Merge branch 'main' into fix/diffusers-embeddings

- also fix a couple places where the wrong base was used for relative model paths

.github/workflows/style-checks.yml

@@ -1,14 +1,13 @@
-name: style checks
+name: Black  # TODO: add isort and flake8 later
-# just formatting for now
-# TODO: add isort and flake8 later
 
 on:
-  pull_request:
+  pull_request: {}
   push:
-    branches: main
+    branches: master
+    tags: "*"
 
 jobs:
-  black:
+  test:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
@@ -20,7 +19,8 @@ jobs:
 
       - name: Install dependencies with pip
         run: |
-          pip install black
+          pip install --upgrade pip wheel
+          pip install .[test]
 
       # - run: isort --check-only .
       - run: black --check .

.github/workflows/test-invoke-pip-skip.yml

@@ -0,0 +1,50 @@
+name: Test invoke.py pip
+
+# This is a dummy stand-in for the actual tests
+# we don't need to run python tests on non-Python changes
+# But PRs require passing tests to be mergeable
+
+on:
+  pull_request:
+    paths:
+      - '**'
+      - '!pyproject.toml'
+      - '!invokeai/**'
+      - '!tests/**'
+      - 'invokeai/frontend/web/**'
+  merge_group:
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  matrix:
+    if: github.event.pull_request.draft == false
+    strategy:
+      matrix:
+        python-version:
+          - '3.10'
+        pytorch:
+          - linux-cuda-11_7
+          - linux-rocm-5_2
+          - linux-cpu
+          - macos-default
+          - windows-cpu
+        include:
+          - pytorch: linux-cuda-11_7
+            os: ubuntu-22.04
+          - pytorch: linux-rocm-5_2
+            os: ubuntu-22.04
+          - pytorch: linux-cpu
+            os: ubuntu-22.04
+          - pytorch: macos-default
+            os: macOS-12
+          - pytorch: windows-cpu
+            os: windows-2022
+    name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
+    runs-on: ${{ matrix.os }}
+    steps:
+      - name: skip
+        run: echo "no build required"

.github/workflows/test-invoke-pip.yml

@@ -3,7 +3,16 @@ on:
   push:
     branches:
       - 'main'
+    paths:
+      - 'pyproject.toml'
+      - 'invokeai/**'
+      - '!invokeai/frontend/web/**'
   pull_request:
+    paths:
+      - 'pyproject.toml'
+      - 'invokeai/**'
+      - 'tests/**'
+      - '!invokeai/frontend/web/**'
     types:
       - 'ready_for_review'
       - 'opened'
@@ -56,23 +65,10 @@ jobs:
         id: checkout-sources
         uses: actions/checkout@v3
 
-      - name: Check for changed python files
-        id: changed-files
-        uses: tj-actions/changed-files@v37
-        with:
-          files_yaml: |
-            python:
-              - 'pyproject.toml'
-              - 'invokeai/**'
-              - '!invokeai/frontend/web/**'
-              - 'tests/**'
-
       - name: set test prompt to main branch validation
-        if: steps.changed-files.outputs.python_any_changed == 'true'
         run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
 
       - name: setup python
-        if: steps.changed-files.outputs.python_any_changed == 'true'
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
@@ -80,7 +76,6 @@ jobs:
           cache-dependency-path: pyproject.toml
 
       - name: install invokeai
-        if: steps.changed-files.outputs.python_any_changed == 'true'
         env:
           PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
         run: >
@@ -88,7 +83,6 @@ jobs:
           --editable=".[test]"
 
       - name: run pytest
-        if: steps.changed-files.outputs.python_any_changed == 'true'
         id: run-pytest
         run: pytest
 

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/cu118
+    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
     ```
 
     _For Linux with an AMD GPU:_
@@ -184,9 +184,8 @@ the command `npm install -g yarn` if needed)
 6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
 
     ```terminal
-    invokeai-configure --root .
+    invokeai-configure
     ```
-	Don't miss the dot at the end!
 
 7. Launch the web server (do it every time you run InvokeAI):
 
@@ -194,9 +193,15 @@ the command `npm install -g yarn` if needed)
     invokeai-web
     ```
 
-8. Point your browser to http://localhost:9090 to bring up the web interface.
+8. Build Node.js assets
 
-9. Type `banana sushi` in the box on the top left and click `Invoke`.
+  ```terminal
+  cd invokeai/frontend/web/
+  yarn vite build
+  ```
+
+9. Point your browser to http://localhost:9090 to bring up the web interface.
+10. Type `banana sushi` in the box on the top left and click `Invoke`.
 
 Be sure to activate the virtual environment each time before re-launching InvokeAI,
 using `source .venv/bin/activate` or `.venv\Scripts\activate`.
@@ -306,30 +311,13 @@ 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
 
-#### Migrating Images
+#### Migration Caveats
 
 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. To do this, you 
+images stored in your 2.3-format outputs directory. You will need to
-need to run an additional step:
+manually import selected images into the 3.0 gallery via drag-and-drop.
-
-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.
 	
-    - ***LICENSE***     
+    - ***Autoimport FolderLICENSE***     
 
     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/cu118
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu117
     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/cu118
+        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
         ```
 
     === "ROCm (AMD)"
@@ -192,11 +192,9 @@ manager, please follow these steps:
     your outputs.
 
     ```terminal
-    invokeai-configure --root .
+    invokeai-configure
     ```
 
-	Don't miss the dot at the end of the command!
-
     The script `invokeai-configure` will interactively guide you through the
     process of downloading and installing the weights files needed for InvokeAI.
     Note that the main Stable Diffusion weights file is protected by a license
@@ -227,6 +225,12 @@ manager, please follow these steps:
 
         !!! warning "Make sure that the virtual environment is activated, which should create `(.venv)` in front of your prompt!"
 
+        === "CLI"
+
+            ```bash
+            invokeai
+            ```
+
         === "local Webserver"
 
             ```bash
@@ -239,12 +243,6 @@ manager, please follow these steps:
             invokeai --web --host 0.0.0.0
             ```
 
-        === "CLI"
-
-            ```bash
-            invokeai
-            ```
-
         If you choose the run the web interface, point your browser at
         http://localhost:9090 in order to load the GUI.
 
@@ -312,7 +310,7 @@ installation protocol (important!)
 
     === "CUDA (NVidia)"
         ```bash
-        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
+        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
         ```
 
     === "ROCm (AMD)"
@@ -356,7 +354,7 @@ 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/cu118
+pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
 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
+Go to [CUDA Toolkit 11.7
-Downloads](https://developer.nvidia.com/cuda-downloads), and use the
+Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive),
-target selection wizard to choose your operating system, hardware
+and use the target selection wizard to choose your operating system,
-platform, and preferred installation method (e.g. "local" versus
+hardware platform, and preferred installation method (e.g. "local"
-"network").
+versus "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/cu118` as described in the [Manual
+https://download.pytorch.org/whl/cu117` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 ## :simple-amd: ROCm

docs/installation/050_INSTALLING_MODELS.md

@@ -124,7 +124,7 @@ installation. Examples:
 invokeai-model-install --list controlnet
 
 # (install the model at the indicated URL)
-invokeai-model-install --add https://civitai.com/api/download/models/128713
+invokeai-model-install --add http://civitai.com/2860
 
 # (delete the named model)
 invokeai-model-install --delete sd-1/main/analog-diffusion

docs/installation/070_INSTALL_XFORMERS.md

@@ -28,21 +28,18 @@ command line, then just be sure to activate it's virtual environment.
 Then run the following three commands:
 
 ```sh
-pip install xformers~=0.0.19
+pip install xformers==0.0.16rc425
-pip install triton    # WON'T WORK ON WINDOWS
+pip install triton
 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. On Windows, please omit the `triton` package,
+installation status. If all goes well, you'll see a report like the
-which is not available on that platform.
-
-If all goes well, you'll see a report like the
 following:
 
 ```sh
-xFormers 0.0.20
+xFormers 0.0.16rc425
 memory_efficient_attention.cutlassF:               available
 memory_efficient_attention.cutlassB:               available
 memory_efficient_attention.flshattF:               available
@@ -51,28 +48,22 @@ 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:                                   2.0.1+cu118
+pytorch.version:                                   1.13.1+cu117
 pytorch.cuda:                                      available
-gpu.compute_capability:                            8.9
+gpu.compute_capability:                            8.6
-gpu.name:                                          NVIDIA GeForce RTX 4070
+gpu.name:                                          NVIDIA RTX A2000 12GB
 build.info:                                        available
-build.cuda_version:                                1108
+build.cuda_version:                                1107
-build.python_version:                              3.10.11
+build.python_version:                              3.10.9
-build.torch_version:                               2.0.1+cu118
+build.torch_version:                               1.13.1+cu117
 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-v0.0.20
+build.env.XFORMERS_PACKAGE_FROM:                   wheel-v0.0.16rc425
-build.nvcc_version:                                11.8.89
 source.privacy:                                    open source
 ```
 
@@ -92,14 +83,14 @@ 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 11.8
+#### 1. Install CUDA Toolkit 11.7
 
 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 11.8
+by NVIDIA itself. Go to [CUDA Toolkit 11.7
-Downloads](https://developer.nvidia.com/cuda-11-8-0-download-archive)
+Downloads](https://developer.nvidia.com/cuda-11-7-0-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.
@@ -110,17 +101,17 @@ example, the install script recipe for Ubuntu 22.04 running on a
 x86_64 system is:
 
 ```
-wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
+wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
-sudo sh cuda_11.8.0_520.61.05_linux.run
+sudo sh cuda_11.7.0_515.43.04_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 2.01 with CUDA 11.8 support
+#### 2. Confirm/Install pyTorch 1.13 with CUDA 11.7 support
 
-If you are using InvokeAI 3.0.2 or higher, these will already be
+If you are using InvokeAI 2.3 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
@@ -133,7 +124,7 @@ Then run the command:
 python -c 'exec("import torch\nprint(torch.__version__)")'
 ```
 
-If it prints __1.13.1+cu118__ you're good. If not, you can install the
+If it prints __1.13.1+cu117__ you're good. If not, you can install the
 most up to date libraries with this command:
 
 ```sh

flake.nix

@@ -34,10 +34,6 @@
             cudaPackages.cudnn
             cudaPackages.cuda_nvrtc
             cudatoolkit
-            pkgconfig
-            libconfig
-            cmake
-            blas
             freeglut
             glib
             gperf
@@ -46,12 +42,6 @@
             libGLU
             linuxPackages.nvidia_x11
             python
-            (opencv4.override {
-              enableGtk3 = true;
-              enableFfmpeg = true;
-              enableCuda = true;
-              enableUnfree = true;
-            })
             stdenv.cc
             stdenv.cc.cc.lib
             xorg.libX11

installer/lib/installer.py

@@ -348,7 +348,7 @@ class InvokeAiInstance:
 
         introduction()
 
-        from invokeai.frontend.install.invokeai_configure import invokeai_configure
+        from invokeai.frontend.install 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/cu118"
+        url = "https://download.pytorch.org/whl/cu117"
         optional_modules = "[xformers,onnx-cuda]"
     if device == "cuda_and_dml":
-        url = "https://download.pytorch.org/whl/cu118"
+        url = "https://download.pytorch.org/whl/cu117"
         optional_modules = "[xformers,onnx-directml]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13

invokeai/app/api/dependencies.py

@@ -2,6 +2,7 @@
 
 from typing import Optional
 from logging import Logger
+import os
 from invokeai.app.services.board_image_record_storage import (
     SqliteBoardImageRecordStorage,
 )
@@ -29,7 +30,6 @@ from ..services.invoker import Invoker
 from ..services.processor import DefaultInvocationProcessor
 from ..services.sqlite import SqliteItemStorage
 from ..services.model_manager_service import ModelManagerService
-from ..services.invocation_stats import InvocationStatsService
 from .events import FastAPIEventService
 
 
@@ -55,7 +55,7 @@ logger = InvokeAILogger.getLogger()
 class ApiDependencies:
     """Contains and initializes all dependencies for the API"""
 
-    invoker: Invoker
+    invoker: Optional[Invoker] = None
 
     @staticmethod
     def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
@@ -68,9 +68,8 @@ class ApiDependencies:
         output_folder = config.output_path
 
         # TODO: build a file/path manager?
-        db_path = config.db_path
+        db_location = config.db_path
-        db_path.parent.mkdir(parents=True, exist_ok=True)
+        db_location.parent.mkdir(parents=True, exist_ok=True)
-        db_location = str(db_path)
 
         graph_execution_manager = SqliteItemStorage[GraphExecutionState](
             filename=db_location, table_name="graph_executions"
@@ -129,7 +128,6 @@ class ApiDependencies:
             graph_execution_manager=graph_execution_manager,
             processor=DefaultInvocationProcessor(),
             configuration=config,
-            performance_statistics=InvocationStatsService(graph_execution_manager),
             logger=logger,
         )
 

invokeai/app/api/routers/board_images.py

@@ -1,30 +1,24 @@
-from fastapi import Body, HTTPException
+from fastapi import Body, HTTPException, Path, Query
 from fastapi.routing import APIRouter
-from pydantic import BaseModel, Field
+from invokeai.app.services.board_record_storage import BoardRecord, BoardChanges
+from invokeai.app.services.image_record_storage import OffsetPaginatedResults
+from invokeai.app.services.models.board_record import BoardDTO
+from invokeai.app.services.models.image_record import ImageDTO
 
 from ..dependencies import ApiDependencies
 
 board_images_router = APIRouter(prefix="/v1/board_images", tags=["boards"])
 
