Merge branch 'main' into feat/onnx

.git-blame-ignore-revs

@@ -1 +1,2 @@
 b3dccfaeb636599c02effc377cdd8a87d658256c
+218b6d0546b990fc449c876fb99f44b50c4daa35

.github/workflows/style-checks.yml

@@ -0,0 +1,27 @@
+name: Black  # TODO: add isort and flake8 later
+
+on:
+  pull_request: {}
+  push:
+    branches: master
+    tags: "*"
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+
+      - name: Install dependencies with pip
+        run: |
+          pip install --upgrade pip wheel
+          pip install .[test]
+
+      # - run: isort --check-only .
+      - run: black --check .
+      # - run: flake8

.gitignore

@@ -38,7 +38,6 @@ develop-eggs/
 downloads/
 eggs/
 .eggs/
-lib/
 lib64/
 parts/
 sdist/

.pre-commit-config.yaml

@@ -0,0 +1,10 @@
+# See https://pre-commit.com/ for usage and config
+repos:
+- repo: local
+  hooks:
+  - id: black
+    name: black
+    stages: [commit]
+    language: system
+    entry: black
+    types: [python]

README.md

@@ -123,7 +123,7 @@ and go to http://localhost:9090.
 
 ### Command-Line Installation (for developers and users familiar with Terminals)
 
-You must have Python 3.9 or 3.10 installed on your machine. Earlier or
+You must have Python 3.9 through 3.11 installed on your machine. Earlier or
 later versions are not supported.
 Node.js also needs to be installed along with yarn (can be installed with
 the command `npm install -g yarn` if needed)

docs/installation/010_INSTALL_AUTOMATED.md

@@ -40,10 +40,8 @@ experimental versions later.
     this, open up a command-line window ("Terminal" on Linux and
     Macintosh, "Command" or "Powershell" on Windows) and type `python
     --version`. If Python is installed, it will print out the version
-    number. If it is version `3.9.*` or `3.10.*`, you meet
+    number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet
-    requirements. We do not recommend using Python 3.11 or higher,
+    requirements.
-    as not all the libraries that InvokeAI depends on work properly
-    with this version.
 
     !!! warning "What to do if you have an unsupported version"
 

docs/installation/020_INSTALL_MANUAL.md

@@ -32,7 +32,7 @@ gaming):
 
 * **Python**
 
-    version 3.9 or 3.10 (3.11 is not recommended).
+    version 3.9 through 3.11
 
 * **CUDA Tools**
 
@@ -65,7 +65,7 @@ gaming):
 To install InvokeAI with virtual environments and the PIP package
 manager, please follow these steps:
 
-1.  Please make sure you are using Python 3.9 or 3.10. The rest of the install
+1.  Please make sure you are using Python 3.9 through 3.11. The rest of the install
     procedure depends on this and will not work with other versions:
 
     ```bash

installer/install.sh.in

@@ -9,16 +9,20 @@ cd $scriptdir
 function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
 
 MINIMUM_PYTHON_VERSION=3.9.0
-MAXIMUM_PYTHON_VERSION=3.11.0
+MAXIMUM_PYTHON_VERSION=3.11.100
 PYTHON=""
-for candidate in python3.10 python3.9 python3 python ; do
+for candidate in python3.11 python3.10 python3.9 python3 python ; do
     if ppath=`which $candidate`; then
+        # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
+        # we check that this found executable can actually run
+        if [ $($candidate --version &>/dev/null; echo ${PIPESTATUS}) -gt 0 ]; then continue; fi
+
         python_version=$($ppath -V | awk '{ print $2 }')
         if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then
-	    if [ $(version $python_version) -lt $(version "$MAXIMUM_PYTHON_VERSION") ]; then
+            if [ $(version $python_version) -le $(version "$MAXIMUM_PYTHON_VERSION") ]; then
-		PYTHON=$ppath
+                PYTHON=$ppath
-		break
+                break
-	    fi
+            fi
         fi
     fi
 done

installer/lib/installer.py

@@ -141,15 +141,16 @@ class Installer:
 
         # upgrade pip in Python 3.9 environments
         if int(platform.python_version_tuple()[1]) == 9:
-
             from plumbum import FG, local
 
             pip = local[get_pip_from_venv(venv_dir)]
-            pip[ "install", "--upgrade", "pip"] & FG
+            pip["install", "--upgrade", "pip"] & FG
 
         return venv_dir
 
-    def install(self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None) -> None:
+    def install(
+        self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None
+    ) -> None:
         """
         Install the InvokeAI application into the given runtime path
 
@@ -175,7 +176,7 @@ class Installer:
         self.instance = InvokeAiInstance(runtime=self.dest, venv=self.venv, version=version)
 
         # install dependencies and the InvokeAI application
-        (extra_index_url,optional_modules) = get_torch_source() if not yes_to_all else (None,None)
+        (extra_index_url, optional_modules) = get_torch_source() if not yes_to_all else (None, None)
         self.instance.install(
             extra_index_url,
             optional_modules,
@@ -188,6 +189,7 @@ class Installer:
         # run through the configuration flow
         self.instance.configure()
 
+
 class InvokeAiInstance:
     """
     Manages an installed instance of InvokeAI, comprising a virtual environment and a runtime directory.
@@ -196,7 +198,6 @@ class InvokeAiInstance:
     """
 
     def __init__(self, runtime: Path, venv: Path, version: str) -> None:
-
         self.runtime = runtime
         self.venv = venv
         self.pip = get_pip_from_venv(venv)
@@ -312,7 +313,7 @@ class InvokeAiInstance:
                 "install",
                 "--require-virtualenv",
                 "--use-pep517",
-                str(src)+(optional_modules if optional_modules else ''),
+                str(src) + (optional_modules if optional_modules else ""),
                 "--find-links" if find_links is not None else None,
                 find_links,
                 "--extra-index-url" if extra_index_url is not None else None,
@@ -329,12 +330,12 @@ class InvokeAiInstance:
 
         # set sys.argv to a consistent state
         new_argv = [sys.argv[0]]
-        for i in range(1,len(sys.argv)):
+        for i in range(1, len(sys.argv)):
             el = sys.argv[i]
-            if el in ['-r','--root']:
+            if el in ["-r", "--root"]:
                 new_argv.append(el)
-                new_argv.append(sys.argv[i+1])
+                new_argv.append(sys.argv[i + 1])
-            elif el in ['-y','--yes','--yes-to-all']:
+            elif el in ["-y", "--yes", "--yes-to-all"]:
                 new_argv.append(el)
         sys.argv = new_argv
 
@@ -353,16 +354,16 @@ class InvokeAiInstance:
             invokeai_configure()
             succeeded = True
         except requests.exceptions.ConnectionError as e:
-            print(f'\nA network error was encountered during configuration and download: {str(e)}')
+            print(f"\nA network error was encountered during configuration and download: {str(e)}")
         except OSError as e:
-            print(f'\nAn OS error was encountered during configuration and download: {str(e)}')
+            print(f"\nAn OS error was encountered during configuration and download: {str(e)}")
         except Exception as e:
-            print(f'\nA problem was encountered during the configuration and download steps: {str(e)}')
+            print(f"\nA problem was encountered during the configuration and download steps: {str(e)}")
         finally:
             if not succeeded:
                 print('To try again, find the "invokeai" directory, run the script "invoke.sh" or "invoke.bat"')
-                print('and choose option 7 to fix a broken install, optionally followed by option 5 to install models.')
+                print("and choose option 7 to fix a broken install, optionally followed by option 5 to install models.")
-                print('Alternatively you can relaunch the installer.')
+                print("Alternatively you can relaunch the installer.")
 
     def install_user_scripts(self):
         """
@@ -371,11 +372,11 @@ class InvokeAiInstance:
 
         ext = "bat" if OS == "Windows" else "sh"
 
