Update version to 3.0.2post1

During install testing I discovered two small problems in the
command-line scripts. These are fixed.

## What type of PR is this? (check all applicable)

- [X Bug Fix

## Have you discussed this change with the InvokeAI team?
- [X] Yes
- 
      
## Have you updated all relevant documentation?
- [X] Yes


## Description

- installer - use correct entry point for invokeai-configure
- model merge script - prevent error when `--root` not provided

README.md

@@ -161,7 +161,7 @@ the command `npm install -g yarn` if needed)
     _For Windows/Linux with an NVIDIA GPU:_
 
     ```terminal
-    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
     ```
 
     _For Linux with an AMD GPU:_
@@ -306,13 +306,30 @@ InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
 You may now launch the WebUI in the usual way, by selecting option [1]
 from the launcher script
 
-#### Migration Caveats
+#### Migrating Images
 
 The migration script will migrate your invokeai settings and models,
 including textual inversion models, LoRAs and merges that you may have
 installed previously. However it does **not** migrate the generated
-images stored in your 2.3-format outputs directory. You will need to
+images stored in your 2.3-format outputs directory. To do this, you 
-manually import selected images into the 3.0 gallery via drag-and-drop.
+need to run an additional step:
+
+1. From a working InvokeAI 3.0 root directory, start the launcher and
+enter menu option [8] to open the "developer's console".
+
+2. At the developer's console command line, type the command:
+
+```bash
+invokeai-import-images
+```
+
+3. This will lead you through the process of confirming the desired
+   source and destination for the imported images. The images will
+   appear in the gallery board of your choice, and contain the
+   original prompt, model name, and other parameters used to generate
+   the image.
+   
+(Many kudos to **techjedi** for contributing this script.)
 
 ## Hardware Requirements
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -264,7 +264,7 @@ experimental versions later.
 	  you can create several levels of subfolders and drop your models into
       whichever ones you want.
 	
-    - ***Autoimport FolderLICENSE***     
+    - ***LICENSE***     
 
     At the bottom of the screen you will see a checkbox for accepting
     the CreativeML Responsible AI Licenses. You need to accept the license
@@ -471,7 +471,7 @@ Then type the following commands:
 
 === "NVIDIA System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu117
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu118
     pip install xformers
     ```
 

docs/installation/020_INSTALL_MANUAL.md

@@ -148,7 +148,7 @@ manager, please follow these steps:
     === "CUDA (NVidia)"
 
         ```bash
-        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
         ```
 
     === "ROCm (AMD)"
@@ -312,7 +312,7 @@ installation protocol (important!)
 
     === "CUDA (NVidia)"
         ```bash
-        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
         ```
 
     === "ROCm (AMD)"
@@ -356,7 +356,7 @@ you can do so using this unsupported recipe:
 mkdir ~/invokeai
 conda create -n invokeai python=3.10
 conda activate invokeai
-pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
 invokeai-configure --root ~/invokeai
 invokeai --root ~/invokeai --web
 ```

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -34,11 +34,11 @@ directly from NVIDIA. **Do not try to install Ubuntu's
 nvidia-cuda-toolkit package. It is out of date and will cause
 conflicts among the NVIDIA driver and binaries.**
 
-Go to [CUDA Toolkit 11.7
+Go to [CUDA Toolkit
-Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive),
+Downloads](https://developer.nvidia.com/cuda-downloads), and use the
-and use the target selection wizard to choose your operating system,
+target selection wizard to choose your operating system, hardware
-hardware platform, and preferred installation method (e.g. "local"
+platform, and preferred installation method (e.g. "local" versus
-versus "network").
+"network").
 
 This will provide you with a downloadable install file or, depending
 on your choices, a recipe for downloading and running a install shell
@@ -61,7 +61,7 @@ Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/cu117` as described in the [Manual
+https://download.pytorch.org/whl/cu118` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 ## :simple-amd: ROCm

docs/installation/070_INSTALL_XFORMERS.md

@@ -28,18 +28,21 @@ command line, then just be sure to activate it's virtual environment.
 Then run the following three commands:
 
 ```sh
-pip install xformers==0.0.16rc425
+pip install xformers~=0.0.19
-pip install triton
+pip install triton    # WON'T WORK ON WINDOWS
 python -m xformers.info output
 ```
 
 The first command installs `xformers`, the second installs the
 `triton` training accelerator, and the third prints out the `xformers`
-installation status. If all goes well, you'll see a report like the
+installation status. On Windows, please omit the `triton` package,
+which is not available on that platform.
+
+If all goes well, you'll see a report like the
 following:
 
 ```sh
-xFormers 0.0.16rc425
+xFormers 0.0.20
 memory_efficient_attention.cutlassF:               available
 memory_efficient_attention.cutlassB:               available
 memory_efficient_attention.flshattF:               available
@@ -48,22 +51,28 @@ memory_efficient_attention.smallkF:                available
 memory_efficient_attention.smallkB:                available
 memory_efficient_attention.tritonflashattF:        available
 memory_efficient_attention.tritonflashattB:        available
+indexing.scaled_index_addF:                        available
+indexing.scaled_index_addB:                        available
+indexing.index_select:                             available
+swiglu.dual_gemm_silu:                             available
+swiglu.gemm_fused_operand_sum:                     available
 swiglu.fused.p.cpp:                                available
 is_triton_available:                               True
 is_functorch_available:                            False
-pytorch.version:                                   1.13.1+cu117
+pytorch.version:                                   2.0.1+cu118
 pytorch.cuda:                                      available
-gpu.compute_capability:                            8.6
+gpu.compute_capability:                            8.9
-gpu.name:                                          NVIDIA RTX A2000 12GB
+gpu.name:                                          NVIDIA GeForce RTX 4070
 build.info:                                        available
-build.cuda_version:                                1107
+build.cuda_version:                                1108
-build.python_version:                              3.10.9
+build.python_version:                              3.10.11
-build.torch_version:                               1.13.1+cu117
+build.torch_version:                               2.0.1+cu118
 build.env.TORCH_CUDA_ARCH_LIST:                    5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6
 build.env.XFORMERS_BUILD_TYPE:                     Release
 build.env.XFORMERS_ENABLE_DEBUG_ASSERTIONS:        None
 build.env.NVCC_FLAGS:                              None
-build.env.XFORMERS_PACKAGE_FROM:                   wheel-v0.0.16rc425
+build.env.XFORMERS_PACKAGE_FROM:                   wheel-v0.0.20
+build.nvcc_version:                                11.8.89
 source.privacy:                                    open source
 ```
 
@@ -83,14 +92,14 @@ installed from source. These instructions were written for a system
 running Ubuntu 22.04, but other Linux distributions should be able to
 adapt this recipe.
 
-#### 1. Install CUDA Toolkit 11.7
+#### 1. Install CUDA Toolkit 11.8
 
 You will need the CUDA developer's toolkit in order to compile and
 install xFormers. **Do not try to install Ubuntu's nvidia-cuda-toolkit
 package.** It is out of date and will cause conflicts among the NVIDIA
 driver and binaries. Instead install the CUDA Toolkit package provided
-by NVIDIA itself. Go to [CUDA Toolkit 11.7
+by NVIDIA itself. Go to [CUDA Toolkit 11.8
-Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive)
+Downloads](https://developer.nvidia.com/cuda-11-8-0-download-archive)
 and use the target selection wizard to choose your platform and Linux
 distribution. Select an installer type of "runfile (local)" at the
 last step.
@@ -101,17 +110,17 @@ example, the install script recipe for Ubuntu 22.04 running on a
 x86_64 system is:
 
 ```
-wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
+wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
-sudo sh cuda_11.7.0_515.43.04_linux.run
+sudo sh cuda_11.8.0_520.61.05_linux.run
 ```
 
 Rather than cut-and-paste this example, We recommend that you walk
 through the toolkit wizard in order to get the most up to date
 installer for your system.
 
-#### 2. Confirm/Install pyTorch 1.13 with CUDA 11.7 support
+#### 2. Confirm/Install pyTorch 2.01 with CUDA 11.8 support
 
-If you are using InvokeAI 2.3 or higher, these will already be
+If you are using InvokeAI 3.0.2 or higher, these will already be
 installed. If not, you can check whether you have the needed libraries
 using a quick command. Activate the invokeai virtual environment,
 either by entering the "developer's console", or manually with a
@@ -124,7 +133,7 @@ Then run the command:
 python -c 'exec("import torch\nprint(torch.__version__)")'
 ```
 