 
-class AddImagesToBoardResult(BaseModel):
-    board_id: str = Field(description="The id of the board the images were added to")
-    added_image_names: list[str] = Field(description="The image names that were added to the board")
-
-
-class RemoveImagesFromBoardResult(BaseModel):
-    removed_image_names: list[str] = Field(description="The image names that were removed from their board")
-
-
 @board_images_router.post(
     "/",
-    operation_id="add_image_to_board",
+    operation_id="create_board_image",
     responses={
         201: {"description": "The image was added to a board successfully"},
     },
     status_code=201,
 )
-async def add_image_to_board(
+async def create_board_image(
     board_id: str = Body(description="The id of the board to add to"),
     image_name: str = Body(description="The name of the image to add"),
 ):
@@ -35,78 +29,26 @@ async def add_image_to_board(
         )
         return result
     except Exception as e:
-        raise HTTPException(status_code=500, detail="Failed to add image to board")
+        raise HTTPException(status_code=500, detail="Failed to add to board")
 
 
 @board_images_router.delete(
     "/",
-    operation_id="remove_image_from_board",
+    operation_id="remove_board_image",
     responses={
         201: {"description": "The image was removed from the board successfully"},
     },
     status_code=201,
 )
-async def remove_image_from_board(
+async def remove_board_image(
-    image_name: str = Body(description="The name of the image to remove", embed=True),
+    board_id: str = Body(description="The id of the board"),
+    image_name: str = Body(description="The name of the image to remove"),
 ):
-    """Removes an image from its board, if it had one"""
+    """Deletes a board_image"""
     try:
-        result = ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name)
+        result = ApiDependencies.invoker.services.board_images.remove_image_from_board(
-        return result
-    except Exception as e:
-        raise HTTPException(status_code=500, detail="Failed to remove image from board")
-
-
-@board_images_router.post(
-    "/batch",
-    operation_id="add_images_to_board",
-    responses={
-        201: {"description": "Images were added to board successfully"},
-    },
-    status_code=201,
-    response_model=AddImagesToBoardResult,
-)
-async def add_images_to_board(
-    board_id: str = Body(description="The id of the board to add to"),
-    image_names: list[str] = Body(description="The names of the images to add", embed=True),
-) -> AddImagesToBoardResult:
-    """Adds a list of images to a board"""
-    try:
-        added_image_names: list[str] = []
-        for image_name in image_names:
-            try:
-                ApiDependencies.invoker.services.board_images.add_image_to_board(
             board_id=board_id, image_name=image_name
         )
-                added_image_names.append(image_name)
+        return result
-            except:
-                pass
-        return AddImagesToBoardResult(board_id=board_id, added_image_names=added_image_names)
     except Exception as e:
-        raise HTTPException(status_code=500, detail="Failed to add images to board")
+        raise HTTPException(status_code=500, detail="Failed to update board")
-
-
-@board_images_router.post(
-    "/batch/delete",
-    operation_id="remove_images_from_board",
-    responses={
-        201: {"description": "Images were removed from board successfully"},
-    },
-    status_code=201,
-    response_model=RemoveImagesFromBoardResult,
-)
-async def remove_images_from_board(
-    image_names: list[str] = Body(description="The names of the images to remove", embed=True),
-) -> RemoveImagesFromBoardResult:
-    """Removes a list of images from their board, if they had one"""
-    try:
-        removed_image_names: list[str] = []
-        for image_name in image_names:
-            try:
-                ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name)
-                removed_image_names.append(image_name)
-            except:
-                pass
-        return RemoveImagesFromBoardResult(removed_image_names=removed_image_names)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail="Failed to remove images from board")

invokeai/app/api/routers/images.py

@@ -1,20 +1,21 @@
 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 pydantic import BaseModel
+from PIL import Image
 
 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"])
@@ -24,7 +25,7 @@ IMAGE_MAX_AGE = 31536000
 
 
 @images_router.post(
-    "/upload",
+    "/",
     operation_id="upload_image",
     responses={
         201: {"description": "The image was uploaded successfully"},
@@ -76,7 +77,7 @@ async def upload_image(
         raise HTTPException(status_code=500, detail="Failed to create image")
 
 
-@images_router.delete("/i/{image_name}", operation_id="delete_image")
+@images_router.delete("/{image_name}", operation_id="delete_image")
 async def delete_image(
     image_name: str = Path(description="The name of the image to delete"),
 ) -> None:
@@ -102,7 +103,7 @@ async def clear_intermediates() -> int:
 
 
 @images_router.patch(
-    "/i/{image_name}",
+    "/{image_name}",
     operation_id="update_image",
     response_model=ImageDTO,
 )
@@ -119,7 +120,7 @@ async def update_image(
 
 
 @images_router.get(
-    "/i/{image_name}",
+    "/{image_name}",
     operation_id="get_image_dto",
     response_model=ImageDTO,
 )
@@ -135,7 +136,7 @@ async def get_image_dto(
 
 
 @images_router.get(
-    "/i/{image_name}/metadata",
+    "/{image_name}/metadata",
     operation_id="get_image_metadata",
     response_model=ImageMetadata,
 )
@@ -150,9 +151,8 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
-@images_router.api_route(
+@images_router.get(
-    "/i/{image_name}/full",
+    "/{image_name}/full",
-    methods=["GET", "HEAD"],
     operation_id="get_image_full",
     response_class=Response,
     responses={
@@ -187,7 +187,7 @@ async def get_image_full(
 
 
 @images_router.get(
-    "/i/{image_name}/thumbnail",
+    "/{image_name}/thumbnail",
     operation_id="get_image_thumbnail",
     response_class=Response,
     responses={
@@ -216,7 +216,7 @@ async def get_image_thumbnail(
 
 
 @images_router.get(
-    "/i/{image_name}/urls",
+    "/{image_name}/urls",
     operation_id="get_image_urls",
     response_model=ImageUrlsDTO,
 )
@@ -265,24 +265,3 @@ async def list_image_dtos(
     )
 
     return image_dtos
-
-
-class DeleteImagesFromListResult(BaseModel):
-    deleted_images: list[str]
-
-
-@images_router.post("/delete", operation_id="delete_images_from_list", response_model=DeleteImagesFromListResult)
-async def delete_images_from_list(
-    image_names: list[str] = Body(description="The list of names of images to delete", embed=True),
-) -> DeleteImagesFromListResult:
-    try:
-        deleted_images: list[str] = []
-        for image_name in image_names:
-            try:
-                ApiDependencies.invoker.services.images.delete(image_name)
-                deleted_images.append(image_name)
-            except:
-                pass
-        return DeleteImagesFromListResult(deleted_images=deleted_images)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail="Failed to delete images")

invokeai/app/api/routers/models.py

@@ -104,12 +104,8 @@ 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/cli_app.py

@@ -37,7 +37,6 @@ from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
 from invokeai.app.services.images import ImageService, ImageServiceDependencies
 from invokeai.app.services.resource_name import SimpleNameService
 from invokeai.app.services.urls import LocalUrlService
-from invokeai.app.services.invocation_stats import InvocationStatsService
 from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
 from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
 
@@ -312,7 +311,6 @@ def invoke_cli():
         graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
         graph_execution_manager=graph_execution_manager,
         processor=DefaultInvocationProcessor(),
-        performance_statistics=InvocationStatsService(graph_execution_manager),
         logger=logger,
         configuration=config,
     )

invokeai/app/invocations/compel.py

@@ -109,15 +109,12 @@ class CompelInvocation(BaseInvocation):
             name = trigger[1:-1]
             try:
                 ti_list.append(
-                    (
-                        name,
                     context.services.model_manager.get_model(
                         model_name=name,
                         base_model=self.clip.text_encoder.base_model,
                         model_type=ModelType.TextualInversion,
                         context=context,
-                        ).context.model,
+                    ).context.model
-                    )
                 )
             except ModelNotFoundException:
                 # print(e)
@@ -176,7 +173,7 @@ class CompelInvocation(BaseInvocation):
 
 
 class SDXLPromptInvocationBase:
-    def run_clip_raw(self, context, clip_field, prompt, get_pooled, lora_prefix):
+    def run_clip_raw(self, context, clip_field, prompt, get_pooled):
         tokenizer_info = context.services.model_manager.get_model(
             **clip_field.tokenizer.dict(),
             context=context,
@@ -200,15 +197,12 @@ class SDXLPromptInvocationBase:
             name = trigger[1:-1]
             try:
                 ti_list.append(
-                    (
-                        name,
                     context.services.model_manager.get_model(
                         model_name=name,
                         base_model=clip_field.text_encoder.base_model,
                         model_type=ModelType.TextualInversion,
                         context=context,
-                        ).context.model,
+                    ).context.model
-                    )
                 )
             except ModelNotFoundException:
                 # print(e)
@@ -216,8 +210,8 @@ class SDXLPromptInvocationBase:
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
-        with ModelPatcher.apply_lora(
+        with ModelPatcher.apply_lora_text_encoder(
-            text_encoder_info.context.model, _lora_loader(), lora_prefix
+            text_encoder_info.context.model, _lora_loader()
         ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
             tokenizer,
             ti_manager,
@@ -253,7 +247,7 @@ class SDXLPromptInvocationBase:
 
         return c, c_pooled, None
 
-    def run_clip_compel(self, context, clip_field, prompt, get_pooled, lora_prefix):
+    def run_clip_compel(self, context, clip_field, prompt, get_pooled):
         tokenizer_info = context.services.model_manager.get_model(
             **clip_field.tokenizer.dict(),
             context=context,
@@ -277,15 +271,12 @@ class SDXLPromptInvocationBase:
             name = trigger[1:-1]
             try:
                 ti_list.append(
-                    (
-                        name,
                     context.services.model_manager.get_model(
                         model_name=name,
                         base_model=clip_field.text_encoder.base_model,
                         model_type=ModelType.TextualInversion,
                         context=context,
-                        ).context.model,
+                    ).context.model
-                    )
                 )
             except ModelNotFoundException:
                 # print(e)
@@ -293,8 +284,8 @@ class SDXLPromptInvocationBase:
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
-        with ModelPatcher.apply_lora(
+        with ModelPatcher.apply_lora_text_encoder(
-            text_encoder_info.context.model, _lora_loader(), lora_prefix
+            text_encoder_info.context.model, _lora_loader()
         ), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
             tokenizer,
             ti_manager,
@@ -366,11 +357,11 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False, "lora_te1_")
+        c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False)
         if self.style.strip() == "":
-            c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True, "lora_te2_")
+            c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True)
         else:
-            c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "lora_te2_")
+            c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
 
         original_size = (self.original_height, self.original_width)
         crop_coords = (self.crop_top, self.crop_left)
@@ -424,8 +415,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        # TODO: if there will appear lora for refiner - write proper prefix
+        c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
-        c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "<NONE>")
 
         original_size = (self.original_height, self.original_width)
         crop_coords = (self.crop_top, self.crop_left)
@@ -477,11 +467,11 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False, "lora_te1_")
+        c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False)
         if self.style.strip() == "":
-            c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True, "lora_te2_")
+            c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True)
         else:
-            c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True, "lora_te2_")
+            c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True)
 
         original_size = (self.original_height, self.original_width)
         crop_coords = (self.crop_top, self.crop_left)
@@ -535,8 +525,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        # TODO: if there will appear lora for refiner - write proper prefix
+        c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True)
-        c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True, "<NONE>")
 
         original_size = (self.original_height, self.original_width)
         crop_coords = (self.crop_top, self.crop_left)

invokeai/app/invocations/generate.py

@@ -1,23 +1,26 @@
 # 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 .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext
+
-from .compel import ConditioningField
-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 ..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
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
 INFILL_METHODS = Literal[tuple(infill_methods())]
@@ -181,8 +184,6 @@ 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,
@@ -192,6 +193,8 @@ 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

@@ -3,7 +3,6 @@
 from typing import Literal, Optional
 
 import numpy
-import cv2
 from PIL import Image, ImageFilter, ImageOps, ImageChops
 from pydantic import Field
 from pathlib import Path
@@ -501,7 +500,7 @@ 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.min
+        image_arr = image_arr * (self.max - self.min) + self.max
 
         lerp_image = Image.fromarray(numpy.uint8(image_arr))
 