-        #scripts = ['invoke', 'update']
+        # scripts = ['invoke', 'update']
-        scripts = ['invoke']
+        scripts = ["invoke"]
 
         for script in scripts:
-            src = Path(__file__).parent / '..' / "templates" / f"{script}.{ext}.in"
+            src = Path(__file__).parent / ".." / "templates" / f"{script}.{ext}.in"
             dest = self.runtime / f"{script}.{ext}"
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
@@ -420,11 +421,7 @@ def set_sys_path(venv_path: Path) -> None:
     # filter out any paths in sys.path that may be system- or user-wide
     # but leave the temporary bootstrap virtualenv as it contains packages we
     # temporarily need at install time
-    sys.path = list(filter(
+    sys.path = list(filter(lambda p: not p.endswith("-packages") or p.find(BOOTSTRAP_VENV_PREFIX) != -1, sys.path))
-        lambda p: not p.endswith("-packages")
-        or p.find(BOOTSTRAP_VENV_PREFIX) != -1,
-        sys.path
-    ))
 
     # determine site-packages/lib directory location for the venv
     lib = "Lib" if OS == "Windows" else f"lib/python{sys.version_info.major}.{sys.version_info.minor}"
@@ -433,7 +430,7 @@ def set_sys_path(venv_path: Path) -> None:
     sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
 
 
-def get_torch_source() -> (Union[str, None],str):
+def get_torch_source() -> (Union[str, None], str):
     """
     Determine the extra index URL for pip to use for torch installation.
     This depends on the OS and the graphics accelerator in use.
@@ -461,9 +458,9 @@ def get_torch_source() -> (Union[str, None],str):
         elif device == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
 
-    if device == 'cuda':
+    if device == "cuda":
-        url = 'https://download.pytorch.org/whl/cu117'
+        url = "https://download.pytorch.org/whl/cu117"
-        optional_modules = '[xformers]'
+        optional_modules = "[xformers]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
 

installer/lib/messages.py

@@ -36,13 +36,15 @@ else:
 
 
 def welcome():
-
     @group()
     def text():
         if (platform_specific := _platform_specific_help()) != "":
             yield platform_specific
             yield ""
-        yield Text.from_markup("Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.", justify="center")
+        yield Text.from_markup(
+            "Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.",
+            justify="center",
+        )
 
     console.rule()
     print(
@@ -58,6 +60,7 @@ def welcome():
     )
     console.line()
 
+
 def confirm_install(dest: Path) -> bool:
     if dest.exists():
         print(f":exclamation: Directory {dest} already exists :exclamation:")
@@ -92,7 +95,6 @@ def dest_path(dest=None) -> Path:
     dest_confirmed = confirm_install(dest)
 
     while not dest_confirmed:
-
         # if the given destination already exists, the starting point for browsing is its parent directory.
         # the user may have made a typo, or otherwise wants to place the root dir next to an existing one.
         # if the destination dir does NOT exist, then the user must have changed their mind about the selection.
@@ -300,15 +302,20 @@ def introduction() -> None:
     )
     console.line(2)
 
-def _platform_specific_help()->str:
+
+def _platform_specific_help() -> str:
     if OS == "Darwin":
-        text = Text.from_markup("""[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/].""")
+        text = Text.from_markup(
+            """[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/]."""
+        )
     elif OS == "Windows":
-        text = Text.from_markup("""[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
+        text = Text.from_markup(
+            """[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
   1. Double-click on the file [b wheat1]WinLongPathsEnabled.reg[/] in order to
      enable long path support on your system.
   2. Make sure you have the [b wheat1]Visual C++ core libraries[/] installed. If not, install from
-     [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]""")
+     [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
+        )
     else:
         text = ""
     return text

invokeai/app/api/routers/models.py

@@ -90,7 +90,7 @@ async def update_model(
                 new_name=info.model_name,
                 new_base=info.base_model,
             )
-            logger.info(f"Successfully renamed {base_model}/{model_name}=>{info.base_model}/{info.model_name}")
+            logger.info(f"Successfully renamed {base_model.value}/{model_name}=>{info.base_model}/{info.model_name}")
             # update information to support an update of attributes
             model_name = info.model_name
             base_model = info.base_model

invokeai/app/api_app.py

@@ -3,6 +3,7 @@ import asyncio
 import sys
 from inspect import signature
 
+import logging
 import uvicorn
 import socket
 
@@ -210,11 +211,25 @@ def invoke_api():
     port = find_port(app_config.port)
     if port != app_config.port:
         logger.warn(f"Port {app_config.port} in use, using port {port}")
+
     # Start our own event loop for eventing usage
     loop = asyncio.new_event_loop()
-    config = uvicorn.Config(app=app, host=app_config.host, port=port, loop=loop)
+    config = uvicorn.Config(
-    # Use access_log to turn off logging
+        app=app,
+        host=app_config.host,
+        port=port,
+        loop=loop,
+        log_level=app_config.log_level,
+    )
     server = uvicorn.Server(config)
+
+    # replace uvicorn's loggers with InvokeAI's for consistent appearance
+    for logname in ["uvicorn.access", "uvicorn"]:
+        l = logging.getLogger(logname)
+        l.handlers.clear()
+        for ch in logger.handlers:
+            l.addHandler(ch)
+
     loop.run_until_complete(server.serve())
 
 

invokeai/app/invocations/latent.py

@@ -12,7 +12,7 @@ from pydantic import BaseModel, Field, validator
 
 from invokeai.app.invocations.metadata import CoreMetadata
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from invokeai.backend.model_management.models.base import ModelType
+from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 
 from ...backend.model_management.lora import ModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
@@ -312,70 +312,71 @@ class TextToLatentsInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
+        with SilenceWarnings():
+            noise = context.services.latents.get(self.noise.latents_name)
 
-        # Get the source node id (we are invoking the prepared node)
+            # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        def step_callback(state: PipelineIntermediateState):
+            def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
+                self.dispatch_progress(context, source_node_id, state)
 
-        def _lora_loader():
+            def _lora_loader():
-            for lora in self.unet.loras:
+                for lora in self.unet.loras:
-                lora_info = context.services.model_manager.get_model(
+                    lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}),
+                        **lora.dict(exclude={"weight"}),
+                        context=context,
+                    )
+                    yield (lora_info.context.model, lora.weight)
+                    del lora_info
+                return
+
+            unet_info = context.services.model_manager.get_model(
+                **self.unet.unet.dict(),
+                context=context,
+            )
+            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
+                unet_info.context.model, _lora_loader()
+            ), unet_info as unet:
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
+
+                scheduler = get_scheduler(
                     context=context,
+                    scheduler_info=self.unet.scheduler,
+                    scheduler_name=self.scheduler,
                 )
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
 
-        unet_info = context.services.model_manager.get_model(
+                pipeline = self.create_pipeline(unet, scheduler)
-            **self.unet.unet.dict(),
+                conditioning_data = self.get_conditioning_data(context, scheduler, unet)
-            context=context,
-        )
-        with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
-            unet_info.context.model, _lora_loader()
-        ), unet_info as unet:
-            noise = noise.to(device=unet.device, dtype=unet.dtype)
 
-            scheduler = get_scheduler(
+                control_data = self.prep_control_data(
-                context=context,
+                    model=pipeline,
-                scheduler_info=self.unet.scheduler,
+                    context=context,
-                scheduler_name=self.scheduler,
+                    control_input=self.control,
-            )
+                    latents_shape=noise.shape,
+                    # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
+                    do_classifier_free_guidance=True,
+                    exit_stack=exit_stack,
+                )
 
-            pipeline = self.create_pipeline(unet, scheduler)
+                # TODO: Verify the noise is the right size
-            conditioning_data = self.get_conditioning_data(context, scheduler, unet)
+                result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+                    latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
+                    noise=noise,
+                    num_inference_steps=self.steps,
+                    conditioning_data=conditioning_data,
+                    control_data=control_data,  # list[ControlNetData]
+                    callback=step_callback,
+                )
 
-            control_data = self.prep_control_data(
+            # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-                model=pipeline,
+            result_latents = result_latents.to("cpu")
-                context=context,
+            torch.cuda.empty_cache()
-                control_input=self.control,
-                latents_shape=noise.shape,
-                # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
-                do_classifier_free_guidance=True,
-                exit_stack=exit_stack,
-            )
 