-If it prints __1.13.1+cu117__ you're good. If not, you can install the
+If it prints __1.13.1+cu118__ you're good. If not, you can install the
 most up to date libraries with this command:
 
 ```sh

installer/lib/installer.py

@@ -348,7 +348,7 @@ class InvokeAiInstance:
 
         introduction()
 
-        from invokeai.frontend.install import invokeai_configure
+        from invokeai.frontend.install.invokeai_configure import invokeai_configure
 
         # NOTE: currently the config script does its own arg parsing! this means the command-line switches
         # from the installer will also automatically propagate down to the config script.
@@ -463,10 +463,10 @@ def get_torch_source() -> (Union[str, None], str):
             url = "https://download.pytorch.org/whl/cpu"
 
     if device == "cuda":
-        url = "https://download.pytorch.org/whl/cu117"
+        url = "https://download.pytorch.org/whl/cu118"
         optional_modules = "[xformers,onnx-cuda]"
     if device == "cuda_and_dml":
-        url = "https://download.pytorch.org/whl/cu117"
+        url = "https://download.pytorch.org/whl/cu118"
         optional_modules = "[xformers,onnx-directml]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13

installer/readme.txt

@@ -8,16 +8,13 @@ Preparations:
   to work. Instructions are given here:
   https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
 
-  NOTE: At this time we do not recommend Python 3.11. We recommend 
-  Version 3.10.9, which has been extensively tested with InvokeAI.
-  
   Before you start the installer, please open up your system's command
   line window (Terminal or Command) and type the commands:
 
      python --version
 
   If all is well, it will print "Python 3.X.X", where the version number
-  is at least 3.9.1, and less than 3.11.
+  is at least 3.9.*, and not higher than 3.11.*.
 
   If this works, check the version of the Python package manager, pip:
 

invokeai/app/api/routers/images.py

@@ -1,22 +1,20 @@
 import io
 from typing import Optional
 
+from PIL import Image
 from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
-from PIL import Image
+from pydantic import BaseModel
-from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.metadata import ImageMetadata
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.item_storage import PaginatedResults
 from invokeai.app.services.models.image_record import (
     ImageDTO,
     ImageRecordChanges,
     ImageUrlsDTO,
 )
-
 from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
@@ -152,8 +150,9 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
-@images_router.get(
+@images_router.api_route(
     "/i/{image_name}/full",
+    methods=["GET", "HEAD"],
     operation_id="get_image_full",
     response_class=Response,
     responses={

invokeai/app/api/routers/models.py

@@ -104,8 +104,12 @@ async def update_model(
             ):  # model manager moved model path during rename - don't overwrite it
                 info.path = new_info.get("path")
 
+        # replace empty string values with None/null to avoid phenomenon of vae: ''
+        info_dict = info.dict()
+        info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
+
         ApiDependencies.invoker.services.model_manager.update_model(
-            model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info.dict()
+            model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info_dict
         )
 
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(

invokeai/app/invocations/generate.py

@@ -1,26 +1,23 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from contextlib import contextmanager, ContextDecorator
 from functools import partial
 from typing import Literal, Optional, get_args
 
-import torch
 from pydantic import Field
 
 from invokeai.app.models.image import ColorField, ImageCategory, ImageField, ResourceOrigin
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 from invokeai.backend.generator.inpaint import infill_methods
-
-from ...backend.generator import Inpaint, InvokeAIGenerator
-from ...backend.stable_diffusion import PipelineIntermediateState
-from ..util.step_callback import stable_diffusion_step_callback
 from .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext
-from .image import ImageOutput
-
-from ...backend.model_management.lora import ModelPatcher
-from ...backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from .model import UNetField, VaeField
 from .compel import ConditioningField
-from contextlib import contextmanager, ExitStack, ContextDecorator
+from .image import ImageOutput
+from .model import UNetField, VaeField
+from ..util.step_callback import stable_diffusion_step_callback
+from ...backend.generator import Inpaint, InvokeAIGenerator
+from ...backend.model_management.lora import ModelPatcher
+from ...backend.stable_diffusion import PipelineIntermediateState
+from ...backend.stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
 INFILL_METHODS = Literal[tuple(infill_methods())]
@@ -184,6 +181,8 @@ class InpaintInvocation(BaseInvocation):
             device = context.services.model_manager.mgr.cache.execution_device
             dtype = context.services.model_manager.mgr.cache.precision
 
+            vae.to(dtype=unet.dtype)
+
             pipeline = StableDiffusionGeneratorPipeline(
                 vae=vae,
                 text_encoder=None,
@@ -193,8 +192,6 @@ class InpaintInvocation(BaseInvocation):
                 safety_checker=None,
                 feature_extractor=None,
                 requires_safety_checker=False,
-                precision="float16" if dtype == torch.float16 else "float32",
-                execution_device=device,
             )
 
             yield OldModelInfo(

invokeai/app/invocations/image.py

@@ -501,7 +501,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
         image = context.services.images.get_pil_image(self.image.image_name)
 
         image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
-        image_arr = image_arr * (self.max - self.min) + self.max
+        image_arr = image_arr * (self.max - self.min) + self.min
 
         lerp_image = Image.fromarray(numpy.uint8(image_arr))
 
@@ -661,27 +661,23 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to adjust")
-    hue: int = Field(default=0, description="The degrees by which to rotate the hue")
+    hue: int = Field(default=0, description="The degrees by which to rotate the hue, 0-360")
     # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         pil_image = context.services.images.get_pil_image(self.image.image_name)
 
-        # Convert PIL image to OpenCV format (numpy array), note color channel
-        # ordering is changed from RGB to BGR
-        image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
-
         # Convert image to HSV color space
-        hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+        hsv_image = numpy.array(pil_image.convert("HSV"))
 
-        # Adjust the hue
+        # Convert hue from 0..360 to 0..256
-        hsv_image[:, :, 0] = (hsv_image[:, :, 0] + self.hue) % 180
+        hue = int(256 * ((self.hue % 360) / 360))
 
-        # Convert image back to BGR color space
+        # Increment each hue and wrap around at 255
-        image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
+        hsv_image[:, :, 0] = (hsv_image[:, :, 0] + hue) % 256
 
         # Convert back to PIL format and to original color mode
-        pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
+        pil_image = Image.fromarray(hsv_image, mode="HSV").convert("RGBA")
 
         image_dto = context.services.images.create(
             image=pil_image,

invokeai/app/invocations/latent.py

@@ -5,15 +5,26 @@ from typing import List, Literal, Optional, Union
 
 import einops
 import torch
-from diffusers import ControlNetModel
 from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
 from diffusers.schedulers import SchedulerMixin as Scheduler
 from pydantic import BaseModel, Field, validator
 
 from invokeai.app.invocations.metadata import CoreMetadata
+from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.model_management.models import ModelType, SilenceWarnings
-
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
+from .compel import ConditioningField
+from .controlnet_image_processors import ControlField
+from .image import ImageOutput
+from .model import ModelInfo, UNetField, VaeField
+from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from ...backend.model_management import ModelPatcher
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
@@ -24,23 +35,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
 )
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.model_management import ModelPatcher
 from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision
-from ..models.image import ImageCategory, ImageField, ResourceOrigin
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
-from .compel import ConditioningField
-from .controlnet_image_processors import ControlField
-from .image import ImageOutput
-from .model import ModelInfo, UNetField, VaeField
-from invokeai.app.util.controlnet_utils import prepare_control_image
-
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    XFormersAttnProcessor,
-)
-
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
@@ -231,7 +226,6 @@ class TextToLatentsInvocation(BaseInvocation):
             safety_checker=None,
             feature_extractor=None,
             requires_safety_checker=False,
-            precision="float16" if unet.dtype == torch.float16 else "float32",
         )
 
     def prep_control_data(

invokeai/app/invocations/metadata.py

@@ -2,6 +2,7 @@ from typing import Literal, Optional, Union
 
 from pydantic import Field
 
+from ...version import __version__
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -23,6 +24,7 @@ class LoRAMetadataField(BaseModelExcludeNull):
 class CoreMetadata(BaseModelExcludeNull):
     """Core generation metadata for an image generated in InvokeAI."""
 
+    app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
     generation_mode: str = Field(
         description="The generation mode that output this image",
     )

invokeai/app/services/config.py

@@ -24,11 +24,10 @@ InvokeAI:
     sequential_guidance: false
     precision: float16
     max_cache_size: 6
-    max_vram_cache_size: 2.7
+    max_vram_cache_size: 0.5
     always_use_cpu: false
     free_gpu_mem: false
   Features:
-    restore: true
     esrgan: true
     patchmatch: true
     internet_available: true
@@ -165,7 +164,7 @@ import pydoc
 import os
 import sys
 from argparse import ArgumentParser
-from omegaconf import OmegaConf, DictConfig
+from omegaconf import OmegaConf, DictConfig, ListConfig
 from pathlib import Path
 from pydantic import BaseSettings, Field, parse_obj_as
 from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
@@ -173,6 +172,7 @@ from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_ty
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
+DEFAULT_MAX_VRAM = 0.5
 
 
 class InvokeAISettings(BaseSettings):
@@ -189,7 +189,12 @@ class InvokeAISettings(BaseSettings):
         opt = parser.parse_args(argv)
         for name in self.__fields__:
             if name not in self._excluded():
-                setattr(self, name, getattr(opt, name))
+                value = getattr(opt, name)
+                if isinstance(value, ListConfig):
+                    value = list(value)
+                elif isinstance(value, DictConfig):
+                    value = dict(value)
+                setattr(self, name, value)
 
     def to_yaml(self) -> str:
         """
@@ -282,14 +287,10 @@ class InvokeAISettings(BaseSettings):
         return [
             "type",
             "initconf",
-            "gpu_mem_reserved",
-            "max_loaded_models",
             "version",
             "from_file",
             "model",
-            "restore",
             "root",
-            "nsfw_checker",
         ]
 
     class Config:
@@ -388,15 +389,11 @@ class InvokeAIAppConfig(InvokeAISettings):
     internet_available  : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
     log_tokenization    : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
     patchmatch          : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
-    restore             : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED')
 
     always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
     free_gpu_mem        : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
-    max_loaded_models   : int = Field(default=3, gt=0, description="(DEPRECATED: use max_cache_size) Maximum number of models to keep in memory for rapid switching", category='DEPRECATED')
     max_cache_size      : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
     max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
-    gpu_mem_reserved    : float = Field(default=2.75, ge=0, description="DEPRECATED: use max_vram_cache_size. Amount of VRAM reserved for model storage", category='DEPRECATED')
-    nsfw_checker        : bool = Field(default=True, description="DEPRECATED: use Web settings to enable/disable", category='DEPRECATED')
     precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance')
     sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
@@ -414,9 +411,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     outdir              : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
     from_file           : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
     use_memory_db       : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
-    ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert')
+    ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
-
-    model               : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
 
     log_handlers        : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
     # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
@@ -426,6 +421,9 @@ class InvokeAIAppConfig(InvokeAISettings):
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
     # fmt: on
 
+    class Config:
+        validate_assignment = True
+
     def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
         """
         Update settings with contents of init file, environment, and

invokeai/backend/generator/img2img.py

@@ -1,25 +1,11 @@
 """
 invokeai.backend.generator.img2img descends from .generator
 """
-from typing import Optional
 
-import torch
-from accelerate.utils import set_seed
-from diffusers import logging
-
-from ..stable_diffusion import (
-    ConditioningData,
-    PostprocessingSettings,
-    StableDiffusionGeneratorPipeline,
-)
 from .base import Generator
 
 
 class Img2Img(Generator):
-    def __init__(self, model, precision):
-        super().__init__(model, precision)
-        self.init_latent = None  # by get_noise()
-
     def get_make_image(
         self,
         sampler,
@@ -42,51 +28,4 @@ class Img2Img(Generator):
         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
+        raise NotImplementedError("replaced by invokeai.app.invocations.latent.LatentsToLatentsInvocation")
-
-        # noinspection PyTypeChecker
-        pipeline: StableDiffusionGeneratorPipeline = self.model
-        pipeline.scheduler = sampler
-
-        uc, c, extra_conditioning_info = conditioning
-        conditioning_data = ConditioningData(
-            uc,
-            c,
-            cfg_scale,
-            extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=threshold,
-                warmup=warmup,
-                h_symmetry_time_pct=h_symmetry_time_pct,
-                v_symmetry_time_pct=v_symmetry_time_pct,
-            ),
-        ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
-
-        def make_image(x_T: torch.Tensor, seed: int):
-            # FIXME: use x_T for initial seeded noise
-            # We're not at the moment because the pipeline automatically resizes init_image if
-            # necessary, which the x_T input might not match.
-            # In the meantime, reset the seed prior to generating pipeline output so we at least get the same result.
-            logging.set_verbosity_error()  # quench safety check warnings
-            pipeline_output = pipeline.img2img_from_embeddings(
-                init_image,
-                strength,
-                steps,
-                conditioning_data,
-                noise_func=self.get_noise_like,
-                callback=step_callback,
-                seed=seed,
-            )
-            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
-                attention_maps_callback(pipeline_output.attention_map_saver)
-            return pipeline.numpy_to_pil(pipeline_output.images)[0]
-
-        return make_image
-
-    def get_noise_like(self, like: torch.Tensor):
-        device = like.device
-        x = torch.randn_like(like, device=device)
-        if self.perlin > 0.0:
-            shape = like.shape
-            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(shape[3], shape[2])
-        return x

invokeai/backend/generator/inpaint.py

@@ -377,3 +377,11 @@ class Inpaint(Img2Img):
         )
 
         return corrected_result
+
+    def get_noise_like(self, like: torch.Tensor):
+        device = like.device
+        x = torch.randn_like(like, device=device)
+        if self.perlin > 0.0:
+            shape = like.shape
+            x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(shape[3], shape[2])
+        return x

invokeai/backend/install/invokeai_configure.py

@@ -10,15 +10,17 @@ import sys
 import argparse
 import io
 import os
+import psutil
 import shutil
 import textwrap
+import torch
 import traceback
 import yaml
 import warnings
 from argparse import Namespace
+from enum import Enum
 from pathlib import Path
 from shutil import get_terminal_size
-from typing import get_type_hints
 from urllib import request
 
 import npyscreen
@@ -44,6 +46,8 @@ from invokeai.app.services.config import (
 )
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
+
+# TO DO - Move all the frontend code into invokeai.frontend.install
 from invokeai.frontend.install.widgets import (
     SingleSelectColumns,
     CenteredButtonPress,
@@ -53,6 +57,7 @@ from invokeai.frontend.install.widgets import (
     CyclingForm,
     MIN_COLS,
     MIN_LINES,
+    WindowTooSmallException,
 )
 from invokeai.backend.install.legacy_arg_parsing import legacy_parser
 from invokeai.backend.install.model_install_backend import (
@@ -61,6 +66,7 @@ from invokeai.backend.install.model_install_backend import (
     ModelInstall,
 )
 from invokeai.backend.model_management.model_probe import ModelType, BaseModelType
+from pydantic.error_wrappers import ValidationError
 
 warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()
@@ -76,6 +82,13 @@ Default_config_file = config.model_conf_path
 SD_Configs = config.legacy_conf_path
 
 PRECISION_CHOICES = ["auto", "float16", "float32"]
+GB = 1073741824  # GB in bytes
+HAS_CUDA = torch.cuda.is_available()
+_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
+
+
+MAX_VRAM /= GB
+MAX_RAM = psutil.virtual_memory().total / GB
 
 INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 # This is the InvokeAI initialization file, which contains command-line default values.
@@ -86,6 +99,12 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 logger = InvokeAILogger.getLogger()
 
 
+class DummyWidgetValue(Enum):
+    zero = 0
+    true = True
+    false = False
+
+
 # --------------------------------------------
 def postscript(errors: None):
     if not any(errors):
@@ -376,15 +395,47 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             max_width=80,
             scroll_exit=True,
         )
-        self.max_cache_size = self.add_widget_intelligent(
+        self.nextrely += 1
-            IntTitleSlider,
+        self.add_widget_intelligent(
-            name="Size of the RAM cache used for fast model switching (GB)",
+            npyscreen.TitleFixedText,
-            value=old_opts.max_cache_size,
+            name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
-            out_of=20,
+            begin_entry_at=0,
-            lowest=3,
+            editable=False,
-            begin_entry_at=6,
+            color="CONTROL",
             scroll_exit=True,
         )
+        self.nextrely -= 1
+        self.max_cache_size = self.add_widget_intelligent(
+            npyscreen.Slider,
+            value=clip(old_opts.max_cache_size, range=(3.0, MAX_RAM), step=0.5),
+            out_of=round(MAX_RAM),
+            lowest=0.0,
+            step=0.5,
+            relx=8,
+            scroll_exit=True,
+        )
+        if HAS_CUDA:
+            self.nextrely += 1
+            self.add_widget_intelligent(
+                npyscreen.TitleFixedText,
+                name="VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
+                begin_entry_at=0,
+                editable=False,
+                color="CONTROL",
+                scroll_exit=True,
+            )
+            self.nextrely -= 1
+            self.max_vram_cache_size = self.add_widget_intelligent(
+                npyscreen.Slider,
+                value=clip(old_opts.max_vram_cache_size, range=(0, MAX_VRAM), step=0.25),
+                out_of=round(MAX_VRAM * 2) / 2,
+                lowest=0.0,
+                relx=8,
+                step=0.25,
+                scroll_exit=True,
+            )
+        else:
+            self.max_vram_cache_size = DummyWidgetValue.zero
         self.nextrely += 1
         self.outdir = self.add_widget_intelligent(
             FileBox,
@@ -401,7 +452,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
         self.autoimport_dirs = {}
         self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
             FileBox,
-            name=f"Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
+            name="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
             value=str(config.root_path / config.autoimport_dir),
             select_dir=True,
             must_exist=False,
@@ -476,6 +527,7 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
