompts(
     template_file: Path,
     run_invoke: bool = False,
     invoke_model: str = None,
-    invoke_outdir: Path = None,
+    invoke_outdir: str = None,
+    invoke_root: str = None,
     processes_per_gpu: int = 1,
 ):
     """
@@ -61,6 +62,8 @@ def expand_prompts(
             invokeai_args = [shutil.which("invokeai"), "--from_file", "-"]
             if invoke_model:
                 invokeai_args.extend(("--model", invoke_model))
+            if invoke_root:
+                invokeai_args.extend(("--root", invoke_root))
             if invoke_outdir:
                 outdir = os.path.expanduser(invoke_outdir)
                 invokeai_args.extend(("--outdir", outdir))
@@ -79,6 +82,11 @@ def expand_prompts(
             )
             import ldm.invoke.CLI
 
+            print(f'DEBUG: BATCH PARENT ENVIRONMENT:')
+            print('<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<')
+            print("\n".join([f'{x}:{os.environ[x]}' for x in os.environ.keys()]))
+            print('<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<')
+            
             parent_conn, child_conn = Pipe()
             children = set()
             for i in range(processes_to_launch):
@@ -99,8 +107,9 @@ def expand_prompts(
             sequence = 0
             for command in commands:
                 sequence += 1
-                parent_conn.send(
-                    command + f' --fnformat="dp.{sequence:04}.{{prompt}}.png"'
+                format = _get_fn_format(outdir, sequence)
+                parent_conn.send_bytes(
+                    (command + f' --fnformat="{format}"').encode('utf-8')
                 )
             parent_conn.close()
         else:
@@ -110,7 +119,27 @@ def expand_prompts(
         for p in children:
             p.terminate()
 
+def _dummy_cli_main():
+    counter = 0
+    while line := sys.stdin.readline():
+        print(f'[{counter}] {os.getpid()} got command {line.rstrip()}\n')
+        counter += 1
+        time.sleep(1)
 
+def _get_fn_format(directory:str, sequence:int)->str:
+    """
+    Get a filename that doesn't exceed filename length restrictions
+    on the current platform.
+    """
+    try:
+        max_length = os.pathconf(directory,'PC_NAME_MAX')
+    except:
+        max_length = 255
+    prefix = f'dp.{sequence:04}.'
+    suffix = '.png'
+    max_length -= len(prefix)+len(suffix)
+    return f'{prefix}{{prompt:0.{max_length}}}{suffix}'
+            
 class MessageToStdin(object):
     def __init__(self, connection: Connection):
         self.connection = connection
@@ -119,7 +148,7 @@ class MessageToStdin(object):
     def readline(self) -> str:
         try:
             if len(self.linebuffer) == 0:
-                message = self.connection.recv()
+                message = self.connection.recv_bytes().decode('utf-8')
                 self.linebuffer = message.split("\n")
             result = self.linebuffer.pop(0)
             return result
@@ -165,9 +194,9 @@ def _run_invoke(
     os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}"
     sys.argv = args
     sys.stdin = MessageToStdin(conn_in)
-    sys.stdout = FilterStream(sys.stdout, include=re.compile("^\[\d+\]"))
-    with open(logfile, "w") as stderr, redirect_stderr(stderr):
-        entry_point()
+#    sys.stdout = FilterStream(sys.stdout, include=re.compile("^\[\d+\]"))
+#    with open(logfile, "w") as stderr, redirect_stderr(stderr):
+    entry_point()
 
 
 def _filter_output(stream: TextIOBase):
@@ -224,6 +253,10 @@ def main():
         default=1,
         help="When executing invokeai, how many parallel processes to execute per CUDA GPU.",
     )
+    parser.add_argument(
+        '--root_dir',
+        default=None,
+        help='Path to directory containing "models", "outputs" and "configs". If not present will read from environment variable INVOKEAI_ROOT. Defaults to ~/invokeai'    )
     opt = parser.parse_args()
 
     if opt.example:
@@ -247,6 +280,7 @@ def main():
         run_invoke=opt.invoke,
         invoke_model=opt.model,
         invoke_outdir=opt.outdir,
+        invoke_root=opt.root,
         processes_per_gpu=opt.processes_per_gpu,
     )
 

ldm/invoke/generator/diffusers_pipeline.py

@@ -29,6 +29,8 @@ from typing_extensions import ParamSpec
 from ldm.invoke.globals import Globals
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings
 from ldm.modules.textual_inversion_manager import TextualInversionManager
+from ldm.modules.lora_manager import LoraManager
+from ldm.modules.peft_manager import PeftManager
 from ..devices import normalize_device, CPU_DEVICE
 from ..offloading import LazilyLoadedModelGroup, FullyLoadedModelGroup, ModelGroup
 from ...models.diffusion.cross_attention_map_saving import AttentionMapSaver
@@ -289,11 +291,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
         )
+        self.lora_manager = LoraManager(self)
+        self.peft_manager = PeftManager()
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward, is_running_diffusers=True)
         use_full_precision = (precision == 'float32' or precision == 'autocast')
         self.textual_inversion_manager = TextualInversionManager(tokenizer=self.tokenizer,
                                                                  text_encoder=self.text_encoder,
                                                                  full_precision=use_full_precision)
+
         # InvokeAI's interface for text embeddings and whatnot
         self.embeddings_provider = EmbeddingsProvider(
             tokenizer=self.tokenizer,

ldm/invoke/generator/inpaint.py

@@ -255,8 +255,8 @@ class Inpaint(Img2Img):
         pipeline.scheduler = sampler
 
         # todo: support cross-attention control
-        uc, c, _ = conditioning
-        conditioning_data = (ConditioningData(uc, c, cfg_scale)
+        uc, c, extra_conditioning_info = conditioning
+        conditioning_data = (ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
                              .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
 
 

ldm/invoke/globals.py

@@ -23,6 +23,7 @@ Globals = Namespace()
 Globals.initfile = 'invokeai.init'
 Globals.models_file = 'models.yaml'
 Globals.models_dir = 'models'
+Globals.lora_models_dir = 'loras'
 Globals.config_dir = 'configs'
 Globals.autoscan_dir = 'weights'
 Globals.converted_ckpts_dir = 'converted_ckpts'
@@ -61,7 +62,7 @@ Globals.sequential_guidance = False
 Globals.full_precision = False
 
 # whether we should convert ckpt files into diffusers models on the fly
-Globals.ckpt_convert = False
+Globals.ckpt_convert = True
 
 # logging tokenization everywhere
 Globals.log_tokenization = False
@@ -75,6 +76,11 @@ def global_config_dir()->Path:
 def global_models_dir()->Path:
     return Path(Globals.root, Globals.models_dir)
 
+def global_lora_models_dir()->Path:
+    return Path(Globals.lora_models_dir) \
+        if Path(Globals.lora_models_dir).is_absolute() \
+           else Path(Globals.root, Globals.lora_models_dir)
+
 def global_autoscan_dir()->Path:
     return Path(Globals.root, Globals.autoscan_dir)
 

ldm/invoke/model_manager.py

@@ -152,19 +152,6 @@ class ModelManager(object):
         """
         return list(self.config.keys())
 
-    def is_legacy(self, model_name: str) -> bool:
-        """
-        Return true if this is a legacy (.ckpt) model
-        """
-        # if we are converting legacy files automatically, then
-        # there are no legacy ckpts!
-        info = self.model_info(model_name)
-        if Globals.ckpt_convert or info.format=='diffusers' or self.is_v2_config(info.config):
-            return False
-        if "weights" in info and info["weights"].endswith((".ckpt", ".safetensors")):
-            return True
-        return False
-
     def list_models(self) -> dict:
         """
         Return a dict of models in the format:
@@ -365,9 +352,6 @@ class ModelManager(object):
         if not os.path.isabs(weights):
             weights = os.path.normpath(os.path.join(Globals.root, weights))
 
-        # check whether this is a v2 file and force conversion
-        convert = Globals.ckpt_convert or self.is_v2_config(config)
-
         if matching_config := self._scan_for_matching_file(Path(weights),suffixes=['.yaml']):
             print(f'   | Using external config file {matching_config}')
             config = matching_config
@@ -382,102 +366,41 @@ class ModelManager(object):
         # then we look for a file with the same basename
         vae_path = vae_path or self._scan_for_matching_file(Path(weights))
             
-        # if converting automatically to diffusers, then we do the conversion and return
-        # a diffusers pipeline
-        if convert:
-            print(
-                f">> Converting legacy checkpoint {model_name} into a diffusers model..."
-            )
-            from ldm.invoke.ckpt_to_diffuser import load_pipeline_from_original_stable_diffusion_ckpt
+        # Do the conversion and return a diffusers pipeline
+        print(
+            f">> Converting legacy checkpoint {model_name} into a diffusers model..."
+        )
+        from ldm.invoke.ckpt_to_diffuser import load_pipeline_from_original_stable_diffusion_ckpt
 
-            try:
-                if self.list_models()[self.current_model]['status'] == 'active':
-                    self.offload_model(self.current_model)
-            except Exception:
-                pass
-            
-            if self._has_cuda():
-                torch.cuda.empty_cache()
-            pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
-                checkpoint_path=weights,
-                original_config_file=config,
-                vae_path=vae_path,
-                return_generator_pipeline=True,
-                precision=torch.float16
-                if self.precision == "float16"
-                else torch.float32,
-            )
-            if self.sequential_offload:
-                pipeline.enable_offload_submodels(self.device)
-            else:
-                pipeline.to(self.device)
+#        try:
+#            if self.list_models()[self.current_model]['status'] == 'active':
+#                self.offload_model(self.current_model)
+#        except Exception:
+#            pass
 