-            # TODO: Verify the noise is the right size
+            name = f"{context.graph_execution_state_id}__{self.id}"
-            result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+            context.services.latents.save(name, result_latents)
-                latents=torch.zeros_like(noise, dtype=torch_dtype(unet.device)),
+            return build_latents_output(latents_name=name, latents=result_latents)
-                noise=noise,
-                num_inference_steps=self.steps,
-                conditioning_data=conditioning_data,
-                control_data=control_data,  # list[ControlNetData]
-                callback=step_callback,
-            )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        result_latents = result_latents.to("cpu")
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, result_latents)
-        return build_latents_output(latents_name=name, latents=result_latents)
 
 
 class LatentsToLatentsInvocation(TextToLatentsInvocation):
@@ -403,82 +404,83 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
+        with SilenceWarnings():  # this quenches NSFW nag from diffusers
-        latent = context.services.latents.get(self.latents.latents_name)
+            noise = context.services.latents.get(self.noise.latents_name)
+            latent = context.services.latents.get(self.latents.latents_name)
 
-        # Get the source node id (we are invoking the prepared node)
+            # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-        def step_callback(state: PipelineIntermediateState):
+            def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
+                self.dispatch_progress(context, source_node_id, state)
 
-        def _lora_loader():
+            def _lora_loader():
-            for lora in self.unet.loras:
+                for lora in self.unet.loras:
-                lora_info = context.services.model_manager.get_model(
+                    lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}),
+                        **lora.dict(exclude={"weight"}),
+                        context=context,
+                    )
+                    yield (lora_info.context.model, lora.weight)
+                    del lora_info
+                return
+
+            unet_info = context.services.model_manager.get_model(
+                **self.unet.unet.dict(),
+                context=context,
+            )
+            with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
+                unet_info.context.model, _lora_loader()
+            ), unet_info as unet:
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
+                latent = latent.to(device=unet.device, dtype=unet.dtype)
+
+                scheduler = get_scheduler(
                     context=context,
+                    scheduler_info=self.unet.scheduler,
+                    scheduler_name=self.scheduler,
                 )
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
 
-        unet_info = context.services.model_manager.get_model(
+                pipeline = self.create_pipeline(unet, scheduler)
-            **self.unet.unet.dict(),
+                conditioning_data = self.get_conditioning_data(context, scheduler, unet)
-            context=context,
-        )
-        with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
-            unet_info.context.model, _lora_loader()
-        ), unet_info as unet:
-            noise = noise.to(device=unet.device, dtype=unet.dtype)
-            latent = latent.to(device=unet.device, dtype=unet.dtype)
 
-            scheduler = get_scheduler(
+                control_data = self.prep_control_data(
-                context=context,
+                    model=pipeline,
-                scheduler_info=self.unet.scheduler,
+                    context=context,
-                scheduler_name=self.scheduler,
+                    control_input=self.control,
-            )
+                    latents_shape=noise.shape,
+                    # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
+                    do_classifier_free_guidance=True,
+                    exit_stack=exit_stack,
+                )
 
-            pipeline = self.create_pipeline(unet, scheduler)
+                # TODO: Verify the noise is the right size
-            conditioning_data = self.get_conditioning_data(context, scheduler, unet)
+                initial_latents = (
+                    latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
+                )
 
-            control_data = self.prep_control_data(
+                timesteps, _ = pipeline.get_img2img_timesteps(
-                model=pipeline,
+                    self.steps,
-                context=context,
+                    self.strength,
-                control_input=self.control,
+                    device=unet.device,
-                latents_shape=noise.shape,
+                )
-                # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
-                do_classifier_free_guidance=True,
-                exit_stack=exit_stack,
-            )
 
-            # TODO: Verify the noise is the right size
+                result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
-            initial_latents = (
+                    latents=initial_latents,
-                latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
+                    timesteps=timesteps,
-            )
+                    noise=noise,
+                    num_inference_steps=self.steps,
+                    conditioning_data=conditioning_data,
+                    control_data=control_data,  # list[ControlNetData]
+                    callback=step_callback,
+                )
 
-            timesteps, _ = pipeline.get_img2img_timesteps(
+            # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-                self.steps,
+            result_latents = result_latents.to("cpu")
-                self.strength,
+            torch.cuda.empty_cache()
-                device=unet.device,
-            )
 
-            result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
+            name = f"{context.graph_execution_state_id}__{self.id}"
-                latents=initial_latents,
+            context.services.latents.save(name, result_latents)
-                timesteps=timesteps,
-                noise=noise,
-                num_inference_steps=self.steps,
-                conditioning_data=conditioning_data,
-                control_data=control_data,  # list[ControlNetData]
-                callback=step_callback,
-            )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        result_latents = result_latents.to("cpu")
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.save(name, result_latents)
         return build_latents_output(latents_name=name, latents=result_latents)
 
 
@@ -491,7 +493,7 @@ class LatentsToImageInvocation(BaseInvocation):
     # Inputs
     latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
     vae: VaeField = Field(default=None, description="Vae submodel")
-    tiled: bool = Field(default=False, description="Decode latents by overlapping tiles(less memory consumption)")
+    tiled: bool = Field(default=False, description="Decode latents by overlaping tiles (less memory consumption)")
     fp32: bool = Field(DEFAULT_PRECISION == "float32", description="Decode in full precision")
     metadata: Optional[CoreMetadata] = Field(
         default=None, description="Optional core metadata to be written to the image"

invokeai/backend/model_management/lora.py

@@ -712,6 +712,7 @@ class TextualInversionManager(BaseTextualInversionManager):
 
 
 class ONNXModelPatcher:
+    from .models.base import IAIOnnxRuntimeModel, OnnxRuntimeModel
     @classmethod
     @contextmanager
     def apply_lora_unet(

invokeai/backend/model_management/model_cache.py

@@ -358,6 +358,7 @@ class ModelCache(object):
             # 2 refs:
             # 1 from cache_entry
             # 1 from getrefcount function
+            # 1 from onnx runtime object
             if not cache_entry.locked and refs <= 3 if "onnx" in model_key else 2:
                 self.logger.debug(
                     f"Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"

invokeai/backend/model_management/model_manager.py

@@ -401,7 +401,11 @@ class ModelManager(object):
         base_model: BaseModelType,
         model_type: ModelType,
     ) -> str:
-        return f"{base_model}/{model_type}/{model_name}"
+        # In 3.11, the behavior of (str,enum) when interpolated into a
+        # string has changed. The next two lines are defensive.
+        base_model = BaseModelType(base_model)
+        model_type = ModelType(model_type)
+        return f"{base_model.value}/{model_type.value}/{model_name}"
 
     @classmethod
     def parse_key(cls, model_key: str) -> Tuple[str, BaseModelType, ModelType]:

invokeai/backend/model_management/models/base.py

@@ -19,13 +19,9 @@ from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callab
 
 import onnx
 from onnx import numpy_helper
-from onnx.external_data_helper import set_external_data
 from onnxruntime import (
     InferenceSession,
-    OrtValue,
     SessionOptions,
-    ExecutionMode,
-    GraphOptimizationLevel,
     get_available_providers,
 )
 

invokeai/backend/model_management/models/lora.py

@@ -57,7 +57,7 @@ class LoRAModel(ModelBase):
 
     @classproperty
     def save_to_config(cls) -> bool:
-        return False
+        return True
 
     @classmethod
     def detect_format(cls, path: str):

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-b3976531.js"></script>
+    <script type="module" crossorigin src="./assets/index-5a784cdd.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -340,6 +340,7 @@
         "allModels": "All Models",
         "checkpointModels": "Checkpoints",
         "diffusersModels": "Diffusers",
+        "loraModels": "LoRAs",
         "safetensorModels": "SafeTensors",
         "onnxModels": "Onnx",
         "oliveModels": "Olives",