             "outdir",
             "free_gpu_mem",
             "max_cache_size",
+            "max_vram_cache_size",
             "xformers_enabled",
             "always_use_cpu",
         ]:
@@ -553,6 +605,16 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections:
     )
 
 
+# -------------------------------------
+def clip(value: float, range: tuple[float, float], step: float) -> float:
+    minimum, maximum = range
+    if value < minimum:
+        value = minimum
+    if value > maximum:
+        value = maximum
+    return round(value / step) * step
+
+
 # -------------------------------------
 def initialize_rootdir(root: Path, yes_to_all: bool = False):
     logger.info("Initializing InvokeAI runtime directory")
@@ -592,13 +654,13 @@ def maybe_create_models_yaml(root: Path):
 
 # -------------------------------------
 def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace):
-    # parse_args() will read from init file if present
     invokeai_opts = default_startup_options(initfile)
     invokeai_opts.root = program_opts.root
 
-    # The third argument is needed in the Windows 11 environment to
+    if not set_min_terminal_size(MIN_COLS, MIN_LINES):
-    # launch a console window running this program.
+        raise WindowTooSmallException(
-    set_min_terminal_size(MIN_COLS, MIN_LINES)
+            "Could not increase terminal size. Try running again with a larger window or smaller font size."
+        )
 
     # the install-models application spawns a subprocess to install
     # models, and will crash unless this is set before running.
@@ -654,10 +716,13 @@ def migrate_init_file(legacy_format: Path):
     old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
     new = InvokeAIAppConfig.get_config()
 
-    fields = list(get_type_hints(InvokeAIAppConfig).keys())
+    fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"]
     for attr in fields:
         if hasattr(old, attr):
+            try:
                 setattr(new, attr, getattr(old, attr))
+            except ValidationError as e:
+                print(f"* Ignoring incompatible value for field {attr}:\n  {str(e)}")
 
     # a few places where the field names have changed and we have to
     # manually add in the new names/values
@@ -777,6 +842,7 @@ def main():
 
         models_to_download = default_user_selections(opt)
         new_init_file = config.root_path / "invokeai.yaml"
+
         if opt.yes_to_all:
             write_default_options(opt, new_init_file)
             init_options = Namespace(precision="float32" if opt.full_precision else "float16")
@@ -802,6 +868,8 @@ def main():
         postscript(errors=errors)
         if not opt.yes_to_all:
             input("Press any key to continue...")
+    except WindowTooSmallException as e:
+        logger.error(str(e))
     except KeyboardInterrupt:
         print("\nGoodbye! Come back soon.")
 

invokeai/backend/install/migrate_to_3.py

@@ -591,7 +591,6 @@ script, which will perform a full upgrade in place.""",
     # TODO: revisit - don't rely on invokeai.yaml to exist yet!
     dest_is_setup = (dest_root / "models/core").exists() and (dest_root / "databases").exists()
     if not dest_is_setup:
-        import invokeai.frontend.install.invokeai_configure
         from invokeai.backend.install.invokeai_configure import initialize_rootdir
 
         initialize_rootdir(dest_root, True)

invokeai/backend/model_management/lora.py

@@ -143,7 +143,7 @@ class ModelPatcher:
                         # with torch.autocast(device_type="cpu"):
                         layer.to(dtype=torch.float32)
                         layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
-                        layer_weight = layer.get_weight() * lora_weight * layer_scale
+                        layer_weight = layer.get_weight(original_weights[module_key]) * lora_weight * layer_scale
 
                         if module.weight.shape != layer_weight.shape:
                             # TODO: debug on lycoris
@@ -361,7 +361,8 @@ class ONNXModelPatcher:
 
                     layer.to(dtype=torch.float32)
                     layer_key = layer_key.replace(prefix, "")
-                    layer_weight = layer.get_weight().detach().cpu().numpy() * lora_weight
+                    # TODO: rewrite to pass original tensor weight(required by ia3)
+                    layer_weight = layer.get_weight(None).detach().cpu().numpy() * lora_weight
                     if layer_key is blended_loras:
                         blended_loras[layer_key] += layer_weight
                     else:

invokeai/backend/model_management/model_manager.py

@@ -228,19 +228,19 @@ the root is the InvokeAI ROOTDIR.
 """
 from __future__ import annotations
 
-import os
 import hashlib
+import os
 import textwrap
-import yaml
+import types
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Literal, Optional, List, Tuple, Union, Dict, Set, Callable, types
 from shutil import rmtree, move
+from typing import Optional, List, Literal, Tuple, Union, Dict, Set, Callable
 
 import torch
+import yaml
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
-
 from pydantic import BaseModel, Field
 
 import invokeai.backend.util.logging as logger
@@ -259,6 +259,7 @@ from .models import (
     ModelNotFoundException,
     InvalidModelException,
     DuplicateModelException,
+    ModelBase,
 )
 
 # We are only starting to number the config file with release 3.
@@ -361,7 +362,7 @@ class ModelManager(object):
             if model_key.startswith("_"):
                 continue
             model_name, base_model, model_type = self.parse_key(model_key)
-            model_class = MODEL_CLASSES[base_model][model_type]
+            model_class = self._get_implementation(base_model, model_type)
             # alias for config file
             model_config["model_format"] = model_config.pop("format")
             self.models[model_key] = model_class.create_config(**model_config)
@@ -381,18 +382,24 @@ class ModelManager(object):
         # causing otherwise unreferenced models to be removed from memory
         self._read_models()
 
-    def model_exists(
+    def model_exists(self, model_name: str, base_model: BaseModelType, model_type: ModelType, *, rescan=False) -> bool:
-        self,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-    ) -> bool:
         """
-        Given a model name, returns True if it is a valid
+        Given a model name, returns True if it is a valid identifier.
-        identifier.
+
+        :param model_name: symbolic name of the model in models.yaml
+        :param model_type: ModelType enum indicating the type of model to return
+        :param base_model: BaseModelType enum indicating the base model used by this model
+        :param rescan: if True, scan_models_directory
         """
         model_key = self.create_key(model_name, base_model, model_type)
-        return model_key in self.models
+        exists = model_key in self.models
+
+        # if model not found try to find it (maybe file just pasted)
+        if rescan and not exists:
+            self.scan_models_directory(base_model=base_model, model_type=model_type)
+            exists = self.model_exists(model_name, base_model, model_type, rescan=False)
+
+        return exists
 
     @classmethod
     def create_key(
@@ -443,39 +450,32 @@ class ModelManager(object):
         :param model_name: symbolic name of the model in models.yaml
         :param model_type: ModelType enum indicating the type of model to return
         :param base_model: BaseModelType enum indicating the base model used by this model
-        :param submode_typel: an ModelType enum indicating the portion of
+        :param submodel_type: an ModelType enum indicating the portion of
                the model to retrieve (e.g. ModelType.Vae)
         """
-        model_class = MODEL_CLASSES[base_model][model_type]
         model_key = self.create_key(model_name, base_model, model_type)
 
-        # if model not found try to find it (maybe file just pasted)
+        if not self.model_exists(model_name, base_model, model_type, rescan=True):
-        if model_key not in self.models:
-            self.scan_models_directory(base_model=base_model, model_type=model_type)
-            if model_key not in self.models:
             raise ModelNotFoundException(f"Model not found - {model_key}")
 
-        model_config = self.models[model_key]
+        model_config = self._get_model_config(base_model, model_name, model_type)
-        model_path = self.resolve_model_path(model_config.path)
+
+        model_path, is_submodel_override = self._get_model_path(model_config, submodel_type)
+
+        if is_submodel_override:
+            model_type = submodel_type
+            submodel_type = None
+
+        model_class = self._get_implementation(base_model, model_type)
 
         if not model_path.exists():
             if model_class.save_to_config:
                 self.models[model_key].error = ModelError.NotFound
-                raise Exception(f'Files for model "{model_key}" not found')
+                raise Exception(f'Files for model "{model_key}" not found at {model_path}')
 
             else:
                 self.models.pop(model_key, None)
-                raise ModelNotFoundException(f"Model not found - {model_key}")
+                raise ModelNotFoundException(f'Files for model "{model_key}" not found at {model_path}')
-
-        # vae/movq override
-        # TODO:
-        if submodel_type is not None and hasattr(model_config, submodel_type):
-            override_path = getattr(model_config, submodel_type)
-            if override_path:
-                model_path = self.resolve_path(override_path)
-                model_type = submodel_type
-                submodel_type = None
-                model_class = MODEL_CLASSES[base_model][model_type]
 
         # TODO: path
         # TODO: is it accurate to use path as id
@@ -513,6 +513,55 @@ class ModelManager(object):
             _cache=self.cache,
         )
 
+    def _get_model_path(
+        self, model_config: ModelConfigBase, submodel_type: Optional[SubModelType] = None
+    ) -> (Path, bool):
+        """Extract a model's filesystem path from its config.
+
+        :return: The fully qualified Path of the module (or submodule).
+        """
+        model_path = model_config.path
+        is_submodel_override = False
+
+        # Does the config explicitly override the submodel?
+        if submodel_type is not None and hasattr(model_config, submodel_type):
+            submodel_path = getattr(model_config, submodel_type)
+            if submodel_path is not None and len(submodel_path) > 0:
+                model_path = getattr(model_config, submodel_type)
+                is_submodel_override = True
+
+        model_path = self.resolve_model_path(model_path)
+        return model_path, is_submodel_override
+
+    def _get_model_config(self, base_model: BaseModelType, model_name: str, model_type: ModelType) -> ModelConfigBase:
+        """Get a model's config object."""
+        model_key = self.create_key(model_name, base_model, model_type)
+        try:
+            model_config = self.models[model_key]
+        except KeyError:
+            raise ModelNotFoundException(f"Model not found - {model_key}")
+        return model_config
+
+    def _get_implementation(self, base_model: BaseModelType, model_type: ModelType) -> type[ModelBase]:
+        """Get the concrete implementation class for a specific model type."""
+        model_class = MODEL_CLASSES[base_model][model_type]
+        return model_class
+
+    def _instantiate(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> ModelBase:
+        """Make a new instance of this model, without loading it."""
+        model_config = self._get_model_config(base_model, model_name, model_type)
+        model_path, is_submodel_override = self._get_model_path(model_config, submodel_type)
+        # FIXME: do non-overriden submodels get the right class?
+        constructor = self._get_implementation(base_model, model_type)
+        instance = constructor(model_path, base_model, model_type)
+        return instance
+
     def model_info(
         self,
         model_name: str,
@@ -546,8 +595,9 @@ class ModelManager(object):
         the combined format of the list_models() method.
         """
         models = self.list_models(base_model, model_type, model_name)
-        if len(models) > 1:
+        if len(models) >= 1:
             return models[0]
+        else:
             return None
 
     def list_models(
@@ -660,7 +710,7 @@ class ModelManager(object):
         if path := model_attributes.get("path"):
             model_attributes["path"] = str(self.relative_model_path(Path(path)))
 
-        model_class = MODEL_CLASSES[base_model][model_type]
+        model_class = self._get_implementation(base_model, model_type)
         model_config = model_class.create_config(**model_attributes)
         model_key = self.create_key(model_name, base_model, model_type)
 
@@ -851,7 +901,7 @@ class ModelManager(object):
 
         for model_key, model_config in self.models.items():
             model_name, base_model, model_type = self.parse_key(model_key)
-            model_class = MODEL_CLASSES[base_model][model_type]
+            model_class = self._get_implementation(base_model, model_type)
             if model_class.save_to_config:
                 # TODO: or exclude_unset better fits here?
                 data_to_save[model_key] = model_config.dict(exclude_defaults=True, exclude={"error"})
@@ -909,7 +959,7 @@ class ModelManager(object):
 
                 model_path = self.resolve_model_path(model_config.path).absolute()
                 if not model_path.exists():
-                    model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
+                    model_class = self._get_implementation(cur_base_model, cur_model_type)
                     if model_class.save_to_config:
                         model_config.error = ModelError.NotFound
                         self.models.pop(model_key, None)
@@ -925,7 +975,7 @@ class ModelManager(object):
                 for cur_model_type in ModelType:
                     if model_type is not None and cur_model_type != model_type:
                         continue
-                    model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
+                    model_class = self._get_implementation(cur_base_model, cur_model_type)
                     models_dir = self.resolve_model_path(Path(cur_base_model.value, cur_model_type.value))
 
                     if not models_dir.exists():
@@ -941,7 +991,9 @@ class ModelManager(object):
                                     raise DuplicateModelException(f"Model with key {model_key} added twice")
 
                                 model_path = self.relative_model_path(model_path)
-                                model_config: ModelConfigBase = model_class.probe_config(str(model_path))
+                                model_config: ModelConfigBase = model_class.probe_config(
+                                    str(model_path), model_base=cur_base_model
+                                )
                                 self.models[model_key] = model_config
                                 new_models_found = True
                             except DuplicateModelException as e:

invokeai/backend/model_management/model_probe.py

@@ -17,6 +17,7 @@ from .models import (
     SilenceWarnings,
     InvalidModelException,
 )
+from .util import lora_token_vector_length
 from .models.base import read_checkpoint_meta
 
 
@@ -315,38 +316,16 @@ class LoRACheckpointProbe(CheckpointProbeBase):
 
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
+        token_vector_length = lora_token_vector_length(checkpoint)
 
-        # SD-2 models are very hard to probe. These probes are brittle and likely to fail in the future
+        if token_vector_length == 768:
-        # There are also some "SD-2 LoRAs" that have identical keys and shapes to SD-1 and will be
-        # misclassified as SD-1
-        key = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
-        if key in checkpoint and checkpoint[key].shape[0] == 320:
-            return BaseModelType.StableDiffusion2
-
-        key = "lora_unet_output_blocks_5_1_transformer_blocks_1_ff_net_2.lora_up.weight"
-        if key in checkpoint:
-            return BaseModelType.StableDiffusionXL
-
-        key1 = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
-        key2 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
-        key3 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.hada_w1_a"
-
-        lora_token_vector_length = (
-            checkpoint[key1].shape[1]
-            if key1 in checkpoint
-            else checkpoint[key2].shape[1]
-            if key2 in checkpoint
-            else checkpoint[key3].shape[0]
-            if key3 in checkpoint
-            else None
-        )
-
-        if lora_token_vector_length == 768:
             return BaseModelType.StableDiffusion1
-        elif lora_token_vector_length == 1024:
+        elif token_vector_length == 1024:
             return BaseModelType.StableDiffusion2
+        elif token_vector_length == 2048:
+            return BaseModelType.StableDiffusionXL
         else:
-            raise InvalidModelException(f"Unknown LoRA type")
+            raise InvalidModelException(f"Unknown LoRA type: {self.checkpoint_path}")
 
 
 class TextualInversionCheckpointProbe(CheckpointProbeBase):

invokeai/backend/model_management/models/lora.py

@@ -1,18 +1,21 @@
+import bisect
 import os
-import torch
 from enum import Enum
-from typing import Optional, Dict, Union, Literal, Any
 from pathlib import Path
+from typing import Dict, Optional, Union
+
+import torch
 from safetensors.torch import load_file
+
 from .base import (
+    BaseModelType,
+    InvalidModelException,
     ModelBase,
     ModelConfigBase,
-    BaseModelType,
+    ModelNotFoundException,
     ModelType,
     SubModelType,
     classproperty,
-    InvalidModelException,
-    ModelNotFoundException,
 )
 
 
@@ -122,41 +125,7 @@ class LoRALayerBase:
         self.rank = None  # set in layer implementation
         self.layer_key = layer_key
 
-    def forward(
+    def get_weight(self, orig_weight: torch.Tensor):
-        self,
-        module: torch.nn.Module,
-        input_h: Any,  # for real looks like Tuple[torch.nn.Tensor] but not sure
-        multiplier: float,
-    ):
-        if type(module) == torch.nn.Conv2d:
-            op = torch.nn.functional.conv2d
-            extra_args = dict(
-                stride=module.stride,
-                padding=module.padding,
-                dilation=module.dilation,
-                groups=module.groups,
-            )
-
-        else:
-            op = torch.nn.functional.linear
-            extra_args = {}
-
-        weight = self.get_weight()
-
-        bias = self.bias if self.bias is not None else 0
-        scale = self.alpha / self.rank if (self.alpha and self.rank) else 1.0
-        return (
-            op(
-                *input_h,
-                (weight + bias).view(module.weight.shape),
-                None,
-                **extra_args,
-            )
-            * multiplier
-            * scale
-        )
-
-    def get_weight(self):
         raise NotImplementedError()
 
     def calc_size(self) -> int:
@@ -197,7 +166,7 @@ class LoRALayer(LoRALayerBase):
 
         self.rank = self.down.shape[0]
 
-    def get_weight(self):
+    def get_weight(self, orig_weight: torch.Tensor):
         if self.mid is not None:
             up = self.up.reshape(self.up.shape[0], self.up.shape[1])
             down = self.down.reshape(self.down.shape[0], self.down.shape[1])
@@ -260,7 +229,7 @@ class LoHALayer(LoRALayerBase):
 
         self.rank = self.w1_b.shape[0]
 
-    def get_weight(self):
+    def get_weight(self, orig_weight: torch.Tensor):
         if self.t1 is None:
             weight = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
 
@@ -342,7 +311,7 @@ class LoKRLayer(LoRALayerBase):
         else:
             self.rank = None  # unscaled
 
-    def get_weight(self):
+    def get_weight(self, orig_weight: torch.Tensor):
         w1 = self.w1
         if w1 is None:
             w1 = self.w1_a @ self.w1_b
@@ -410,7 +379,7 @@ class FullLayer(LoRALayerBase):
 
         self.rank = None  # unscaled
 
-    def get_weight(self):
+    def get_weight(self, orig_weight: torch.Tensor):
         return self.weight
 
     def calc_size(self) -> int:
@@ -428,6 +397,45 @@ class FullLayer(LoRALayerBase):
         self.weight = self.weight.to(device=device, dtype=dtype)
 
 
+class IA3Layer(LoRALayerBase):
+    # weight: torch.Tensor
+    # on_input: torch.Tensor
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: dict,
+    ):
+        super().__init__(layer_key, values)
+
+        self.weight = values["weight"]
+        self.on_input = values["on_input"]
+
+        self.rank = None  # unscaled
+
+    def get_weight(self, orig_weight: torch.Tensor):
+        weight = self.weight
+        if not self.on_input:
+            weight = weight.reshape(-1, 1)
+        return orig_weight * weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        model_size += self.weight.nelement() * self.weight.element_size()
+        model_size += self.on_input.nelement() * self.on_input.element_size()
+        return model_size
+
+    def to(
+        self,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        super().to(device=device, dtype=dtype)
+
+        self.weight = self.weight.to(device=device, dtype=dtype)
+        self.on_input = self.on_input.to(device=device, dtype=dtype)
+
+
 # TODO: rename all methods used in model logic with Info postfix and remove here Raw postfix
 class LoRAModelRaw:  # (torch.nn.Module):
     _name: str
@@ -477,30 +485,61 @@ class LoRAModelRaw:  # (torch.nn.Module):
         return model_size
 
     @classmethod
-    def _convert_sdxl_compvis_keys(cls, state_dict):
+    def _convert_sdxl_keys_to_diffusers_format(cls, state_dict):