-            return (
-                pipeline,
-                width,
-                height,
-                "NOHASH",
-            )
-
-        # for usage statistics
         if self._has_cuda():
-            torch.cuda.reset_peak_memory_stats()
             torch.cuda.empty_cache()
-
-        # this does the work
-        if not os.path.isabs(config):
-            config = os.path.join(Globals.root, config)
-        omega_config = OmegaConf.load(config)
-        with open(weights, "rb") as f:
-            weight_bytes = f.read()
-        model_hash = self._cached_sha256(weights, weight_bytes)
-        sd = None
-
-        if weights.endswith(".ckpt"):
-            self.scan_model(model_name, weights)
-            sd = torch.load(io.BytesIO(weight_bytes), map_location="cpu")
+        pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
+            checkpoint_path=weights,
+            original_config_file=config,
+            vae_path=vae_path,
+            return_generator_pipeline=True,
+            precision=torch.float16
+            if self.precision == "float16"
+            else torch.float32,
+        )
+        if self.sequential_offload:
+            pipeline.enable_offload_submodels(self.device)
         else:
-            sd = safetensors.torch.load(weight_bytes)
+            pipeline.to(self.device)
 
-        del weight_bytes
-        # merged models from auto11 merge board are flat for some reason
-        if "state_dict" in sd:
-            sd = sd["state_dict"]
+        return (
+            pipeline,
+            width,
+            height,
+            "NOHASH",
+        )
 
-        print("   | Forcing garbage collection prior to loading new model")
-        gc.collect()
-        model = instantiate_from_config(omega_config.model)
-        model.load_state_dict(sd, strict=False)
-
-        if self.precision == "float16":
-            print("   | Using faster float16 precision")
-            model = model.to(torch.float16)
-        else:
-            print("   | Using more accurate float32 precision")
-
-        # look and load a matching vae file. Code borrowed from AUTOMATIC1111 modules/sd_models.py
-        if vae_path:
-            print(f"   | Loading VAE weights from: {vae_path}")
-            if vae_path.suffix in [".ckpt", ".pt"]:
-                self.scan_model(vae_path.name, vae_path)
-                vae_ckpt = torch.load(vae_path, map_location="cpu")
-            else:
-                vae_ckpt = safetensors.torch.load_file(vae_path)
-            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
-            model.first_stage_model.load_state_dict(vae_dict, strict=False)
-        else:
-            print("   | Using VAE built into model.")
-
-        model.to(self.device)
-        # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
-        model.cond_stage_model.device = self.device
-
-        model.eval()
-
-        for module in model.modules():
-            if isinstance(module, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
-                module._orig_padding_mode = module.padding_mode
-        return model, width, height, model_hash
 
     def _load_diffusers_model(self, mconfig):
         name_or_path = self.model_name_or_path(mconfig)
@@ -760,7 +683,6 @@ class ModelManager(object):
     def heuristic_import(
         self,
         path_url_or_repo: str,
-        convert: bool = False,
         model_name: str = None,
         description: str = None,
         model_config_file: Path = None,
@@ -782,9 +704,6 @@ class ModelManager(object):
         The model_name and/or description can be provided. If not, they will
         be generated automatically.
 
-        If convert is true, legacy models will be converted to diffusers
-        before importing.
-
         If commit_to_conf is provided, the newly loaded model will be written
         to the `models.yaml` file at the indicated path. Otherwise, the changes
         will only remain in memory.
@@ -840,7 +759,6 @@ class ModelManager(object):
                 ):
                     if model_name := self.heuristic_import(
                         str(m),
-                        convert,
                         commit_to_conf=commit_to_conf,
                         config_file_callback=config_file_callback,
                     ):
@@ -919,48 +837,28 @@ class ModelManager(object):
             if not model_config_file:
                 return
 
-        if self.is_v2_config(model_config_file):
-            convert = True
-            print("   | This SD-v2 model will be converted to diffusers format for use")
-
         if (vae_path := self._scan_for_matching_file(model_path)):
             print(f"   | Using VAE file {vae_path.name}")
         
-        if convert:
-            diffuser_path = Path(
-                Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
-            )
-            vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
-            model_name = self.convert_and_import(
-                model_path,
-                diffusers_path=diffuser_path,
-                vae=vae,
-                vae_path=vae_path,
-                model_name=model_name,
-                model_description=description,
-                original_config_file=model_config_file,
-                commit_to_conf=commit_to_conf,
-                scan_needed=False,
-            )
-            # in the event that this file was downloaded automatically prior to conversion
-            # we do not keep the original .ckpt/.safetensors around
-            if is_temporary:
-                model_path.unlink(missing_ok=True)
-        else:
-            model_name = self.import_ckpt_model(
-                model_path,
-                config=model_config_file,
-                model_name=model_name,
-                model_description=description,
-                vae=str(
-                    vae_path
-                    or Path(
-                        Globals.root,
-                        "models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt",
-                    )
-                ),
-                commit_to_conf=commit_to_conf,
-            )
+        diffuser_path = Path(
+            Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
+        )
+        vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
+        model_name = self.convert_and_import(
+            model_path,
+            diffusers_path=diffuser_path,
+            vae=vae,
+            vae_path=vae_path,
+            model_name=model_name,
+            model_description=description,
+            original_config_file=model_config_file,
+            commit_to_conf=commit_to_conf,
+            scan_needed=False,
+        )
+        # in the event that this file was downloaded automatically prior to conversion
+        # we do not keep the original .ckpt/.safetensors around
+        if is_temporary:
+            model_path.unlink(missing_ok=True)
         if commit_to_conf:
             self.commit(commit_to_conf)
         return model_name

ldm/invoke/readline.py

@@ -11,9 +11,11 @@ seeds:
 import os
 import re
 import atexit
+from typing import List
 from ldm.invoke.args import Args
 from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.invoke.globals import Globals
+from ldm.modules.lora_manager import LoraManager
 
 # ---------------readline utilities---------------------
 try:
@@ -136,6 +138,9 @@ class Completer(object):
             elif re.search('<[\w-]*$',buffer):
                 self.matches= self._concept_completions(text,state)
 
+            elif re.search('withLora\(?[a-zA-Z0-9._-]*$',buffer):
+                self.matches= self._lora_completions(text,state)
+
             # looking for a model
             elif re.match('^'+'|'.join(MODEL_COMMANDS),buffer):
                 self.matches= self._model_completions(text, state)
@@ -298,6 +303,15 @@ class Completer(object):
         matches.sort()
         return matches
 
+    def _lora_completions(self, text, state)->List[str]:
+        loras: dict = LoraManager.list_loras()
+        lora_names = [f'withLora({x},1)' for x in loras.keys()]
+        matches = list()
+        for lora in lora_names:
+            if lora.startswith(text):
+                matches.append(lora)
+        return sorted(matches)
+
     def _model_completions(self, text, state, ckpt_only=False):
         m = re.search('(!switch\s+)(\w*)',text)
         if m:

ldm/models/diffusion/cross_attention_control.py

@@ -289,10 +289,10 @@ class InvokeAICrossAttentionMixin:
 
 
 
-def restore_default_cross_attention(model, is_running_diffusers: bool, restore_attention_processor: Optional[AttnProcessor]=None):
+def restore_default_cross_attention(model, is_running_diffusers: bool, processors_to_restore: Optional[AttnProcessor]=None):
     if is_running_diffusers:
         unet = model
-        unet.set_attn_processor(restore_attention_processor or CrossAttnProcessor())
+        unet.set_attn_processor(processors_to_restore or CrossAttnProcessor())
     else:
         remove_attention_function(model)
 
@@ -334,11 +334,9 @@ def override_cross_attention(model, context: Context, is_running_diffusers = Fal
             default_slice_size = 4
             slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
             unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
-        return old_attn_processors
     else:
         context.register_cross_attention_modules(model)
         inject_attention_function(model, context)
-        return None
 
 
 

ldm/models/diffusion/ddim.py

@@ -19,7 +19,7 @@ class DDIMSampler(Sampler):
         all_timesteps_count = kwargs.get('all_timesteps_count', t_enc)
 
         if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
-            self.invokeai_diffuser.override_cross_attention(extra_conditioning_info, step_count = all_timesteps_count)
+            self.invokeai_diffuser.override_attention_processors(extra_conditioning_info, step_count = all_timesteps_count)
         else:
             self.invokeai_diffuser.restore_default_cross_attention()
 

ldm/models/diffusion/ksampler.py

@@ -43,7 +43,7 @@ class CFGDenoiser(nn.Module):
         extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
 
         if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
-            self.invokeai_diffuser.override_cross_attention(extra_conditioning_info, step_count = t_enc)
+            self.invokeai_diffuser.override_attention_processors(extra_conditioning_info, step_count = t_enc)
         else:
             self.invokeai_diffuser.restore_default_cross_attention()
 

ldm/models/diffusion/plms.py

@@ -21,7 +21,7 @@ class PLMSSampler(Sampler):
         all_timesteps_count = kwargs.get('all_timesteps_count', t_enc)
 
         if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
-            self.invokeai_diffuser.override_cross_attention(extra_conditioning_info, step_count = all_timesteps_count)
+            self.invokeai_diffuser.override_attention_processors(extra_conditioning_info, step_count = all_timesteps_count)
         else:
             self.invokeai_diffuser.restore_default_cross_attention()
 

ldm/models/diffusion/shared_invokeai_diffusion.py

@@ -9,17 +9,28 @@ from diffusers.models.cross_attention import AttnProcessor
 from typing_extensions import TypeAlias
 
 from ldm.invoke.globals import Globals
-from ldm.models.diffusion.cross_attention_control import Arguments, \
-    restore_default_cross_attention, override_cross_attention, Context, get_cross_attention_modules, \
-    CrossAttentionType, SwapCrossAttnContext
+from ldm.models.diffusion.cross_attention_control import (
+    Arguments,
+    restore_default_cross_attention,
+    override_cross_attention,
+    Context,
+    get_cross_attention_modules,
+    CrossAttentionType,
+    SwapCrossAttnContext,
+)
 from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
+from ldm.modules.lora_manager import LoraCondition
 