@@ -651,143 +650,3 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
             width=image_dto.width,
             height=image_dto.height,
         )
-
-
-class ImageHueAdjustmentInvocation(BaseInvocation):
-    """Adjusts the Hue of an image."""
-
-    # fmt: off
-    type: Literal["img_hue_adjust"] = "img_hue_adjust"
-
-    # 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, 0-360")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        pil_image = context.services.images.get_pil_image(self.image.image_name)
-
-        # Convert image to HSV color space
-        hsv_image = numpy.array(pil_image.convert("HSV"))
-
-        # Convert hue from 0..360 to 0..256
-        hue = int(256 * ((self.hue % 360) / 360))
-
-        # Increment each hue and wrap around at 255
-        hsv_image[:, :, 0] = (hsv_image[:, :, 0] + hue) % 256
-
-        # Convert back to PIL format and to original color mode
-        pil_image = Image.fromarray(hsv_image, mode="HSV").convert("RGBA")
-
-        image_dto = context.services.images.create(
-            image=pil_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-            session_id=context.graph_execution_state_id,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class ImageLuminosityAdjustmentInvocation(BaseInvocation):
-    """Adjusts the Luminosity (Value) of an image."""
-
-    # fmt: off
-    type: Literal["img_luminosity_adjust"] = "img_luminosity_adjust"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to adjust")
-    luminosity: float = Field(default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)")
-    # 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)
-
-        # Adjust the luminosity (value)
-        hsv_image[:, :, 2] = numpy.clip(hsv_image[:, :, 2] * self.luminosity, 0, 255)
-
-        # Convert image back to BGR color space
-        image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
-
-        # Convert back to PIL format and to original color mode
-        pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
-
-        image_dto = context.services.images.create(
-            image=pil_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-            session_id=context.graph_execution_state_id,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class ImageSaturationAdjustmentInvocation(BaseInvocation):
-    """Adjusts the Saturation of an image."""
-
-    # fmt: off
-    type: Literal["img_saturation_adjust"] = "img_saturation_adjust"
-
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to adjust")
-    saturation: float = Field(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation")
-    # 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)
-
-        # Adjust the saturation
-        hsv_image[:, :, 1] = numpy.clip(hsv_image[:, :, 1] * self.saturation, 0, 255)
-
-        # Convert image back to BGR color space
-        image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
-
-        # Convert back to PIL format and to original color mode
-        pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
-
-        image_dto = context.services.images.create(
-            image=pil_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
-            session_id=context.graph_execution_state_id,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/latent.py

@@ -5,27 +5,16 @@ 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 ...backend.model_management.lora import ModelPatcher
-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 (
     ConditioningData,
@@ -35,7 +24,23 @@ 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())
 
@@ -226,6 +231,7 @@ 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

@@ -1,8 +1,7 @@
 from typing import Literal, Optional, Union
 
-from pydantic import Field
+from pydantic import BaseModel, Field
 
-from ...version import __version__
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -11,20 +10,18 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 
 
-class LoRAMetadataField(BaseModelExcludeNull):
+class LoRAMetadataField(BaseModel):
     """LoRA metadata for an image generated in InvokeAI."""
 
     lora: LoRAModelField = Field(description="The LoRA model")
     weight: float = Field(description="The weight of the LoRA model")
 
 
-class CoreMetadata(BaseModelExcludeNull):
+class CoreMetadata(BaseModel):
     """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",
     )
@@ -73,7 +70,7 @@ class CoreMetadata(BaseModelExcludeNull):
     refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
 
 
-class ImageMetadata(BaseModelExcludeNull):
+class ImageMetadata(BaseModel):
     """An image's generation metadata"""
 
     metadata: Optional[dict] = Field(

invokeai/app/invocations/model.py

@@ -262,103 +262,6 @@ class LoraLoaderInvocation(BaseInvocation):
         return output
 
 
-class SDXLLoraLoaderOutput(BaseInvocationOutput):
-    """Model loader output"""
-
-    # fmt: off
-    type: Literal["sdxl_lora_loader_output"] = "sdxl_lora_loader_output"
-
-    unet: Optional[UNetField] = Field(default=None, description="UNet submodel")
-    clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels")
-    clip2: Optional[ClipField] = Field(default=None, description="Tokenizer2 and text_encoder2 submodels")
-    # fmt: on
-
-
-class SDXLLoraLoaderInvocation(BaseInvocation):
-    """Apply selected lora to unet and text_encoder."""
-
-    type: Literal["sdxl_lora_loader"] = "sdxl_lora_loader"
-
-    lora: Union[LoRAModelField, None] = Field(default=None, description="Lora model name")
-    weight: float = Field(default=0.75, description="With what weight to apply lora")
-
-    unet: Optional[UNetField] = Field(description="UNet model for applying lora")
-    clip: Optional[ClipField] = Field(description="Clip model for applying lora")
-    clip2: Optional[ClipField] = Field(description="Clip2 model for applying lora")
-
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "title": "SDXL Lora Loader",
-                "tags": ["lora", "loader"],
-                "type_hints": {"lora": "lora_model"},
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
-        if self.lora is None:
-            raise Exception("No LoRA provided")
-
-        base_model = self.lora.base_model
-        lora_name = self.lora.model_name
-
-        if not context.services.model_manager.model_exists(
-            base_model=base_model,
-            model_name=lora_name,
-            model_type=ModelType.Lora,
-        ):
-            raise Exception(f"Unknown lora name: {lora_name}!")
-
-        if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
-            raise Exception(f'Lora "{lora_name}" already applied to unet')
-
-        if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
-            raise Exception(f'Lora "{lora_name}" already applied to clip')
-
-        if self.clip2 is not None and any(lora.model_name == lora_name for lora in self.clip2.loras):
-            raise Exception(f'Lora "{lora_name}" already applied to clip2')
-
-        output = SDXLLoraLoaderOutput()
-
-        if self.unet is not None:
-            output.unet = copy.deepcopy(self.unet)
-            output.unet.loras.append(
-                LoraInfo(
-                    base_model=base_model,
-                    model_name=lora_name,
-                    model_type=ModelType.Lora,
-                    submodel=None,
-                    weight=self.weight,
-                )
-            )
-
-        if self.clip is not None:
-            output.clip = copy.deepcopy(self.clip)
-            output.clip.loras.append(
-                LoraInfo(
-                    base_model=base_model,
-                    model_name=lora_name,
-                    model_type=ModelType.Lora,
-                    submodel=None,
-                    weight=self.weight,
-                )
-            )
-
-        if self.clip2 is not None:
-            output.clip2 = copy.deepcopy(self.clip2)
-            output.clip2.loras.append(
-                LoraInfo(
-                    base_model=base_model,
-                    model_name=lora_name,
-                    model_type=ModelType.Lora,
-                    submodel=None,
-                    weight=self.weight,
-                )
-            )
-
-        return output
-
-
 class VAEModelField(BaseModel):
     """Vae model field"""
 

invokeai/app/invocations/onnx.py

@@ -65,6 +65,7 @@ class ONNXPromptInvocation(BaseInvocation):
             **self.clip.text_encoder.dict(),
         )
         with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder, ExitStack() as stack:
+            # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.clip.loras]
             loras = [
                 (context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)
                 for lora in self.clip.loras
@@ -75,14 +76,18 @@ class ONNXPromptInvocation(BaseInvocation):
                 name = trigger[1:-1]
                 try:
                     ti_list.append(
-                        (
+                        # stack.enter_context(
-                            name,
+                        #    context.services.model_manager.get_model(
+                        #        model_name=name,
+                        #        base_model=self.clip.text_encoder.base_model,
+                        #        model_type=ModelType.TextualInversion,
+                        #    )
+                        # )
                         context.services.model_manager.get_model(
                             model_name=name,
                             base_model=self.clip.text_encoder.base_model,
                             model_type=ModelType.TextualInversion,
-                            ).context.model,
+                        ).context.model
-                        )
                     )
                 except Exception:
                     # print(e)

invokeai/app/invocations/sdxl.py

@@ -5,7 +5,7 @@ from typing import List, Literal, Optional, Union
 
 from pydantic import Field, validator
 
-from ...backend.model_management import ModelType, SubModelType, ModelPatcher
+from ...backend.model_management import ModelType, SubModelType
 from invokeai.app.util.step_callback import stable_diffusion_xl_step_callback
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
 
@@ -293,20 +293,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
 
         num_inference_steps = self.steps
 
-        def _lora_loader():
-            for lora in self.unet.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}),
-                    context=context,
-                )
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
-
         unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context)
         do_classifier_free_guidance = True
         cross_attention_kwargs = None
-        with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
+        with unet_info as unet:
             scheduler.set_timesteps(num_inference_steps, device=unet.device)
             timesteps = scheduler.timesteps
 
@@ -553,19 +543,9 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
             context=context,
         )
 
-        def _lora_loader():
-            for lora in self.unet.loras:
-                lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}),
-                    context=context,
-                )
-                yield (lora_info.context.model, lora.weight)
-                del lora_info
-            return
-
         do_classifier_free_guidance = True
         cross_attention_kwargs = None
-        with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
+        with unet_info as unet:
             # apply denoising_start
             num_inference_steps = self.steps
             scheduler.set_timesteps(num_inference_steps, device=unet.device)

invokeai/app/services/board_image_record_storage.py

@@ -25,6 +25,7 @@ class BoardImageRecordStorageBase(ABC):
     @abstractmethod
     def remove_image_from_board(
         self,
+        board_id: str,
         image_name: str,
     ) -> None:
         """Removes an image from a board."""
@@ -153,6 +154,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
 
     def remove_image_from_board(
         self,
+        board_id: str,
         image_name: str,
     ) -> None:
         try:
@@ -160,9 +162,9 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
             self._cursor.execute(
                 """--sql
                 DELETE FROM board_images
-                WHERE image_name = ?;
+                WHERE board_id = ? AND image_name = ?;
                 """,
-                (image_name,),
+                (board_id, image_name),
             )
             self._conn.commit()
         except sqlite3.Error as e:

invokeai/app/services/board_images.py

@@ -31,6 +31,7 @@ class BoardImagesServiceABC(ABC):
     @abstractmethod
     def remove_image_from_board(
         self,
+        board_id: str,
         image_name: str,
     ) -> None:
         """Removes an image from a board."""
@@ -92,9 +93,10 @@ class BoardImagesService(BoardImagesServiceABC):
 
     def remove_image_from_board(
         self,
+        board_id: str,
         image_name: str,
     ) -> None:
-        self._services.board_image_records.remove_image_from_board(image_name)
+        self._services.board_image_records.remove_image_from_board(board_id, image_name)
 
     def get_all_board_image_names_for_board(
         self,

invokeai/app/services/config.py

@@ -24,10 +24,11 @@ InvokeAI:
     sequential_guidance: false
     precision: float16
     max_cache_size: 6
-    max_vram_cache_size: 0.5
+    max_vram_cache_size: 2.7
     always_use_cpu: false
     free_gpu_mem: false
   Features:
+    restore: true
     esrgan: true
     patchmatch: true
     internet_available: true
@@ -164,7 +165,7 @@ import pydoc
 import os
 import sys
 from argparse import ArgumentParser
-from omegaconf import OmegaConf, DictConfig, ListConfig
+from omegaconf import OmegaConf, DictConfig
 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
@@ -172,7 +173,6 @@ 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):
@@ -189,12 +189,7 @@ class InvokeAISettings(BaseSettings):
         opt = parser.parse_args(argv)
         for name in self.__fields__:
             if name not in self._excluded():
-                value = getattr(opt, name)
+                setattr(self, name, 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:
         """
@@ -279,7 +274,7 @@ class InvokeAISettings(BaseSettings):
     @classmethod
     def _excluded(self) -> List[str]:
         # internal fields that shouldn't be exposed as command line options
-        return ["type", "initconf"]
+        return ["type", "initconf", "cached_root"]
 
     @classmethod
     def _excluded_from_yaml(self) -> List[str]:
@@ -287,10 +282,15 @@ class InvokeAISettings(BaseSettings):
         return [
             "type",
             "initconf",
+            "gpu_mem_reserved",
+            "max_loaded_models",
             "version",
             "from_file",
             "model",
+            "restore",
             "root",
+            "nsfw_checker",
+            "cached_root",
         ]
 
     class Config:
@@ -356,7 +356,7 @@ class InvokeAISettings(BaseSettings):
 def _find_root() -> Path:
     venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
     if os.environ.get("INVOKEAI_ROOT"):
-        root = Path(os.environ["INVOKEAI_ROOT"])
+        root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
     elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
         root = (venv.parent).resolve()
     else:
@@ -389,17 +389,21 @@ 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')
     tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
 
-    root                : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths')
+    root                : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths')
     autoimport_dir      : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
     lora_dir            : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
     embedding_dir       : Path = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='Paths')
@@ -411,7 +415,8 @@ 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', 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
@@ -419,11 +424,9 @@ class InvokeAIAppConfig(InvokeAISettings):
     log_level           : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or  higher", category="Logging")
 