+        """Convert the keys of an SDXL LoRA state_dict to diffusers format.
+
+        The input state_dict can be in either Stability AI format or diffusers format. If the state_dict is already in
+        diffusers format, then this function will have no effect.
+
+        This function is adapted from:
+        https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L385-L409
+
+        Args:
+            state_dict (Dict[str, Tensor]): The SDXL LoRA state_dict.
+
+        Raises:
+            ValueError: If state_dict contains an unrecognized key, or not all keys could be converted.
+
+        Returns:
+            Dict[str, Tensor]: The diffusers-format state_dict.
+        """
+        converted_count = 0  # The number of Stability AI keys converted to diffusers format.
+        not_converted_count = 0  # The number of keys that were not converted.
+
+        # Get a sorted list of Stability AI UNet keys so that we can efficiently search for keys with matching prefixes.
+        # For example, we want to efficiently find `input_blocks_4_1` in the list when searching for
+        # `input_blocks_4_1_proj_in`.
+        stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP)
+        stability_unet_keys.sort()
+
         new_state_dict = dict()
         for full_key, value in state_dict.items():
-            if full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
+            if full_key.startswith("lora_unet_"):
-                continue  # clip same
+                search_key = full_key.replace("lora_unet_", "")
-
+                # Use bisect to find the key in stability_unet_keys that *may* match the search_key's prefix.
-            if not full_key.startswith("lora_unet_"):
+                position = bisect.bisect_right(stability_unet_keys, search_key)
-                raise NotImplementedError(f"Unknown prefix for sdxl lora key - {full_key}")
+                map_key = stability_unet_keys[position - 1]
-            src_key = full_key.replace("lora_unet_", "")
+                # Now, check if the map_key *actually* matches the search_key.
-            try:
+                if search_key.startswith(map_key):
-                dst_key = None
+                    new_key = full_key.replace(map_key, SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP[map_key])
-                while "_" in src_key:
-                    if src_key in SDXL_UNET_COMPVIS_MAP:
-                        dst_key = SDXL_UNET_COMPVIS_MAP[src_key]
-                        break
-                    src_key = "_".join(src_key.split("_")[:-1])
-
-                if dst_key is None:
-                    raise Exception(f"Unknown sdxl lora key - {full_key}")
-                new_key = full_key.replace(src_key, dst_key)
-            except:
-                print(SDXL_UNET_COMPVIS_MAP)
-                raise
                     new_state_dict[new_key] = value
+                    converted_count += 1
+                else:
+                    new_state_dict[full_key] = value
+                    not_converted_count += 1
+            elif full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
+                # The CLIP text encoders have the same keys in both Stability AI and diffusers formats.
+                new_state_dict[full_key] = value
+                continue
+            else:
+                raise ValueError(f"Unrecognized SDXL LoRA key prefix: '{full_key}'.")
+
+        if converted_count > 0 and not_converted_count > 0:
+            raise ValueError(
+                f"The SDXL LoRA could only be partially converted to diffusers format. converted={converted_count},"
+                f" not_converted={not_converted_count}"
+            )
+
         return new_state_dict
 
     @classmethod
@@ -532,7 +571,7 @@ class LoRAModelRaw:  # (torch.nn.Module):
         state_dict = cls._group_state(state_dict)
 
         if base_model == BaseModelType.StableDiffusionXL:
-            state_dict = cls._convert_sdxl_compvis_keys(state_dict)
+            state_dict = cls._convert_sdxl_keys_to_diffusers_format(state_dict)
 
         for layer_key, values in state_dict.items():
             # lora and locon
@@ -547,11 +586,15 @@ class LoRAModelRaw:  # (torch.nn.Module):
             elif "lokr_w1_b" in values or "lokr_w1" in values:
                 layer = LoKRLayer(layer_key, values)
 
+            # diff
             elif "diff" in values:
                 layer = FullLayer(layer_key, values)
 
+            # ia3
+            elif "weight" in values and "on_input" in values:
+                layer = IA3Layer(layer_key, values)
+
             else:
-                # TODO: ia3/... format
                 print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}")
                 raise Exception("Unknown lora format!")
 
@@ -579,6 +622,7 @@ class LoRAModelRaw:  # (torch.nn.Module):
 # code from
 # https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32
 def make_sdxl_unet_conversion_map():
+    """Create a dict mapping state_dict keys from Stability AI SDXL format to diffusers SDXL format."""
     unet_conversion_map_layer = []
 
     for i in range(3):  # num_blocks is 3 in sdxl
@@ -662,7 +706,6 @@ def make_sdxl_unet_conversion_map():
     return unet_conversion_map
 
 
-SDXL_UNET_COMPVIS_MAP = {
+SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP = {
-    f"{sd}".rstrip(".").replace(".", "_"): f"{hf}".rstrip(".").replace(".", "_")
+    sd.rstrip(".").replace(".", "_"): hf.rstrip(".").replace(".", "_") for sd, hf in make_sdxl_unet_conversion_map()
-    for sd, hf in make_sdxl_unet_conversion_map()
 }

invokeai/backend/model_management/models/sdxl.py

@@ -1,6 +1,5 @@
 import os
 import json
-import invokeai.backend.util.logging as logger
 from enum import Enum
 from pydantic import Field
 from typing import Literal, Optional
@@ -12,6 +11,7 @@ from .base import (
     DiffusersModel,
     read_checkpoint_meta,
     classproperty,
+    InvalidModelException,
 )
 from omegaconf import OmegaConf
 
@@ -65,7 +65,7 @@ class StableDiffusionXLModel(DiffusersModel):
                 in_channels = unet_config["in_channels"]
 
             else:
-                raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
+                raise InvalidModelException(f"{path} is not a recognized Stable Diffusion diffusers model")
 
         else:
             raise NotImplementedError(f"Unknown stable diffusion 2.* format: {model_format}")
@@ -80,8 +80,10 @@ class StableDiffusionXLModel(DiffusersModel):
             raise Exception("Unkown stable diffusion 2.* model format")
 
         if ckpt_config_path is None:
-            # TO DO: implement picking
+            # avoid circular import
-            pass
+            from .stable_diffusion import _select_ckpt_config
+
+            ckpt_config_path = _select_ckpt_config(kwargs.get("model_base", BaseModelType.StableDiffusionXL), variant)
 
         return cls.create_config(
             path=path,

invokeai/backend/model_management/models/vae.py

@@ -1,9 +1,14 @@
 import os
-import torch
-import safetensors
 from enum import Enum
 from pathlib import Path
-from typing import Optional, Union, Literal
+from typing import Optional
+
+import safetensors
+import torch
+from diffusers.utils import is_safetensors_available
+from omegaconf import OmegaConf
+
+from invokeai.app.services.config import InvokeAIAppConfig
 from .base import (
     ModelBase,
     ModelConfigBase,
@@ -18,9 +23,6 @@ from .base import (
     InvalidModelException,
     ModelNotFoundException,
 )
-from invokeai.app.services.config import InvokeAIAppConfig
-from diffusers.utils import is_safetensors_available
-from omegaconf import OmegaConf
 
 
 class VaeModelFormat(str, Enum):
@@ -80,7 +82,7 @@ class VaeModel(ModelBase):
     @classmethod
     def detect_format(cls, path: str):
         if not os.path.exists(path):
-            raise ModelNotFoundException()
+            raise ModelNotFoundException(f"Does not exist as local file: {path}")
 
         if os.path.isdir(path):
             if os.path.exists(os.path.join(path, "config.json")):

invokeai/backend/model_management/util.py

@@ -0,0 +1,75 @@
+# Copyright (c) 2023 The InvokeAI Development Team
+"""Utilities used by the Model Manager"""
+
+
+def lora_token_vector_length(checkpoint: dict) -> int:
+    """
+    Given a checkpoint in memory, return the lora token vector length
+
+    :param checkpoint: The checkpoint
+    """
+
+    def _get_shape_1(key, tensor, checkpoint):
+        lora_token_vector_length = None
+
+        if "." not in key:
+            return lora_token_vector_length  # wrong key format
+        model_key, lora_key = key.split(".", 1)
+
+        # check lora/locon
+        if lora_key == "lora_down.weight":
+            lora_token_vector_length = tensor.shape[1]
+
+        # check loha (don't worry about hada_t1/hada_t2 as it used only in 4d shapes)
+        elif lora_key in ["hada_w1_b", "hada_w2_b"]:
+            lora_token_vector_length = tensor.shape[1]
+
+        # check lokr (don't worry about lokr_t2 as it used only in 4d shapes)
+        elif "lokr_" in lora_key:
+            if model_key + ".lokr_w1" in checkpoint:
+                _lokr_w1 = checkpoint[model_key + ".lokr_w1"]
+            elif model_key + "lokr_w1_b" in checkpoint:
+                _lokr_w1 = checkpoint[model_key + ".lokr_w1_b"]
+            else:
+                return lora_token_vector_length  # unknown format
+
+            if model_key + ".lokr_w2" in checkpoint:
+                _lokr_w2 = checkpoint[model_key + ".lokr_w2"]
+            elif model_key + "lokr_w2_b" in checkpoint:
+                _lokr_w2 = checkpoint[model_key + ".lokr_w2_b"]
+            else:
+                return lora_token_vector_length  # unknown format
+
+            lora_token_vector_length = _lokr_w1.shape[1] * _lokr_w2.shape[1]
+
+        elif lora_key == "diff":
+            lora_token_vector_length = tensor.shape[1]
+
+        # ia3 can be detected only by shape[0] in text encoder
+        elif lora_key == "weight" and "lora_unet_" not in model_key:
+            lora_token_vector_length = tensor.shape[0]
+
+        return lora_token_vector_length
+
+    lora_token_vector_length = None
+    lora_te1_length = None
+    lora_te2_length = None
+    for key, tensor in checkpoint.items():
+        if key.startswith("lora_unet_") and ("_attn2_to_k." in key or "_attn2_to_v." in key):
+            lora_token_vector_length = _get_shape_1(key, tensor, checkpoint)
+        elif key.startswith("lora_te") and "_self_attn_" in key:
+            tmp_length = _get_shape_1(key, tensor, checkpoint)
+            if key.startswith("lora_te_"):
+                lora_token_vector_length = tmp_length
+            elif key.startswith("lora_te1_"):
+                lora_te1_length = tmp_length
+            elif key.startswith("lora_te2_"):
+                lora_te2_length = tmp_length
+
+        if lora_te1_length is not None and lora_te2_length is not None:
+            lora_token_vector_length = lora_te1_length + lora_te2_length
+
+        if lora_token_vector_length is not None:
+            break
+
+    return lora_token_vector_length

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -4,25 +4,21 @@ import dataclasses
 import inspect
 import math
 import secrets
-from collections.abc import Sequence
 from dataclasses import dataclass, field
 from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
-from pydantic import Field
 
-import einops
 import PIL.Image
-import numpy as np
+import einops
-from accelerate.utils import set_seed
 import psutil
 import torch
 import torchvision.transforms as T
+from accelerate.utils import set_seed
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.models.controlnet import ControlNetModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
     StableDiffusionPipeline,
 )
-
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import (
     StableDiffusionImg2ImgPipeline,
 )
@@ -31,21 +27,20 @@ from diffusers.pipelines.stable_diffusion.safety_checker import (
 )
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
-from diffusers.utils import PIL_INTERPOLATION
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.outputs import BaseOutput
+from pydantic import Field
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from typing_extensions import ParamSpec
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from ..util import CPU_DEVICE, normalize_device
 from .diffusion import (
     AttentionMapSaver,
     InvokeAIDiffuserComponent,
     PostprocessingSettings,
 )
-from .offloading import FullyLoadedModelGroup, ModelGroup
+from ..util import normalize_device
 
 
 @dataclass
@@ -289,8 +284,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
-    _model_group: ModelGroup
-
     ID_LENGTH = 8
 
     def __init__(
@@ -303,9 +296,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         safety_checker: Optional[StableDiffusionSafetyChecker],
         feature_extractor: Optional[CLIPFeatureExtractor],
         requires_safety_checker: bool = False,
-        precision: str = "float32",
         control_model: ControlNetModel = None,
-        execution_device: Optional[torch.device] = None,
     ):
         super().__init__(
             vae,
@@ -330,9 +321,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             # control_model=control_model,
         )
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
-
-        self._model_group = FullyLoadedModelGroup(execution_device or self.unet.device)
-        self._model_group.install(*self._submodels)
         self.control_model = control_model
 
     def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
@@ -368,72 +356,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             else:
                 self.disable_attention_slicing()
 
-    def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings=False):
-        # overridden method; types match the superclass.
-        if torch_device is None:
-            return self
-        self._model_group.set_device(torch.device(torch_device))
-        self._model_group.ready()
-
-    @property
-    def device(self) -> torch.device:
-        return self._model_group.execution_device
-
-    @property
-    def _submodels(self) -> Sequence[torch.nn.Module]:
-        module_names, _, _ = self.extract_init_dict(dict(self.config))
-        submodels = []
-        for name in module_names.keys():
-            if hasattr(self, name):
-                value = getattr(self, name)
-            else:
-                value = getattr(self.config, name)
-            if isinstance(value, torch.nn.Module):
-                submodels.append(value)
-        return submodels
-
-    def image_from_embeddings(
-        self,
-        latents: torch.Tensor,
-        num_inference_steps: int,
-        conditioning_data: ConditioningData,
-        *,
-        noise: torch.Tensor,
-        callback: Callable[[PipelineIntermediateState], None] = None,
-        run_id=None,
-    ) -> InvokeAIStableDiffusionPipelineOutput:
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        :param conditioning_data:
-        :param latents: Pre-generated un-noised latents, to be used as inputs for
-            image generation. Can be used to tweak the same generation with different prompts.
-        :param num_inference_steps: The number of denoising steps. More denoising steps usually lead to a higher quality
-            image at the expense of slower inference.
-        :param noise: Noise to add to the latents, sampled from a Gaussian distribution.
-        :param callback:
-        :param run_id:
-        """
-        result_latents, result_attention_map_saver = self.latents_from_embeddings(
-            latents,
-            num_inference_steps,
-            conditioning_data,
-            noise=noise,
-            run_id=run_id,
-            callback=callback,
-        )
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        with torch.inference_mode():
-            image = self.decode_latents(result_latents)
-            output = InvokeAIStableDiffusionPipelineOutput(
-                images=image,
-                nsfw_content_detected=[],
-                attention_map_saver=result_attention_map_saver,
-            )
-            return self.check_for_safety(output, dtype=conditioning_data.dtype)
-
     def latents_from_embeddings(
         self,
         latents: torch.Tensor,
@@ -450,7 +372,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if self.scheduler.config.get("cpu_only", False):
             scheduler_device = torch.device("cpu")
         else:
-            scheduler_device = self._model_group.device_for(self.unet)
+            scheduler_device = self.unet.device
 
         if timesteps is None:
             self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
@@ -504,7 +426,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 (batch_size,),
                 timesteps[0],
                 dtype=timesteps.dtype,
-                device=self._model_group.device_for(self.unet),
+                device=self.unet.device,
             )
             latents = self.scheduler.add_noise(latents, noise, batched_t)
 
@@ -700,79 +622,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             **kwargs,
         ).sample
 
-    def img2img_from_embeddings(
-        self,
-        init_image: Union[torch.FloatTensor, PIL.Image.Image],
-        strength: float,
-        num_inference_steps: int,
-        conditioning_data: ConditioningData,
-        *,
-        callback: Callable[[PipelineIntermediateState], None] = None,
-        run_id=None,
-        noise_func=None,
-        seed=None,
-    ) -> InvokeAIStableDiffusionPipelineOutput:
-        if isinstance(init_image, PIL.Image.Image):
-            init_image = image_resized_to_grid_as_tensor(init_image.convert("RGB"))
-
-        if init_image.dim() == 3:
-            init_image = einops.rearrange(init_image, "c h w -> 1 c h w")
-
-        # 6. Prepare latent variables
-        initial_latents = self.non_noised_latents_from_image(
-            init_image,
-            device=self._model_group.device_for(self.unet),
-            dtype=self.unet.dtype,
-        )
-        if seed is not None:
-            set_seed(seed)
-        noise = noise_func(initial_latents)
-
-        return self.img2img_from_latents_and_embeddings(
-            initial_latents,
-            num_inference_steps,
-            conditioning_data,
-            strength,
-            noise,
-            run_id,
-            callback,
-        )
-
-    def img2img_from_latents_and_embeddings(
-        self,
-        initial_latents,
-        num_inference_steps,
-        conditioning_data: ConditioningData,
-        strength,
-        noise: torch.Tensor,
-        run_id=None,
-        callback=None,
-    ) -> InvokeAIStableDiffusionPipelineOutput:
-        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
-        result_latents, result_attention_maps = self.latents_from_embeddings(
-            latents=initial_latents
-            if strength < 1.0
-            else torch.zeros_like(initial_latents, device=initial_latents.device, dtype=initial_latents.dtype),
-            num_inference_steps=num_inference_steps,
-            conditioning_data=conditioning_data,
-            timesteps=timesteps,
-            noise=noise,
-            run_id=run_id,
-            callback=callback,
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        with torch.inference_mode():
-            image = self.decode_latents(result_latents)
-            output = InvokeAIStableDiffusionPipelineOutput(
-                images=image,
-                nsfw_content_detected=[],
-                attention_map_saver=result_attention_maps,
-            )
-            return self.check_for_safety(output, dtype=conditioning_data.dtype)
-
     def get_img2img_timesteps(self, num_inference_steps: int, strength: float, device=None) -> (torch.Tensor, int):
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         assert img2img_pipeline.scheduler is self.scheduler
@@ -780,7 +629,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if self.scheduler.config.get("cpu_only", False):
             scheduler_device = torch.device("cpu")
         else:
-            scheduler_device = self._model_group.device_for(self.unet)
+            scheduler_device = self.unet.device
 
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
         timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
@@ -806,7 +655,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         noise_func=None,
         seed=None,
     ) -> InvokeAIStableDiffusionPipelineOutput:
-        device = self._model_group.device_for(self.unet)
+        device = self.unet.device
         latents_dtype = self.unet.dtype
 
         if isinstance(init_image, PIL.Image.Image):
@@ -877,42 +726,17 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 nsfw_content_detected=[],
                 attention_map_saver=result_attention_maps,
             )
-            return self.check_for_safety(output, dtype=conditioning_data.dtype)
+            return output
 
     def non_noised_latents_from_image(self, init_image, *, device: torch.device, dtype):
         init_image = init_image.to(device=device, dtype=dtype)
         with torch.inference_mode():
-            self._model_group.load(self.vae)
             init_latent_dist = self.vae.encode(init_image).latent_dist
             init_latents = init_latent_dist.sample().to(dtype=dtype)  # FIXME: uses torch.randn. make reproducible!
 
         init_latents = 0.18215 * init_latents
         return init_latents
 
-    def check_for_safety(self, output, dtype):
-        with torch.inference_mode():
-            screened_images, has_nsfw_concept = self.run_safety_checker(output.images, dtype=dtype)
-        screened_attention_map_saver = None
-        if has_nsfw_concept is None or not has_nsfw_concept:
-            screened_attention_map_saver = output.attention_map_saver
-        return InvokeAIStableDiffusionPipelineOutput(
-            screened_images,
-            has_nsfw_concept,
-            # block the attention maps if NSFW content is detected