 ModelForwardCallback: TypeAlias = Union[
     # x, t, conditioning, Optional[cross-attention kwargs]
-    Callable[[torch.Tensor, torch.Tensor, torch.Tensor, Optional[dict[str, Any]]], torch.Tensor],
-    Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
+    Callable[
+        [torch.Tensor, torch.Tensor, torch.Tensor, Optional[dict[str, Any]]],
+        torch.Tensor,
+    ],
+    Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
 ]
 
+
 @dataclass(frozen=True)
 class PostprocessingSettings:
     threshold: float
@@ -29,31 +40,39 @@ class PostprocessingSettings:
 
 
 class InvokeAIDiffuserComponent:
-    '''
+    """
     The aim of this component is to provide a single place for code that can be applied identically to
     all InvokeAI diffusion procedures.
 
     At the moment it includes the following features:
     * Cross attention control ("prompt2prompt")
     * Hybrid conditioning (used for inpainting)
-    '''
+    * "LoRA" and "PEFT" augmentions to the unet's attention weights
+    """
+
     debug_thresholding = False
     sequential_guidance = False
 
     @dataclass
     class ExtraConditioningInfo:
-
         tokens_count_including_eos_bos: int
         cross_attention_control_args: Optional[Arguments] = None
+        lora_conditions: Optional[list[LoraCondition]] = None
 
         @property
         def wants_cross_attention_control(self):
             return self.cross_attention_control_args is not None
 
+        @property
+        def has_lora_conditions(self):
+            return self.lora_conditions is not None
 
-    def __init__(self, model, model_forward_callback: ModelForwardCallback,
-                 is_running_diffusers: bool=False,
-                 ):
+    def __init__(
+        self,
+        model,
+        model_forward_callback: ModelForwardCallback,
+        is_running_diffusers: bool = False,
+    ):
         """
         :param model: the unet model to pass through to cross attention control
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
@@ -66,42 +85,64 @@ class InvokeAIDiffuserComponent:
         self.sequential_guidance = Globals.sequential_guidance
 
     @contextmanager
-    def custom_attention_context(self,
-                                 extra_conditioning_info: Optional[ExtraConditioningInfo],
-                                 step_count: int):
-        do_swap = extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control
+    def custom_attention_context(
+        self, extra_conditioning_info: Optional[ExtraConditioningInfo], step_count: int
+    ):
         old_attn_processor = None
-        if do_swap:
-            old_attn_processor = self.override_cross_attention(extra_conditioning_info,
-                                                               step_count=step_count)
+        if extra_conditioning_info and (
+            extra_conditioning_info.wants_cross_attention_control
+            | extra_conditioning_info.has_lora_conditions
+        ):
+            old_attn_processor = self.override_attention_processors(
+                extra_conditioning_info, step_count=step_count
+            )
+
         try:
             yield None
         finally:
             if old_attn_processor is not None:
                 self.restore_default_cross_attention(old_attn_processor)
+            if extra_conditioning_info and extra_conditioning_info.has_lora_conditions:
+                for lora_condition in extra_conditioning_info.lora_conditions:
+                    lora_condition.unload()
             # TODO resuscitate attention map saving
-            #self.remove_attention_map_saving()
+            # self.remove_attention_map_saving()
 
-    def override_cross_attention(self, conditioning: ExtraConditioningInfo, step_count: int) -> Dict[str, AttnProcessor]:
+    def override_attention_processors(
+        self, conditioning: ExtraConditioningInfo, step_count: int
+    ) -> Dict[str, AttnProcessor]:
         """
         setup cross attention .swap control. for diffusers this replaces the attention processor, so
         the previous attention processor is returned so that the caller can restore it later.
         """
-        self.conditioning = conditioning
-        self.cross_attention_control_context = Context(
-            arguments=self.conditioning.cross_attention_control_args,
-            step_count=step_count
-        )
-        return override_cross_attention(self.model,
-                                        self.cross_attention_control_context,
-                                        is_running_diffusers=self.is_running_diffusers)
+        old_attn_processors = self.model.attn_processors
 
-    def restore_default_cross_attention(self, restore_attention_processor: Optional['AttnProcessor']=None):
-        self.conditioning = None
+        # Load lora conditions into the model
+        if conditioning.has_lora_conditions:
+            for condition in conditioning.lora_conditions:
+                condition(self.model)
+
+        if conditioning.wants_cross_attention_control:
+            self.cross_attention_control_context = Context(
+                arguments=conditioning.cross_attention_control_args,
+                step_count=step_count,
+            )
+            override_cross_attention(
+                self.model,
+                self.cross_attention_control_context,
+                is_running_diffusers=self.is_running_diffusers,
+            )
+        return old_attn_processors
+
+    def restore_default_cross_attention(
+        self, processors_to_restore: Optional[dict[str, "AttnProcessor"]] = None
+    ):
         self.cross_attention_control_context = None
-        restore_default_cross_attention(self.model,
-                                        is_running_diffusers=self.is_running_diffusers,
-                                        restore_attention_processor=restore_attention_processor)
+        restore_default_cross_attention(
+            self.model,
+            is_running_diffusers=self.is_running_diffusers,
+            processors_to_restore=processors_to_restore,
+        )
 
     def setup_attention_map_saving(self, saver: AttentionMapSaver):
         def callback(slice, dim, offset, slice_size, key):
@@ -110,26 +151,40 @@ class InvokeAIDiffuserComponent:
                 return
             saver.add_attention_maps(slice, key)
 
-        tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
+        tokens_cross_attention_modules = get_cross_attention_modules(
+            self.model, CrossAttentionType.TOKENS
+        )
         for identifier, module in tokens_cross_attention_modules:
-            key = ('down' if identifier.startswith('down') else
-                   'up' if identifier.startswith('up') else
-                   'mid')
+            key = (
+                "down"
+                if identifier.startswith("down")
+                else "up"
+                if identifier.startswith("up")
+                else "mid"
+            )
             module.set_attention_slice_calculated_callback(
-                lambda slice, dim, offset, slice_size, key=key: callback(slice, dim, offset, slice_size, key))
+                lambda slice, dim, offset, slice_size, key=key: callback(
+                    slice, dim, offset, slice_size, key
+                )
+            )
 
     def remove_attention_map_saving(self):
-        tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
+        tokens_cross_attention_modules = get_cross_attention_modules(
+            self.model, CrossAttentionType.TOKENS
+        )
         for _, module in tokens_cross_attention_modules:
             module.set_attention_slice_calculated_callback(None)
 
-    def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
-                                unconditioning: Union[torch.Tensor,dict],
-                                conditioning: Union[torch.Tensor,dict],
-                                unconditional_guidance_scale: float,
-                                step_index: Optional[int]=None,
-                                total_step_count: Optional[int]=None,
-                          ):
+    def do_diffusion_step(
+        self,
+        x: torch.Tensor,
+        sigma: torch.Tensor,
+        unconditioning: Union[torch.Tensor, dict],
+        conditioning: Union[torch.Tensor, dict],
+        unconditional_guidance_scale: float,
+        step_index: Optional[int] = None,
+        total_step_count: Optional[int] = None,
+    ):
         """
         :param x: current latents
         :param sigma: aka t, passed to the internal model to control how much denoising will occur
@@ -140,33 +195,55 @@ class InvokeAIDiffuserComponent:
         :return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
         """
 
-
         cross_attention_control_types_to_do = []
         context: Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
-            percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
-            cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(percent_through)
+            percent_through = self.calculate_percent_through(
+                sigma, step_index, total_step_count
+            )
+            cross_attention_control_types_to_do = (
+                context.get_active_cross_attention_control_types_for_step(
+                    percent_through
+                )
+            )
 
-        wants_cross_attention_control = (len(cross_attention_control_types_to_do) > 0)
+        wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
         wants_hybrid_conditioning = isinstance(conditioning, dict)
 
         if wants_hybrid_conditioning:
-            unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(x, sigma, unconditioning,
-                                                                                       conditioning)
+            unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(
+                x, sigma, unconditioning, conditioning
+            )
         elif wants_cross_attention_control:
-            unconditioned_next_x, conditioned_next_x = self._apply_cross_attention_controlled_conditioning(x, sigma,
-                                                                                                           unconditioning,
-                                                                                                           conditioning,
-                                                                                                           cross_attention_control_types_to_do)
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_cross_attention_controlled_conditioning(
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                cross_attention_control_types_to_do,
+            )
         elif self.sequential_guidance:
-            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning_sequentially(
-                x, sigma, unconditioning, conditioning)
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_standard_conditioning_sequentially(
+                x, sigma, unconditioning, conditioning
+            )
 
         else:
-            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning(
-                x, sigma, unconditioning, conditioning)
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_standard_conditioning(
+                x, sigma, unconditioning, conditioning
+            )
 