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

@@ -10,8 +10,11 @@ export const addAppConfigReceivedListener = () => {
   startAppListening({
     matcher: appInfoApi.endpoints.getAppConfig.matchFulfilled,
     effect: async (action, { getState, dispatch }) => {
-      const { infill_methods, nsfw_methods, watermarking_methods } =
+      const {
-        action.payload;
+        infill_methods = [],
+        nsfw_methods = [],
+        watermarking_methods = [],
+      } = action.payload;
       const infillMethod = getState().generation.infillMethod;
 
       if (!infill_methods.includes(infillMethod)) {

invokeai/frontend/web/src/features/parameters/components/Parameters/Core/ParamPositiveConditioning.tsx

@@ -148,7 +148,7 @@ const ParamPositiveConditioning = () => {
         <Box
           sx={{
             position: 'absolute',
-            top: shouldPinParametersPanel ? 6 : 0,
+            top: shouldPinParametersPanel ? 5 : 0,
             insetInlineEnd: 0,
           }}
         >

invokeai/frontend/web/src/features/parameters/components/Parameters/Prompt/ParamPromptArea.tsx

@@ -0,0 +1,17 @@
+import { Flex } from '@chakra-ui/react';
+import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
+import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
+
+export default function ParamPromptArea() {
+  return (
+    <Flex
+      sx={{
+        flexDirection: 'column',
+        gap: 2,
+      }}
+    >
+      <ParamPositiveConditioning />
+      <ParamNegativeConditioning />
+    </Flex>
+  );
+}

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

@@ -1,3 +1,5 @@
+import { components } from 'services/api/schema';
+
 export const MODEL_TYPE_MAP = {
   'sd-1': 'Stable Diffusion 1.x',
   'sd-2': 'Stable Diffusion 2.x',
@@ -5,6 +7,13 @@ export const MODEL_TYPE_MAP = {
   'sdxl-refiner': 'Stable Diffusion XL Refiner',
 };
 
+export const MODEL_TYPE_SHORT_MAP = {
+  'sd-1': 'SD1',
+  'sd-2': 'SD2',
+  sdxl: 'SDXL',
+  'sdxl-refiner': 'SDXLR',
+};
+
 export const clipSkipMap = {
   'sd-1': {
     maxClip: 12,
@@ -23,3 +32,12 @@ export const clipSkipMap = {
     markers: [0, 1, 2, 3, 5, 10, 15, 20, 24],
   },
 };
+
+type LoRAModelFormatMap = {
+  [key in components['schemas']['LoRAModelFormat']]: string;
+};
+
+export const LORA_MODEL_FORMAT_MAP: LoRAModelFormatMap = {
+  lycoris: 'LyCORIS',
+  diffusers: 'Diffusers',
+};

invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatButton.tsx

@@ -0,0 +1,43 @@
+import { RootState } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { FaLink } from 'react-icons/fa';
+import { setShouldConcatSDXLStylePrompt } from '../store/sdxlSlice';
+
+export default function ParamSDXLConcatButton() {
+  const shouldConcatSDXLStylePrompt = useAppSelector(
+    (state: RootState) => state.sdxl.shouldConcatSDXLStylePrompt
+  );
+
+  const shouldPinParametersPanel = useAppSelector(
+    (state: RootState) => state.ui.shouldPinParametersPanel
+  );
+
+  const dispatch = useAppDispatch();
+
+  const handleShouldConcatPromptChange = () => {
+    dispatch(setShouldConcatSDXLStylePrompt(!shouldConcatSDXLStylePrompt));
+  };
+
+  return (
+    <IAIIconButton
+      aria-label="Concat"
+      tooltip="Concatenates Basic Prompt with Style (Recommended)"
+      variant="outline"
+      isChecked={shouldConcatSDXLStylePrompt}
+      onClick={handleShouldConcatPromptChange}
+      icon={<FaLink />}
+      size="xs"
+      sx={{
+        position: 'absolute',
+        insetInlineEnd: 1,
+        top: shouldPinParametersPanel ? 12 : 20,
+        border: 'none',
+        color: shouldConcatSDXLStylePrompt ? 'accent.500' : 'base.500',
+        _hover: {
+          bg: 'none',
+        },
+      }}
+    ></IAIIconButton>
+  );
+}

invokeai/frontend/web/src/features/sdxl/components/ParamSDXLConcatPrompt.tsx

@@ -1,33 +0,0 @@
-import { Box } from '@chakra-ui/react';
-import { RootState } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAISwitch from 'common/components/IAISwitch';
-import { ChangeEvent } from 'react';
-import { setShouldConcatSDXLStylePrompt } from '../store/sdxlSlice';
-
-export default function ParamSDXLConcatPrompt() {
-  const shouldConcatSDXLStylePrompt = useAppSelector(
-    (state: RootState) => state.sdxl.shouldConcatSDXLStylePrompt
-  );
-
-  const dispatch = useAppDispatch();
-
-  const handleShouldConcatPromptChange = (e: ChangeEvent<HTMLInputElement>) => {
-    dispatch(setShouldConcatSDXLStylePrompt(e.target.checked));
-  };
-
-  return (
-    <Box
-      sx={{
-        px: 2,
-      }}
-    >
-      <IAISwitch
-        label="Concat Style Prompt"
-        tooltip="Concatenates Basic Prompt with Style (Recommended)"
-        isChecked={shouldConcatSDXLStylePrompt}
-        onChange={handleShouldConcatPromptChange}
-      />
-    </Box>
-  );
-}

invokeai/frontend/web/src/features/sdxl/components/ParamSDXLPromptArea.tsx

@@ -0,0 +1,61 @@
+import { Box, Flex } from '@chakra-ui/react';
+import { RootState } from 'app/store/store';
+import { useAppSelector } from 'app/store/storeHooks';
+import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
+import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
+import { AnimatePresence } from 'framer-motion';
+import ParamSDXLConcatButton from './ParamSDXLConcatButton';
+import ParamSDXLNegativeStyleConditioning from './ParamSDXLNegativeStyleConditioning';
+import ParamSDXLPositiveStyleConditioning from './ParamSDXLPositiveStyleConditioning';
+import SDXLConcatLink from './SDXLConcatLink';
+
+export default function ParamSDXLPromptArea() {
+  const shouldPinParametersPanel = useAppSelector(
+    (state: RootState) => state.ui.shouldPinParametersPanel
+  );
+
+  const shouldConcatSDXLStylePrompt = useAppSelector(
+    (state: RootState) => state.sdxl.shouldConcatSDXLStylePrompt
+  );
+
+  return (
+    <Flex
+      sx={{
+        flexDirection: 'column',
+        gap: 2,
+      }}
+    >
+      <AnimatePresence>
+        {shouldConcatSDXLStylePrompt && (
+          <Box
+            sx={{
+              position: 'absolute',
+              w: 'full',
+              top: shouldPinParametersPanel ? '119px' : '175px',
+            }}
+          >
+            <SDXLConcatLink />
+          </Box>
+        )}
+      </AnimatePresence>
+      <AnimatePresence>
+        {shouldConcatSDXLStylePrompt && (
+          <Box
+            sx={{
+              position: 'absolute',
+              w: 'full',
+              top: shouldPinParametersPanel ? '263px' : '319px',
+            }}
+          >
+            <SDXLConcatLink />
+          </Box>
+        )}
+      </AnimatePresence>
+      <ParamPositiveConditioning />
+      <ParamSDXLConcatButton />
+      <ParamSDXLPositiveStyleConditioning />
+      <ParamNegativeConditioning />
+      <ParamSDXLNegativeStyleConditioning />
+    </Flex>
+  );
+}