-            attention_map_saver=screened_attention_map_saver,
-        )
-
-    def run_safety_checker(self, image, device=None, dtype=None):
-        # overriding to use the model group for device info instead of requiring the caller to know.
-        if self.safety_checker is not None:
-            device = self._model_group.device_for(self.safety_checker)
-        return super().run_safety_checker(image, device, dtype)
-
-    def decode_latents(self, latents):
-        # Explicit call to get the vae loaded, since `decode` isn't the forward method.
-        self._model_group.load(self.vae)
-        return super().decode_latents(latents)
-
     def debug_latents(self, latents, msg):
         from invokeai.backend.image_util import debug_image
 

invokeai/backend/stable_diffusion/offloading.py

@@ -1,253 +0,0 @@
-from __future__ import annotations
-
-import warnings
-import weakref
-from abc import ABCMeta, abstractmethod
-from collections.abc import MutableMapping
-from typing import Callable, Union
-
-import torch
-from accelerate.utils import send_to_device
-from torch.utils.hooks import RemovableHandle
-
-OFFLOAD_DEVICE = torch.device("cpu")
-
-
-class _NoModel:
-    """Symbol that indicates no model is loaded.
-
-    (We can't weakref.ref(None), so this was my best idea at the time to come up with something
-    type-checkable.)
-    """
-
-    def __bool__(self):
-        return False
-
-    def to(self, device: torch.device):
-        pass
-
-    def __repr__(self):
-        return "<NO MODEL>"
-
-
-NO_MODEL = _NoModel()
-
-
-class ModelGroup(metaclass=ABCMeta):
-    """
-    A group of models.
-
-    The use case I had in mind when writing this is the sub-models used by a DiffusionPipeline,
-    e.g. its text encoder, U-net, VAE, etc.
-
-    Those models are :py:class:`diffusers.ModelMixin`, but "model" is interchangeable with
-    :py:class:`torch.nn.Module` here.
-    """
-
-    def __init__(self, execution_device: torch.device):
-        self.execution_device = execution_device
-
-    @abstractmethod
-    def install(self, *models: torch.nn.Module):
-        """Add models to this group."""
-        pass
-
-    @abstractmethod
-    def uninstall(self, models: torch.nn.Module):
-        """Remove models from this group."""
-        pass
-
-    @abstractmethod
-    def uninstall_all(self):
-        """Remove all models from this group."""
-
-    @abstractmethod
-    def load(self, model: torch.nn.Module):
-        """Load this model to the execution device."""
-        pass
-
-    @abstractmethod
-    def offload_current(self):
-        """Offload the current model(s) from the execution device."""
-        pass
-
-    @abstractmethod
-    def ready(self):
-        """Ready this group for use."""
-        pass
-
-    @abstractmethod
-    def set_device(self, device: torch.device):
-        """Change which device models from this group will execute on."""
-        pass
-
-    @abstractmethod
-    def device_for(self, model) -> torch.device:
-        """Get the device the given model will execute on.
-
-        The model should already be a member of this group.
-        """
-        pass
-
-    @abstractmethod
-    def __contains__(self, model):
-        """Check if the model is a member of this group."""
-        pass
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} object at {id(self):x}: " f"device={self.execution_device} >"
-
-
-class LazilyLoadedModelGroup(ModelGroup):
-    """
-    Only one model from this group is loaded on the GPU at a time.
-
-    Running the forward method of a model will displace the previously-loaded model,
-    offloading it to CPU.
-
-    If you call other methods on the model, e.g. ``model.encode(x)`` instead of ``model(x)``,
-    you will need to explicitly load it with :py:method:`.load(model)`.
-
-    This implementation relies on pytorch forward-pre-hooks, and it will copy forward arguments
-    to the appropriate execution device, as long as they are positional arguments and not keyword
-    arguments. (I didn't make the rules; that's the way the pytorch 1.13 API works for hooks.)
-    """
-
-    _hooks: MutableMapping[torch.nn.Module, RemovableHandle]
-    _current_model_ref: Callable[[], Union[torch.nn.Module, _NoModel]]
-
-    def __init__(self, execution_device: torch.device):
-        super().__init__(execution_device)
-        self._hooks = weakref.WeakKeyDictionary()
-        self._current_model_ref = weakref.ref(NO_MODEL)
-
-    def install(self, *models: torch.nn.Module):
-        for model in models:
-            self._hooks[model] = model.register_forward_pre_hook(self._pre_hook)
-
-    def uninstall(self, *models: torch.nn.Module):
-        for model in models:
-            hook = self._hooks.pop(model)
-            hook.remove()
-            if self.is_current_model(model):
-                # no longer hooked by this object, so don't claim to manage it
-                self.clear_current_model()
-
-    def uninstall_all(self):
-        self.uninstall(*self._hooks.keys())
-
-    def _pre_hook(self, module: torch.nn.Module, forward_input):
-        self.load(module)
-        if len(forward_input) == 0:
-            warnings.warn(
-                f"Hook for {module.__class__.__name__} got no input. " f"Inputs must be positional, not keywords.",
-                stacklevel=3,
-            )
-        return send_to_device(forward_input, self.execution_device)
-
-    def load(self, module):
-        if not self.is_current_model(module):
-            self.offload_current()
-            self._load(module)
-
-    def offload_current(self):
-        module = self._current_model_ref()
-        if module is not NO_MODEL:
-            module.to(OFFLOAD_DEVICE)
-        self.clear_current_model()
-
-    def _load(self, module: torch.nn.Module) -> torch.nn.Module:
-        assert self.is_empty(), f"A model is already loaded: {self._current_model_ref()}"
-        module = module.to(self.execution_device)
-        self.set_current_model(module)
-        return module
-
-    def is_current_model(self, model: torch.nn.Module) -> bool:
-        """Is the given model the one currently loaded on the execution device?"""
-        return self._current_model_ref() is model
-
-    def is_empty(self):
-        """Are none of this group's models loaded on the execution device?"""
-        return self._current_model_ref() is NO_MODEL
-
-    def set_current_model(self, value):
-        self._current_model_ref = weakref.ref(value)
-
-    def clear_current_model(self):
-        self._current_model_ref = weakref.ref(NO_MODEL)
-
-    def set_device(self, device: torch.device):
-        if device == self.execution_device:
-            return
-        self.execution_device = device
-        current = self._current_model_ref()
-        if current is not NO_MODEL:
-            current.to(device)
-
-    def device_for(self, model):
-        if model not in self:
-            raise KeyError(f"This does not manage this model {type(model).__name__}", model)
-        return self.execution_device  # this implementation only dispatches to one device
-
-    def ready(self):
-        pass  # always ready to load on-demand
-
-    def __contains__(self, model):
-        return model in self._hooks
-
-    def __repr__(self) -> str:
-        return (
-            f"<{self.__class__.__name__} object at {id(self):x}: "
-            f"current_model={type(self._current_model_ref()).__name__} >"
-        )
-
-
-class FullyLoadedModelGroup(ModelGroup):
-    """
-    A group of models without any implicit loading or unloading.
-
-    :py:meth:`.ready` loads _all_ the models to the execution device at once.
-    """
-
-    _models: weakref.WeakSet
-
-    def __init__(self, execution_device: torch.device):
-        super().__init__(execution_device)
-        self._models = weakref.WeakSet()
-
-    def install(self, *models: torch.nn.Module):
-        for model in models:
-            self._models.add(model)
-            model.to(self.execution_device)
-
-    def uninstall(self, *models: torch.nn.Module):
-        for model in models:
-            self._models.remove(model)
-
-    def uninstall_all(self):
-        self.uninstall(*self._models)
-
-    def load(self, model):
-        model.to(self.execution_device)
-
-    def offload_current(self):
-        for model in self._models:
-            model.to(OFFLOAD_DEVICE)
-
-    def ready(self):
-        for model in self._models:
-            self.load(model)
-
-    def set_device(self, device: torch.device):
-        self.execution_device = device
-        for model in self._models:
-            if model.device != OFFLOAD_DEVICE:
-                model.to(device)
-
-    def device_for(self, model):
-        if model not in self:
-            raise KeyError("This does not manage this model f{type(model).__name__}", model)
-        return self.execution_device  # this implementation only dispatches to one device
-
-    def __contains__(self, model):
-        return model in self._models

invokeai/frontend/install/__init__.py

@@ -1,6 +1,3 @@
 """
 Initialization file for invokeai.frontend.config
 """
-from .invokeai_configure import main as invokeai_configure
-from .invokeai_update import main as invokeai_update
-from .model_install import main as invokeai_model_install

invokeai/frontend/install/import_images.py

@@ -0,0 +1,795 @@
+# Copyright (c) 2023 - The InvokeAI Team
+# Primary Author: David Lovell (github @f412design, discord @techjedi)
+# co-author, minor tweaks - Lincoln Stein
+
+# pylint: disable=line-too-long
+# pylint: disable=broad-exception-caught
+"""Script to import images into the new database system for 3.0.0"""
+
+import os
+import datetime
+import shutil
+import locale
+import sqlite3
+import json
+import glob
+import re
+import uuid
+import yaml
+import PIL
+import PIL.ImageOps
+import PIL.PngImagePlugin
+
+from pathlib import Path
+from prompt_toolkit import prompt
+from prompt_toolkit.shortcuts import message_dialog
+from prompt_toolkit.completion import PathCompleter
+from prompt_toolkit.key_binding import KeyBindings
+
+from invokeai.app.services.config import InvokeAIAppConfig
+
+app_config = InvokeAIAppConfig.get_config()
+
+bindings = KeyBindings()
+
+
+@bindings.add("c-c")
+def _(event):
+    raise KeyboardInterrupt
+
+
+# release notes
+# "Use All" with size dimensions not selectable in the UI will not load dimensions
+
+
+class Config:
+    """Configuration loader."""
+
+    def __init__(self):
+        pass
+
+    TIMESTAMP_STRING = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%SZ")
+
+    INVOKE_DIRNAME = "invokeai"
+    YAML_FILENAME = "invokeai.yaml"
+    DATABASE_FILENAME = "invokeai.db"
+
+    database_path = None
+    database_backup_dir = None
+    outputs_path = None
+    thumbnail_path = None
+
+    def find_and_load(self):
+        """find the yaml config file and load"""
+        root = app_config.root_path
+        if not self.confirm_and_load(os.path.abspath(root)):
+            print("\r\nSpecify custom database and outputs paths:")
+            self.confirm_and_load_from_user()
+
+        self.database_backup_dir = os.path.join(os.path.dirname(self.database_path), "backup")
+        self.thumbnail_path = os.path.join(self.outputs_path, "thumbnails")
+
+    def confirm_and_load(self, invoke_root):
+        """Validates a yaml path exists, confirms the user wants to use it and loads config."""
+        yaml_path = os.path.join(invoke_root, self.YAML_FILENAME)
+        if os.path.exists(yaml_path):
+            db_dir, outdir = self.load_paths_from_yaml(yaml_path)
+            if os.path.isabs(db_dir):
+                database_path = os.path.join(db_dir, self.DATABASE_FILENAME)
+            else:
+                database_path = os.path.join(invoke_root, db_dir, self.DATABASE_FILENAME)
+
+            if os.path.isabs(outdir):
+                outputs_path = os.path.join(outdir, "images")
+            else:
+                outputs_path = os.path.join(invoke_root, outdir, "images")
+
+            db_exists = os.path.exists(database_path)
+            outdir_exists = os.path.exists(outputs_path)
+
+            text = f"Found {self.YAML_FILENAME} file at {yaml_path}:"
+            text += f"\n  Database : {database_path}"
+            text += f"\n  Outputs  : {outputs_path}"
+            text += "\n\nUse these paths for import (yes) or choose different ones (no) [Yn]: "
+
+            if db_exists and outdir_exists:
+                if (prompt(text).strip() or "Y").upper().startswith("Y"):
+                    self.database_path = database_path
+                    self.outputs_path = outputs_path
+                    return True
+                else:
+                    return False
+            else:
+                print("  Invalid: One or more paths in this config did not exist and cannot be used.")
+
+        else:
+            message_dialog(
+                title="Path not found",
+                text=f"Auto-discovery of configuration failed! Could not find ({yaml_path}), Custom paths can be specified.",
+            ).run()
+            return False
+
+    def confirm_and_load_from_user(self):
+        default = ""
+        while True:
+            database_path = os.path.expanduser(
+                prompt(
+                    "Database: Specify absolute path to the database to import into: ",
+                    completer=PathCompleter(
+                        expanduser=True, file_filter=lambda x: Path(x).is_dir() or x.endswith((".db"))
+                    ),
+                    default=default,
+                )
+            )
+            if database_path.endswith(".db") and os.path.isabs(database_path) and os.path.exists(database_path):
+                break
+            default = database_path + "/" if Path(database_path).is_dir() else database_path
+
+        default = ""
+        while True:
+            outputs_path = os.path.expanduser(
+                prompt(
+                    "Outputs: Specify absolute path to outputs/images directory to import into: ",
+                    completer=PathCompleter(expanduser=True, only_directories=True),
+                    default=default,
+                )
+            )
+
+            if outputs_path.endswith("images") and os.path.isabs(outputs_path) and os.path.exists(outputs_path):
+                break
+            default = outputs_path + "/" if Path(outputs_path).is_dir() else outputs_path
+
+        self.database_path = database_path
+        self.outputs_path = outputs_path
+
+        return
+
+    def load_paths_from_yaml(self, yaml_path):
+        """Load an Invoke AI yaml file and get the database and outputs paths."""
+        try:
+            with open(yaml_path, "rt", encoding=locale.getpreferredencoding()) as file:
+                yamlinfo = yaml.safe_load(file)
+                db_dir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("db_dir", None)
+                outdir = yamlinfo.get("InvokeAI", {}).get("Paths", {}).get("outdir", None)
+                return db_dir, outdir
+        except Exception:
+            print(f"Failed to load paths from yaml file! {yaml_path}!")
+            return None, None
+
+
+class ImportStats:
+    """DTO for tracking work progress."""
+
+    def __init__(self):
+        pass
+
+    time_start = datetime.datetime.utcnow()
+    count_source_files = 0
+    count_skipped_file_exists = 0
+    count_skipped_db_exists = 0
+    count_imported = 0
+    count_imported_by_version = {}
+    count_file_errors = 0
+
+    @staticmethod
+    def get_elapsed_time_string():
+        """Get a friendly time string for the  time elapsed since processing start."""
+        time_now = datetime.datetime.utcnow()
+        total_seconds = (time_now - ImportStats.time_start).total_seconds()
+        hours = int((total_seconds) / 3600)
+        minutes = int(((total_seconds) % 3600) / 60)
+        seconds = total_seconds % 60
+        out_str = f"{hours} hour(s) -" if hours > 0 else ""
+        out_str += f"{minutes} minute(s) -" if minutes > 0 else ""
+        out_str += f"{seconds:.2f} second(s)"
+        return out_str
+
+
+class InvokeAIMetadata:
+    """DTO for core Invoke AI generation properties parsed from metadata."""
+
+    def __init__(self):
+        pass
+
+    def __str__(self):
+        formatted_str = f"{self.generation_mode}~{self.steps}~{self.cfg_scale}~{self.model_name}~{self.scheduler}~{self.seed}~{self.width}~{self.height}~{self.rand_device}~{self.strength}~{self.init_image}"
+        formatted_str += f"\r\npositive_prompt: {self.positive_prompt}"
+        formatted_str += f"\r\nnegative_prompt: {self.negative_prompt}"
+        return formatted_str
+
+    generation_mode = None
+    steps = None
+    cfg_scale = None
+    model_name = None
+    scheduler = None
+    seed = None
+    width = None
+    height = None
+    rand_device = None
+    strength = None
+    init_image = None
+    positive_prompt = None
+    negative_prompt = None
+    imported_app_version = None
+
+    def to_json(self):
+        """Convert the active instance to json format."""
+        prop_dict = {}
+        prop_dict["generation_mode"] = self.generation_mode
+        # dont render prompt nodes if neither are set to avoid the ui thinking it can set them
+        # if at least one exists, render them both, but use empty string instead of None if one of them is empty
+        # this allows the field that is empty to actually be cleared byt he UI instead of leaving the previous value
+        if self.positive_prompt or self.negative_prompt:
+            prop_dict["positive_prompt"] = "" if self.positive_prompt is None else self.positive_prompt
+            prop_dict["negative_prompt"] = "" if self.negative_prompt is None else self.negative_prompt
+        prop_dict["width"] = self.width
+        prop_dict["height"] = self.height
+        # only render seed if it has a value to avoid ui thinking it can set this and then error
+        if self.seed:
+            prop_dict["seed"] = self.seed
+        prop_dict["rand_device"] = self.rand_device
+        prop_dict["cfg_scale"] = self.cfg_scale
+        prop_dict["steps"] = self.steps
+        prop_dict["scheduler"] = self.scheduler
+        prop_dict["clip_skip"] = 0
+        prop_dict["model"] = {}
+        prop_dict["model"]["model_name"] = self.model_name
+        prop_dict["model"]["base_model"] = None
+        prop_dict["controlnets"] = []
+        prop_dict["loras"] = []
+        prop_dict["vae"] = None
+        prop_dict["strength"] = self.strength
+        prop_dict["init_image"] = self.init_image
+        prop_dict["positive_style_prompt"] = None
+        prop_dict["negative_style_prompt"] = None
+        prop_dict["refiner_model"] = None
+        prop_dict["refiner_cfg_scale"] = None
+        prop_dict["refiner_steps"] = None
+        prop_dict["refiner_scheduler"] = None
+        prop_dict["refiner_aesthetic_store"] = None
+        prop_dict["refiner_start"] = None
+        prop_dict["imported_app_version"] = self.imported_app_version
+
+        return json.dumps(prop_dict)
+
+
+class InvokeAIMetadataParser:
+    """Parses strings with json data  to find Invoke AI core metadata properties."""
+
+    def __init__(self):
+        pass
+
+    def parse_meta_tag_dream(self, dream_string):
+        """Take as input an png metadata json node for the 'dream' field variant from prior to 1.15"""
+        props = InvokeAIMetadata()
+
+        props.imported_app_version = "pre1.15"
+        seed_match = re.search("-S\\s*(\\d+)", dream_string)
+        if seed_match is not None:
+            try:
+                props.seed = int(seed_match[1])
+            except ValueError:
+                props.seed = None
+            raw_prompt = re.sub("(-S\\s*\\d+)", "", dream_string)
+        else:
+            raw_prompt = dream_string
+
+        pos_prompt, neg_prompt = self.split_prompt(raw_prompt)
+
+        props.positive_prompt = pos_prompt
+        props.negative_prompt = neg_prompt
+
+        return props
+
+    def parse_meta_tag_sd_metadata(self, tag_value):
+        """Take as input an png metadata json node for the 'sd-metadata' field variant from 1.15 through 2.3.5 post 2"""
+        props = InvokeAIMetadata()
+
+        props.imported_app_version = tag_value.get("app_version")
+        props.model_name = tag_value.get("model_weights")
+        img_node = tag_value.get("image")
+        if img_node is not None:
+            props.generation_mode = img_node.get("type")
+            props.width = img_node.get("width")
+            props.height = img_node.get("height")
+            props.seed = img_node.get("seed")
+            props.rand_device = "cuda"  # hardcoded since all generations pre 3.0 used cuda random noise instead of cpu
+            props.cfg_scale = img_node.get("cfg_scale")
+            props.steps = img_node.get("steps")
+            props.scheduler = self.map_scheduler(img_node.get("sampler"))
+            props.strength = img_node.get("strength")
+            if props.strength is None:
+                props.strength = img_node.get("strength_steps")  # try second name for this property
+            props.init_image = img_node.get("init_image_path")
+            if props.init_image is None:  # try second name for this property
+                props.init_image = img_node.get("init_img")
+            # remove the path info from init_image so if we move the init image, it will be correctly relative in the new location
+            if props.init_image is not None:
+                props.init_image = os.path.basename(props.init_image)
+            raw_prompt = img_node.get("prompt")
+            if isinstance(raw_prompt, list):
+                raw_prompt = raw_prompt[0].get("prompt")
+
+            props.positive_prompt, props.negative_prompt = self.split_prompt(raw_prompt)
+
+        return props
+
+    def parse_meta_tag_invokeai(self, tag_value):
+        """Take as input an png metadata json node for the 'invokeai' field variant from 3.0.0 beta 1 through 5"""
+        props = InvokeAIMetadata()
+
+        props.imported_app_version = "3.0.0 or later"
+        props.generation_mode = tag_value.get("type")