-        combined_next_x = self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
+        combined_next_x = self._combine(
+            unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
+        )
 
         return combined_next_x
 
@@ -176,24 +253,33 @@ class InvokeAIDiffuserComponent:
         latents: torch.Tensor,
         sigma,
         step_index,
-        total_step_count
+        total_step_count,
     ) -> torch.Tensor:
         if postprocessing_settings is not None:
-            percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
-            latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
-            latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
+            percent_through = self.calculate_percent_through(
+                sigma, step_index, total_step_count
+            )
+            latents = self.apply_threshold(
+                postprocessing_settings, latents, percent_through
+            )
+            latents = self.apply_symmetry(
+                postprocessing_settings, latents, percent_through
+            )
         return latents
 
     def calculate_percent_through(self, sigma, step_index, total_step_count):
         if step_index is not None and total_step_count is not None:
             # 🧨diffusers codepath
-            percent_through = step_index / total_step_count  # will never reach 1.0 - this is deliberate
+            percent_through = (
+                step_index / total_step_count
+            )  # will never reach 1.0 - this is deliberate
         else:
             # legacy compvis codepath
             # TODO remove when compvis codepath support is dropped
             if step_index is None and sigma is None:
                 raise ValueError(
-                    f"Either step_index or sigma is required when doing cross attention control, but both are None.")
+                    f"Either step_index or sigma is required when doing cross attention control, but both are None."
+                )
             percent_through = self.estimate_percent_through(step_index, sigma)
         return percent_through
 
@@ -204,24 +290,30 @@ class InvokeAIDiffuserComponent:
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
         both_conditionings = torch.cat([unconditioning, conditioning])
-        both_results = self.model_forward_callback(x_twice, sigma_twice, both_conditionings)
+        both_results = self.model_forward_callback(
+            x_twice, sigma_twice, both_conditionings
+        )
         unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
-        if conditioned_next_x.device.type == 'mps':
+        if conditioned_next_x.device.type == "mps":
             # prevent a result filled with zeros. seems to be a torch bug.
             conditioned_next_x = conditioned_next_x.clone()
         return unconditioned_next_x, conditioned_next_x
 
-
-    def _apply_standard_conditioning_sequentially(self, x: torch.Tensor, sigma, unconditioning: torch.Tensor, conditioning: torch.Tensor):
+    def _apply_standard_conditioning_sequentially(
+        self,
+        x: torch.Tensor,
+        sigma,
+        unconditioning: torch.Tensor,
+        conditioning: torch.Tensor,
+    ):
         # low-memory sequential path
         unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
         conditioned_next_x = self.model_forward_callback(x, sigma, conditioning)
-        if conditioned_next_x.device.type == 'mps':
+        if conditioned_next_x.device.type == "mps":
             # prevent a result filled with zeros. seems to be a torch bug.
             conditioned_next_x = conditioned_next_x.clone()
         return unconditioned_next_x, conditioned_next_x
 
-
     def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning):
         assert isinstance(conditioning, dict)
         assert isinstance(unconditioning, dict)
@@ -236,48 +328,80 @@ class InvokeAIDiffuserComponent:
                 ]
             else:
                 both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]])
-        unconditioned_next_x, conditioned_next_x = self.model_forward_callback(x_twice, sigma_twice, both_conditionings).chunk(2)
+        unconditioned_next_x, conditioned_next_x = self.model_forward_callback(
+            x_twice, sigma_twice, both_conditionings
+        ).chunk(2)
         return unconditioned_next_x, conditioned_next_x
 
-
-    def _apply_cross_attention_controlled_conditioning(self,
-                                                     x: torch.Tensor,
-                                                     sigma,
-                                                     unconditioning,
-                                                     conditioning,
-                                                     cross_attention_control_types_to_do):
+    def _apply_cross_attention_controlled_conditioning(
+        self,
+        x: torch.Tensor,
+        sigma,
+        unconditioning,
+        conditioning,
+        cross_attention_control_types_to_do,
+    ):
         if self.is_running_diffusers:
-            return self._apply_cross_attention_controlled_conditioning__diffusers(x, sigma, unconditioning,
-                                                                                  conditioning,
-                                                                                  cross_attention_control_types_to_do)
+            return self._apply_cross_attention_controlled_conditioning__diffusers(
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                cross_attention_control_types_to_do,
+            )
         else:
-            return self._apply_cross_attention_controlled_conditioning__compvis(x, sigma, unconditioning, conditioning,
-                                                                                cross_attention_control_types_to_do)
+            return self._apply_cross_attention_controlled_conditioning__compvis(
+                x,
+                sigma,
+                unconditioning,
+                conditioning,
+                cross_attention_control_types_to_do,
+            )
 
-    def _apply_cross_attention_controlled_conditioning__diffusers(self,
-                                                                 x: torch.Tensor,
-                                                                 sigma,
-                                                                 unconditioning,
-                                                                 conditioning,
-                                                                 cross_attention_control_types_to_do):
+    def _apply_cross_attention_controlled_conditioning__diffusers(
+        self,
+        x: torch.Tensor,
+        sigma,
+        unconditioning,
+        conditioning,
+        cross_attention_control_types_to_do,
+    ):
         context: Context = self.cross_attention_control_context
 
-        cross_attn_processor_context = SwapCrossAttnContext(modified_text_embeddings=context.arguments.edited_conditioning,
-                                                            index_map=context.cross_attention_index_map,
-                                                            mask=context.cross_attention_mask,
-                                                            cross_attention_types_to_do=[])
+        cross_attn_processor_context = SwapCrossAttnContext(
+            modified_text_embeddings=context.arguments.edited_conditioning,
+            index_map=context.cross_attention_index_map,
+            mask=context.cross_attention_mask,
+            cross_attention_types_to_do=[],
+        )
         # no cross attention for unconditioning (negative prompt)
-        unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning,
-                                                           {"swap_cross_attn_context": cross_attn_processor_context})
+        unconditioned_next_x = self.model_forward_callback(
+            x,
+            sigma,
+            unconditioning,
+            {"swap_cross_attn_context": cross_attn_processor_context},
+        )
 
         # do requested cross attention types for conditioning (positive prompt)
-        cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do
-        conditioned_next_x = self.model_forward_callback(x, sigma, conditioning,
-                                                         {"swap_cross_attn_context": cross_attn_processor_context})
+        cross_attn_processor_context.cross_attention_types_to_do = (
+            cross_attention_control_types_to_do
+        )
+        conditioned_next_x = self.model_forward_callback(
+            x,
+            sigma,
+            conditioning,
+            {"swap_cross_attn_context": cross_attn_processor_context},
+        )
         return unconditioned_next_x, conditioned_next_x
 
-
-    def _apply_cross_attention_controlled_conditioning__compvis(self, x:torch.Tensor, sigma, unconditioning, conditioning, cross_attention_control_types_to_do):
+    def _apply_cross_attention_controlled_conditioning__compvis(
+        self,
+        x: torch.Tensor,
+        sigma,
+        unconditioning,
+        conditioning,
+        cross_attention_control_types_to_do,
+    ):
         # print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
         # slower non-batched path (20% slower on mac MPS)
         # We are only interested in using attention maps for conditioned_next_x, but batching them with generation of
@@ -287,24 +411,26 @@ class InvokeAIDiffuserComponent:
         # representing batched uncond + cond, but then when it comes to applying the saved attention, the
         # wrangler gets an attention tensor which only has shape[0]=8, representing just self.edited_conditionings.)
         # todo: give CrossAttentionControl's `wrangler` function more info so it can work with a batched call as well.
-        context:Context = self.cross_attention_control_context
+        context: Context = self.cross_attention_control_context
 
         try:
             unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
 
             # process x using the original prompt, saving the attention maps
-            #print("saving attention maps for", cross_attention_control_types_to_do)
+            # print("saving attention maps for", cross_attention_control_types_to_do)
             for ca_type in cross_attention_control_types_to_do:
                 context.request_save_attention_maps(ca_type)
             _ = self.model_forward_callback(x, sigma, conditioning)
             context.clear_requests(cleanup=False)
 
             # process x again, using the saved attention maps to control where self.edited_conditioning will be applied
-            #print("applying saved attention maps for", cross_attention_control_types_to_do)
+            # print("applying saved attention maps for", cross_attention_control_types_to_do)
             for ca_type in cross_attention_control_types_to_do:
                 context.request_apply_saved_attention_maps(ca_type)
-            edited_conditioning = self.conditioning.cross_attention_control_args.edited_conditioning
-            conditioned_next_x = self.model_forward_callback(x, sigma, edited_conditioning)
+            edited_conditioning = context.arguments.edited_conditioning
+            conditioned_next_x = self.model_forward_callback(
+                x, sigma, edited_conditioning
+            )
             context.clear_requests(cleanup=True)
 
         except:
@@ -323,17 +449,21 @@ class InvokeAIDiffuserComponent:
         self,
         postprocessing_settings: PostprocessingSettings,
         latents: torch.Tensor,
-        percent_through: float
+        percent_through: float,
     ) -> torch.Tensor:
-
-        if postprocessing_settings.threshold is None or postprocessing_settings.threshold == 0.0:
+        if (
+            postprocessing_settings.threshold is None
+            or postprocessing_settings.threshold == 0.0
+        ):
             return latents
 
         threshold = postprocessing_settings.threshold
         warmup = postprocessing_settings.warmup
 
         if percent_through < warmup:
-            current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup))
+            current_threshold = threshold + threshold * 5 * (
+                1 - (percent_through / warmup)
+            )
         else:
             current_threshold = threshold
 