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
+    cached_root         : Path = Field(default=None,  description="internal use only", category="DEPRECATED")
     # 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
@@ -469,12 +472,15 @@ class InvokeAIAppConfig(InvokeAISettings):
         """
         Path to the runtime root directory
         """
-        if self.root:
+        # we cache value of root to protect against it being '.' and the cwd changing
+        if self.cached_root:
+            root = self.cached_root
+        elif self.root:
             root = Path(self.root).expanduser().absolute()
         else:
-            root = self.find_root().expanduser().absolute()
+            root = self.find_root()
-        self.root = root  # insulate ourselves from relative paths that may change
+        self.cached_root = root
-        return root
+        return self.cached_root
 
     @property
     def root_dir(self) -> Path:

invokeai/app/services/images.py

@@ -289,10 +289,9 @@ class ImageService(ImageServiceABC):
     def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
         try:
             image_record = self._services.image_records.get(image_name)
-            metadata = self._services.image_records.get_metadata(image_name)
 
             if not image_record.session_id:
-                return ImageMetadata(metadata=metadata)
+                return ImageMetadata()
 
             session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id)
             graph = None
@@ -304,6 +303,7 @@ class ImageService(ImageServiceABC):
                     self._services.logger.warn(f"Failed to parse session graph: {e}")
                     graph = None
 
+            metadata = self._services.image_records.get_metadata(image_name)
             return ImageMetadata(graph=graph, metadata=metadata)
         except ImageRecordNotFoundException:
             self._services.logger.error("Image record not found")

invokeai/app/services/invocation_services.py

@@ -32,7 +32,6 @@ class InvocationServices:
     logger: "Logger"
     model_manager: "ModelManagerServiceBase"
     processor: "InvocationProcessorABC"
-    performance_statistics: "InvocationStatsServiceBase"
     queue: "InvocationQueueABC"
 
     def __init__(
@@ -48,7 +47,6 @@ class InvocationServices:
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
         processor: "InvocationProcessorABC",
-        performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
     ):
         self.board_images = board_images
@@ -63,5 +61,4 @@ class InvocationServices:
         self.logger = logger
         self.model_manager = model_manager
         self.processor = processor
-        self.performance_statistics = performance_statistics
         self.queue = queue

invokeai/app/services/invocation_stats.py

@@ -1,223 +0,0 @@
-# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
-"""Utility to collect execution time and GPU usage stats on invocations in flight"""
-
-"""
-Usage:
-
-statistics = InvocationStatsService(graph_execution_manager)
-with statistics.collect_stats(invocation, graph_execution_state.id):
-      ... execute graphs...
-statistics.log_stats()
-
-Typical output:
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node                 Calls  Seconds  VRAM Used
-[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader        1   0.005s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip                1   0.004s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel                   2   0.512s     0.26G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int                 1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size            1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate                  1   0.001s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator     1   0.002s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise                    1   0.002s     0.01G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l                      1   3.541s     1.93G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i                      1   0.679s     0.58G
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME:  4.749s
-[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
-
-The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
-writes to the system log is stored in InvocationServices.performance_statistics.
-"""
-
-import time
-from abc import ABC, abstractmethod
-from contextlib import AbstractContextManager
-from dataclasses import dataclass, field
-from typing import Dict
-
-import torch
-
-import invokeai.backend.util.logging as logger
-
-from ..invocations.baseinvocation import BaseInvocation
-from .graph import GraphExecutionState
-from .item_storage import ItemStorageABC
-
-
-class InvocationStatsServiceBase(ABC):
-    "Abstract base class for recording node memory/time performance statistics"
-
-    @abstractmethod
-    def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]):
-        """
-        Initialize the InvocationStatsService and reset counters to zero
-        :param graph_execution_manager: Graph execution manager for this session
-        """
-        pass
-
-    @abstractmethod
-    def collect_stats(
-        self,
-        invocation: BaseInvocation,
-        graph_execution_state_id: str,
-    ) -> AbstractContextManager:
-        """
-        Return a context object that will capture the statistics on the execution
-        of invocaation. Use with: to place around the part of the code that executes the invocation.
-        :param invocation: BaseInvocation object from the current graph.
-        :param graph_execution_state: GraphExecutionState object from the current session.
-        """
-        pass
-
-    @abstractmethod
-    def reset_stats(self, graph_execution_state_id: str):
-        """
-        Reset all statistics for the indicated graph
-        :param graph_execution_state_id
-        """
-        pass
-
-    @abstractmethod
-    def reset_all_stats(self):
-        """Zero all statistics"""
-        pass
-
-    @abstractmethod
-    def update_invocation_stats(
-        self,
-        graph_id: str,
-        invocation_type: str,
-        time_used: float,
-        vram_used: float,
-    ):
-        """
-        Add timing information on execution of a node. Usually
-        used internally.
-        :param graph_id: ID of the graph that is currently executing
-        :param invocation_type: String literal type of the node
-        :param time_used: Time used by node's exection (sec)
-        :param vram_used: Maximum VRAM used during exection (GB)
-        """
-        pass
-
-    @abstractmethod
-    def log_stats(self):
-        """
-        Write out the accumulated statistics to the log or somewhere else.
-        """
-        pass
-
-
-@dataclass
-class NodeStats:
-    """Class for tracking execution stats of an invocation node"""
-
-    calls: int = 0
-    time_used: float = 0.0  # seconds
-    max_vram: float = 0.0  # GB
-
-
-@dataclass
-class NodeLog:
-    """Class for tracking node usage"""
-
-    # {node_type => NodeStats}
-    nodes: Dict[str, NodeStats] = field(default_factory=dict)
-
-
-class InvocationStatsService(InvocationStatsServiceBase):
-    """Accumulate performance information about a running graph. Collects time spent in each node,
-    as well as the maximum and current VRAM utilisation for CUDA systems"""
-
-    def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]):
-        self.graph_execution_manager = graph_execution_manager
-        # {graph_id => NodeLog}
-        self._stats: Dict[str, NodeLog] = {}
-
-    class StatsContext:
-        def __init__(self, invocation: BaseInvocation, graph_id: str, collector: "InvocationStatsServiceBase"):
-            self.invocation = invocation
-            self.collector = collector
-            self.graph_id = graph_id
-            self.start_time = 0
-
-        def __enter__(self):
-            self.start_time = time.time()
-            if torch.cuda.is_available():
-                torch.cuda.reset_peak_memory_stats()
-
-        def __exit__(self, *args):
-            self.collector.update_invocation_stats(
-                self.graph_id,
-                self.invocation.type,
-                time.time() - self.start_time,
-                torch.cuda.max_memory_allocated() / 1e9 if torch.cuda.is_available() else 0.0,
-            )
-
-    def collect_stats(
-        self,
-        invocation: BaseInvocation,
-        graph_execution_state_id: str,
-    ) -> StatsContext:
-        """
-        Return a context object that will capture the statistics.
-        :param invocation: BaseInvocation object from the current graph.
-        :param graph_execution_state: GraphExecutionState object from the current session.
-        """
-        if not self._stats.get(graph_execution_state_id):  # first time we're seeing this
-            self._stats[graph_execution_state_id] = NodeLog()
-        return self.StatsContext(invocation, graph_execution_state_id, self)
-
-    def reset_all_stats(self):
-        """Zero all statistics"""
-        self._stats = {}
-
-    def reset_stats(self, graph_execution_id: str):
-        """Zero the statistics for the indicated graph."""
-        try:
-            self._stats.pop(graph_execution_id)
-        except KeyError:
-            logger.warning(f"Attempted to clear statistics for unknown graph {graph_execution_id}")
-
-    def update_invocation_stats(self, graph_id: str, invocation_type: str, time_used: float, vram_used: float):
-        """
-        Add timing information on execution of a node. Usually
-        used internally.
-        :param graph_id: ID of the graph that is currently executing
-        :param invocation_type: String literal type of the node
-        :param time_used: Floating point seconds used by node's exection
-        """
-        if not self._stats[graph_id].nodes.get(invocation_type):
-            self._stats[graph_id].nodes[invocation_type] = NodeStats()
-        stats = self._stats[graph_id].nodes[invocation_type]
-        stats.calls += 1
-        stats.time_used += time_used
-        stats.max_vram = max(stats.max_vram, vram_used)
-
-    def log_stats(self):
-        """
-        Send the statistics to the system logger at the info level.
-        Stats will only be printed if when the execution of the graph
-        is complete.
-        """
-        completed = set()
-        for graph_id, node_log in self._stats.items():
-            current_graph_state = self.graph_execution_manager.get(graph_id)
-            if not current_graph_state.is_complete():
-                continue
-
-            total_time = 0
-            logger.info(f"Graph stats: {graph_id}")
-            logger.info("Node                 Calls    Seconds VRAM Used")
-            for node_type, stats in self._stats[graph_id].nodes.items():
-                logger.info(f"{node_type:<20} {stats.calls:>5}   {stats.time_used:7.3f}s     {stats.max_vram:4.2f}G")
-                total_time += stats.time_used
-
-            logger.info(f"TOTAL GRAPH EXECUTION TIME:  {total_time:7.3f}s")
-            if torch.cuda.is_available():
-                logger.info("Current VRAM utilization " + "%4.2fG" % (torch.cuda.memory_allocated() / 1e9))
-
-            completed.add(graph_id)
-
-        for graph_id in completed:
-            del self._stats[graph_id]

invokeai/app/services/model_manager_service.py

@@ -3,10 +3,9 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from logging import Logger
 from pathlib import Path
 from pydantic import Field
-from typing import Literal, Optional, Union, Callable, List, Tuple, TYPE_CHECKING
+from typing import Optional, Union, Callable, List, Tuple, TYPE_CHECKING
 from types import ModuleType
 
 from invokeai.backend.model_management import (
@@ -194,7 +193,7 @@ class ModelManagerServiceBase(ABC):
         self,
         model_name: str,
         base_model: BaseModelType,
-        model_type: Literal[ModelType.Main, ModelType.Vae],
+        model_type: Union[ModelType.Main, ModelType.Vae],
     ) -> AddModelResult:
         """
         Convert a checkpoint file into a diffusers folder, deleting the cached
@@ -293,7 +292,7 @@ class ModelManagerService(ModelManagerServiceBase):
     def __init__(
         self,
         config: InvokeAIAppConfig,
-        logger: Logger,
+        logger: ModuleType,
     ):
         """
         Initialize with the path to the models.yaml config file.
@@ -397,7 +396,7 @@ class ModelManagerService(ModelManagerServiceBase):
             model_type,
         )
 
-    def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
+    def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
         """
         Given a model name returns a dict-like (OmegaConf) object describing it.
         """
@@ -417,7 +416,7 @@ class ModelManagerService(ModelManagerServiceBase):
         """
         return self.mgr.list_models(base_model, model_type)
 
-    def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
+    def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
         """
         Return information about the model using the same format as list_models()
         """
@@ -430,7 +429,7 @@ class ModelManagerService(ModelManagerServiceBase):
         model_type: ModelType,
         model_attributes: dict,
         clobber: bool = False,
-    ) -> AddModelResult:
+    ) -> None:
         """
         Update the named model with a dictionary of attributes. Will fail with an
         assertion error if the name already exists. Pass clobber=True to overwrite.
@@ -479,7 +478,7 @@ class ModelManagerService(ModelManagerServiceBase):
         self,
         model_name: str,
         base_model: BaseModelType,
-        model_type: Literal[ModelType.Main, ModelType.Vae],
+        model_type: Union[ModelType.Main, ModelType.Vae],
         convert_dest_directory: Optional[Path] = Field(
             default=None, description="Optional directory location for merged model"
         ),
@@ -574,9 +573,9 @@ class ModelManagerService(ModelManagerServiceBase):
             default=None, description="Base model shared by all models to be merged"
         ),
         merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
-        alpha: float = 0.5,
+        alpha: Optional[float] = 0.5,
         interp: Optional[MergeInterpolationMethod] = None,
-        force: bool = False,
+        force: Optional[bool] = False,
         merge_dest_directory: Optional[Path] = Field(
             default=None, description="Optional directory location for merged model"
         ),
@@ -634,8 +633,8 @@ class ModelManagerService(ModelManagerServiceBase):
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
-        new_name: Optional[str] = None,
+        new_name: str = None,
-        new_base: Optional[BaseModelType] = None,
+        new_base: BaseModelType = None,
     ):
         """
         Rename the indicated model. Can provide a new name and/or a new base.

invokeai/app/services/models/board_image.py

@@ -1,8 +0,0 @@
-from pydantic import Field
-
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
-
-
-class BoardImage(BaseModelExcludeNull):
-    board_id: str = Field(description="The id of the board")
-    image_name: str = Field(description="The name of the image")

invokeai/app/services/models/board_record.py

@@ -1,11 +1,10 @@
 from typing import Optional, Union
 from datetime import datetime
-from pydantic import Field
+from pydantic import BaseModel, Extra, Field, StrictBool, StrictStr
 from invokeai.app.util.misc import get_iso_timestamp
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 
 
-class BoardRecord(BaseModelExcludeNull):
+class BoardRecord(BaseModel):
     """Deserialized board record."""
 
     board_id: str = Field(description="The unique ID of the board.")

invokeai/app/services/models/image_record.py

@@ -1,14 +1,13 @@
 import datetime
 from typing import Optional, Union
 
-from pydantic import Extra, Field, StrictBool, StrictStr
+from pydantic import BaseModel, Extra, Field, StrictBool, StrictStr
 
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from invokeai.app.util.misc import get_iso_timestamp
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 
 
-class ImageRecord(BaseModelExcludeNull):
+class ImageRecord(BaseModel):
     """Deserialized image record without metadata."""
 
     image_name: str = Field(description="The unique name of the image.")
@@ -41,7 +40,7 @@ class ImageRecord(BaseModelExcludeNull):
     """The node ID that generated this image, if it is a generated image."""
 