+        if props.generation_mode is not None:
+            props.generation_mode = props.generation_mode.replace("t2l", "txt2img").replace("l2l", "img2img")
+
+        props.width = tag_value.get("width")
+        props.height = tag_value.get("height")
+        props.seed = tag_value.get("seed")
+        props.cfg_scale = tag_value.get("cfg_scale")
+        props.steps = tag_value.get("steps")
+        props.scheduler = tag_value.get("scheduler")
+        props.strength = tag_value.get("strength")
+        props.positive_prompt = tag_value.get("positive_conditioning")
+        props.negative_prompt = tag_value.get("negative_conditioning")
+
+        return props
+
+    def map_scheduler(self, old_scheduler):
+        """Convert the legacy sampler names to matching 3.0 schedulers"""
+        if old_scheduler is None:
+            return None
+
+        match (old_scheduler):
+            case "ddim":
+                return "ddim"
+            case "plms":
+                return "pnmd"
+            case "k_lms":
+                return "lms"
+            case "k_dpm_2":
+                return "kdpm_2"
+            case "k_dpm_2_a":
+                return "kdpm_2_a"
+            case "dpmpp_2":
+                return "dpmpp_2s"
+            case "k_dpmpp_2":
+                return "dpmpp_2m"
+            case "k_dpmpp_2_a":
+                return None  # invalid, in 2.3.x, selecting this sample would just fallback to last run or plms if new session
+            case "k_euler":
+                return "euler"
+            case "k_euler_a":
+                return "euler_a"
+            case "k_heun":
+                return "heun"
+        return None
+
+    def split_prompt(self, raw_prompt: str):
+        """Split the unified prompt strings by extracting all negative prompt blocks out into the negative prompt."""
+        if raw_prompt is None:
+            return "", ""
+        raw_prompt_search = raw_prompt.replace("\r", "").replace("\n", "")
+        matches = re.findall(r"\[(.+?)\]", raw_prompt_search)
+        if len(matches) > 0:
+            negative_prompt = ""
+            if len(matches) == 1:
+                negative_prompt = matches[0].strip().strip(",")
+            else:
+                for match in matches:
+                    negative_prompt += f"({match.strip().strip(',')})"
+            positive_prompt = re.sub(r"(\[.+?\])", "", raw_prompt_search).strip()
+        else:
+            positive_prompt = raw_prompt_search.strip()
+            negative_prompt = ""
+
+        return positive_prompt, negative_prompt
+
+
+class DatabaseMapper:
+    """Class to abstract database functionality."""
+
+    def __init__(self, database_path, database_backup_dir):
+        self.database_path = database_path
+        self.database_backup_dir = database_backup_dir
+        self.connection = None
+        self.cursor = None
+
+    def connect(self):
+        """Open connection to the database."""
+        self.connection = sqlite3.connect(self.database_path)
+        self.cursor = self.connection.cursor()
+
+    def get_board_names(self):
+        """Get a list of the current board names from the database."""
+        sql_get_board_name = "SELECT board_name FROM boards"
+        self.cursor.execute(sql_get_board_name)
+        rows = self.cursor.fetchall()
+        return [row[0] for row in rows]
+
+    def does_image_exist(self, image_name):
+        """Check database if a image name already exists and return a boolean."""
+        sql_get_image_by_name = f"SELECT image_name FROM images WHERE image_name='{image_name}'"
+        self.cursor.execute(sql_get_image_by_name)
+        rows = self.cursor.fetchall()
+        return True if len(rows) > 0 else False
+
+    def add_new_image_to_database(self, filename, width, height, metadata, modified_date_string):
+        """Add an image to the database."""
+        sql_add_image = f"""INSERT INTO images (image_name, image_origin, image_category, width, height, session_id, node_id, metadata, is_intermediate, created_at, updated_at)
+VALUES ('{filename}', 'internal', 'general', {width}, {height}, null, null, '{metadata}', 0, '{modified_date_string}', '{modified_date_string}')"""
+        self.cursor.execute(sql_add_image)
+        self.connection.commit()
+
+    def get_board_id_with_create(self, board_name):
+        """Get the board id for supplied name, and create the board if one does not exist."""
+        sql_find_board = f"SELECT board_id FROM boards WHERE board_name='{board_name}' COLLATE NOCASE"
+        self.cursor.execute(sql_find_board)
+        rows = self.cursor.fetchall()
+        if len(rows) > 0:
+            return rows[0][0]
+        else:
+            board_date_string = datetime.datetime.utcnow().date().isoformat()
+            new_board_id = str(uuid.uuid4())
+            sql_insert_board = f"INSERT INTO boards (board_id, board_name, created_at, updated_at) VALUES ('{new_board_id}', '{board_name}', '{board_date_string}', '{board_date_string}')"
+            self.cursor.execute(sql_insert_board)
+            self.connection.commit()
+            return new_board_id
+
+    def add_image_to_board(self, filename, board_id):
+        """Add an image mapping to a board."""
+        add_datetime_str = datetime.datetime.utcnow().isoformat()
+        sql_add_image_to_board = f"""INSERT INTO board_images (board_id, image_name, created_at, updated_at)
+            VALUES ('{board_id}', '{filename}', '{add_datetime_str}', '{add_datetime_str}')"""
+        self.cursor.execute(sql_add_image_to_board)
+        self.connection.commit()
+
+    def disconnect(self):
+        """Disconnect from the db, cleaning up connections and cursors."""
+        if self.cursor is not None:
+            self.cursor.close()
+        if self.connection is not None:
+            self.connection.close()
+
+    def backup(self, timestamp_string):
+        """Take a backup of the database."""
+        if not os.path.exists(self.database_backup_dir):
+            print(f"Database backup directory {self.database_backup_dir} does not exist -> creating...", end="")
+            os.makedirs(self.database_backup_dir)
+            print("Done!")
+        database_backup_path = os.path.join(self.database_backup_dir, f"backup-{timestamp_string}-invokeai.db")
+        print(f"Making DB Backup at {database_backup_path}...", end="")
+        shutil.copy2(self.database_path, database_backup_path)
+        print("Done!")
+
+
+class MediaImportProcessor:
+    """Containing class for script functionality."""
+
+    def __init__(self):
+        pass
+
+    board_name_id_map = {}
+
+    def get_import_file_list(self):
+        """Ask the user for the import folder and scan for the list of files to return."""
+        while True:
+            default = ""
+            while True:
+                import_dir = os.path.expanduser(
+                    prompt(
+                        "Inputs: Specify absolute path containing InvokeAI .png images to import: ",
+                        completer=PathCompleter(expanduser=True, only_directories=True),
+                        default=default,
+                    )
+                )
+                if len(import_dir) > 0 and Path(import_dir).is_dir():
+                    break
+                default = import_dir
+
+            recurse_directories = (
+                (prompt("Include files from subfolders recursively [yN]? ").strip() or "N").upper().startswith("N")
+            )
+            if recurse_directories:
+                is_recurse = False
+                matching_file_list = glob.glob(import_dir + "/*.png", recursive=False)
+            else:
+                is_recurse = True
+                matching_file_list = glob.glob(import_dir + "/**/*.png", recursive=True)
+
+            if len(matching_file_list) > 0:
+                return import_dir, is_recurse, matching_file_list
+            else:
+                print(f"The specific path {import_dir} exists, but does not contain .png files!")
+
+    def get_file_details(self, filepath):
+        """Retrieve the embedded metedata fields and dimensions from an image file."""
+        with PIL.Image.open(filepath) as img:
+            img.load()
+            png_width, png_height = img.size
+            img_info = img.info
+        return img_info, png_width, png_height
+
+    def select_board_option(self, board_names, timestamp_string):
+        """Allow the user to choose how a board is selected for imported files."""
+        while True:
+            print("\r\nOptions for board selection for imported images:")
+            print(f"1) Select an existing board name. (found {len(board_names)})")
+            print("2) Specify a board name to create/add to.")
+            print("3) Create/add to board named 'IMPORT'.")
+            print(
+                f"4) Create/add to board named 'IMPORT' with the current datetime string appended (.e.g IMPORT_{timestamp_string})."
+            )
+            print(
+                "5) Create/add to board named 'IMPORT' with a the original file app_version appended (.e.g IMPORT_2.2.5)."
+            )
+            input_option = input("Specify desired board option: ")
+            match (input_option):
+                case "1":
+                    if len(board_names) < 1:
+                        print("\r\nThere are no existing board names to choose from. Select another option!")
+                        continue
+                    board_name = self.select_item_from_list(
+                        board_names, "board name", True, "Cancel, go back and choose a different board option."
+                    )
+                    if board_name is not None:
+                        return board_name
+                case "2":
+                    while True:
+                        board_name = input("Specify new/existing board name: ")
+                        if board_name:
+                            return board_name
+                case "3":
+                    return "IMPORT"
+                case "4":
+                    return f"IMPORT_{timestamp_string}"
+                case "5":
+                    return "IMPORT_APPVERSION"
+
+    def select_item_from_list(self, items, entity_name, allow_cancel, cancel_string):
+        """A general function to render a list of items to select in the console, prompt the user for a selection and ensure a valid entry is selected."""
+        print(f"Select a {entity_name.lower()} from the following list:")
+        index = 1
+        for item in items:
+            print(f"{index}) {item}")
+            index += 1
+        if allow_cancel:
+            print(f"{index}) {cancel_string}")
+        while True:
+            try:
+                option_number = int(input("Specify number of selection: "))
+            except ValueError:
+                continue
+            if allow_cancel and option_number == index:
+                return None
+            if option_number >= 1 and option_number <= len(items):
+                return items[option_number - 1]
+
+    def import_image(self, filepath: str, board_name_option: str, db_mapper: DatabaseMapper, config: Config):
+        """Import a single file by its path"""
+        parser = InvokeAIMetadataParser()
+        file_name = os.path.basename(filepath)
+        file_destination_path = os.path.join(config.outputs_path, file_name)
+
+        print("===============================================================================")
+        print(f"Importing {filepath}")
+
+        # check destination to see if the file was previously imported
+        if os.path.exists(file_destination_path):
+            print("File already exists in the destination, skipping!")
+            ImportStats.count_skipped_file_exists += 1
+            return
+
+        # check if file name is already referenced in the database
+        if db_mapper.does_image_exist(file_name):
+            print("A reference to a file with this name already exists in the database, skipping!")
+            ImportStats.count_skipped_db_exists += 1
+            return
+
+        # load image info and dimensions
+        img_info, png_width, png_height = self.get_file_details(filepath)
+
+        # parse metadata
+        destination_needs_meta_update = True
+        log_version_note = "(Unknown)"
+        if "invokeai_metadata" in img_info:
+            # for the latest, we will just re-emit the same json, no need to parse/modify
+            converted_field = None
+            latest_json_string = img_info.get("invokeai_metadata")
+            log_version_note = "3.0.0+"
+            destination_needs_meta_update = False
+        else:
+            if "sd-metadata" in img_info:
+                converted_field = parser.parse_meta_tag_sd_metadata(json.loads(img_info.get("sd-metadata")))
+            elif "invokeai" in img_info:
+                converted_field = parser.parse_meta_tag_invokeai(json.loads(img_info.get("invokeai")))
+            elif "dream" in img_info:
+                converted_field = parser.parse_meta_tag_dream(img_info.get("dream"))
+            elif "Dream" in img_info:
+                converted_field = parser.parse_meta_tag_dream(img_info.get("Dream"))
+            else:
+                converted_field = InvokeAIMetadata()
+                destination_needs_meta_update = False
+                print("File does not have metadata from known Invoke AI versions, add only, no update!")
+
+            # use the loaded img dimensions if the metadata didnt have them
+            if converted_field.width is None:
+                converted_field.width = png_width
+            if converted_field.height is None:
+                converted_field.height = png_height
+
+            log_version_note = converted_field.imported_app_version if converted_field else "NoVersion"
+            log_version_note = log_version_note or "NoVersion"
+
+            latest_json_string = converted_field.to_json()
+
+        print(f"From Invoke AI Version {log_version_note} with dimensions {png_width} x {png_height}.")
+
+        # if metadata needs update, then update metdata and copy in one shot
+        if destination_needs_meta_update:
+            print("Updating metadata while copying...", end="")
+            self.update_file_metadata_while_copying(
+                filepath, file_destination_path, "invokeai_metadata", latest_json_string
+            )
+            print("Done!")
+        else:
+            print("No metadata update necessary, copying only...", end="")
+            shutil.copy2(filepath, file_destination_path)
+            print("Done!")
+
+        # create thumbnail
+        print("Creating thumbnail...", end="")
+        thumbnail_path = os.path.join(config.thumbnail_path, os.path.splitext(file_name)[0]) + ".webp"
+        thumbnail_size = 256, 256
+        with PIL.Image.open(filepath) as source_image:
+            source_image.thumbnail(thumbnail_size)
+            source_image.save(thumbnail_path, "webp")
+        print("Done!")
+
+        # finalize the dynamic board name if there is an APPVERSION token in it.
+        if converted_field is not None:
+            board_name = board_name_option.replace("APPVERSION", converted_field.imported_app_version or "NoVersion")
+        else:
+            board_name = board_name_option.replace("APPVERSION", "Latest")
+
+        # maintain a map of alrady created/looked up ids to avoid DB queries
+        print("Finding/Creating board...", end="")
+        if board_name in self.board_name_id_map:
+            board_id = self.board_name_id_map[board_name]
+        else:
+            board_id = db_mapper.get_board_id_with_create(board_name)
+            self.board_name_id_map[board_name] = board_id
+        print("Done!")
+
+        # add image to db
+        print("Adding image to database......", end="")
+        modified_time = datetime.datetime.utcfromtimestamp(os.path.getmtime(filepath))
+        db_mapper.add_new_image_to_database(file_name, png_width, png_height, latest_json_string, modified_time)
+        print("Done!")
+
+        # add image to board
+        print("Adding image to board......", end="")
+        db_mapper.add_image_to_board(file_name, board_id)
+        print("Done!")
+
+        ImportStats.count_imported += 1
+        if log_version_note in ImportStats.count_imported_by_version:
+            ImportStats.count_imported_by_version[log_version_note] += 1
+        else:
+            ImportStats.count_imported_by_version[log_version_note] = 1
+
+    def update_file_metadata_while_copying(self, filepath, file_destination_path, tag_name, tag_value):
+        """Perform a metadata update with save to a new destination which accomplishes a copy while updating metadata."""
+        with PIL.Image.open(filepath) as target_image:
+            existing_img_info = target_image.info
+            metadata = PIL.PngImagePlugin.PngInfo()
+            # re-add any existing invoke ai tags unless they are the one we are trying to add
+            for key in existing_img_info:
+                if key != tag_name and key in ("dream", "Dream", "sd-metadata", "invokeai", "invokeai_metadata"):
+                    metadata.add_text(key, existing_img_info[key])
+            metadata.add_text(tag_name, tag_value)
+            target_image.save(file_destination_path, pnginfo=metadata)
+
+    def process(self):
+        """Begin main processing."""
+
+        print("===============================================================================")
+        print("This script will import images generated by earlier versions of")
+        print("InvokeAI into the currently installed root directory:")
+        print(f"   {app_config.root_path}")
+        print("If this is not what you want to do, type ctrl-C now to cancel.")
+
+        # load config
+        print("===============================================================================")
+        print("= Configuration & Settings")
+
+        config = Config()
+        config.find_and_load()
+        db_mapper = DatabaseMapper(config.database_path, config.database_backup_dir)
+        db_mapper.connect()
+
+        import_dir, is_recurse, import_file_list = self.get_import_file_list()
+        ImportStats.count_source_files = len(import_file_list)
+
+        board_names = db_mapper.get_board_names()
+        board_name_option = self.select_board_option(board_names, config.TIMESTAMP_STRING)
+
+        print("\r\n===============================================================================")
+        print("= Import Settings Confirmation")
+
+        print()
+        print(f"Database File Path               : {config.database_path}")
+        print(f"Outputs/Images Directory         : {config.outputs_path}")
+        print(f"Import Image Source Directory    : {import_dir}")
+        print(f"  Recurse Source SubDirectories  : {'Yes' if is_recurse else 'No'}")
+        print(f"Count of .png file(s) found      : {len(import_file_list)}")
+        print(f"Board name option specified      : {board_name_option}")
+        print(f"Database backup will be taken at : {config.database_backup_dir}")
+
+        print("\r\nNotes about the import process:")
+        print("- Source image files will not be modified, only copied to the outputs directory.")
+        print("- If the same file name already exists in the destination, the file will be skipped.")
+        print("- If the same file name already has a record in the database, the file will be skipped.")
+        print("- Invoke AI metadata tags will be updated/written into the imported copy only.")
+        print(
+            "- On the imported copy, only Invoke AI known tags (latest and legacy) will be retained (dream, sd-metadata, invokeai, invokeai_metadata)"
+        )
+        print(
+            "- A property 'imported_app_version' will be added to metadata that can be viewed in the UI's metadata viewer."
+        )
+        print(
+            "- The new 3.x InvokeAI outputs folder structure is flat so recursively found source imges will all be placed into the single outputs/images folder."
+        )
+
+        while True:
+            should_continue = prompt("\nDo you wish to continue with the import [Yn] ? ").lower() or "y"
+            if should_continue == "n":
+                print("\r\nCancelling Import")
+                return
+            elif should_continue == "y":
+                print()
+                break
+
+        db_mapper.backup(config.TIMESTAMP_STRING)
+
+        print()
+        ImportStats.time_start = datetime.datetime.utcnow()
+
+        for filepath in import_file_list:
+            try:
+                self.import_image(filepath, board_name_option, db_mapper, config)
+            except sqlite3.Error as sql_ex:
+                print(f"A database related exception was found processing {filepath}, will continue to next file. ")
+                print("Exception detail:")
+                print(sql_ex)
+                ImportStats.count_file_errors += 1
+            except Exception as ex:
+                print(f"Exception processing {filepath}, will continue to next file. ")
+                print("Exception detail:")
+                print(ex)
+                ImportStats.count_file_errors += 1
+
+        print("\r\n===============================================================================")
+        print(f"= Import Complete - Elpased Time: {ImportStats.get_elapsed_time_string()}")
+        print()
+        print(f"Source File(s)                          : {ImportStats.count_source_files}")
+        print(f"Total Imported                          : {ImportStats.count_imported}")
+        print(f"Skipped b/c file already exists on disk : {ImportStats.count_skipped_file_exists}")
+        print(f"Skipped b/c file already exists in db   : {ImportStats.count_skipped_db_exists}")
+        print(f"Errors during import                    : {ImportStats.count_file_errors}")
+        if ImportStats.count_imported > 0:
+            print("\r\nBreakdown of imported files by version:")
+            for key, version in ImportStats.count_imported_by_version.items():