@@ -347,10 +477,14 @@ class InvokeAIDiffuserComponent:
 
         if self.debug_thresholding:
             std, mean = [i.item() for i in torch.std_mean(latents)]
-            outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
-            print(f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
-                  f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
-                  f"  | {outside / latents.numel() * 100:.2f}% values outside threshold")
+            outside = torch.count_nonzero(
+                (latents < -current_threshold) | (latents > current_threshold)
+            )
+            print(
+                f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
+                f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
+                f"  | {outside / latents.numel() * 100:.2f}% values outside threshold"
+            )
 
         if maxval < current_threshold and minval > -current_threshold:
             return latents
@@ -363,17 +497,23 @@ class InvokeAIDiffuserComponent:
             latents = torch.clone(latents)
             maxval = np.clip(maxval * scale, 1, current_threshold)
             num_altered += torch.count_nonzero(latents > maxval)
-            latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval
+            latents[latents > maxval] = (
+                torch.rand_like(latents[latents > maxval]) * maxval
+            )
 
         if minval < -current_threshold:
             latents = torch.clone(latents)
             minval = np.clip(minval * scale, -current_threshold, -1)
             num_altered += torch.count_nonzero(latents < minval)
-            latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval
+            latents[latents < minval] = (
+                torch.rand_like(latents[latents < minval]) * minval
+            )
 
         if self.debug_thresholding:
-            print(f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
-                  f"  | {num_altered / latents.numel() * 100:.2f}% values altered")
+            print(
+                f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
+                f"  | {num_altered / latents.numel() * 100:.2f}% values altered"
+            )
 
         return latents
 
@@ -381,9 +521,8 @@ class InvokeAIDiffuserComponent:
         self,
         postprocessing_settings: PostprocessingSettings,
         latents: torch.Tensor,
-        percent_through: float
+        percent_through: float,
     ) -> torch.Tensor:
-
         # Reset our last percent through if this is our first step.
         if percent_through == 0.0:
             self.last_percent_through = 0.0
@@ -393,36 +532,52 @@ class InvokeAIDiffuserComponent:
 
         # Check for out of bounds
         h_symmetry_time_pct = postprocessing_settings.h_symmetry_time_pct
-        if (h_symmetry_time_pct is not None and (h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0)):
+        if h_symmetry_time_pct is not None and (
+            h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0
+        ):
             h_symmetry_time_pct = None
 
         v_symmetry_time_pct = postprocessing_settings.v_symmetry_time_pct
-        if (v_symmetry_time_pct is not None and (v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0)):
+        if v_symmetry_time_pct is not None and (
+            v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0
+        ):
             v_symmetry_time_pct = None
 
         dev = latents.device.type
 
-        latents.to(device='cpu')
+        latents.to(device="cpu")
 
         if (
-            h_symmetry_time_pct != None and
-            self.last_percent_through < h_symmetry_time_pct and
-            percent_through >= h_symmetry_time_pct
+            h_symmetry_time_pct != None
+            and self.last_percent_through < h_symmetry_time_pct
+            and percent_through >= h_symmetry_time_pct
         ):
             # Horizontal symmetry occurs on the 3rd dimension of the latent
             width = latents.shape[3]
             x_flipped = torch.flip(latents, dims=[3])
-            latents = torch.cat([latents[:, :, :, 0:int(width/2)], x_flipped[:, :, :, int(width/2):int(width)]], dim=3)
+            latents = torch.cat(
+                [
+                    latents[:, :, :, 0 : int(width / 2)],
+                    x_flipped[:, :, :, int(width / 2) : int(width)],
+                ],
+                dim=3,
+            )
 
         if (
-            v_symmetry_time_pct != None and
-            self.last_percent_through < v_symmetry_time_pct and
-            percent_through >= v_symmetry_time_pct
+            v_symmetry_time_pct != None
+            and self.last_percent_through < v_symmetry_time_pct
+            and percent_through >= v_symmetry_time_pct
         ):
             # Vertical symmetry occurs on the 2nd dimension of the latent
             height = latents.shape[2]
             y_flipped = torch.flip(latents, dims=[2])
-            latents = torch.cat([latents[:, :, 0:int(height / 2)], y_flipped[:, :, int(height / 2):int(height)]], dim=2)
+            latents = torch.cat(
+                [
+                    latents[:, :, 0 : int(height / 2)],
+                    y_flipped[:, :, int(height / 2) : int(height)],
+                ],
+                dim=2,
+            )
 
         self.last_percent_through = percent_through
         return latents.to(device=dev)
@@ -430,7 +585,9 @@ class InvokeAIDiffuserComponent:
     def estimate_percent_through(self, step_index, sigma):
         if step_index is not None and self.cross_attention_control_context is not None:
             # percent_through will never reach 1.0 (but this is intended)
-            return float(step_index) / float(self.cross_attention_control_context.step_count)
+            return float(step_index) / float(
+                self.cross_attention_control_context.step_count
+            )
         # find the best possible index of the current sigma in the sigma sequence
         smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma)
         sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0
@@ -439,33 +596,38 @@ class InvokeAIDiffuserComponent:
         return 1.0 - float(sigma_index + 1) / float(self.model.sigmas.shape[0])
         # print('estimated percent_through', percent_through, 'from sigma', sigma.item())
 
-
     # todo: make this work
     @classmethod
-    def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
+    def apply_conjunction(
+        cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale
+    ):
         x_in = torch.cat([x] * 2)
-        t_in = torch.cat([t] * 2) # aka sigmas
+        t_in = torch.cat([t] * 2)  # aka sigmas
 
         deltas = None
         uncond_latents = None
-        weighted_cond_list = c_or_weighted_c_list if type(c_or_weighted_c_list) is list else [(c_or_weighted_c_list, 1)]
+        weighted_cond_list = (
+            c_or_weighted_c_list
+            if type(c_or_weighted_c_list) is list
+            else [(c_or_weighted_c_list, 1)]
+        )
 
         # below is fugly omg
         num_actual_conditionings = len(c_or_weighted_c_list)
-        conditionings = [uc] + [c for c,weight in weighted_cond_list]
-        weights = [1] + [weight for c,weight in weighted_cond_list]
-        chunk_count = ceil(len(conditionings)/2)
+        conditionings = [uc] + [c for c, weight in weighted_cond_list]
+        weights = [1] + [weight for c, weight in weighted_cond_list]
+        chunk_count = ceil(len(conditionings) / 2)
         deltas = None
         for chunk_index in range(chunk_count):
-            offset = chunk_index*2
-            chunk_size = min(2, len(conditionings)-offset)
+            offset = chunk_index * 2
+            chunk_size = min(2, len(conditionings) - offset)
 
             if chunk_size == 1:
                 c_in = conditionings[offset]
                 latents_a = forward_func(x_in[:-1], t_in[:-1], c_in)
                 latents_b = None
             else:
-                c_in = torch.cat(conditionings[offset:offset+2])
+                c_in = torch.cat(conditionings[offset : offset + 2])
                 latents_a, latents_b = forward_func(x_in, t_in, c_in).chunk(2)
 
             # first chunk is guaranteed to be 2 entries: uncond_latents + first conditioining
@@ -478,11 +640,15 @@ class InvokeAIDiffuserComponent:
                     deltas = torch.cat((deltas, latents_b - uncond_latents))
 
         # merge the weighted deltas together into a single merged delta
-        per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
+        per_delta_weights = torch.tensor(
+            weights[1:], dtype=deltas.dtype, device=deltas.device
+        )
         normalize = False
         if normalize:
             per_delta_weights /= torch.sum(per_delta_weights)
-        reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
+        reshaped_weights = per_delta_weights.reshape(
+            per_delta_weights.shape + (1, 1, 1)
+        )
         deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)
 