 
-class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid):
+class ImageRecordChanges(BaseModel, extra=Extra.forbid):
     """A set of changes to apply to an image record.
 
     Only limited changes are valid:
@@ -61,7 +60,7 @@ class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid):
     """The image's new `is_intermediate` flag."""
 
 
-class ImageUrlsDTO(BaseModelExcludeNull):
+class ImageUrlsDTO(BaseModel):
     """The URLs for an image and its thumbnail."""
 
     image_name: str = Field(description="The unique name of the image.")
@@ -77,15 +76,11 @@ class ImageDTO(ImageRecord, ImageUrlsDTO):
 
     board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.")
     """The id of the board the image belongs to, if one exists."""
-
     pass
 
 
 def image_record_to_dto(
-    image_record: ImageRecord,
+    image_record: ImageRecord, image_url: str, thumbnail_url: str, board_id: Optional[str]
-    image_url: str,
-    thumbnail_url: str,
-    board_id: Optional[str],
 ) -> ImageDTO:
     """Converts an image record to an image DTO."""
     return ImageDTO(

invokeai/app/services/processor.py

@@ -1,14 +1,13 @@
 import time
 import traceback
-from threading import BoundedSemaphore, Event, Thread
+from threading import Event, Thread, BoundedSemaphore
-
-import invokeai.backend.util.logging as logger
 
 from ..invocations.baseinvocation import InvocationContext
-from ..models.exceptions import CanceledException
 from .invocation_queue import InvocationQueueItem
-from .invocation_stats import InvocationStatsServiceBase
 from .invoker import InvocationProcessorABC, Invoker
+from ..models.exceptions import CanceledException
+
+import invokeai.backend.util.logging as logger
 
 
 class DefaultInvocationProcessor(InvocationProcessorABC):
@@ -36,8 +35,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
     def __process(self, stop_event: Event):
         try:
             self.__threadLimit.acquire()
-            statistics: InvocationStatsServiceBase = self.__invoker.services.performance_statistics
-
             while not stop_event.is_set():
                 try:
                     queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
@@ -86,7 +83,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
 
                 # Invoke
                 try:
-                    with statistics.collect_stats(invocation, graph_execution_state.id):
                     outputs = invocation.invoke(
                         InvocationContext(
                             services=self.__invoker.services,
@@ -111,13 +107,11 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         source_node_id=source_node_id,
                         result=outputs.dict(),
                     )
-                    statistics.log_stats()
 
                 except KeyboardInterrupt:
                     pass
 
                 except CanceledException:
-                    statistics.reset_stats(graph_execution_state.id)
                     pass
 
                 except Exception as e:
@@ -139,7 +133,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         error_type=e.__class__.__name__,
                         error=error,
                     )
-                    statistics.reset_stats(graph_execution_state.id)
+
                     pass
 
                 # Check queue to see if this is canceled, and skip if so

invokeai/app/services/urls.py

@@ -20,6 +20,6 @@ class LocalUrlService(UrlServiceBase):
 
         # These paths are determined by the routes in invokeai/app/api/routers/images.py
         if thumbnail:
-            return f"{self._base_url}/images/i/{image_basename}/thumbnail"
+            return f"{self._base_url}/images/{image_basename}/thumbnail"
 
-        return f"{self._base_url}/images/i/{image_basename}/full"
+        return f"{self._base_url}/images/{image_basename}/full"

invokeai/app/util/misc.py

@@ -18,5 +18,5 @@ SEED_MAX = np.iinfo(np.uint32).max
 
 
 def get_random_seed():
-    rng = np.random.default_rng(seed=None)
+    rng = np.random.default_rng(seed=0)
     return int(rng.integers(0, SEED_MAX))

invokeai/app/util/model_exclude_null.py

@@ -1,23 +0,0 @@
-from typing import Any
-from pydantic import BaseModel
-
-
-"""
-We want to exclude null values from objects that make their way to the client.
-
-Unfortunately there is no built-in way to do this in pydantic, so we need to override the default
-dict method to do this.
-
-From https://github.com/tiangolo/fastapi/discussions/8882#discussioncomment-5154541
-"""
-
-
-class BaseModelExcludeNull(BaseModel):
-    def dict(self, *args, **kwargs) -> dict[str, Any]:
-        """
-        Override the default dict method to exclude None values in the response
-        """
-        kwargs.pop("exclude_none", None)
-        return super().dict(*args, exclude_none=True, **kwargs)
-
-    pass

invokeai/backend/generator/img2img.py

@@ -1,11 +1,25 @@
 """
 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,
@@ -28,4 +42,51 @@ 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.
         """
-        raise NotImplementedError("replaced by invokeai.app.invocations.latent.LatentsToLatentsInvocation")
+        self.perlin = perlin
+
+        # 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

@@ -377,11 +377,3 @@ 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/check_root.py

@@ -12,7 +12,6 @@ def check_invokeai_root(config: InvokeAIAppConfig):
         assert config.model_conf_path.exists(), f"{config.model_conf_path} not found"
         assert config.db_path.parent.exists(), f"{config.db_path.parent} not found"
         assert config.models_path.exists(), f"{config.models_path} not found"
-        if not config.ignore_missing_core_models:
         for model in [
             "CLIP-ViT-bigG-14-laion2B-39B-b160k",
             "bert-base-uncased",
@@ -33,10 +32,5 @@ def check_invokeai_root(config: InvokeAIAppConfig):
         print(
             '** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **'
         )
-        print(
-            '** (To skip this check completely, add "--ignore_missing_core_models" to your CLI args. Not installing '
-            "these core models will prevent the loading of some or all .safetensors and .ckpt files. However, you can "
-            "always come back and install these core models in the future.)"
-        )
         input("Press any key to continue...")
         sys.exit(0)

invokeai/backend/install/invokeai_configure.py

@@ -10,17 +10,15 @@ 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
@@ -46,8 +44,6 @@ 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,
@@ -57,7 +53,6 @@ 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 (
@@ -66,7 +61,6 @@ 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()
@@ -82,13 +76,6 @@ 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.
@@ -99,12 +86,6 @@ 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):
@@ -395,47 +376,15 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             max_width=80,
             scroll_exit=True,
         )
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -= 1
         self.max_cache_size = self.add_widget_intelligent(
-            npyscreen.Slider,
+            IntTitleSlider,
-            value=clip(old_opts.max_cache_size, range=(3.0, MAX_RAM), step=0.5),
+            name="Size of the RAM cache used for fast model switching (GB)",
-            out_of=round(MAX_RAM),
+            value=old_opts.max_cache_size,
-            lowest=0.0,
+            out_of=20,
-            step=0.5,
+            lowest=3,
-            relx=8,
+            begin_entry_at=6,
             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,
@@ -452,7 +401,7 @@ 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="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
+            name=f"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,
@@ -527,7 +476,6 @@ 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",
         ]:
@@ -605,16 +553,6 @@ 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")
@@ -654,13 +592,13 @@ 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
 
-    if not set_min_terminal_size(MIN_COLS, MIN_LINES):
+    # The third argument is needed in the Windows 11 environment to
-        raise WindowTooSmallException(
+    # launch a console window running this program.
-            "Could not increase terminal size. Try running again with a larger window or smaller font size."
+    set_min_terminal_size(MIN_COLS, MIN_LINES)
-        )
 
     # the install-models application spawns a subprocess to install
     # models, and will crash unless this is set before running.
@@ -716,13 +654,10 @@ def migrate_init_file(legacy_format: Path):
     old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
     new = InvokeAIAppConfig.get_config()
 
-    fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"]
+    fields = list(get_type_hints(InvokeAIAppConfig).keys())
     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
@@ -842,7 +777,6 @@ 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")
@@ -868,8 +802,6 @@ 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

@@ -591,6 +591,7 @@ 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/install/model_install_backend.py

@@ -13,7 +13,6 @@ import requests
 from diffusers import DiffusionPipeline
 from diffusers import logging as dlogging
 import onnx
-import torch
 from huggingface_hub import hf_hub_url, HfFolder, HfApi
 from omegaconf import OmegaConf
 from tqdm import tqdm
@@ -24,7 +23,6 @@ from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult
 from invokeai.backend.model_management.model_probe import ModelProbe, SchedulerPredictionType, ModelProbeInfo
 from invokeai.backend.util import download_with_resume
-from invokeai.backend.util.devices import torch_dtype, choose_torch_device
 from ..util.logging import InvokeAILogger
 
 warnings.filterwarnings("ignore")
@@ -101,9 +99,9 @@ class ModelInstall(object):
     def __init__(
         self,
         config: InvokeAIAppConfig,
-        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+        prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
-        model_manager: Optional[ModelManager] = None,
+        model_manager: ModelManager = None,
-        access_token: Optional[str] = None,
+        access_token: str = None,
     ):
         self.config = config
         self.mgr = model_manager or ModelManager(config.model_conf_path)
@@ -305,7 +303,7 @@ class ModelInstall(object):
 
         with TemporaryDirectory(dir=self.config.models_path) as staging:
             staging = Path(staging)
-            if "model_index.json" in files:
+            if "model_index.json" in files and "unet/model.onnx" not in files:
                 location = self._download_hf_pipeline(repo_id, staging)  # pipeline
             elif "unet/model.onnx" in files:
                 location = self._download_hf_model(repo_id, files, staging)
@@ -418,25 +416,15 @@ class ModelInstall(object):
         does a save_pretrained() to the indicated staging area.
         """
         _, name = repo_id.split("/")
-        precision = torch_dtype(choose_torch_device())
+        revisions = ["fp16", "main"] if self.config.precision == "float16" else ["main"]
-        variants = ["fp16", None] if precision == torch.float16 else [None, "fp16"]
-
         model = None
-        for variant in variants:
+        for revision in revisions:
             try:
-                model = DiffusionPipeline.from_pretrained(
+                model = DiffusionPipeline.from_pretrained(repo_id, revision=revision, safety_checker=None)
-                    repo_id,
+            except:  # most errors are due to fp16 not being present. Fix this to catch other errors
-                    variant=variant,
+                pass
-                    torch_dtype=precision,
-                    safety_checker=None,
-                )
-            except Exception as e:  # most errors are due to fp16 not being present. Fix this to catch other errors
-                if "fp16" not in str(e):
-                    print(e)
-
             if model:
                 break
-
         if not model:
             logger.error(f"Diffusers model {repo_id} could not be downloaded. Skipping.")
             return None

invokeai/backend/model_management/__init__.py

@@ -13,4 +13,3 @@ from .models import (
     DuplicateModelException,
 )
 from .model_merge import ModelMerger, MergeInterpolationMethod
-from .lora import ModelPatcher