invokeai/frontend/web/src/features/sdxl/components/SDXLConcatLink.tsx

@@ -0,0 +1,109 @@
+import { Box, Flex } from '@chakra-ui/react';
+import { CSSObject } from '@emotion/react';
+import { motion } from 'framer-motion';
+import { FaLink } from 'react-icons/fa';
+
+const sharedConcatLinkStyle: CSSObject = {
+  position: 'absolute',
+  bg: 'none',
+  w: 'full',
+  minH: 2,
+  borderRadius: 0,
+  borderLeft: 'none',
+  borderRight: 'none',
+  zIndex: 2,
+  maskImage:
+    'radial-gradient(circle at center, black, black 65%, black 30%, black 15%, transparent)',
+};
+
+export default function SDXLConcatLink() {
+  return (
+    <Flex
+      sx={{
+        h: 0.5,
+        placeContent: 'center',
+        gap: 2,
+        flexDirection: 'column',
+      }}
+    >
+      <Box
+        as={motion.div}
+        initial={{
+          scaleX: 0,
+          borderWidth: 0,
+          display: 'none',
+        }}
+        animate={{
+          display: ['block', 'block', 'block', 'none'],
+          scaleX: [0, 0.25, 0.5, 1],
+          borderWidth: [0, 3, 3, 0],
+          transition: { duration: 0.37, times: [0, 0.25, 0.5, 1] },
+        }}
+        sx={{
+          top: '1px',
+          borderTop: 'none',
+          borderColor: 'base.400',
+          zIndex: 2,
+          ...sharedConcatLinkStyle,
+          _dark: {
+            borderColor: 'accent.500',
+          },
+        }}
+      />
+      <Box
+        as={motion.div}
+        initial={{
+          opacity: 0,
+          scale: 0,
+        }}
+        animate={{
+          opacity: [0, 1, 1, 1],
+          scale: [0, 0.75, 1.5, 1],
+          transition: { duration: 0.42, times: [0, 0.25, 0.5, 1] },
+        }}
+        exit={{
+          opacity: 0,
+          scale: 0,
+        }}
+        sx={{
+          zIndex: 3,
+          position: 'absolute',
+          left: '48%',
+          top: '3px',
+          p: 1,
+          borderRadius: 4,
+          bg: 'accent.400',
+          color: 'base.50',
+          _dark: {
+            bg: 'accent.500',
+          },
+        }}
+      >
+        <FaLink size={12} />
+      </Box>
+      <Box
+        as={motion.div}
+        initial={{
+          scaleX: 0,
+          borderWidth: 0,
+          display: 'none',
+        }}
+        animate={{
+          display: ['block', 'block', 'block', 'none'],
+          scaleX: [0, 0.25, 0.5, 1],
+          borderWidth: [0, 3, 3, 0],
+          transition: { duration: 0.37, times: [0, 0.25, 0.5, 1] },
+        }}
+        sx={{
+          top: '17px',
+          borderBottom: 'none',
+          borderColor: 'base.400',
+          ...sharedConcatLinkStyle,
+          _dark: {
+            borderColor: 'accent.500',
+          },
+        }}
+      />
+    </Flex>
+  );
+}

invokeai/frontend/web/src/features/sdxl/components/SDXLImageToImageTabParameters.tsx

@@ -1,34 +1,14 @@
-import { Flex } from '@chakra-ui/react';
 import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
-import ParamSDXLConcatPrompt from './ParamSDXLConcatPrompt';
+import ParamSDXLPromptArea from './ParamSDXLPromptArea';
-import ParamSDXLNegativeStyleConditioning from './ParamSDXLNegativeStyleConditioning';
-import ParamSDXLPositiveStyleConditioning from './ParamSDXLPositiveStyleConditioning';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
 import SDXLImageToImageTabCoreParameters from './SDXLImageToImageTabCoreParameters';
 
 const SDXLImageToImageTabParameters = () => {
   return (
     <>
-      <Flex
+      <ParamSDXLPromptArea />
-        sx={{
-          flexDirection: 'column',
-          gap: 2,
-          p: 2,
-          borderRadius: 4,
-          bg: 'base.100',
-          _dark: { bg: 'base.850' },
-        }}
-      >
-        <ParamPositiveConditioning />
-        <ParamSDXLPositiveStyleConditioning />
-        <ParamNegativeConditioning />
-        <ParamSDXLNegativeStyleConditioning />
-        <ParamSDXLConcatPrompt />
-      </Flex>
       <ProcessButtons />
       <SDXLImageToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />

invokeai/frontend/web/src/features/sdxl/components/SDXLTextToImageTabParameters.tsx

@@ -1,34 +1,14 @@
-import { Flex } from '@chakra-ui/react';
 import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/ParamDynamicPromptsCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
 import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters';
-import ParamSDXLConcatPrompt from './ParamSDXLConcatPrompt';
+import ParamSDXLPromptArea from './ParamSDXLPromptArea';
-import ParamSDXLNegativeStyleConditioning from './ParamSDXLNegativeStyleConditioning';
-import ParamSDXLPositiveStyleConditioning from './ParamSDXLPositiveStyleConditioning';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
 
 const SDXLTextToImageTabParameters = () => {
   return (
     <>
-      <Flex
+      <ParamSDXLPromptArea />
-        sx={{
-          flexDirection: 'column',
-          gap: 2,
-          p: 2,
-          borderRadius: 4,
-          bg: 'base.100',
-          _dark: { bg: 'base.850' },
-        }}
-      >
-        <ParamPositiveConditioning />
-        <ParamSDXLPositiveStyleConditioning />
-        <ParamNegativeConditioning />
-        <ParamSDXLNegativeStyleConditioning />
-        <ParamSDXLConcatPrompt />
-      </Flex>
       <ProcessButtons />
       <TextToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />

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

@@ -227,7 +227,8 @@ const InvokeTabs = () => {
                 id="gallery"
                 order={3}
                 defaultSize={
-                  galleryMinSizePct > DEFAULT_GALLERY_PCT
+                  galleryMinSizePct > DEFAULT_GALLERY_PCT &&
+                  galleryMinSizePct < 100 // prevent this error https://github.com/bvaughn/react-resizable-panels/blob/main/packages/react-resizable-panels/src/Panel.ts#L96
                     ? galleryMinSizePct
                     : DEFAULT_GALLERY_PCT
                 }

invokeai/frontend/web/src/features/ui/components/tabs/ImageToImage/ImageToImageTabParameters.tsx

@@ -2,20 +2,18 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/Para
 import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
 import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 // import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
+import ParamPromptArea from 'features/parameters/components/Parameters/Prompt/ParamPromptArea';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
 import ImageToImageTabCoreParameters from './ImageToImageTabCoreParameters';
 
 const ImageToImageTabParameters = () => {
   return (
     <>
-      <ParamPositiveConditioning />
+      <ParamPromptArea />
-      <ParamNegativeConditioning />
       <ProcessButtons />
       <ImageToImageTabCoreParameters />
       <ParamControlNetCollapse />

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

@@ -3,20 +3,31 @@ import { Flex, Text } from '@chakra-ui/react';
 import { useState } from 'react';
 import {
   MainModelConfigEntity,
+  DiffusersModelConfigEntity,
+  LoRAModelConfigEntity,
   useGetMainModelsQuery,
+  useGetLoRAModelsQuery,
 } from 'services/api/endpoints/models';
 import CheckpointModelEdit from './ModelManagerPanel/CheckpointModelEdit';
 import DiffusersModelEdit from './ModelManagerPanel/DiffusersModelEdit';
+import LoRAModelEdit from './ModelManagerPanel/LoRAModelEdit';
 import ModelList from './ModelManagerPanel/ModelList';
 import { ALL_BASE_MODELS } from 'services/api/constants';
 
 export default function ModelManagerPanel() {
   const [selectedModelId, setSelectedModelId] = useState<string>();
-  const { model } = useGetMainModelsQuery(ALL_BASE_MODELS, {
+  const { mainModel } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      model: selectedModelId ? data?.entities[selectedModelId] : undefined,
+      mainModel: selectedModelId ? data?.entities[selectedModelId] : undefined,
     }),
   });
+  const { loraModel } = useGetLoRAModelsQuery(undefined, {
+    selectFromResult: ({ data }) => ({
+      loraModel: selectedModelId ? data?.entities[selectedModelId] : undefined,
+    }),
+  });
+
+  const model = mainModel ? mainModel : loraModel;
 
   return (
     <Flex sx={{ gap: 8, w: 'full', h: 'full' }}>
@@ -30,7 +41,7 @@ export default function ModelManagerPanel() {
 }
 