         # old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)

ldm/modules/kohya_lora_manager.py

@@ -0,0 +1,367 @@
+import re
+from pathlib import Path
+from typing import Optional
+
+import torch
+from compel import Compel
+from diffusers.models import UNet2DConditionModel
+from safetensors.torch import load_file
+from torch.utils.hooks import RemovableHandle
+from transformers import CLIPTextModel
+
+from ldm.invoke.devices import choose_torch_device
+
+"""
+This module supports loading LoRA weights trained with https://github.com/kohya-ss/sd-scripts
+To be removed once support for diffusers LoRA weights is well supported
+"""
+
+
+class LoRALayer:
+    lora_name: str
+    name: str
+    scale: float
+
+    up: torch.nn.Module
+    mid: Optional[torch.nn.Module] = None
+    down: torch.nn.Module
+
+    def __init__(self, lora_name: str, name: str, rank=4, alpha=1.0):
+        self.lora_name = lora_name
+        self.name = name
+        self.scale = alpha / rank if (alpha and rank) else 1.0
+
+    def forward(self, lora, input_h, output):
+        if self.mid is None:
+            output = (
+                output
+                + self.up(self.down(*input_h)) * lora.multiplier * self.scale
+            )
+        else:
+            output = (
+                output
+                + self.up(self.mid(self.down(*input_h))) * lora.multiplier * self.scale
+            )
+        return output
+
+class LoHALayer:
+    lora_name: str
+    name: str
+    scale: float
+
+    w1_a: torch.Tensor
+    w1_b: torch.Tensor
+    w2_a: torch.Tensor
+    w2_b: torch.Tensor
+    t1: Optional[torch.Tensor] = None
+    t2: Optional[torch.Tensor] = None
+    bias: Optional[torch.Tensor] = None
+
+    org_module: torch.nn.Module
+
+    def __init__(self, lora_name: str, name: str, rank=4, alpha=1.0):
+        self.lora_name = lora_name
+        self.name = name
+        self.scale = alpha / rank if (alpha and rank) else 1.0
+
+    def forward(self, lora, input_h, output):
+
+        if type(self.org_module) == torch.nn.Conv2d:
+            op = torch.nn.functional.conv2d
+            extra_args = dict(
+                stride=self.org_module.stride,
+                padding=self.org_module.padding,
+                dilation=self.org_module.dilation,
+                groups=self.org_module.groups,
+            )
+
+        else:
+            op = torch.nn.functional.linear
+            extra_args = {}
+
+        if self.t1 is None:
+            weight = ((self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b))
+
+        else:
+            rebuild1 = torch.einsum('i j k l, j r, i p -> p r k l', self.t1, self.w1_b, self.w1_a)
+            rebuild2 = torch.einsum('i j k l, j r, i p -> p r k l', self.t2, self.w2_b, self.w2_a)
+            weight = rebuild1 * rebuild2
+        
+        bias = self.bias if self.bias is not None else 0
+        return output + op(
+            *input_h,
+            (weight + bias).view(self.org_module.weight.shape),
+            None,
+            **extra_args,
+        ) * lora.multiplier * self.scale
+
+
+class LoRAModuleWrapper:
+    unet: UNet2DConditionModel
+    text_encoder: CLIPTextModel
+    hooks: list[RemovableHandle]
+
+    def __init__(self, unet, text_encoder):
+        self.unet = unet
+        self.text_encoder = text_encoder
+        self.hooks = []
+        self.text_modules = None
+        self.unet_modules = None
+
+        self.applied_loras = {}
+        self.loaded_loras = {}
+
+        self.UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention", "ResnetBlock2D", "Downsample2D", "Upsample2D", "SpatialTransformer"]
+        self.TEXT_ENCODER_TARGET_REPLACE_MODULE = ["ResidualAttentionBlock", "CLIPAttention", "CLIPMLP"]
+        self.LORA_PREFIX_UNET = "lora_unet"
+        self.LORA_PREFIX_TEXT_ENCODER = "lora_te"
+
+
+        def find_modules(
+            prefix, root_module: torch.nn.Module, target_replace_modules
+        ) -> dict[str, torch.nn.Module]:
+            mapping = {}
+            for name, module in root_module.named_modules():
+                if module.__class__.__name__ in target_replace_modules:
+                    for child_name, child_module in module.named_modules():
+                        layer_type = child_module.__class__.__name__
+                        if layer_type == "Linear" or (
+                            layer_type == "Conv2d"
+                            and child_module.kernel_size in [(1, 1), (3, 3)]
+                        ):
+                            lora_name = prefix + "." + name + "." + child_name
+                            lora_name = lora_name.replace(".", "_")
+                            mapping[lora_name] = child_module
+                            self.apply_module_forward(child_module, lora_name)
+            return mapping
+
+        if self.text_modules is None:
+            self.text_modules = find_modules(
+                self.LORA_PREFIX_TEXT_ENCODER,
+                text_encoder,
+                self.TEXT_ENCODER_TARGET_REPLACE_MODULE,
+            )
+
+        if self.unet_modules is None:
+            self.unet_modules = find_modules(
+                self.LORA_PREFIX_UNET, unet, self.UNET_TARGET_REPLACE_MODULE
+            )
+
+
+    def lora_forward_hook(self, name):
+        wrapper = self
+
+        def lora_forward(module, input_h, output):
+            if len(wrapper.loaded_loras) == 0:
+                return output
+
+            for lora in wrapper.applied_loras.values():
+                layer = lora.layers.get(name, None)
+                if layer is None:
+                    continue
+                output = layer.forward(lora, input_h, output)
+            return output
+
+        return lora_forward
+
+    def apply_module_forward(self, module, name):
+        handle = module.register_forward_hook(self.lora_forward_hook(name))
+        self.hooks.append(handle)
+
+    def clear_hooks(self):
+        for hook in self.hooks:
+            hook.remove()
+
+        self.hooks.clear()
+
+    def clear_applied_loras(self):
+        self.applied_loras.clear()
+
+    def clear_loaded_loras(self):
+        self.loaded_loras.clear()
+
+class LoRA:
+    name: str
+    layers: dict[str, LoRALayer]
+    device: torch.device
+    dtype: torch.dtype
+    wrapper: LoRAModuleWrapper
+    multiplier: float
+
+    def __init__(self, name: str, device, dtype, wrapper, multiplier=1.0):
+        self.name = name
+        self.layers = {}
+        self.multiplier = multiplier
+        self.device = device
+        self.dtype = dtype
+        self.wrapper = wrapper
+
+    def load_from_dict(self, state_dict):
+        state_dict_groupped = dict()
+
+        for key, value in state_dict.items():
+            stem, leaf = key.split(".", 1)
+            if stem not in state_dict_groupped:
+                state_dict_groupped[stem] = dict()
+            state_dict_groupped[stem][leaf] = value
+
+
+        for stem, values in state_dict_groupped.items():
+            if stem.startswith(self.wrapper.LORA_PREFIX_TEXT_ENCODER):
+                wrapped = self.wrapper.text_modules.get(stem, None)
+            elif stem.startswith(self.wrapper.LORA_PREFIX_UNET):
+                wrapped = self.wrapper.unet_modules.get(stem, None)
+            else:
+                continue
+
+            if wrapped is None:
+                print(f">> Missing layer: {stem}")
+                continue
+
+            # TODO: diff key
+
+            bias = None
+            alpha = None
+
+            if "alpha" in values:
+                alpha = values["alpha"].item()
+
+            if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
+                bias = torch.sparse_coo_tensor(
+                    values["bias_indices"],
+                    values["bias_values"],
+                    tuple(values["bias_size"]),
+                ).to(device=self.device, dtype=self.dtype)
+
+
+            # lora and locon
+            if "lora_down.weight" in values:
+                value_down = values["lora_down.weight"]
+                value_mid  = values.get("lora_mid.weight", None)
+                value_up   = values["lora_up.weight"]
+
+                if type(wrapped) == torch.nn.Conv2d:
+                    if value_mid is not None:
+                        layer_down = torch.nn.Conv2d(value_down.shape[1], value_down.shape[0], (1, 1), bias=False)
+                        layer_mid  = torch.nn.Conv2d(value_mid.shape[1], value_mid.shape[0], wrapped.kernel_size, wrapped.stride, wrapped.padding, bias=False)
+                    else:
+                        layer_down = torch.nn.Conv2d(value_down.shape[1], value_down.shape[0], wrapped.kernel_size, wrapped.stride, wrapped.padding, bias=False)
+                        layer_mid  = None
+
+                    layer_up = torch.nn.Conv2d(value_up.shape[1], value_up.shape[0], (1, 1), bias=False)
+
+                elif type(wrapped) == torch.nn.Linear:
+                    layer_down = torch.nn.Linear(value_down.shape[1], value_down.shape[0], bias=False)
+                    layer_mid  = None
+                    layer_up   = torch.nn.Linear(value_up.shape[1], value_up.shape[0], bias=False)
+
+                else:
+                    print(
+                        f">> Encountered unknown lora layer module in {self.name}: {stem} - {type(wrapped).__name__}"
+                    )
+                    return
+
+
+                with torch.no_grad():
+                    layer_down.weight.copy_(value_down)
+                    if layer_mid is not None:
+                        layer_mid.weight.copy_(value_mid)
+                    layer_up.weight.copy_(value_up)
+
+
+                layer_down.to(device=self.device, dtype=self.dtype)
+                if layer_mid is not None:
+                    layer_mid.to(device=self.device, dtype=self.dtype)
+                layer_up.to(device=self.device, dtype=self.dtype)
+
+
+                rank = value_down.shape[0]
+
+                layer = LoRALayer(self.name, stem, rank, alpha)
+                #layer.bias = bias # TODO: find and debug lora/locon with bias
+                layer.down = layer_down
+                layer.mid = layer_mid
+                layer.up = layer_up
+
+            # loha
+            elif "hada_w1_b" in values:
+
+                rank = values["hada_w1_b"].shape[0]
+
+                layer = LoHALayer(self.name, stem, rank, alpha)
+                layer.org_module = wrapped
+                layer.bias = bias
+
+                layer.w1_a = values["hada_w1_a"].to(device=self.device, dtype=self.dtype)
+                layer.w1_b = values["hada_w1_b"].to(device=self.device, dtype=self.dtype)
+                layer.w2_a = values["hada_w2_a"].to(device=self.device, dtype=self.dtype)
+                layer.w2_b = values["hada_w2_b"].to(device=self.device, dtype=self.dtype)
+
+                if "hada_t1" in values:
+                    layer.t1 = values["hada_t1"].to(device=self.device, dtype=self.dtype)
+                else:
+                    layer.t1 = None
+
+                if "hada_t2" in values:
+                    layer.t2 = values["hada_t2"].to(device=self.device, dtype=self.dtype)
+                else:
+                    layer.t2 = None
+
+            else:
+                print(
+                    f">> Encountered unknown lora layer module in {self.name}: {stem} - {type(wrapped).__name__}"
+                )
+                return
+
+            self.layers[stem] = layer
+
+
+class KohyaLoraManager:
+    def __init__(self, pipe, lora_path):
+        self.unet = pipe.unet
+        self.lora_path = lora_path
+        self.wrapper = LoRAModuleWrapper(pipe.unet, pipe.text_encoder)
+        self.text_encoder = pipe.text_encoder
+        self.device = torch.device(choose_torch_device())
+        self.dtype = pipe.unet.dtype
+
+    def load_lora_module(self, name, path_file, multiplier: float = 1.0):
+        print(f"   | Found lora {name} at {path_file}")
+        if path_file.suffix == ".safetensors":
+            checkpoint = load_file(path_file.absolute().as_posix(), device="cpu")
+        else:
+            checkpoint = torch.load(path_file, map_location="cpu")
+
+        lora = LoRA(name, self.device, self.dtype, self.wrapper, multiplier)
+        lora.load_from_dict(checkpoint)
+        self.wrapper.loaded_loras[name] = lora
+
+        return lora
+
+    def apply_lora_model(self, name, mult: float = 1.0):
+        for suffix in ["ckpt", "safetensors", "pt"]:
+            path_file = Path(self.lora_path, f"{name}.{suffix}")
+            if path_file.exists():
+                print(f"   | Loading lora {path_file.name} with weight {mult}")
+                break
+        if not path_file.exists():
+            print(f"   ** Unable to find lora: {name}")
+            return
+
+        lora = self.wrapper.loaded_loras.get(name, None)
+        if lora is None:
+            lora = self.load_lora_module(name, path_file, mult)
+
+        lora.multiplier = mult
+        self.wrapper.applied_loras[name] = lora
+
+    def unload_applied_lora(self, lora_name: str):
+        if lora_name in self.wrapper.applied_loras:
+            del self.wrapper.applied_loras[lora_name]
+
+    def unload_lora(self, lora_name: str):
+        if lora_name in self.wrapper.loaded_loras:
+            del self.wrapper.loaded_loras[lora_name]
+
+    def clear_loras(self):
+        self.wrapper.clear_applied_loras()