+                print(f"  {key:20} : {version}")
+
+
+def main():
+    try:
+        processor = MediaImportProcessor()
+        processor.process()
+    except KeyboardInterrupt:
+        print("\r\n\r\nUser cancelled execution.")
+
+
+if __name__ == "__main__":
+    main()

invokeai/frontend/install/invokeai_configure.py

@@ -1,4 +1,4 @@
 """
 Wrapper for invokeai.backend.configure.invokeai_configure
 """
-from ...backend.install.invokeai_configure import main
+from ...backend.install.invokeai_configure import main as invokeai_configure

invokeai/frontend/install/model_install.py

@@ -28,7 +28,6 @@ from npyscreen import widget
 from invokeai.backend.util.logging import InvokeAILogger
 
 from invokeai.backend.install.model_install_backend import (
-    ModelInstallList,
     InstallSelections,
     ModelInstall,
     SchedulerPredictionType,
@@ -41,12 +40,12 @@ from invokeai.frontend.install.widgets import (
     SingleSelectColumns,
     TextBox,
     BufferBox,
-    FileBox,
     set_min_terminal_size,
     select_stable_diffusion_config_file,
     CyclingForm,
     MIN_COLS,
     MIN_LINES,
+    WindowTooSmallException,
 )
 from invokeai.app.services.config import InvokeAIAppConfig
 
@@ -156,7 +155,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             BufferBox,
             name="Log Messages",
             editable=False,
-            max_height=15,
+            max_height=6,
         )
 
         self.nextrely += 1
@@ -693,7 +692,11 @@ def select_and_download_models(opt: Namespace):
         # needed to support the probe() method running under a subprocess
         torch.multiprocessing.set_start_method("spawn")
 
-        set_min_terminal_size(MIN_COLS, MIN_LINES)
+        if not set_min_terminal_size(MIN_COLS, MIN_LINES):
+            raise WindowTooSmallException(
+                "Could not increase terminal size. Try running again with a larger window or smaller font size."
+            )
+
         installApp = AddModelApplication(opt)
         try:
             installApp.run()
@@ -787,6 +790,8 @@ def main():
         curses.echo()
         curses.endwin()
         logger.info("Goodbye! Come back soon.")
+    except WindowTooSmallException as e:
+        logger.error(str(e))
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
             logger.error("Insufficient vertical space for the interface. Please make your window taller and try again")

invokeai/frontend/install/widgets.py

@@ -21,13 +21,19 @@ MIN_COLS = 130
 MIN_LINES = 38
 
 
+class WindowTooSmallException(Exception):
+    pass
+
+
 # -------------------------------------
-def set_terminal_size(columns: int, lines: int):
+def set_terminal_size(columns: int, lines: int) -> bool:
+    OS = platform.uname().system
+    screen_ok = False
+    while not screen_ok:
         ts = get_terminal_size()
         width = max(columns, ts.columns)
         height = max(lines, ts.lines)
 
-    OS = platform.uname().system
         if OS == "Windows":
             pass
             # not working reliably - ask user to adjust the window
@@ -37,15 +43,18 @@ def set_terminal_size(columns: int, lines: int):
 
         # check whether it worked....
         ts = get_terminal_size()
-    pause = False
+        if ts.columns < columns or ts.lines < lines:
-    if ts.columns < columns:
+            print(
-        print("\033[1mThis window is too narrow for the user interface.\033[0m")
+                f"\033[1mThis window is too small for the interface. InvokeAI requires {columns}x{lines} (w x h) characters, but window is {ts.columns}x{ts.lines}\033[0m"
-        pause = True
+            )
-    if ts.lines < lines:
+            resp = input(
-        print("\033[1mThis window is too short for the user interface.\033[0m")
+                "Maximize the window and/or decrease the font size then press any key to continue. Type [Q] to give up.."
-        pause = True
+            )
-    if pause:
+            if resp.upper().startswith("Q"):
-        input("Maximize the window then press any key to continue..")
+                break
+        else:
+            screen_ok = True
+    return screen_ok
 
 
 def _set_terminal_size_powershell(width: int, height: int):
@@ -80,14 +89,14 @@ def _set_terminal_size_unix(width: int, height: int):
     sys.stdout.flush()
 
 
-def set_min_terminal_size(min_cols: int, min_lines: int):
+def set_min_terminal_size(min_cols: int, min_lines: int) -> bool:
     # make sure there's enough room for the ui
     term_cols, term_lines = get_terminal_size()
     if term_cols >= min_cols and term_lines >= min_lines:
-        return
+        return True
     cols = max(term_cols, min_cols)
     lines = max(term_lines, min_lines)
-    set_terminal_size(cols, lines)
+    return set_terminal_size(cols, lines)
 
 
 class IntSlider(npyscreen.Slider):
@@ -164,7 +173,7 @@ class FloatSlider(npyscreen.Slider):
 
 
 class FloatTitleSlider(npyscreen.TitleText):
-    _entry_type = FloatSlider
+    _entry_type = npyscreen.Slider
 
 
 class SelectColumnBase:

invokeai/frontend/merge/merge_diffusers.py

@@ -382,6 +382,7 @@ def run_cli(args: Namespace):
 
 def main():
     args = _parse_args()
+    if args.root_dir:
         config.parse_args(["--root", str(args.root_dir)])
 
     try:

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-1a474d08.js

@@ -1,4 +1,4 @@
-import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-de589048.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-11348abc.js";var za=String.raw,Ca=za`
+import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-deaa1f26.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-b4489359.js";var za=String.raw,Ca=za`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-de589048.js"></script>
+    <script type="module" crossorigin src="./assets/index-deaa1f26.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -20,7 +20,7 @@ export const addStagingAreaImageSavedListener = () => {
         // we may need to add it to the autoadd board
         const { autoAddBoardId } = getState().gallery;
 
-        if (autoAddBoardId) {
+        if (autoAddBoardId && autoAddBoardId !== 'none') {
           await dispatch(
             imagesApi.endpoints.addImageToBoard.initiate({
               imageDTO: newImageDTO,

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

@@ -1,55 +1,58 @@
 import { modelChanged } from 'features/parameters/store/generationSlice';
 import { setActiveTab } from 'features/ui/store/uiSlice';
-import { forEach } from 'lodash-es';
 import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
-import {
+import { mainModelsAdapter, modelsApi } from 'services/api/endpoints/models';
-  MainModelConfigEntity,
-  modelsApi,
-} from 'services/api/endpoints/models';
 import { startAppListening } from '..';
 
 export const addTabChangedListener = () => {
   startAppListening({
     actionCreator: setActiveTab,
-    effect: (action, { getState, dispatch }) => {
+    effect: async (action, { getState, dispatch }) => {
       const activeTabName = action.payload;
       if (activeTabName === 'unifiedCanvas') {
-        // grab the models from RTK Query cache
+        const currentBaseModel = getState().generation.model?.base_model;
-        const { data } = modelsApi.endpoints.getMainModels.select(
-          NON_REFINER_BASE_MODELS
-        )(getState());
 
-        if (!data) {
+        if (currentBaseModel && ['sd-1', 'sd-2'].includes(currentBaseModel)) {
-          // no models yet, so we can't do anything
+          // if we're already on a valid model, no change needed
+          return;
+        }
+
+        try {
+          // just grab fresh models
+          const modelsRequest = dispatch(
+            modelsApi.endpoints.getMainModels.initiate(NON_REFINER_BASE_MODELS)
+          );
+          const models = await modelsRequest.unwrap();
+          // cancel this cache subscription
+          modelsRequest.unsubscribe();
+
+          if (!models.ids.length) {
+            // no valid canvas models
             dispatch(modelChanged(null));
             return;
           }
 
-        // need to filter out all the invalid canvas models (currently, this is just sdxl)
+          // need to filter out all the invalid canvas models (currently sdxl & refiner)
-        const validCanvasModels: MainModelConfigEntity[] = [];
+          const validCanvasModels = mainModelsAdapter
+            .getSelectors()
+            .selectAll(models)
+            .filter((model) => ['sd-1', 'sd-2'].includes(model.base_model));
 
-        forEach(data.entities, (entity) => {
-          if (!entity) {
-            return;
-          }
-          if (['sd-1', 'sd-2'].includes(entity.base_model)) {
-            validCanvasModels.push(entity);
-          }
-        });
-
-        // this could still be undefined even tho TS doesn't say so
           const firstValidCanvasModel = validCanvasModels[0];
 
           if (!firstValidCanvasModel) {
-          // uh oh, we have no models that are valid for canvas
+            // no valid canvas models
             dispatch(modelChanged(null));
             return;
           }
 
-        // only store the model name and base model in redux
           const { base_model, model_name, model_type } = firstValidCanvasModel;
 
           dispatch(modelChanged({ base_model, model_name, model_type }));
+        } catch {
+          // network request failed, bail
+          dispatch(modelChanged(null));
+        }
       }
     },
   });

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

@@ -96,7 +96,8 @@ export type AppFeature =
   | 'consoleLogging'
   | 'dynamicPrompting'
   | 'batches'
-  | 'syncModels';
+  | 'syncModels'
+  | 'multiselect';
 
 /**
  * A disable-able Stable Diffusion feature

invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts.ts

@@ -9,6 +9,7 @@ import { useListImagesQuery } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
 import { selectionChanged } from '../store/gallerySlice';
 import { imagesSelectors } from 'services/api/util';
+import { useFeatureStatus } from '../../system/hooks/useFeatureStatus';
 
 const selector = createSelector(
   [stateSelector, selectListImagesBaseQueryArgs],
@@ -33,11 +34,18 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
     }),
   });
 
+  const isMultiSelectEnabled = useFeatureStatus('multiselect').isFeatureEnabled;
+
   const handleClick = useCallback(
     (e: MouseEvent<HTMLDivElement>) => {
       if (!imageDTO) {
         return;
       }
+      if (!isMultiSelectEnabled) {
+        dispatch(selectionChanged([imageDTO]));
+        return;
+      }
+
       if (e.shiftKey) {
         const rangeEndImageName = imageDTO.image_name;
         const lastSelectedImage = selection[selection.length - 1]?.image_name;
@@ -71,7 +79,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
         dispatch(selectionChanged([imageDTO]));
       }
     },
-    [dispatch, imageDTO, imageDTOs, selection]
+    [dispatch, imageDTO, imageDTOs, selection, isMultiSelectEnabled]
   );
 
   const isSelected = useMemo(

invokeai/frontend/web/src/features/lora/components/ParamLoraCollapse.tsx

@@ -31,7 +31,7 @@ const ParamLoraCollapse = () => {
   }
 
   return (
-    <IAICollapse label={'LoRA'} activeLabel={activeLabel}>
+    <IAICollapse label="LoRA" activeLabel={activeLabel}>
       <Flex sx={{ flexDir: 'column', gap: 2 }}>
         <ParamLoRASelect />
         <ParamLoraList />

invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx

@@ -1,3 +1,4 @@
+import { Divider } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
@@ -8,20 +9,21 @@ import ParamLora from './ParamLora';
 const selector = createSelector(
   stateSelector,
   ({ lora }) => {
-    const { loras } = lora;
+    return { lorasArray: map(lora.loras) };
-
-    return { loras };
   },
   defaultSelectorOptions
 );
 
 const ParamLoraList = () => {
-  const { loras } = useAppSelector(selector);
+  const { lorasArray } = useAppSelector(selector);
 
   return (
     <>
-      {map(loras, (lora) => (
+      {lorasArray.map((lora, i) => (
+        <>
+          {i > 0 && <Divider key={`${lora.model_name}-divider`} pt={1} />}
           <ParamLora key={lora.model_name} lora={lora} />
+        </>
       ))}
     </>
   );

invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx

@@ -54,12 +54,7 @@ const ParamLoRASelect = () => {
       });
     });
 
-    // Sort Alphabetically
+    return data.sort((a, b) => (a.disabled && !b.disabled ? 1 : -1));
-    data.sort((a, b) =>
-      a.label && b.label ? (a.label?.localeCompare(b.label) ? 1 : -1) : -1
-    );
-
-    return data.sort((a, b) => (a.disabled && !b.disabled ? -1 : 1));
   }, [loras, loraModels, currentMainModel?.base_model]);
 
   const handleChange = useCallback(

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

@@ -9,7 +9,6 @@ import {
   CLIP_SKIP,
   LORA_LOADER,
   MAIN_MODEL_LOADER,
-  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
@@ -36,15 +35,11 @@ export const addLoRAsToGraph = (
     | undefined;
 
   if (loraCount > 0) {
-    // Remove MAIN_MODEL_LOADER unet connection to feed it to LoRAs
+    // Remove modelLoaderNodeId unet connection to feed it to LoRAs
     graph.edges = graph.edges.filter(
       (e) =>
         !(
-          e.source.node_id === MAIN_MODEL_LOADER &&
+          e.source.node_id === modelLoaderNodeId &&
-          ['unet'].includes(e.source.field)
-        ) &&
-        !(
-          e.source.node_id === ONNX_MODEL_LOADER &&
           ['unet'].includes(e.source.field)
         )
     );

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

@@ -0,0 +1,212 @@
+import { RootState } from 'app/store/store';
+import { NonNullableGraph } from 'features/nodes/types/types';
+import { forEach, size } from 'lodash-es';
+import {
+  MetadataAccumulatorInvocation,
+  SDXLLoraLoaderInvocation,
+} from 'services/api/types';
+import {
+  LORA_LOADER,
+  METADATA_ACCUMULATOR,
+  NEGATIVE_CONDITIONING,
+  POSITIVE_CONDITIONING,
+  SDXL_MODEL_LOADER,
+} from './constants';
+
+export const addSDXLLoRAsToGraph = (
+  state: RootState,
+  graph: NonNullableGraph,
+  baseNodeId: string,
+  modelLoaderNodeId: string = SDXL_MODEL_LOADER
+): void => {
+  /**
+   * LoRA nodes get the UNet and CLIP models from the main model loader and apply the LoRA to them.
+   * They then output the UNet and CLIP models references on to either the next LoRA in the chain,
+   * or to the inference/conditioning nodes.
+   *
+   * So we need to inject a LoRA chain into the graph.
+   */
+
+  const { loras } = state.lora;
+  const loraCount = size(loras);
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
+    | MetadataAccumulatorInvocation
+    | undefined;
+
+  if (loraCount > 0) {
+    // Remove modelLoaderNodeId unet/clip/clip2 connections to feed it to LoRAs
+    graph.edges = graph.edges.filter(
+      (e) =>
+        !(
+          e.source.node_id === modelLoaderNodeId &&
+          ['unet'].includes(e.source.field)
+        ) &&
+        !(
+          e.source.node_id === modelLoaderNodeId &&
+          ['clip'].includes(e.source.field)
+        ) &&
+        !(
+          e.source.node_id === modelLoaderNodeId &&
+          ['clip2'].includes(e.source.field)
+        )
+    );
+  }
+
+  // we need to remember the last lora so we can chain from it
+  let lastLoraNodeId = '';
+  let currentLoraIndex = 0;
+
+  forEach(loras, (lora) => {
+    const { model_name, base_model, weight } = lora;
+    const currentLoraNodeId = `${LORA_LOADER}_${model_name.replace('.', '_')}`;
+
+    const loraLoaderNode: SDXLLoraLoaderInvocation = {
+      type: 'sdxl_lora_loader',
+      id: currentLoraNodeId,
+      is_intermediate: true,
+      lora: { model_name, base_model },
+      weight,
+    };
+
+    // add the lora to the metadata accumulator
+    if (metadataAccumulator) {
+      metadataAccumulator.loras.push({
+        lora: { model_name, base_model },
+        weight,
+      });
+    }
+
+    // add to graph
+    graph.nodes[currentLoraNodeId] = loraLoaderNode;
+    if (currentLoraIndex === 0) {
+      // first lora = start the lora chain, attach directly to model loader
+      graph.edges.push({
+        source: {
+          node_id: modelLoaderNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'unet',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: modelLoaderNodeId,
+          field: 'clip',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'clip',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: modelLoaderNodeId,
+          field: 'clip2',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'clip2',
+        },
+      });
+    } else {
+      // we are in the middle of the lora chain, instead connect to the previous lora
+      graph.edges.push({
+        source: {
+          node_id: lastLoraNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'unet',
+        },
+      });
+      graph.edges.push({
+        source: {
+          node_id: lastLoraNodeId,
+          field: 'clip',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'clip',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: lastLoraNodeId,
+          field: 'clip2',
+        },
+        destination: {
+          node_id: currentLoraNodeId,
+          field: 'clip2',
+        },
+      });
+    }
+
+    if (currentLoraIndex === loraCount - 1) {
+      // final lora, end the lora chain - we need to connect up to inference and conditioning nodes
+      graph.edges.push({
+        source: {
+          node_id: currentLoraNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: baseNodeId,
+          field: 'unet',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: currentLoraNodeId,
+          field: 'clip',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'clip',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: currentLoraNodeId,
+          field: 'clip',
+        },
+        destination: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'clip',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: currentLoraNodeId,
+          field: 'clip2',
+        },
+        destination: {
+          node_id: POSITIVE_CONDITIONING,
+          field: 'clip2',
+        },
+      });
+
+      graph.edges.push({
+        source: {
+          node_id: currentLoraNodeId,
+          field: 'clip2',
+        },
+        destination: {
+          node_id: NEGATIVE_CONDITIONING,
+          field: 'clip2',
+        },
+      });
+    }
+
+    // increment the lora for the next one in the chain
+    lastLoraNodeId = currentLoraNodeId;
+    currentLoraIndex += 1;
+  });
+};

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

@@ -22,6 +22,7 @@ import {
   SDXL_LATENTS_TO_LATENTS,
   SDXL_MODEL_LOADER,
 } from './constants';
+import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 
 /**
  * Builds the Image to Image tab graph.
@@ -364,6 +365,8 @@ export const buildLinearSDXLImageToImageGraph = (
     },
   });
 
+  addSDXLLoRAsToGraph(state, graph, SDXL_LATENTS_TO_LATENTS, SDXL_MODEL_LOADER);
+
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
     addSDXLRefinerToGraph(state, graph, SDXL_LATENTS_TO_LATENTS);

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

@@ -4,6 +4,7 @@ import { NonNullableGraph } from 'features/nodes/types/types';
 import { initialGenerationState } from 'features/parameters/store/generationSlice';
 import { addDynamicPromptsToGraph } from './addDynamicPromptsToGraph';
 import { addNSFWCheckerToGraph } from './addNSFWCheckerToGraph';
+import { addSDXLLoRAsToGraph } from './addSDXLLoRAstoGraph';
 import { addSDXLRefinerToGraph } from './addSDXLRefinerToGraph';
 import { addWatermarkerToGraph } from './addWatermarkerToGraph';
 import {
@@ -246,6 +247,8 @@ export const buildLinearSDXLTextToImageGraph = (
     },
   });
 
+  addSDXLLoRAsToGraph(state, graph, SDXL_TEXT_TO_LATENTS, SDXL_MODEL_LOADER);
+
   // Add Refiner if enabled
   if (shouldUseSDXLRefiner) {
     addSDXLRefinerToGraph(state, graph, SDXL_TEXT_TO_LATENTS);

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

@@ -4,6 +4,7 @@ import ProcessButtons from 'features/parameters/components/ProcessButtons/Proces
 import ParamSDXLPromptArea from './ParamSDXLPromptArea';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
 import SDXLImageToImageTabCoreParameters from './SDXLImageToImageTabCoreParameters';
+import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 
 const SDXLImageToImageTabParameters = () => {
   return (
@@ -12,6 +13,7 @@ const SDXLImageToImageTabParameters = () => {
       <ProcessButtons />
       <SDXLImageToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />
+      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamNoiseCollapse />
     </>

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

@@ -4,6 +4,7 @@ import ProcessButtons from 'features/parameters/components/ProcessButtons/Proces
 import TextToImageTabCoreParameters from 'features/ui/components/tabs/TextToImage/TextToImageTabCoreParameters';
 import ParamSDXLPromptArea from './ParamSDXLPromptArea';
 import ParamSDXLRefinerCollapse from './ParamSDXLRefinerCollapse';
+import ParamLoraCollapse from 'features/lora/components/ParamLoraCollapse';
 
 const SDXLTextToImageTabParameters = () => {
   return (
@@ -12,6 +13,7 @@ const SDXLTextToImageTabParameters = () => {
       <ProcessButtons />
       <TextToImageTabCoreParameters />
       <ParamSDXLRefinerCollapse />
+      <ParamLoraCollapse />
       <ParamDynamicPromptsCollapse />
       <ParamNoiseCollapse />
     </>

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

@@ -365,12 +365,19 @@ export const systemSlice = createSlice({
       state.statusTranslationKey = 'common.statusConnected';
       state.progressImage = null;
 
+      let errorDescription = undefined;
+
+      if (action.payload?.status === 422) {
+        errorDescription = 'Validation Error';
+      } else if (action.payload?.error) {
+        errorDescription = action.payload?.error as string;
+      }
+
       state.toastQueue.push(
         makeToast({
           title: t('toast.serverError'),
           status: 'error',
-          description:
+          description: errorDescription,
-            action.payload?.status === 422 ? 'Validation Error' : undefined,