 type ModelEditProps = {
-  model: MainModelConfigEntity | undefined;
+  model: MainModelConfigEntity | LoRAModelConfigEntity | undefined;
 };
 
 const ModelEdit = (props: ModelEditProps) => {
@@ -41,7 +52,16 @@ const ModelEdit = (props: ModelEditProps) => {
   }
 
   if (model?.model_format === 'diffusers') {
-    return <DiffusersModelEdit key={model.id} model={model} />;
+    return (
+      <DiffusersModelEdit
+        key={model.id}
+        model={model as DiffusersModelConfigEntity}
+      />
+    );
+  }
+
+  if (model?.model_type === 'lora') {
+    return <LoRAModelEdit key={model.id} model={model} />;
   }
 
   return (

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

@@ -0,0 +1,137 @@
+import { Divider, Flex, Text } from '@chakra-ui/react';
+import { useForm } from '@mantine/form';
+import { makeToast } from 'features/system/util/makeToast';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIButton from 'common/components/IAIButton';
+import IAIMantineTextInput from 'common/components/IAIMantineInput';
+import { selectIsBusy } from 'features/system/store/systemSelectors';
+import { addToast } from 'features/system/store/systemSlice';
+import { useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import {
+  LORA_MODEL_FORMAT_MAP,
+  MODEL_TYPE_MAP,
+} from 'features/parameters/types/constants';
+import {
+  LoRAModelConfigEntity,
+  useUpdateLoRAModelsMutation,
+} from 'services/api/endpoints/models';
+import { LoRAModelConfig } from 'services/api/types';
+import BaseModelSelect from '../shared/BaseModelSelect';
+
+type LoRAModelEditProps = {
+  model: LoRAModelConfigEntity;
+};
+
+export default function LoRAModelEdit(props: LoRAModelEditProps) {
+  const isBusy = useAppSelector(selectIsBusy);
+
+  const { model } = props;
+
+  const [updateLoRAModel, { isLoading }] = useUpdateLoRAModelsMutation();
+
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const loraEditForm = useForm<LoRAModelConfig>({
+    initialValues: {
+      model_name: model.model_name ? model.model_name : '',
+      base_model: model.base_model,
+      model_type: 'lora',
+      path: model.path ? model.path : '',
+      description: model.description ? model.description : '',
+      model_format: model.model_format,
+    },
+    validate: {
+      path: (value) =>
+        value.trim().length === 0 ? 'Must provide a path' : null,
+    },
+  });
+
+  const editModelFormSubmitHandler = useCallback(
+    (values: LoRAModelConfig) => {
+      const responseBody = {
+        base_model: model.base_model,
+        model_name: model.model_name,
+        body: values,
+      };
+
+      updateLoRAModel(responseBody)
+        .unwrap()
+        .then((payload) => {
+          loraEditForm.setValues(payload as LoRAModelConfig);
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.modelUpdated'),
+                status: 'success',
+              })
+            )
+          );
+        })
+        .catch((_) => {
+          loraEditForm.reset();
+          dispatch(
+            addToast(
+              makeToast({
+                title: t('modelManager.modelUpdateFailed'),
+                status: 'error',
+              })
+            )
+          );
+        });
+    },
+    [
+      dispatch,
+      loraEditForm,
+      model.base_model,
+      model.model_name,
+      t,
+      updateLoRAModel,
+    ]
+  );
+
+  return (
+    <Flex flexDirection="column" rowGap={4} width="100%">
+      <Flex flexDirection="column">
+        <Text fontSize="lg" fontWeight="bold">
+          {model.model_name}
+        </Text>
+        <Text fontSize="sm" color="base.400">
+          {MODEL_TYPE_MAP[model.base_model]} Model ⋅{' '}
+          {LORA_MODEL_FORMAT_MAP[model.model_format]} format
+        </Text>
+      </Flex>
+      <Divider />
+
+      <form
+        onSubmit={loraEditForm.onSubmit((values) =>
+          editModelFormSubmitHandler(values)
+        )}
+      >
+        <Flex flexDirection="column" overflowY="scroll" gap={4}>
+          <IAIMantineTextInput
+            label={t('modelManager.name')}
+            {...loraEditForm.getInputProps('model_name')}
+          />
+          <IAIMantineTextInput
+            label={t('modelManager.description')}
+            {...loraEditForm.getInputProps('description')}
+          />
+          <BaseModelSelect {...loraEditForm.getInputProps('base_model')} />
+          <IAIMantineTextInput
+            label={t('modelManager.modelLocation')}
+            {...loraEditForm.getInputProps('path')}
+          />
+          <IAIButton
+            type="submit"
+            isDisabled={isBusy || isLoading}
+            isLoading={isLoading}
+          >
+            {t('modelManager.updateModel')}
+          </IAIButton>
+        </Flex>
+      </form>
+    </Flex>
+  );
+}

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

@@ -11,6 +11,8 @@ import {
   OnnxModelConfigEntity,
   useGetMainModelsQuery,
   useGetOnnxModelsQuery,
+  useGetLoRAModelsQuery,
+  LoRAModelConfigEntity,
 } from 'services/api/endpoints/models';
 import ModelListItem from './ModelListItem';
 import { ALL_BASE_MODELS } from 'services/api/constants';
@@ -22,22 +24,42 @@ type ModelListProps = {
 
 type ModelFormat = 'images' | 'checkpoint' | 'diffusers' | 'olive' | 'onnx';
 
+type ModelType = 'main' | 'lora';
+
+type CombinedModelFormat = ModelFormat | 'lora';
+
 const ModelList = (props: ModelListProps) => {
   const { selectedModelId, setSelectedModelId } = props;
   const { t } = useTranslation();
   const [nameFilter, setNameFilter] = useState<string>('');
   const [modelFormatFilter, setModelFormatFilter] =
-    useState<ModelFormat>('images');
+    useState<CombinedModelFormat>('images');
 
   const { filteredDiffusersModels } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      filteredDiffusersModels: modelsFilter(data, 'diffusers', nameFilter),
+      filteredDiffusersModels: modelsFilter(
+        data,
+        'main',
+        'diffusers',
+        nameFilter
+      ),
     }),
   });
 
   const { filteredCheckpointModels } = useGetMainModelsQuery(ALL_BASE_MODELS, {
     selectFromResult: ({ data }) => ({
-      filteredCheckpointModels: modelsFilter(data, 'checkpoint', nameFilter),
+      filteredCheckpointModels: modelsFilter(
+        data,
+        'main',
+        'checkpoint',
+        nameFilter
+      ),
+    }),
+  });
+
+  const { filteredLoraModels } = useGetLoRAModelsQuery(undefined, {
+    selectFromResult: ({ data }) => ({
+      filteredLoraModels: modelsFilter(data, 'lora', undefined, nameFilter),
     }),
   });
 
@@ -89,6 +111,13 @@ const ModelList = (props: ModelListProps) => {
           >
             {t('modelManager.oliveModels')}
           </IAIButton>
+          <IAIButton
+            size="sm"
+            onClick={() => setModelFormatFilter('lora')}
+            isChecked={modelFormatFilter === 'lora'}
+          >
+            {t('modelManager.loraModels')}
+          </IAIButton>
         </ButtonGroup>
 
         <IAIInput
@@ -175,6 +204,24 @@ const ModelList = (props: ModelListProps) => {
                 </Flex>
               </StyledModelContainer>
             )}
+          {['images', 'lora'].includes(modelFormatFilter) &&
+            filteredLoraModels.length > 0 && (
+              <StyledModelContainer>
+                <Flex sx={{ gap: 2, flexDir: 'column' }}>
+                  <Text variant="subtext" fontSize="sm">
+                    LoRAs
+                  </Text>
+                  {filteredLoraModels.map((model) => (
+                    <ModelListItem
+                      key={model.id}
+                      model={model}
+                      isSelected={selectedModelId === model.id}
+                      setSelectedModelId={setSelectedModelId}
+                    />
+                  ))}
+                </Flex>
+              </StyledModelContainer>
+            )}
         </Flex>
       </Flex>
     </Flex>
@@ -183,15 +230,18 @@ const ModelList = (props: ModelListProps) => {
 
 export default ModelList;
 