invokeai/backend/model_management/lora.py

@@ -20,6 +20,424 @@ from diffusers.models import UNet2DConditionModel
 from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
+# TODO: rename and split this file
+
+
+class LoRALayerBase:
+    # rank: Optional[int]
+    # alpha: Optional[float]
+    # bias: Optional[torch.Tensor]
+    # layer_key: str
+
+    # @property
+    # def scale(self):
+    #    return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: dict,
+    ):
+        if "alpha" in values:
+            self.alpha = values["alpha"].item()
+        else:
+            self.alpha = None
+
+        if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
+            self.bias = torch.sparse_coo_tensor(
+                values["bias_indices"],
+                values["bias_values"],
+                tuple(values["bias_size"]),
+            )
+
+        else:
+            self.bias = None
+
+        self.rank = None  # set in layer implementation
+        self.layer_key = layer_key
+
+    def forward(
+        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:
+        model_size = 0
+        for val in [self.bias]:
+            if val is not None:
+                model_size += val.nelement() * val.element_size()
+        return model_size
+
+    def to(
+        self,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        if self.bias is not None:
+            self.bias = self.bias.to(device=device, dtype=dtype)
+
+
+# TODO: find and debug lora/locon with bias
+class LoRALayer(LoRALayerBase):
+    # up: torch.Tensor
+    # mid: Optional[torch.Tensor]
+    # down: torch.Tensor
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: dict,
+    ):
+        super().__init__(layer_key, values)
+
+        self.up = values["lora_up.weight"]
+        self.down = values["lora_down.weight"]
+        if "lora_mid.weight" in values:
+            self.mid = values["lora_mid.weight"]
+        else:
+            self.mid = None
+
+        self.rank = self.down.shape[0]
+
+    def get_weight(self):
+        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])
+            weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
+        else:
+            weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
+
+        return weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        for val in [self.up, self.mid, self.down]:
+            if val is not None:
+                model_size += val.nelement() * val.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.up = self.up.to(device=device, dtype=dtype)
+        self.down = self.down.to(device=device, dtype=dtype)
+
+        if self.mid is not None:
+            self.mid = self.mid.to(device=device, dtype=dtype)
+
+
+class LoHALayer(LoRALayerBase):
+    # w1_a: torch.Tensor
+    # w1_b: torch.Tensor
+    # w2_a: torch.Tensor
+    # w2_b: torch.Tensor
+    # t1: Optional[torch.Tensor] = None
+    # t2: Optional[torch.Tensor] = None
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: dict,
+    ):
+        super().__init__(layer_key, values)
+
+        self.w1_a = values["hada_w1_a"]
+        self.w1_b = values["hada_w1_b"]
+        self.w2_a = values["hada_w2_a"]
+        self.w2_b = values["hada_w2_b"]
+
+        if "hada_t1" in values:
+            self.t1 = values["hada_t1"]
+        else:
+            self.t1 = None
+
+        if "hada_t2" in values:
+            self.t2 = values["hada_t2"]
+        else:
+            self.t2 = None
+
+        self.rank = self.w1_b.shape[0]
+
+    def get_weight(self):
+        if self.t1 is None:
+            weight = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
+
+        else:
+            rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a)
+            rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a)
+            weight = rebuild1 * rebuild2
+
+        return weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        for val in [self.w1_a, self.w1_b, self.w2_a, self.w2_b, self.t1, self.t2]:
+            if val is not None:
+                model_size += val.nelement() * val.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.w1_a = self.w1_a.to(device=device, dtype=dtype)
+        self.w1_b = self.w1_b.to(device=device, dtype=dtype)
+        if self.t1 is not None:
+            self.t1 = self.t1.to(device=device, dtype=dtype)
+
+        self.w2_a = self.w2_a.to(device=device, dtype=dtype)
+        self.w2_b = self.w2_b.to(device=device, dtype=dtype)
+        if self.t2 is not None:
+            self.t2 = self.t2.to(device=device, dtype=dtype)
+
+
+class LoKRLayer(LoRALayerBase):
+    # w1: Optional[torch.Tensor] = None
+    # w1_a: Optional[torch.Tensor] = None
+    # w1_b: Optional[torch.Tensor] = None
+    # w2: Optional[torch.Tensor] = None
+    # w2_a: Optional[torch.Tensor] = None
+    # w2_b: Optional[torch.Tensor] = None
+    # t2: Optional[torch.Tensor] = None
+
+    def __init__(
+        self,
+        layer_key: str,
+        values: dict,
+    ):
+        super().__init__(layer_key, values)
+
+        if "lokr_w1" in values:
+            self.w1 = values["lokr_w1"]
+            self.w1_a = None
+            self.w1_b = None
+        else:
+            self.w1 = None
+            self.w1_a = values["lokr_w1_a"]
+            self.w1_b = values["lokr_w1_b"]
+
+        if "lokr_w2" in values:
+            self.w2 = values["lokr_w2"]
+            self.w2_a = None
+            self.w2_b = None
+        else:
+            self.w2 = None
+            self.w2_a = values["lokr_w2_a"]
+            self.w2_b = values["lokr_w2_b"]
+
+        if "lokr_t2" in values:
+            self.t2 = values["lokr_t2"]
+        else:
+            self.t2 = None
+
+        if "lokr_w1_b" in values:
+            self.rank = values["lokr_w1_b"].shape[0]
+        elif "lokr_w2_b" in values:
+            self.rank = values["lokr_w2_b"].shape[0]
+        else:
+            self.rank = None  # unscaled
+
+    def get_weight(self):
+        w1 = self.w1
+        if w1 is None:
+            w1 = self.w1_a @ self.w1_b
+
+        w2 = self.w2
+        if w2 is None:
+            if self.t2 is None:
+                w2 = self.w2_a @ self.w2_b
+            else:
+                w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
+
+        if len(w2.shape) == 4:
+            w1 = w1.unsqueeze(2).unsqueeze(2)
+        w2 = w2.contiguous()
+        weight = torch.kron(w1, w2)
+
+        return weight
+
+    def calc_size(self) -> int:
+        model_size = super().calc_size()
+        for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]:
+            if val is not None:
+                model_size += val.nelement() * val.element_size()
+        return model_size
+
+    def to(
+        self,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        super().to(device=device, dtype=dtype)
+
+        if self.w1 is not None:
+            self.w1 = self.w1.to(device=device, dtype=dtype)
+        else:
+            self.w1_a = self.w1_a.to(device=device, dtype=dtype)
+            self.w1_b = self.w1_b.to(device=device, dtype=dtype)
+
+        if self.w2 is not None:
+            self.w2 = self.w2.to(device=device, dtype=dtype)
+        else:
+            self.w2_a = self.w2_a.to(device=device, dtype=dtype)
+            self.w2_b = self.w2_b.to(device=device, dtype=dtype)
+
+        if self.t2 is not None:
+            self.t2 = self.t2.to(device=device, dtype=dtype)
+
+
+class LoRAModel:  # (torch.nn.Module):
+    _name: str
+    layers: Dict[str, LoRALayer]
+    _device: torch.device
+    _dtype: torch.dtype
+
+    def __init__(
+        self,
+        name: str,
+        layers: Dict[str, LoRALayer],
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        self._name = name
+        self._device = device or torch.cpu
+        self._dtype = dtype or torch.float32
+        self.layers = layers
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    def to(
+        self,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> LoRAModel:
+        # TODO: try revert if exception?
+        for key, layer in self.layers.items():
+            layer.to(device=device, dtype=dtype)
+        self._device = device
+        self._dtype = dtype
+
+    def calc_size(self) -> int:
+        model_size = 0
+        for _, layer in self.layers.items():
+            model_size += layer.calc_size()
+        return model_size
+
+    @classmethod
+    def from_checkpoint(
+        cls,
+        file_path: Union[str, Path],
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or torch.device("cpu")
+        dtype = dtype or torch.float32
+
+        if isinstance(file_path, str):
+            file_path = Path(file_path)
+
+        model = cls(
+            device=device,
+            dtype=dtype,
+            name=file_path.stem,  # TODO:
+            layers=dict(),
+        )
+
+        if file_path.suffix == ".safetensors":
+            state_dict = load_file(file_path.absolute().as_posix(), device="cpu")
+        else:
+            state_dict = torch.load(file_path, map_location="cpu")
+
+        state_dict = cls._group_state(state_dict)
+
+        for layer_key, values in state_dict.items():
+            # lora and locon
+            if "lora_down.weight" in values:
+                layer = LoRALayer(layer_key, values)
+
+            # loha
+            elif "hada_w1_b" in values:
+                layer = LoHALayer(layer_key, values)
+
+            # lokr
+            elif "lokr_w1_b" in values or "lokr_w1" in values:
+                layer = LoKRLayer(layer_key, values)
+
+            else:
+                # TODO: diff/ia3/... format
+                print(f">> Encountered unknown lora layer module in {model.name}: {layer_key}")
+                return
+
+            # lower memory consumption by removing already parsed layer values
+            state_dict[layer_key].clear()
+
+            layer.to(device=device, dtype=dtype)
+            model.layers[layer_key] = layer
+
+        return model
+
+    @staticmethod
+    def _group_state(state_dict: dict):
+        state_dict_groupped = dict()
+
+        for key, value in state_dict.items():
+            stem, leaf = key.split(".", 1)
+            if stem not in state_dict_groupped:
+                state_dict_groupped[stem] = dict()
+            state_dict_groupped[stem][leaf] = value
+
+        return state_dict_groupped
+
+
 """
 loras = [
     (lora_model1, 0.7),
@@ -98,26 +516,6 @@ class ModelPatcher:
         with cls.apply_lora(text_encoder, loras, "lora_te_"):
             yield
 
-    @classmethod
-    @contextmanager
-    def apply_sdxl_lora_text_encoder(
-        cls,
-        text_encoder: CLIPTextModel,
-        loras: List[Tuple[LoRAModel, float]],
-    ):
-        with cls.apply_lora(text_encoder, loras, "lora_te1_"):
-            yield
-
-    @classmethod
-    @contextmanager
-    def apply_sdxl_lora_text_encoder2(
-        cls,
-        text_encoder: CLIPTextModel,
-        loras: List[Tuple[LoRAModel, float]],
-    ):
-        with cls.apply_lora(text_encoder, loras, "lora_te2_"):
-            yield
-
     @classmethod
     @contextmanager
     def apply_lora(
@@ -143,7 +541,7 @@ 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(original_weights[module_key]) * lora_weight * layer_scale
+                        layer_weight = layer.get_weight() * lora_weight * layer_scale
 
                         if module.weight.shape != layer_weight.shape:
                             # TODO: debug on lycoris
@@ -164,7 +562,7 @@ class ModelPatcher:
         cls,
         tokenizer: CLIPTokenizer,
         text_encoder: CLIPTextModel,
-        ti_list: List[Tuple[str, Any]],
+        ti_list: List[Any],
     ) -> Tuple[CLIPTokenizer, TextualInversionManager]:
         init_tokens_count = None
         new_tokens_added = None
@@ -174,27 +572,27 @@ class ModelPatcher:
             ti_manager = TextualInversionManager(ti_tokenizer)
             init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings
 
-            def _get_trigger(ti_name, index):
+            def _get_trigger(ti, index):
-                trigger = ti_name
+                trigger = ti.name
                 if index > 0:
                     trigger += f"-!pad-{i}"
                 return f"<{trigger}>"
 
             # modify tokenizer
             new_tokens_added = 0
-            for ti_name, ti in ti_list:
+            for ti in ti_list:
                 for i in range(ti.embedding.shape[0]):
-                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
+                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti, i))
 
             # modify text_encoder
             text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added)
             model_embeddings = text_encoder.get_input_embeddings()
 
-            for ti_name, ti in ti_list:
+            for ti in ti_list:
                 ti_tokens = []
                 for i in range(ti.embedding.shape[0]):
                     embedding = ti.embedding[i]
-                    trigger = _get_trigger(ti_name, i)
+                    trigger = _get_trigger(ti, i)
 
                     token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
                     if token_id == ti_tokenizer.unk_token_id:
@@ -239,6 +637,7 @@ class ModelPatcher:
 
 
 class TextualInversionModel:
+    name: str
     embedding: torch.Tensor  # [n, 768]|[n, 1280]
 
     @classmethod
@@ -252,6 +651,10 @@ class TextualInversionModel:
             file_path = Path(file_path)
 
         result = cls()  # TODO:
+        if file_path.name == "learned_embeds.bin":
+            result.name = file_path.parent.name
+        else:
+            result.name = file_path.stem
 
         if file_path.suffix == ".safetensors":
             state_dict = load_file(file_path.absolute().as_posix(), device="cpu")
@@ -361,8 +764,7 @@ class ONNXModelPatcher:
 
                     layer.to(dtype=torch.float32)
                     layer_key = layer_key.replace(prefix, "")
-                    # TODO: rewrite to pass original tensor weight(required by ia3)
+                    layer_weight = layer.get_weight().detach().cpu().numpy() * lora_weight
-                    layer_weight = layer.get_weight(None).detach().cpu().numpy() * lora_weight
                     if layer_key is blended_loras:
                         blended_loras[layer_key] += layer_weight
                     else:
@@ -429,7 +831,7 @@ class ONNXModelPatcher:
         cls,
         tokenizer: CLIPTokenizer,
         text_encoder: IAIOnnxRuntimeModel,
-        ti_list: List[Tuple[str, Any]],
+        ti_list: List[Any],
     ) -> Tuple[CLIPTokenizer, TextualInversionManager]:
         from .models.base import IAIOnnxRuntimeModel
 
@@ -442,17 +844,17 @@ class ONNXModelPatcher:
             ti_tokenizer = copy.deepcopy(tokenizer)
             ti_manager = TextualInversionManager(ti_tokenizer)
 
-            def _get_trigger(ti_name, index):
+            def _get_trigger(ti, index):
-                trigger = ti_name
+                trigger = ti.name
                 if index > 0:
                     trigger += f"-!pad-{i}"
                 return f"<{trigger}>"
 
             # modify tokenizer
             new_tokens_added = 0
-            for ti_name, ti in ti_list:
+            for ti in ti_list:
                 for i in range(ti.embedding.shape[0]):
-                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
+                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti, i))
 
             # modify text_encoder
             orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
@@ -462,11 +864,11 @@ class ONNXModelPatcher:
                 axis=0,
             )
 