ldm/modules/lora_manager.py

@@ -0,0 +1,66 @@
+import os
+from pathlib import Path
+from ldm.invoke.globals import global_lora_models_dir
+from .kohya_lora_manager import KohyaLoraManager
+from typing import Optional, Dict
+
+class LoraCondition:
+    name: str
+    weight: float
+
+    def __init__(self, name, weight: float = 1.0, kohya_manager: Optional[KohyaLoraManager]=None):
+        self.name = name
+        self.weight = weight
+        self.kohya_manager = kohya_manager
+
+    def __call__(self, model):
+        # TODO: make model able to load from huggingface, rather then just local files
+        path = Path(global_lora_models_dir(), self.name)
+        if path.is_dir():
+            if model.load_attn_procs:
+                file = Path(path, "pytorch_lora_weights.bin")
+                if file.is_file():
+                    print(f">> Loading LoRA: {path}")
+                    model.load_attn_procs(path.absolute().as_posix())
+                else:
+                    print(f"   ** Unable to find valid LoRA at: {path}")
+            else:
+                print("   ** Invalid Model to load LoRA")
+        elif self.kohya_manager:
+            self.kohya_manager.apply_lora_model(self.name,self.weight)
+        else:
+            print("   ** Unable to load LoRA")
+
+    def unload(self):
+        if self.kohya_manager:
+            print(f'>> unloading LoRA {self.name}')
+            self.kohya_manager.unload_applied_lora(self.name)
+
+class LoraManager:
+    def __init__(self, pipe):
+        # Kohya class handles lora not generated through diffusers
+        self.kohya = KohyaLoraManager(pipe, global_lora_models_dir())
+
+    def set_loras_conditions(self, lora_weights: list):
+        conditions = []
+        if len(lora_weights) > 0:
+            for lora in lora_weights:
+                conditions.append(LoraCondition(lora.model, lora.weight, self.kohya))
+
+        if len(conditions) > 0:
+            return conditions
+
+        return None
+
+    @classmethod
+    def list_loras(self)->Dict[str, Path]:
+        path = Path(global_lora_models_dir())
+        models_found = dict()
+        for root,_,files in os.walk(path):
+            for x in files:
+                name = Path(x).stem
+                suffix = Path(x).suffix
+                if suffix in [".ckpt", ".pt", ".safetensors"]:
+                    models_found[name]=Path(root,x)
+        return models_found
+            

ldm/modules/peft_manager.py

@@ -0,0 +1,95 @@
+from peft import LoraModel, LoraConfig, set_peft_model_state_dict
+import torch
+import json
+from pathlib import Path
+from ldm.invoke.globals import global_lora_models_dir
+
+class LoraPeftModule:
+    def __init__(self, lora_dir, multiplier: float = 1.0):
+        self.lora_dir = lora_dir
+        self.multiplier = multiplier
+        self.config = self.load_config()
+        self.checkpoint = self.load_checkpoint()
+
+    def load_config(self):
+        lora_config_file = Path(self.lora_dir, f'lora_config.json')
+        with open(lora_config_file, "r") as f:
+            return json.load(f)
+
+    def load_checkpoint(self):
+        return torch.load(Path(self.lora_dir, f'lora.pt'))
+
+    def unet(self, text_encoder):
+        lora_ds = {
+            k.replace("text_encoder_", ""): v for k, v in self.checkpoint.items() if "text_encoder_" in k
+        }
+        config = LoraConfig(**self.config["peft_config"])
+        model = LoraModel(config, text_encoder)
+        set_peft_model_state_dict(model, lora_ds)
+        return model
+
+    def text_encoder(self, unet):
+        lora_ds = {
+            k: v for k, v in self.checkpoint.items() if "text_encoder_" not in k
+        }
+        config = LoraConfig(**self.config["text_encoder_peft_config"])
+        model = LoraModel(config, unet)
+        set_peft_model_state_dict(model, lora_ds)
+        return model
+
+    def apply(self, pipe, dtype):
+        pipe.unet = self.unet(pipe.unet)
+        if "text_encoder_peft_config" in self.config:
+            pipe.text_encoder = self.text_encoder(pipe.text_encoder)
+
+        if dtype in (torch.float16, torch.bfloat16):
+            pipe.unet.half()
+            pipe.text_encoder.half()
+
+        return pipe
+
+
+class PeftManager:
+    modules: list[LoraPeftModule]
+
+    def __init__(self):
+        self.lora_path = global_lora_models_dir()
+        self.modules = []
+
+    def set_loras(self, lora_weights: list):
+        if len(lora_weights) > 0:
+            for lora in lora_weights:
+                self.add(lora.model, lora.weight)
+
+    def add(self, name, multiplier: float = 1.0):
+        lora_dir = Path(self.lora_path, name)
+
+        if lora_dir.exists():
+            lora_config_file = Path(lora_dir, f'lora_config.json')
+            lora_checkpoint = Path(lora_dir, f'lora.pt')
+
+            if lora_config_file.exists() and lora_checkpoint.exists():
+                self.modules.append(LoraPeftModule(lora_dir, multiplier))
+                return
+
+        print(f">> Failed to load lora {name}")
+
+    def load(self, pipe, dtype):
+        if len(self.modules) > 0:
+            for module in self.modules:
+                pipe = module.apply(pipe, dtype)
+
+        return pipe
+
+    # Simple check to allow previous functionality
+    def should_use(self, lora_weights: list):
+        if len(lora_weights) > 0:
+            for lora in lora_weights:
+                lora_dir = Path(self.lora_path, lora.model)
+                if lora_dir.exists():
+                    lora_config_file = Path(lora_dir, f'lora_config.json')
+                    lora_checkpoint = Path(lora_dir, f'lora.pt')
+                    if lora_config_file.exists() and lora_checkpoint.exists():
+                        return False
+
+        return True

pyproject.toml

@@ -32,13 +32,16 @@ dependencies = [
   "albumentations",
   "click",
   "clip_anytorch",
-  "compel==0.1.7",
+  "compel~=1.1.0",
   "datasets",
   "diffusers[torch]~=0.14",
   "dnspython==2.2.1",
   "einops",
   "eventlet",
   "facexlib",
+  "fastapi==0.85.0",
+  "fastapi-events==0.6.0",
+  "fastapi-socketio==0.0.9",
   "flask==2.1.3",
   "flask_cors==3.0.10",
   "flask_socketio==5.3.0",
@@ -54,6 +57,7 @@ dependencies = [
   "numpy<1.24",
   "omegaconf",
   "opencv-python",
+  "peft",
   "picklescan",
   "pillow",
   "prompt-toolkit",
@@ -61,6 +65,7 @@ dependencies = [
   "packaging",
   "pypatchmatch",
   "pyreadline3",
+  "python-multipart==0.0.5",
   "pytorch-lightning==1.7.7",
   "realesrgan",
   "requests==2.28.2",

scripts/invoke-new.py

@@ -0,0 +1,20 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+import os
+import sys
+
+def main():
+    # Change working directory to the repo root
+    os.chdir(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+    if '--web' in sys.argv:
+        from ldm.invoke.app.api_app import invoke_api
+        invoke_api()
+    else:
+        # TODO: Parse some top-level args here.
+        from ldm.invoke.app.cli_app import invoke_cli
+        invoke_cli()
+
+
+if __name__ == '__main__':
+    main()