         })
       );
     });

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

@@ -4,6 +4,7 @@ import {
   ASSETS_CATEGORIES,
   BoardId,
   IMAGE_CATEGORIES,
+  IMAGE_LIMIT,
 } from 'features/gallery/store/types';
 import { keyBy } from 'lodash';
 import { ApiFullTagDescription, LIST_TAG, api } from '..';
@@ -167,7 +168,14 @@ export const imagesApi = api.injectEndpoints({
           },
         };
       },
-      invalidatesTags: (result, error, imageDTOs) => [],
+      invalidatesTags: (result, error, { imageDTOs }) => {
+        // for now, assume bulk delete is all on one board
+        const boardId = imageDTOs[0]?.board_id;
+        return [
+          { type: 'BoardImagesTotal', id: boardId ?? 'none' },
+          { type: 'BoardAssetsTotal', id: boardId ?? 'none' },
+        ];
+      },
       async onQueryStarted({ imageDTOs }, { dispatch, queryFulfilled }) {
         /**
          * Cache changes for `deleteImages`:
@@ -889,18 +897,25 @@ export const imagesApi = api.injectEndpoints({
           board_id,
         },
       }),
-      invalidatesTags: (result, error, { board_id }) => [
+      invalidatesTags: (result, error, { imageDTOs, board_id }) => {
+        //assume all images are being moved from one board for now
+        const oldBoardId = imageDTOs[0]?.board_id;
+        return [
           // update the destination board
           { type: 'Board', id: board_id ?? 'none' },
-        // update old board totals
+          // update new board totals
           { type: 'BoardImagesTotal', id: board_id ?? 'none' },
           { type: 'BoardAssetsTotal', id: board_id ?? 'none' },
+          // update old board totals
+          { type: 'BoardImagesTotal', id: oldBoardId ?? 'none' },
+          { type: 'BoardAssetsTotal', id: oldBoardId ?? 'none' },
           // update the no_board totals
           { type: 'BoardImagesTotal', id: 'none' },
           { type: 'BoardAssetsTotal', id: 'none' },
-      ],
+        ];
+      },
       async onQueryStarted(
-        { board_id, imageDTOs },
+        { board_id: new_board_id, imageDTOs },
         { dispatch, queryFulfilled, getState }
       ) {
         try {
@@ -920,7 +935,7 @@ export const imagesApi = api.injectEndpoints({
                 'getImageDTO',
                 image_name,
                 (draft) => {
-                  draft.board_id = board_id;
+                  draft.board_id = new_board_id;
                 }
               )
             );
@@ -946,7 +961,7 @@ export const imagesApi = api.injectEndpoints({
             );
 
             const queryArgs = {
-              board_id,
+              board_id: new_board_id,
               categories,
             };
 
@@ -954,23 +969,24 @@ export const imagesApi = api.injectEndpoints({
               queryArgs
             )(getState());
 
-            const { data: total } = IMAGE_CATEGORIES.includes(
+            const { data: previousTotal } = IMAGE_CATEGORIES.includes(
               imageDTO.image_category
             )
               ? boardsApi.endpoints.getBoardImagesTotal.select(
-                  imageDTO.board_id ?? 'none'
+                  new_board_id ?? 'none'
                 )(getState())
               : boardsApi.endpoints.getBoardAssetsTotal.select(
-                  imageDTO.board_id ?? 'none'
+                  new_board_id ?? 'none'
                 )(getState());
 
             const isCacheFullyPopulated =
-              currentCache.data && currentCache.data.ids.length >= (total ?? 0);
+              currentCache.data &&
+              currentCache.data.ids.length >= (previousTotal ?? 0);
 
-            const isInDateRange = getIsImageInDateRange(
+            const isInDateRange =
-              currentCache.data,
+              (previousTotal || 0) >= IMAGE_LIMIT
-              imageDTO
+                ? getIsImageInDateRange(currentCache.data, imageDTO)
-            );
+                : true;
 
             if (isCacheFullyPopulated || isInDateRange) {
               // *upsert* to $cache
@@ -981,7 +997,7 @@ export const imagesApi = api.injectEndpoints({
                   (draft) => {
                     imagesAdapter.upsertOne(draft, {
                       ...imageDTO,
-                      board_id,
+                      board_id: new_board_id,
                     });
                   }
                 )
@@ -1097,10 +1113,10 @@ export const imagesApi = api.injectEndpoints({
             const isCacheFullyPopulated =
               currentCache.data && currentCache.data.ids.length >= (total ?? 0);
 
-            const isInDateRange = getIsImageInDateRange(
+            const isInDateRange =
-              currentCache.data,
+              (total || 0) >= IMAGE_LIMIT
-              imageDTO
+                ? getIsImageInDateRange(currentCache.data, imageDTO)
-            );
+                : true;
 
             if (isCacheFullyPopulated || isInDateRange) {
               // *upsert* to $cache
@@ -1111,7 +1127,7 @@ export const imagesApi = api.injectEndpoints({
                   (draft) => {
                     imagesAdapter.upsertOne(draft, {
                       ...imageDTO,
-                      board_id: undefined,
+                      board_id: 'none',
                     });
                   }
                 )

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

@@ -1443,7 +1443,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -1486,7 +1486,7 @@ export type components = {
        * @description The results of node executions
        */
       results: {
-        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
+        [key: string]: (components["schemas"]["ImageOutput"] | components["schemas"]["MaskOutput"] | components["schemas"]["ControlOutput"] | components["schemas"]["ModelLoaderOutput"] | components["schemas"]["LoraLoaderOutput"] | components["schemas"]["SDXLLoraLoaderOutput"] | components["schemas"]["VaeLoaderOutput"] | components["schemas"]["MetadataAccumulatorOutput"] | components["schemas"]["CompelOutput"] | components["schemas"]["ClipSkipInvocationOutput"] | components["schemas"]["LatentsOutput"] | components["schemas"]["SDXLModelLoaderOutput"] | components["schemas"]["SDXLRefinerModelLoaderOutput"] | components["schemas"]["ONNXModelLoaderOutput"] | components["schemas"]["PromptOutput"] | components["schemas"]["PromptCollectionOutput"] | components["schemas"]["IntOutput"] | components["schemas"]["FloatOutput"] | components["schemas"]["StringOutput"] | components["schemas"]["IntCollectionOutput"] | components["schemas"]["FloatCollectionOutput"] | components["schemas"]["ImageCollectionOutput"] | components["schemas"]["NoiseOutput"] | components["schemas"]["GraphInvocationOutput"] | components["schemas"]["IterateInvocationOutput"] | components["schemas"]["CollectInvocationOutput"]) | undefined;
       };
       /**
        * Errors 
@@ -1904,6 +1904,40 @@ export type components = {
        */
       image_name: string;
     };
+    /**
+     * ImageHueAdjustmentInvocation 
+     * @description Adjusts the Hue of an image.
+     */
+    ImageHueAdjustmentInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default img_hue_adjust 
+       * @enum {string}
+       */
+      type?: "img_hue_adjust";
+      /**
+       * Image 
+       * @description The image to adjust
+       */
+      image?: components["schemas"]["ImageField"];
+      /**
+       * Hue 
+       * @description The degrees by which to rotate the hue, 0-360 
+       * @default 0
+       */
+      hue?: number;
+    };
     /**
      * ImageInverseLerpInvocation 
      * @description Inverse linear interpolation of all pixels of an image
@@ -1984,6 +2018,40 @@ export type components = {
        */
       max?: number;
     };
+    /**
+     * ImageLuminosityAdjustmentInvocation 
+     * @description Adjusts the Luminosity (Value) of an image.
+     */
+    ImageLuminosityAdjustmentInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default img_luminosity_adjust 
+       * @enum {string}
+       */
+      type?: "img_luminosity_adjust";
+      /**
+       * Image 
+       * @description The image to adjust
+       */
+      image?: components["schemas"]["ImageField"];
+      /**
+       * Luminosity 
+       * @description The factor by which to adjust the luminosity (value) 
+       * @default 1
+       */
+      luminosity?: number;
+    };
     /**
      * ImageMetadata 
      * @description An image's generation metadata
@@ -2239,6 +2307,40 @@ export type components = {
        */
       resample_mode?: "nearest" | "box" | "bilinear" | "hamming" | "bicubic" | "lanczos";
     };
+    /**
+     * ImageSaturationAdjustmentInvocation 
+     * @description Adjusts the Saturation of an image.
+     */
+    ImageSaturationAdjustmentInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default img_saturation_adjust 
+       * @enum {string}
+       */
+      type?: "img_saturation_adjust";
+      /**
+       * Image 
+       * @description The image to adjust
+       */
+      image?: components["schemas"]["ImageField"];
+      /**
+       * Saturation 
+       * @description The factor by which to adjust the saturation 
+       * @default 1
+       */
+      saturation?: number;
+    };
     /**
      * ImageScaleInvocation 
      * @description Scales an image by a factor
@@ -4912,6 +5014,82 @@ export type components = {
        */
       denoising_end?: number;
     };
+    /**
+     * SDXLLoraLoaderInvocation 
+     * @description Apply selected lora to unet and text_encoder.
+     */
+    SDXLLoraLoaderInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default sdxl_lora_loader 
+       * @enum {string}
+       */
+      type?: "sdxl_lora_loader";
+      /**
+       * Lora 
+       * @description Lora model name
+       */
+      lora?: components["schemas"]["LoRAModelField"];
+      /**
+       * Weight 
+       * @description With what weight to apply lora 
+       * @default 0.75
+       */
+      weight?: number;
+      /**
+       * Unet 
+       * @description UNet model for applying lora
+       */
+      unet?: components["schemas"]["UNetField"];
+      /**
+       * Clip 
+       * @description Clip model for applying lora
+       */
+      clip?: components["schemas"]["ClipField"];
+      /**
+       * Clip2 
+       * @description Clip2 model for applying lora
+       */
+      clip2?: components["schemas"]["ClipField"];
+    };
+    /**
+     * SDXLLoraLoaderOutput 
+     * @description Model loader output
+     */
+    SDXLLoraLoaderOutput: {
+      /**
+       * Type 
+       * @default sdxl_lora_loader_output 
+       * @enum {string}
+       */
+      type?: "sdxl_lora_loader_output";
+      /**
+       * Unet 
+       * @description UNet submodel
+       */
+      unet?: components["schemas"]["UNetField"];
+      /**
+       * Clip 
+       * @description Tokenizer and text_encoder submodels
+       */
+      clip?: components["schemas"]["ClipField"];
+      /**
+       * Clip2 
+       * @description Tokenizer2 and text_encoder2 submodels
+       */
+      clip2?: components["schemas"]["ClipField"];
+    };
     /**
      * SDXLModelLoaderInvocation 
      * @description Loads an sdxl base model, outputting its submodels.
@@ -5961,6 +6139,24 @@ export type components = {
        */
       image?: components["schemas"]["ImageField"];
     };
+    /**
+     * ControlNetModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    ControlNetModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusionXLModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
+    /**
+     * StableDiffusion1ModelFormat 
+     * @description An enumeration. 
+     * @enum {string}
+     */
+    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusionOnnxModelFormat 
      * @description An enumeration. 
@@ -5973,24 +6169,6 @@ export type components = {
      * @enum {string}
      */
     StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
-    /**
-     * StableDiffusion1ModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
-    /**
-     * StableDiffusionXLModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    StableDiffusionXLModelFormat: "checkpoint" | "diffusers";
-    /**
-     * ControlNetModelFormat 
-     * @description An enumeration. 
-     * @enum {string}
-     */
-    ControlNetModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;
@@ -6101,7 +6279,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -6138,7 +6316,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["SDXLLoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["SDXLCompelPromptInvocation"] | components["schemas"]["SDXLRefinerCompelPromptInvocation"] | components["schemas"]["SDXLRawPromptInvocation"] | components["schemas"]["SDXLRefinerRawPromptInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ImageNSFWBlurInvocation"] | components["schemas"]["ImageWatermarkInvocation"] | components["schemas"]["ImageHueAdjustmentInvocation"] | components["schemas"]["ImageLuminosityAdjustmentInvocation"] | components["schemas"]["ImageSaturationAdjustmentInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["SDXLModelLoaderInvocation"] | components["schemas"]["SDXLRefinerModelLoaderInvocation"] | components["schemas"]["SDXLTextToLatentsInvocation"] | components["schemas"]["SDXLLatentsToLatentsInvocation"] | components["schemas"]["ONNXPromptInvocation"] | components["schemas"]["ONNXTextToLatentsInvocation"] | components["schemas"]["ONNXLatentsToImageInvocation"] | components["schemas"]["ONNXSD1ModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["PromptsFromFileInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["ParamStringInvocation"] | components["schemas"]["ParamPromptInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["ESRGANInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {

invokeai/frontend/web/src/services/api/thunks/session.ts

@@ -60,6 +60,9 @@ type InvokedSessionThunkConfig = {
 const isErrorWithStatus = (error: unknown): error is { status: number } =>
   isObject(error) && 'status' in error;
 
+const isErrorWithDetail = (error: unknown): error is { detail: string } =>
+  isObject(error) && 'detail' in error;
+
 /**
  * `SessionsService.invokeSession()` thunk
  */
@@ -85,6 +88,14 @@ export const sessionInvoked = createAsyncThunk<
         error: (error as any).body.detail,
       });
     }
+    if (isErrorWithDetail(error) && response.status === 403) {
+      return rejectWithValue({
+        arg,
+        status: response.status,
+        error: error.detail
+      });
+    }
+    if (error)
       return rejectWithValue({ arg, status: response.status, error });
   }
 });

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

@@ -166,6 +166,9 @@ export type OnnxModelLoaderInvocation = TypeReq<
 export type LoraLoaderInvocation = TypeReq<
   components['schemas']['LoraLoaderInvocation']
 >;
+export type SDXLLoraLoaderInvocation = TypeReq<
+  components['schemas']['SDXLLoraLoaderInvocation']
+>;
 export type MetadataAccumulatorInvocation = TypeReq<
   components['schemas']['MetadataAccumulatorInvocation']
 >;

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "3.0.2a1"
+__version__ = "3.0.2post1"

pyproject.toml

@@ -77,7 +77,7 @@ dependencies = [
   "realesrgan",
   "requests~=2.28.2",
   "rich~=13.3",
-  "safetensors~=0.3.0",
+  "safetensors==0.3.1",
   "scikit-image~=0.21.0",
   "send2trash",
   "test-tube~=0.7.5",
@@ -100,7 +100,7 @@ dependencies = [
 "dev" = [
   "pudb",
 ]
-"test" = ["pytest>6.0.0", "pytest-cov", "black"]
+"test" = ["pytest>6.0.0", "pytest-cov", "pytest-datadir", "black"]
 "xformers" = [
 	   "xformers~=0.0.19; sys_platform!='darwin'",
 	   "triton; sys_platform=='linux'",
@@ -118,7 +118,7 @@ dependencies = [
 [project.scripts]
 
 # legacy entrypoints; provided for backwards compatibility
-"configure_invokeai.py" = "invokeai.frontend.install:invokeai_configure"
+"configure_invokeai.py" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
 "textual_inversion.py" = "invokeai.frontend.training:invokeai_textual_inversion"
 
 # shortcut commands to start cli and web
@@ -130,15 +130,16 @@ dependencies = [
 "invokeai-web" = "invokeai.app.api_app:invoke_api"
 
 # full commands
-"invokeai-configure" = "invokeai.frontend.install:invokeai_configure"
+"invokeai-configure" = "invokeai.frontend.install.invokeai_configure:invokeai_configure"
 "invokeai-merge" = "invokeai.frontend.merge:invokeai_merge_diffusers"
 "invokeai-ti" = "invokeai.frontend.training:invokeai_textual_inversion"
-"invokeai-model-install" = "invokeai.frontend.install:invokeai_model_install"
+"invokeai-model-install" = "invokeai.frontend.install.model_install:main"
 "invokeai-migrate3" = "invokeai.backend.install.migrate_to_3:main"
-"invokeai-update" = "invokeai.frontend.install:invokeai_update"
+"invokeai-update" = "invokeai.frontend.install.invokeai_update:main"
 "invokeai-metadata" = "invokeai.frontend.CLI.sd_metadata:print_metadata"
 "invokeai-node-cli" = "invokeai.app.cli_app:invoke_cli"
 "invokeai-node-web" = "invokeai.app.api_app:invoke_api"
+"invokeai-import-images" = "invokeai.frontend.install.import_images:main"
 
 [project.urls]
 "Homepage" = "https://invoke-ai.github.io/InvokeAI/"

scripts/create_checkpoint_template.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python
+"""
+Read a checkpoint/safetensors file and write out a template .json file containing
+its metadata for use in fast model probing.
+"""
+
+import sys
+import argparse
+import json
+
+from pathlib import Path
+
+from invokeai.backend.model_management.models.base import read_checkpoint_meta
+
+parser = argparse.ArgumentParser(description="Create a .json template from checkpoint/safetensors model")
+parser.add_argument("--checkpoint", "--in", type=Path, help="Path to the input checkpoint/safetensors file")
+parser.add_argument("--template", "--out", type=Path, help="Path to the output .json file")
+
+opt = parser.parse_args()
+ckpt = read_checkpoint_meta(opt.checkpoint)
+while "state_dict" in ckpt:
+    ckpt = ckpt["state_dict"]
+
+tmpl = {}
+
+for key, tensor in ckpt.items():
+    tmpl[key] = list(tensor.shape)
+
+try:
+    with open(opt.template, "w") as f:
+        json.dump(tmpl, f)
+    print(f"Template written out as {opt.template}")
+except Exception as e:
+    print(f"An exception occurred while writing template: {str(e)}")

scripts/verify_checkpoint_template.py

@@ -0,0 +1,37 @@
+#!/usr/bin/env python
+"""
+Read a checkpoint/safetensors file and compare it to a template .json.
+Returns True if their metadata match.
+"""
+
+import sys
+import argparse
+import json
+
+from pathlib import Path
+
+from invokeai.backend.model_management.models.base import read_checkpoint_meta
+
+parser = argparse.ArgumentParser(description="Compare a checkpoint/safetensors file to a JSON metadata template.")
+parser.add_argument("--checkpoint", "--in", type=Path, help="Path to the input checkpoint/safetensors file")
+parser.add_argument("--template", "--out", type=Path, help="Path to the template .json file to match against")
+
+opt = parser.parse_args()
+ckpt = read_checkpoint_meta(opt.checkpoint)
+while "state_dict" in ckpt:
+    ckpt = ckpt["state_dict"]
+
+checkpoint_metadata = {}
+
+for key, tensor in ckpt.items():
+    checkpoint_metadata[key] = list(tensor.shape)
+
+with open(opt.template, "r") as f:
+    template = json.load(f)
+
+if checkpoint_metadata == template:
+    print("True")
+    sys.exit(0)
+else:
+    print("False")
+    sys.exit(-1)

tests/test_model_manager.py

@@ -0,0 +1,47 @@
+from pathlib import Path
+
+import pytest
+
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend import ModelManager, BaseModelType, ModelType, SubModelType
+
+BASIC_MODEL_NAME = ("SDXL base", BaseModelType.StableDiffusionXL, ModelType.Main)
+VAE_OVERRIDE_MODEL_NAME = ("SDXL with VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
+VAE_NULL_OVERRIDE_MODEL_NAME = ("SDXL with empty VAE", BaseModelType.StableDiffusionXL, ModelType.Main)
+
+
+@pytest.fixture
+def model_manager(datadir) -> ModelManager:
+    InvokeAIAppConfig.get_config(root=datadir)
+    return ModelManager(datadir / "configs" / "relative_sub.models.yaml")
+
+
+def test_get_model_names(model_manager: ModelManager):
+    names = model_manager.model_names()
+    assert names[:2] == [BASIC_MODEL_NAME, VAE_OVERRIDE_MODEL_NAME]
+
+
+def test_get_model_path_for_diffusers(model_manager: ModelManager, datadir: Path):
+    model_config = model_manager._get_model_config(BASIC_MODEL_NAME[1], BASIC_MODEL_NAME[0], BASIC_MODEL_NAME[2])
+    top_model_path, is_override = model_manager._get_model_path(model_config)
+    expected_model_path = datadir / "models" / "sdxl" / "main" / "SDXL base 1_0"
+    assert top_model_path == expected_model_path
+    assert not is_override
+
+
+def test_get_model_path_for_overridden_vae(model_manager: ModelManager, datadir: Path):
+    model_config = model_manager._get_model_config(
+        VAE_OVERRIDE_MODEL_NAME[1], VAE_OVERRIDE_MODEL_NAME[0], VAE_OVERRIDE_MODEL_NAME[2]
+    )
+    vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
+    expected_vae_path = datadir / "models" / "sdxl" / "vae" / "sdxl-vae-fp16-fix"
+    assert vae_model_path == expected_vae_path
+    assert is_override
+
+
+def test_get_model_path_for_null_overridden_vae(model_manager: ModelManager, datadir: Path):
+    model_config = model_manager._get_model_config(
+        VAE_NULL_OVERRIDE_MODEL_NAME[1], VAE_NULL_OVERRIDE_MODEL_NAME[0], VAE_NULL_OVERRIDE_MODEL_NAME[2]
+    )
+    vae_model_path, is_override = model_manager._get_model_path(model_config, SubModelType.Vae)
+    assert not is_override

tests/test_model_manager/configs/relative_sub.models.yaml

@@ -0,0 +1,22 @@
+__metadata__:
+  version: 3.0.0
+
+sdxl/main/SDXL base:
+  path: sdxl/main/SDXL base 1_0
+  description: SDXL base v1.0
+  variant: normal
+  format: diffusers
+
+sdxl/main/SDXL with VAE:
+  path: sdxl/main/SDXL base 1_0
+  description: SDXL with customized VAE
+  vae: sdxl/vae/sdxl-vae-fp16-fix/
+  variant: normal
+  format: diffusers
+
+sdxl/main/SDXL with empty VAE:
+  path: sdxl/main/SDXL base 1_0
+  description: SDXL with customized VAE
+  vae: ''
+  variant: normal
+  format: diffusers