-            for ti_name, ti in ti_list:
+            for ti in ti_list:
                 ti_tokens = []
                 for i in range(ti.embedding.shape[0]):
                     embedding = ti.embedding[i].detach().numpy()
-                    trigger = _get_trigger(ti_name, i)
+                    trigger = _get_trigger(ti, i)
 
                     token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
                     if token_id == ti_tokenizer.unk_token_id:

invokeai/backend/model_management/model_cache.py

@@ -28,6 +28,8 @@ import torch
 
 import logging
 import invokeai.backend.util.logging as logger
+from invokeai.app.services.config import get_invokeai_config
+from .lora import LoRAModel, TextualInversionModel
 from .models import BaseModelType, ModelType, SubModelType, ModelBase
 
 # Maximum size of the cache, in gigs

invokeai/backend/model_management/model_manager.py

@@ -228,19 +228,19 @@ the root is the InvokeAI ROOTDIR.
 """
 from __future__ import annotations
 
-import hashlib
 import os
+import hashlib
 import textwrap
-import types
+import yaml
 from dataclasses import dataclass
 from pathlib import Path
+from typing import 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
@@ -259,7 +259,6 @@ from .models import (
     ModelNotFoundException,
     InvalidModelException,
     DuplicateModelException,
-    ModelBase,
 )
 
 # We are only starting to number the config file with release 3.
@@ -362,7 +361,7 @@ class ModelManager(object):
             if model_key.startswith("_"):
                 continue
             model_name, base_model, model_type = self.parse_key(model_key)
-            model_class = self._get_implementation(base_model, model_type)
+            model_class = MODEL_CLASSES[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)
@@ -382,24 +381,18 @@ class ModelManager(object):
         # causing otherwise unreferenced models to be removed from memory
         self._read_models()
 
-    def model_exists(self, model_name: str, base_model: BaseModelType, model_type: ModelType, *, rescan=False) -> bool:
+    def model_exists(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ) -> bool:
         """
-        Given a model name, returns True if it is a valid identifier.
+        Given a model name, returns True if it is a valid
-
+        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)
-        exists = model_key in self.models
+        return 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(
@@ -450,32 +443,39 @@ 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 submodel_type: an ModelType enum indicating the portion of
+        :param submode_typel: an ModelType enum indicating the portion of
                the model to retrieve (e.g. ModelType.Vae)
         """
+        model_class = MODEL_CLASSES[base_model][model_type]
         model_key = self.create_key(model_name, base_model, model_type)
 
-        if not self.model_exists(model_name, base_model, model_type, rescan=True):
+        # if model not found try to find it (maybe file just pasted)
+        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._get_model_config(base_model, model_name, model_type)
+        model_config = self.models[model_key]
-
+        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 at {model_path}')
+                raise Exception(f'Files for model "{model_key}" not found')
 
             else:
                 self.models.pop(model_key, None)
-                raise ModelNotFoundException(f'Files for model "{model_key}" not found at {model_path}')
+                raise ModelNotFoundException(f"Model not found - {model_key}")
+
+        # 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
@@ -513,61 +513,12 @@ 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,
         base_model: BaseModelType,
         model_type: ModelType,
-    ) -> Union[dict, None]:
+    ) -> dict:
         """
         Given a model name returns the OmegaConf (dict-like) object describing it.
         """
@@ -589,16 +540,13 @@ class ModelManager(object):
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
-    ) -> Union[dict, None]:
+    ) -> dict:
         """
         Returns a dict describing one installed model, using
         the combined format of the list_models() method.
         """
         models = self.list_models(base_model, model_type, model_name)
-        if len(models) >= 1:
+        return models[0] if models else None
-            return models[0]
-        else:
-            return None
 
     def list_models(
         self,
@@ -612,7 +560,7 @@ class ModelManager(object):
 
         model_keys = (
             [self.create_key(model_name, base_model, model_type)]
-            if model_name and base_model and model_type
+            if model_name
             else sorted(self.models, key=str.casefold)
         )
         models = []
@@ -648,7 +596,7 @@ class ModelManager(object):
         Print a table of models and their descriptions. This needs to be redone
         """
         # TODO: redo
-        for model_dict in self.list_models():
+        for model_type, model_dict in self.list_models().items():
             for model_name, model_info in model_dict.items():
                 line = f'{model_info["name"]:25s} {model_info["type"]:10s} {model_info["description"]}'
                 print(line)
@@ -710,7 +658,7 @@ class ModelManager(object):
         if path := model_attributes.get("path"):
             model_attributes["path"] = str(self.relative_model_path(Path(path)))
 
-        model_class = self._get_implementation(base_model, model_type)
+        model_class = MODEL_CLASSES[base_model][model_type]
         model_config = model_class.create_config(**model_attributes)
         model_key = self.create_key(model_name, base_model, model_type)
 
@@ -751,8 +699,8 @@ class ModelManager(object):
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
-        new_name: Optional[str] = None,
+        new_name: str = None,
-        new_base: Optional[BaseModelType] = None,
+        new_base: BaseModelType = None,
     ):
         """
         Rename or rebase a model.
@@ -805,7 +753,7 @@ class ModelManager(object):
         self,
         model_name: str,
         base_model: BaseModelType,
-        model_type: Literal[ModelType.Main, ModelType.Vae],
+        model_type: Union[ModelType.Main, ModelType.Vae],
         dest_directory: Optional[Path] = None,
     ) -> AddModelResult:
         """
@@ -819,10 +767,6 @@ class ModelManager(object):
         This will raise a ValueError unless the model is a checkpoint.
         """
         info = self.model_info(model_name, base_model, model_type)
-
-        if info is None:
-            raise FileNotFoundError(f"model not found: {model_name}")
-
         if info["model_format"] != "checkpoint":
             raise ValueError(f"not a checkpoint format model: {model_name}")
 
@@ -892,7 +836,7 @@ class ModelManager(object):
 
         return search_folder, found_models
 
-    def commit(self, conf_file: Optional[Path] = None) -> None:
+    def commit(self, conf_file: Path = None) -> None:
         """
         Write current configuration out to the indicated file.
         """
@@ -901,7 +845,7 @@ 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 = self._get_implementation(base_model, model_type)
+            model_class = MODEL_CLASSES[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"})
@@ -959,7 +903,7 @@ class ModelManager(object):
 
                 model_path = self.resolve_model_path(model_config.path).absolute()
                 if not model_path.exists():
-                    model_class = self._get_implementation(cur_base_model, cur_model_type)
+                    model_class = MODEL_CLASSES[cur_base_model][cur_model_type]
                     if model_class.save_to_config:
                         model_config.error = ModelError.NotFound
                         self.models.pop(model_key, None)
@@ -975,7 +919,7 @@ class ModelManager(object):
                 for cur_model_type in ModelType:
                     if model_type is not None and cur_model_type != model_type:
                         continue
-                    model_class = self._get_implementation(cur_base_model, cur_model_type)
+                    model_class = MODEL_CLASSES[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():
@@ -991,9 +935,7 @@ 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(
+                                model_config: ModelConfigBase = model_class.probe_config(str(model_path))
-                                    str(model_path), model_base=cur_base_model
-                                )
                                 self.models[model_key] = model_config
                                 new_models_found = True
                             except DuplicateModelException as e:
@@ -1041,7 +983,7 @@ class ModelManager(object):
         # LS: hacky
         # Patch in the SD VAE from core so that it is available for use by the UI
         try:
-            self.heuristic_import({str(self.resolve_model_path("core/convert/sd-vae-ft-mse"))})
+            self.heuristic_import({self.resolve_model_path("core/convert/sd-vae-ft-mse")})
         except:
             pass
 
@@ -1069,7 +1011,7 @@ class ModelManager(object):
     def heuristic_import(
         self,
         items_to_import: Set[str],
-        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+        prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
     ) -> Dict[str, AddModelResult]:
         """Import a list of paths, repo_ids or URLs. Returns the set of
         successfully imported items.

invokeai/backend/model_management/model_merge.py

@@ -33,7 +33,7 @@ class ModelMerger(object):
         self,
         model_paths: List[Path],
         alpha: float = 0.5,
-        interp: Optional[MergeInterpolationMethod] = None,
+        interp: MergeInterpolationMethod = None,
         force: bool = False,
         **kwargs,
     ) -> DiffusionPipeline:
@@ -73,7 +73,7 @@ class ModelMerger(object):
         base_model: Union[BaseModelType, str],
         merged_model_name: str,
         alpha: float = 0.5,
-        interp: Optional[MergeInterpolationMethod] = None,
+        interp: MergeInterpolationMethod = None,
         force: bool = False,
         merge_dest_directory: Optional[Path] = None,
         **kwargs,
@@ -122,7 +122,7 @@ class ModelMerger(object):
         dump_path.mkdir(parents=True, exist_ok=True)
         dump_path = dump_path / merged_model_name
 
-        merged_pipe.save_pretrained(dump_path, safe_serialization=True)
+        merged_pipe.save_pretrained(dump_path, safe_serialization=1)
         attributes = dict(
             path=str(dump_path),
             description=f"Merge of models {', '.join(model_names)}",

invokeai/backend/model_management/model_probe.py

@@ -17,7 +17,6 @@ from .models import (
     SilenceWarnings,
     InvalidModelException,
 )
-from .util import lora_token_vector_length
 from .models.base import read_checkpoint_meta
 
 
@@ -316,16 +315,21 @@ class LoRACheckpointProbe(CheckpointProbeBase):
 
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
-        token_vector_length = lora_token_vector_length(checkpoint)
+        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.hada_w1_a"
-        if token_vector_length == 768:
+        lora_token_vector_length = (
+            checkpoint[key1].shape[1]
+            if key1 in checkpoint
+            else checkpoint[key2].shape[0]
+            if key2 in checkpoint
+            else 768
+        )
+        if lora_token_vector_length == 768:
             return BaseModelType.StableDiffusion1
-        elif token_vector_length == 1024:
+        elif lora_token_vector_length == 1024:
             return BaseModelType.StableDiffusion2
-        elif token_vector_length == 2048:
-            return BaseModelType.StableDiffusionXL
         else:
-            raise InvalidModelException(f"Unknown LoRA type: {self.checkpoint_path}")
+            return None
 
 
 class TextualInversionCheckpointProbe(CheckpointProbeBase):

invokeai/backend/model_management/models/base.py

@@ -292,9 +292,8 @@ class DiffusersModel(ModelBase):
                 )
                 break
             except Exception as e:
-                if not str(e).startswith("Error no file"):
+                # print("====ERR LOAD====")
-                    print("====ERR LOAD====")
+                # print(f"{variant}: {e}")
-                    print(f"{variant}: {e}")
                 pass
         else:
             raise Exception(f"Failed to load {self.base_model}:{self.model_type}:{child_type} model")

invokeai/backend/model_management/models/lora.py

@@ -1,9 +1,7 @@
 import os
 import torch
 from enum import Enum
-from typing import Optional, Dict, Union, Literal, Any
+from typing import Optional, Union, Literal
-from pathlib import Path
-from safetensors.torch import load_file
 from .base import (
     ModelBase,
     ModelConfigBase,
@@ -15,6 +13,9 @@ from .base import (
     ModelNotFoundException,
 )
 
+# TODO: naming
+from ..lora import LoRAModel as LoRAModelRaw
+
 
 class LoRAModelFormat(str, Enum):
     LyCORIS = "lycoris"
@@ -49,7 +50,6 @@ class LoRAModel(ModelBase):
         model = LoRAModelRaw.from_checkpoint(
             file_path=self.model_path,
             dtype=torch_dtype,
-            base_model=self.base_model,
         )
 