-const modelsFilter = (
+const modelsFilter = <
-  data:
+  T extends
-    | EntityState<MainModelConfigEntity>
+    | MainModelConfigEntity
-    | EntityState<OnnxModelConfigEntity>
+    | LoRAModelConfigEntity
-    | undefined,
+    | OnnxModelConfigEntity
-  model_format: ModelFormat,
+>(
+  data: EntityState<T> | undefined,
+  model_type: ModelType,
+  model_format: ModelFormat | undefined,
   nameFilter: string
 ) => {
-  const filteredModels: MainModelConfigEntity[] = [];
+  const filteredModels: T[] = [];
   forEach(data?.entities, (model) => {
     if (!model) {
       return;
@@ -201,9 +251,11 @@ const modelsFilter = (
       .toLowerCase()
       .includes(nameFilter.toLowerCase());
 
-    const matchesFormat = model.model_format === model_format;
+    const matchesFormat =
+      model_format === undefined || model.model_format === model_format;
+    const matchesType = model.model_type === model_type;
 
-    if (matchesFilter && matchesFormat) {
+    if (matchesFilter && matchesFormat && matchesType) {
       filteredModels.push(model);
     }
   });

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

@@ -9,29 +9,26 @@ import { selectIsBusy } from 'features/system/store/systemSelectors';
 import { addToast } from 'features/system/store/systemSlice';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
+import { MODEL_TYPE_SHORT_MAP } from 'features/parameters/types/constants';
 import {
   MainModelConfigEntity,
+  LoRAModelConfigEntity,
   useDeleteMainModelsMutation,
+  useDeleteLoRAModelsMutation,
 } from 'services/api/endpoints/models';
 
 type ModelListItemProps = {
-  model: MainModelConfigEntity;
+  model: MainModelConfigEntity | LoRAModelConfigEntity;
   isSelected: boolean;
   setSelectedModelId: (v: string | undefined) => void;
 };
 
-const modelBaseTypeMap = {
-  'sd-1': 'SD1',
-  'sd-2': 'SD2',
-  sdxl: 'SDXL',
-  'sdxl-refiner': 'SDXLR',
-};
-
 export default function ModelListItem(props: ModelListItemProps) {
   const isBusy = useAppSelector(selectIsBusy);
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const [deleteMainModel] = useDeleteMainModelsMutation();
+  const [deleteLoRAModel] = useDeleteLoRAModelsMutation();
 
   const { model, isSelected, setSelectedModelId } = props;
 
@@ -40,7 +37,10 @@ export default function ModelListItem(props: ModelListItemProps) {
   }, [model.id, setSelectedModelId]);
 
   const handleModelDelete = useCallback(() => {
-    deleteMainModel(model)
+    const method = { main: deleteMainModel, lora: deleteLoRAModel }[
+      model.model_type
+    ];
+    method(model)
       .unwrap()
       .then((_) => {
         dispatch(
@@ -60,14 +60,21 @@ export default function ModelListItem(props: ModelListItemProps) {
                 title: `${t('modelManager.modelDeleteFailed')}: ${
                   model.model_name
                 }`,
-                status: 'success',
+                status: 'error',
               })
             )
           );
         }
       });
     setSelectedModelId(undefined);
-  }, [deleteMainModel, model, setSelectedModelId, dispatch, t]);
+  }, [
+    deleteMainModel,
+    deleteLoRAModel,
+    model,
+    setSelectedModelId,
+    dispatch,
+    t,
+  ]);
 
   return (
     <Flex sx={{ gap: 2, alignItems: 'center', w: 'full' }}>
@@ -100,8 +107,8 @@ export default function ModelListItem(props: ModelListItemProps) {
         <Flex gap={4} alignItems="center">
           <Badge minWidth={14} p={0.5} fontSize="sm" variant="solid">
             {
-              modelBaseTypeMap[
+              MODEL_TYPE_SHORT_MAP[
-                model.base_model as keyof typeof modelBaseTypeMap
+                model.base_model as keyof typeof MODEL_TYPE_SHORT_MAP
               ]
             }
           </Badge>

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

@@ -2,20 +2,18 @@ import ParamDynamicPromptsCollapse from 'features/dynamicPrompts/components/Para
 import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamNoiseCollapse from 'features/parameters/components/Parameters/Noise/ParamNoiseCollapse';
 import ParamSeamlessCollapse from 'features/parameters/components/Parameters/Seamless/ParamSeamlessCollapse';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 // import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
+import ParamPromptArea from '../../../../parameters/components/Parameters/Prompt/ParamPromptArea';
 import TextToImageTabCoreParameters from './TextToImageTabCoreParameters';
 
 const TextToImageTabParameters = () => {
   return (
     <>
-      <ParamPositiveConditioning />
+      <ParamPromptArea />
-      <ParamNegativeConditioning />
       <ProcessButtons />
       <TextToImageTabCoreParameters />
       <ParamControlNetCollapse />

invokeai/frontend/web/src/features/ui/components/tabs/UnifiedCanvas/UnifiedCanvasParameters.tsx

@@ -4,18 +4,16 @@ import ParamAdvancedCollapse from 'features/parameters/components/Parameters/Adv
 import ParamInfillAndScalingCollapse from 'features/parameters/components/Parameters/Canvas/InfillAndScaling/ParamInfillAndScalingCollapse';
 import ParamSeamCorrectionCollapse from 'features/parameters/components/Parameters/Canvas/SeamCorrection/ParamSeamCorrectionCollapse';
 import ParamControlNetCollapse from 'features/parameters/components/Parameters/ControlNet/ParamControlNetCollapse';
-import ParamNegativeConditioning from 'features/parameters/components/Parameters/Core/ParamNegativeConditioning';
-import ParamPositiveConditioning from 'features/parameters/components/Parameters/Core/ParamPositiveConditioning';
 import ParamSymmetryCollapse from 'features/parameters/components/Parameters/Symmetry/ParamSymmetryCollapse';
 // import ParamVariationCollapse from 'features/parameters/components/Parameters/Variations/ParamVariationCollapse';
+import ParamPromptArea from 'features/parameters/components/Parameters/Prompt/ParamPromptArea';
 import ProcessButtons from 'features/parameters/components/ProcessButtons/ProcessButtons';
 import UnifiedCanvasCoreParameters from './UnifiedCanvasCoreParameters';
 
 const UnifiedCanvasParameters = () => {
   return (
     <>
-      <ParamPositiveConditioning />
+      <ParamPromptArea />
-      <ParamNegativeConditioning />
       <ProcessButtons />
       <UnifiedCanvasCoreParameters />
       <ParamControlNetCollapse />

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

@@ -57,9 +57,17 @@ type UpdateMainModelArg = {
   body: MainModelConfig;
 };
 
+type UpdateLoRAModelArg = {
+  base_model: BaseModelType;
+  model_name: string;
+  body: LoRAModelConfig;
+};
+
 type UpdateMainModelResponse =
   paths['/api/v1/models/{base_model}/{model_type}/{model_name}']['patch']['responses']['200']['content']['application/json'];
 
+type UpdateLoRAModelResponse = UpdateMainModelResponse;
+
 type DeleteMainModelArg = {
   base_model: BaseModelType;
   model_name: string;
@@ -68,6 +76,10 @@ type DeleteMainModelArg = {
 
 type DeleteMainModelResponse = void;
 