static/dream_web/test.html

@@ -0,0 +1,206 @@
+<html lang="en">
+
+<head>
+  <title>InvokeAI Test</title>
+  <meta charset="utf-8">
+  <link rel="icon" type="image/x-icon" href="static/dream_web/favicon.ico" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+  <!--<script src="config.js"></script>-->
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/socket.io/4.0.1/socket.io.js"
+    integrity="sha512-q/dWJ3kcmjBLU4Qc47E4A9kTB4m3wuTY7vkFJDTZKjTs8jhyGQnaUrxa0Ytd0ssMZhbNua9hE+E7Qv1j+DyZwA=="
+    crossorigin="anonymous"></script>
+
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/fslightbox/3.0.9/index.js"></script>
+
+  <style>
+      body {
+          background:#334;
+      }
+
+      #gallery > a.image {
+          display:inline-block;
+          width:128px;
+          height:128px;
+          margin:8px;
+          background-size:contain;
+          background-repeat:no-repeat;
+          background-position:center;
+      }
+
+      .results {
+        border-color:green;
+        border-width:1px;
+        border-style:solid;
+      }
+
+      .intermediates {
+        border-color:yellow;
+        border-width:1px;
+        border-style:solid;
+      }
+  </style>
+</head>
+
+<body>
+    <button onclick="dostuff();">Test Invoke</button>
+    <div id="gallery"></div>
+    <div id="textlog">
+        <textarea id='log' rows=10 cols=60 autofocus>
+        </textarea>
+    </div>
+
+    <script type="text/javascript">
+        const socket_url = `ws://${window.location.host}`;
+        const socket = io(socket_url, {
+            path: "/ws/socket.io"
+        });
+        socket.on('connect', (data) => {
+            //socket.emit('subscribe', { 'session': 'WcBtYATwT92Mrb9zLgeyNw==' });
+        });
+
+        function addGalleryImage(src, className) {
+                let gallery = document.getElementById("gallery");
+                let div = document.createElement("a");
+                div.href = src;
+                div.setAttribute('data-fslightbox', '');
+                div.classList.add("image");
+                div.classList.add(className);
+                div.style.backgroundImage = `url('${src}')`;
+                gallery.appendChild(div);
+
+                refreshFsLightbox();
+        }
+
+        function log(event, data) {
+            document.getElementById("log").value += `${event} => ${JSON.stringify(data)}\n`;
+
+            if (data.result?.image?.image_type) {
+                addGalleryImage(`/api/v1/images/${data.result.image.image_type}/${data.result.image.image_name}`, data.result.image.image_type);
+            }
+            if (data.result?.mask?.image_type) {
+                addGalleryImage(`/api/v1/images/${data.result.mask.image_type}/${data.result.mask.image_name}`, data.result.mask.image_type);
+            }
+
+            console.log(`${event} => ${JSON.stringify(data)}`);
+        }
+
+        socket.on('generator_progress', (data) => log('generator_progress', data));
+        socket.on('invocation_complete', (data) => log('invocation_complete', data));
+        socket.on('invocation_started', (data) => log('invocation_started', data));
+        socket.on('session_complete', (data) => {
+            log('session_complete', data);
+
+            // NOTE: you may not want to unsubscribe if you plan to continue using this session,
+            //       just make sure you unsubscribe eventually
+            socket.emit('unsubscribe', { 'session': data.session_id });
+        });
+
+        function dostuff() {
+            let prompt = "hyper detailed 4k cryengine 3D render of a cat in a dune buggy, trending on artstation, soft atmospheric lighting, volumetric lighting, cinematic still, golden hour, crepuscular rays, smooth [grainy]";
+            let strength = 0.95;
+            let sampler = 'keuler_a';
+            let steps = 50;
+            let seed = -1;
+
+            // Start building nodes
+            var id = 1;
+            var initialNode = {"id": id.toString(), "type": "txt2img", "prompt": prompt, "sampler": sampler, "steps": steps, "seed": seed};
+            id++;
+            var upscaleNode = {"id": id.toString(), "type": "show_image" };
+            id++
+
+            nodes = {};
+            nodes[initialNode.id] = initialNode;
+            nodes[upscaleNode.id] = upscaleNode;
+            links = [
+                [{ "node_id": initialNode.id, field: "image" },
+                 { "node_id": upscaleNode.id, field: "image" }]
+            ];
+            // expandSize = 128;
+            // for (var i = 0; i < 6; ++i) {
+            //     var i_seed = (seed == -1) ? -1 : seed + i + 1;
+            //     var startid = id - 1;
+            //     var offset = (i < 3) ? -expandSize : (3 * expandSize) + ((i - 3 + 1) * expandSize);
+            //     nodes.push({"id": id.toString(), "type": "crop", "x": offset, "y": 0, "width": 512, "height": 512});
+            //     let id_gen = id;
+            //     links.push({"from_node": {"id": startid.toString(), "field": "image"},"to_node": {"id": id_gen.toString(),"field": "image"}});
+            //     id++;
+                
+            //     nodes.push({"id": id.toString(), "type": "tomask"});
+            //     let id_mask = id;
+            //     links.push({"from_node": {"id": id_gen.toString(), "field": "image"},"to_node": {"id": id_mask.toString(),"field": "image"}});
+            //     id++;
+
+            //     nodes.push({"id": id.toString(), "type": "blur", "radius": 32});
+            //     let id_mask_blur = id;
+            //     links.push({"from_node": {"id": id_mask.toString(), "field": "mask"},"to_node": {"id": id_mask_blur.toString(),"field": "image"}});
+            //     id++
+
+            //     nodes.push({"id": id.toString(), "type": "ilerp", "min": 128, "max": 255});
+            //     let id_ilerp = id;
+            //     links.push({"from_node": {"id": id_mask_blur.toString(), "field": "image"},"to_node": {"id": id_ilerp.toString(),"field": "image"}});
+            //     id++
+
+            //     nodes.push({"id": id.toString(), "type": "cv_inpaint"});
+            //     let id_cv_inpaint = id;
+            //     links.push({"from_node": {"id": id_gen.toString(), "field": "image"},"to_node": {"id": id_cv_inpaint.toString(),"field": "image"}});
+            //     links.push({"from_node": {"id": id_mask.toString(), "field": "mask"},"to_node": {"id": id_cv_inpaint.toString(),"field": "mask"}});
+            //     id++;
+
+            //     nodes.push({"id": id.toString(), "type": "img2img", "prompt": prompt, "strength": strength, "sampler": sampler, "steps": steps, "seed": i_seed, "color_match": true, "inpaint_replace": inpaint_replace});
+            //     let id_i2i = id;
+            //     links.push({"from_node": {"id": id_cv_inpaint.toString(), "field": "image"},"to_node": {"id": id_i2i.toString(),"field": "image"}});
+            //     links.push({"from_node": {"id": id_ilerp.toString(), "field": "image"},"to_node": {"id": id_i2i.toString(),"field": "mask"}});
+            //     id++;
+
+            //     nodes.push({"id": id.toString(), "type": "paste", "x": offset, "y": 0});
+            //     let id_paste = id;
+            //     links.push({"from_node": {"id": startid.toString(), "field": "image"},"to_node": {"id": id_paste.toString(),"field": "base_image"}});
+            //     links.push({"from_node": {"id": id_i2i.toString(), "field": "image"},"to_node": {"id": id_paste.toString(),"field": "image"}});
+            //     links.push({"from_node": {"id": id_ilerp.toString(), "field": "image"},"to_node": {"id": id_paste.toString(),"field": "mask"}});
+            //     id++;
+            // }
+
+            var graph = {
+                "nodes": nodes,
+                "edges": links
+            };
+
+            // var defaultGraph = {"nodes": [
+            //     {"id": "1", "type": "txt2img", "prompt": prompt},
+            //     {"id": "2", "type": "crop", "x": -256, "y": 128, "width": 512, "height": 512},
+            //     {"id": "3", "type": "tomask"},
+            //     {"id": "4", "type": "cv_inpaint"},
+            //     {"id": "5", "type": "img2img", "prompt": prompt, "strength": 0.9},
+            //     {"id": "6", "type": "paste", "x": -256, "y": 128},
+            // ],
+            // "links": [
+            //     {"from_node": {"id": "1","field": "image"},"to_node": {"id": "2","field": "image"}},
+            //     {"from_node": {"id": "2","field": "image"},"to_node": {"id": "3","field": "image"}},
+            //     {"from_node": {"id": "2","field": "image"},"to_node": {"id": "4","field": "image"}},
+            //     {"from_node": {"id": "3","field": "mask"},"to_node": {"id": "4","field": "mask"}},
+            //     {"from_node": {"id": "4","field": "image"},"to_node": {"id": "5","field": "image"}},
+            //     {"from_node": {"id": "3","field": "mask"},"to_node": {"id": "5","field": "mask"}},
+            //     {"from_node": {"id": "1","field": "image"},"to_node": {"id": "6","field": "base_image"}},
+            //     {"from_node": {"id": "5","field": "image"},"to_node": {"id": "6","field": "image"}}
+            // ]};
+            fetch('/api/v1/sessions/', {
+                method: 'POST',
+                headers: new Headers({'content-type': 'application/json'}),
+                body: JSON.stringify(graph)
+            }).then(response => response.json())
+            .then(data => {
+                sessionId = data.id;
+                socket.emit('subscribe', { 'session': sessionId });
+                fetch(`/api/v1/sessions/${sessionId}/invoke?all=true`, {
+                    method: 'PUT',
+                    headers: new Headers({'content-type': 'application/json'}),
+                    body: ''
+                });
+            });
+        }
+    </script>
+</body>
+
+</html>