+type DeleteLoRAModelArg = DeleteMainModelArg;
+
+type DeleteLoRAModelResponse = void;
+
 type ConvertMainModelArg = {
   base_model: BaseModelType;
   model_name: string;
@@ -373,6 +385,31 @@ export const modelsApi = api.injectEndpoints({
         );
       },
     }),
+    updateLoRAModels: build.mutation<
+      UpdateLoRAModelResponse,
+      UpdateLoRAModelArg
+    >({
+      query: ({ base_model, model_name, body }) => {
+        return {
+          url: `models/${base_model}/lora/${model_name}`,
+          method: 'PATCH',
+          body: body,
+        };
+      },
+      invalidatesTags: [{ type: 'LoRAModel', id: LIST_TAG }],
+    }),
+    deleteLoRAModels: build.mutation<
+      DeleteLoRAModelResponse,
+      DeleteLoRAModelArg
+    >({
+      query: ({ base_model, model_name }) => {
+        return {
+          url: `models/${base_model}/lora/${model_name}`,
+          method: 'DELETE',
+        };
+      },
+      invalidatesTags: [{ type: 'LoRAModel', id: LIST_TAG }],
+    }),
     getControlNetModels: build.query<
       EntityState<ControlNetModelConfigEntity>,
       void
@@ -521,6 +558,8 @@ export const {
   useAddMainModelsMutation,
   useConvertMainModelsMutation,
   useMergeMainModelsMutation,
+  useDeleteLoRAModelsMutation,
+  useUpdateLoRAModelsMutation,
   useSyncModelsMutation,
   useGetModelsInFolderQuery,
   useGetCheckpointConfigsQuery,

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -5857,11 +5857,11 @@ export type components = {
       image?: components["schemas"]["ImageField"];
     };
     /**
-     * StableDiffusion1ModelFormat 
+     * StableDiffusion2ModelFormat 
      * @description An enumeration. 
      * @enum {string}
      */
-    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
+    StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusionXLModelFormat 
      * @description An enumeration. 
@@ -5880,6 +5880,12 @@ export type components = {
      * @enum {string}
      */
     ControlNetModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion1ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;

invokeai/frontend/web/src/services/api/types.d.ts

@@ -43,8 +43,13 @@ export type ControlField = components['schemas']['ControlField'];
 // Model Configs
 export type LoRAModelConfig = components['schemas']['LoRAModelConfig'];
 export type VaeModelConfig = components['schemas']['VaeModelConfig'];
+export type ControlNetModelCheckpointConfig =
+  components['schemas']['ControlNetModelCheckpointConfig'];
+export type ControlNetModelDiffusersConfig =
+  components['schemas']['ControlNetModelDiffusersConfig'];
 export type ControlNetModelConfig =
-  components['schemas']['ControlNetModelConfig'];
+  | ControlNetModelCheckpointConfig
+  | ControlNetModelDiffusersConfig;
 export type TextualInversionModelConfig =
   components['schemas']['TextualInversionModelConfig'];
 export type DiffusersModelConfig =

invokeai/frontend/web/src/theme/components/textarea.ts

@@ -13,6 +13,15 @@ const invokeAI = defineStyle((props) => ({
       var(--invokeai-colors-base-200) 70%,
       var(--invokeai-colors-base-200) 100%)`,
   },
+  _disabled: {
+    '::-webkit-resizer': {
+      backgroundImage: `linear-gradient(135deg,
+        var(--invokeai-colors-base-50) 0%,
+        var(--invokeai-colors-base-50) 70%,
+        var(--invokeai-colors-base-200) 70%,
+        var(--invokeai-colors-base-200) 100%)`,
+    },
+  },
   _dark: {
     '::-webkit-resizer': {
       backgroundImage: `linear-gradient(135deg,
@@ -21,6 +30,15 @@ const invokeAI = defineStyle((props) => ({
         var(--invokeai-colors-base-800) 70%,
         var(--invokeai-colors-base-800) 100%)`,
     },
+    _disabled: {
+      '::-webkit-resizer': {
+        backgroundImage: `linear-gradient(135deg,
+          var(--invokeai-colors-base-900) 0%,
+          var(--invokeai-colors-base-900) 70%,
+          var(--invokeai-colors-base-800) 70%,
+          var(--invokeai-colors-base-800) 100%)`,
+      },
+    },
   },
 }));
 

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "3.0.1rc1"
+__version__ = "3.0.1rc2"

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "InvokeAI"
 description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
-requires-python = ">=3.9, <3.11"
+requires-python = ">=3.9, <3.12"
 readme = { content-type = "text/markdown", file = "README.md" }
 keywords = ["stable-diffusion", "AI"]
 dynamic = ["version"]
@@ -32,16 +32,16 @@ classifiers = [
   'Topic :: Scientific/Engineering :: Image Processing',
 ]
 dependencies = [
-  "accelerate~=0.16",
+  "accelerate~=0.21.0",
   "albumentations",
   "click",
-  "clip_anytorch",          # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
+  "clip_anytorch",  # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
-  "compel==2.0.0",
+  "compel~=2.0.0",
   "controlnet-aux>=0.0.6",
-  "timm==0.6.13",           # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
+  "timm==0.6.13",   # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
   "datasets",
-  "diffusers[torch]~=0.18.1",
+  "diffusers[torch]~=0.19.0",
-  "dnspython==2.2.1",
+  "dnspython~=2.4.0",
   "dynamicprompts",
   "easing-functions",
   "einops",
@@ -54,13 +54,12 @@ dependencies = [
   "flask_cors==3.0.10",
   "flask_socketio==5.3.0",
   "flaskwebgui==1.0.3",
-  "gfpgan==1.3.8",
   "huggingface-hub>=0.11.1",
-  "invisible-watermark>=0.2.0", # needed to install SDXL base and refiner using their repo_ids
+  "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "matplotlib",                 # needed for plotting of Penner easing functions
   "mediapipe",                  # needed for "mediapipeface" controlnet model
   "npyscreen",
-  "numpy<1.24",
+  "numpy==1.24.4",
   "omegaconf",
   "onnx",
   "onnxruntime",
@@ -68,25 +67,26 @@ dependencies = [
   "picklescan",
   "pillow",
   "prompt-toolkit",
-  "pympler==1.0.1",
+  "pydantic==1.10.10",
+  "pympler~=1.0.1",
   "pypatchmatch",
   'pyperclip',
   "pyreadline3",
-  "python-multipart==0.0.6",
+  "python-multipart",
-  "pytorch-lightning==1.7.7",
+  "pytorch-lightning",
   "realesrgan",
-  "requests==2.28.2",
+  "requests~=2.28.2",
   "rich~=13.3",
   "safetensors~=0.3.0",
-  "scikit-image>=0.19",
+  "scikit-image~=0.21.0",
   "send2trash",
-  "test-tube>=0.7.5",
+  "test-tube~=0.7.5",
-  "torch~=2.0.0",
+  "torch~=2.0.1",
-  "torchvision>=0.14.1",
+  "torchvision~=0.15.2",
-  "torchmetrics==0.11.4",
+  "torchmetrics~=1.0.1",
-  "torchsde==0.2.5",
+  "torchsde~=0.2.5",
   "transformers~=4.31.0",
-  "uvicorn[standard]==0.21.1",
+  "uvicorn[standard]~=0.21.1",
   "windows-curses; sys_platform=='win32'",
 ]
 
@@ -100,7 +100,7 @@ dependencies = [
 "dev" = [
   "pudb",
 ]
-"test" = ["pytest>6.0.0", "pytest-cov"]
+"test" = ["pytest>6.0.0", "pytest-cov", "black"]
 "xformers" = [
 	   "xformers~=0.0.19; sys_platform!='darwin'",
 	   "triton; sys_platform=='linux'",
@@ -187,7 +187,7 @@ directory = "coverage/html"
 output = "coverage/index.xml"
 #=== End: PyTest and Coverage
 
-[flake8]
+[tool.flake8]
 max-line-length = 120
 
 [tool.black]

scripts/probe-model.py

@@ -1,8 +1,16 @@
 #!/bin/env python
 
+import argparse
 import sys
 from pathlib import Path
 from invokeai.backend.model_management.model_probe import ModelProbe
 
-info = ModelProbe().probe(Path(sys.argv[1]))
+parser = argparse.ArgumentParser(description="Probe model type")
+parser.add_argument(
+    "model_path",
+    type=Path,
+)
+args = parser.parse_args()
+
+info = ModelProbe().probe(args.model_path)
 print(info)