         self.model_size = model.calc_size()
@@ -87,591 +87,3 @@ class LoRAModel(ModelBase):
             raise NotImplementedError("Diffusers lora not supported")
         else:
             return model_path
-
-
-class LoRALayerBase:
-    # rank: Optional[int]
-    # alpha: Optional[float]
-    # bias: Optional[torch.Tensor]
-    # layer_key: str
-
-    # @property
-    # def scale(self):
-    #    return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: dict,
-    ):
-        if "alpha" in values:
-            self.alpha = values["alpha"].item()
-        else:
-            self.alpha = None
-
-        if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
-            self.bias = torch.sparse_coo_tensor(
-                values["bias_indices"],
-                values["bias_values"],
-                tuple(values["bias_size"]),
-            )
-
-        else:
-            self.bias = None
-
-        self.rank = None  # set in layer implementation
-        self.layer_key = layer_key
-
-    def get_weight(self, orig_weight: torch.Tensor):
-        raise NotImplementedError()
-
-    def calc_size(self) -> int:
-        model_size = 0
-        for val in [self.bias]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(
-        self,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        if self.bias is not None:
-            self.bias = self.bias.to(device=device, dtype=dtype)
-
-
-# TODO: find and debug lora/locon with bias
-class LoRALayer(LoRALayerBase):
-    # up: torch.Tensor
-    # mid: Optional[torch.Tensor]
-    # down: torch.Tensor
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: dict,
-    ):
-        super().__init__(layer_key, values)
-
-        self.up = values["lora_up.weight"]
-        self.down = values["lora_down.weight"]
-        if "lora_mid.weight" in values:
-            self.mid = values["lora_mid.weight"]
-        else:
-            self.mid = None
-
-        self.rank = self.down.shape[0]
-
-    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])
-            weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
-        else:
-            weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
-
-        return weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        for val in [self.up, self.mid, self.down]:
-            if val is not None:
-                model_size += val.nelement() * val.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.up = self.up.to(device=device, dtype=dtype)
-        self.down = self.down.to(device=device, dtype=dtype)
-
-        if self.mid is not None:
-            self.mid = self.mid.to(device=device, dtype=dtype)
-
-
-class LoHALayer(LoRALayerBase):
-    # w1_a: torch.Tensor
-    # w1_b: torch.Tensor
-    # w2_a: torch.Tensor
-    # w2_b: torch.Tensor
-    # t1: Optional[torch.Tensor] = None
-    # t2: Optional[torch.Tensor] = None
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: dict,
-    ):
-        super().__init__(layer_key, values)
-
-        self.w1_a = values["hada_w1_a"]
-        self.w1_b = values["hada_w1_b"]
-        self.w2_a = values["hada_w2_a"]
-        self.w2_b = values["hada_w2_b"]
-
-        if "hada_t1" in values:
-            self.t1 = values["hada_t1"]
-        else:
-            self.t1 = None
-
-        if "hada_t2" in values:
-            self.t2 = values["hada_t2"]
-        else:
-            self.t2 = None
-
-        self.rank = self.w1_b.shape[0]
-
-    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)
-
-        else:
-            rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a)
-            rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a)
-            weight = rebuild1 * rebuild2
-
-        return weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        for val in [self.w1_a, self.w1_b, self.w2_a, self.w2_b, self.t1, self.t2]:
-            if val is not None:
-                model_size += val.nelement() * val.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.w1_a = self.w1_a.to(device=device, dtype=dtype)
-        self.w1_b = self.w1_b.to(device=device, dtype=dtype)
-        if self.t1 is not None:
-            self.t1 = self.t1.to(device=device, dtype=dtype)
-
-        self.w2_a = self.w2_a.to(device=device, dtype=dtype)
-        self.w2_b = self.w2_b.to(device=device, dtype=dtype)
-        if self.t2 is not None:
-            self.t2 = self.t2.to(device=device, dtype=dtype)
-
-
-class LoKRLayer(LoRALayerBase):
-    # w1: Optional[torch.Tensor] = None
-    # w1_a: Optional[torch.Tensor] = None
-    # w1_b: Optional[torch.Tensor] = None
-    # w2: Optional[torch.Tensor] = None
-    # w2_a: Optional[torch.Tensor] = None
-    # w2_b: Optional[torch.Tensor] = None
-    # t2: Optional[torch.Tensor] = None
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: dict,
-    ):
-        super().__init__(layer_key, values)
-
-        if "lokr_w1" in values:
-            self.w1 = values["lokr_w1"]
-            self.w1_a = None
-            self.w1_b = None
-        else:
-            self.w1 = None
-            self.w1_a = values["lokr_w1_a"]
-            self.w1_b = values["lokr_w1_b"]
-
-        if "lokr_w2" in values:
-            self.w2 = values["lokr_w2"]
-            self.w2_a = None
-            self.w2_b = None
-        else:
-            self.w2 = None
-            self.w2_a = values["lokr_w2_a"]
-            self.w2_b = values["lokr_w2_b"]
-
-        if "lokr_t2" in values:
-            self.t2 = values["lokr_t2"]
-        else:
-            self.t2 = None
-
-        if "lokr_w1_b" in values:
-            self.rank = values["lokr_w1_b"].shape[0]
-        elif "lokr_w2_b" in values:
-            self.rank = values["lokr_w2_b"].shape[0]
-        else:
-            self.rank = None  # unscaled
-
-    def get_weight(self, orig_weight: torch.Tensor):
-        w1 = self.w1
-        if w1 is None:
-            w1 = self.w1_a @ self.w1_b
-
-        w2 = self.w2
-        if w2 is None:
-            if self.t2 is None:
-                w2 = self.w2_a @ self.w2_b
-            else:
-                w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
-
-        if len(w2.shape) == 4:
-            w1 = w1.unsqueeze(2).unsqueeze(2)
-        w2 = w2.contiguous()
-        weight = torch.kron(w1, w2)
-
-        return weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        for val in [self.w1, self.w1_a, self.w1_b, self.w2, self.w2_a, self.w2_b, self.t2]:
-            if val is not None:
-                model_size += val.nelement() * val.element_size()
-        return model_size
-
-    def to(
-        self,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        super().to(device=device, dtype=dtype)
-
-        if self.w1 is not None:
-            self.w1 = self.w1.to(device=device, dtype=dtype)
-        else:
-            self.w1_a = self.w1_a.to(device=device, dtype=dtype)
-            self.w1_b = self.w1_b.to(device=device, dtype=dtype)
-
-        if self.w2 is not None:
-            self.w2 = self.w2.to(device=device, dtype=dtype)
-        else:
-            self.w2_a = self.w2_a.to(device=device, dtype=dtype)
-            self.w2_b = self.w2_b.to(device=device, dtype=dtype)
-
-        if self.t2 is not None:
-            self.t2 = self.t2.to(device=device, dtype=dtype)
-
-
-class FullLayer(LoRALayerBase):
-    # weight: torch.Tensor
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: dict,
-    ):
-        super().__init__(layer_key, values)
-
-        self.weight = values["diff"]
-
-        if len(values.keys()) > 1:
-            _keys = list(values.keys())
-            _keys.remove("diff")
-            raise NotImplementedError(f"Unexpected keys in lora diff layer: {_keys}")
-
-        self.rank = None  # unscaled
-
-    def get_weight(self, orig_weight: torch.Tensor):
-        return self.weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        model_size += self.weight.nelement() * self.weight.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)
-
-
-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
-    layers: Dict[str, LoRALayer]
-    _device: torch.device
-    _dtype: torch.dtype
-
-    def __init__(
-        self,
-        name: str,
-        layers: Dict[str, LoRALayer],
-        device: torch.device,
-        dtype: torch.dtype,
-    ):
-        self._name = name
-        self._device = device or torch.cpu
-        self._dtype = dtype or torch.float32
-        self.layers = layers
-
-    @property
-    def name(self):
-        return self._name
-
-    @property
-    def device(self):
-        return self._device
-
-    @property
-    def dtype(self):
-        return self._dtype
-
-    def to(
-        self,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        # TODO: try revert if exception?
-        for key, layer in self.layers.items():
-            layer.to(device=device, dtype=dtype)
-        self._device = device
-        self._dtype = dtype
-
-    def calc_size(self) -> int:
-        model_size = 0
-        for _, layer in self.layers.items():
-            model_size += layer.calc_size()
-        return model_size
-
-    @classmethod
-    def _convert_sdxl_compvis_keys(cls, state_dict):
-        new_state_dict = dict()
-        for full_key, value in state_dict.items():
-            if full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
-                continue  # clip same
-
-            if not full_key.startswith("lora_unet_"):
-                raise NotImplementedError(f"Unknown prefix for sdxl lora key - {full_key}")
-            src_key = full_key.replace("lora_unet_", "")
-            try:
-                dst_key = None
-                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
-        return new_state_dict
-
-    @classmethod
-    def from_checkpoint(
-        cls,
-        file_path: Union[str, Path],
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-        base_model: Optional[BaseModelType] = None,
-    ):
-        device = device or torch.device("cpu")
-        dtype = dtype or torch.float32
-
-        if isinstance(file_path, str):
-            file_path = Path(file_path)
-
-        model = cls(
-            device=device,
-            dtype=dtype,
-            name=file_path.stem,  # TODO:
-            layers=dict(),
-        )
-
-        if file_path.suffix == ".safetensors":
-            state_dict = load_file(file_path.absolute().as_posix(), device="cpu")
-        else:
-            state_dict = torch.load(file_path, map_location="cpu")
-
-        state_dict = cls._group_state(state_dict)
-
-        if base_model == BaseModelType.StableDiffusionXL:
-            state_dict = cls._convert_sdxl_compvis_keys(state_dict)
-
-        for layer_key, values in state_dict.items():
-            # lora and locon
-            if "lora_down.weight" in values:
-                layer = LoRALayer(layer_key, values)
-
-            # loha
-            elif "hada_w1_b" in values:
-                layer = LoHALayer(layer_key, values)
-
-            # lokr
-            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:
-                print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}")
-                raise Exception("Unknown lora format!")
-
-            # lower memory consumption by removing already parsed layer values
-            state_dict[layer_key].clear()
-
-            layer.to(device=device, dtype=dtype)
-            model.layers[layer_key] = layer
-
-        return model
-
-    @staticmethod
-    def _group_state(state_dict: dict):
-        state_dict_groupped = dict()
-
-        for key, value in state_dict.items():
-            stem, leaf = key.split(".", 1)
-            if stem not in state_dict_groupped:
-                state_dict_groupped[stem] = dict()
-            state_dict_groupped[stem][leaf] = value
-
-        return state_dict_groupped
-
-
-# code from
-# https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L15C1-L97C32
-def make_sdxl_unet_conversion_map():
-    unet_conversion_map_layer = []
-
-    for i in range(3):  # num_blocks is 3 in sdxl
-        # loop over downblocks/upblocks
-        for j in range(2):
-            # loop over resnets/attentions for downblocks
-            hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-            sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
-            unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-            if i < 3:
-                # no attention layers in down_blocks.3
-                hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-                sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
-                unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-        for j in range(3):
-            # loop over resnets/attentions for upblocks
-            hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-            sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
-            unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-            # if i > 0: commentout for sdxl
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-        if i < 3:
-            # no downsample in down_blocks.3
-            hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-            sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
-            unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-            # no upsample in up_blocks.3
-            hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-            sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}."  # change for sdxl
-            unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-    hf_mid_atn_prefix = "mid_block.attentions.0."
-    sd_mid_atn_prefix = "middle_block.1."
-    unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-    for j in range(2):
-        hf_mid_res_prefix = f"mid_block.resnets.{j}."
-        sd_mid_res_prefix = f"middle_block.{2*j}."
-        unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-    unet_conversion_map_resnet = [
-        # (stable-diffusion, HF Diffusers)
-        ("in_layers.0.", "norm1."),
-        ("in_layers.2.", "conv1."),
-        ("out_layers.0.", "norm2."),
-        ("out_layers.3.", "conv2."),
-        ("emb_layers.1.", "time_emb_proj."),
-        ("skip_connection.", "conv_shortcut."),
-    ]
-
-    unet_conversion_map = []
-    for sd, hf in unet_conversion_map_layer:
-        if "resnets" in hf:
-            for sd_res, hf_res in unet_conversion_map_resnet:
-                unet_conversion_map.append((sd + sd_res, hf + hf_res))
-        else:
-            unet_conversion_map.append((sd, hf))
-
-    for j in range(2):
-        hf_time_embed_prefix = f"time_embedding.linear_{j+1}."
-        sd_time_embed_prefix = f"time_embed.{j*2}."
-        unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix))
-
-    for j in range(2):
-        hf_label_embed_prefix = f"add_embedding.linear_{j+1}."
-        sd_label_embed_prefix = f"label_emb.0.{j*2}."
-        unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix))
-
-    unet_conversion_map.append(("input_blocks.0.0.", "conv_in."))
-    unet_conversion_map.append(("out.0.", "conv_norm_out."))
-    unet_conversion_map.append(("out.2.", "conv_out."))
-
-    return unet_conversion_map
-
-
-SDXL_UNET_COMPVIS_MAP = {
-    f"{sd}".rstrip(".").replace(".", "_"): f"{hf}".rstrip(".").replace(".", "_")
-    for sd, hf in make_sdxl_unet_conversion_map()
-}

invokeai/backend/model_management/models/sdxl.py

@@ -80,10 +80,8 @@ class StableDiffusionXLModel(DiffusersModel):
             raise Exception("Unkown stable diffusion 2.* model format")
 
         if ckpt_config_path is None:
-            # avoid circular import
+            # TO DO: implement picking
-            from .stable_diffusion import _select_ckpt_config
+            pass
-
-            ckpt_config_path = _select_ckpt_config(kwargs.get("model_base", BaseModelType.StableDiffusionXL), variant)
 
         return cls.create_config(
             path=path,

invokeai/backend/model_management/models/stable_diffusion.py

@@ -4,7 +4,6 @@ from enum import Enum
 from pydantic import Field
 from pathlib import Path
 from typing import Literal, Optional, Union
-from diffusers import StableDiffusionInpaintPipeline, StableDiffusionPipeline
 from .base import (
     ModelConfigBase,
     BaseModelType,
@@ -264,8 +263,6 @@ def _convert_ckpt_and_cache(
     weights = app_config.models_path / model_config.path
     config_file = app_config.root_path / model_config.config
     output_path = Path(output_path)
-    variant = model_config.variant
-    pipeline_class = StableDiffusionInpaintPipeline if variant == "inpaint" else StableDiffusionPipeline
 
     # return cached version if it exists
     if output_path.exists():
@@ -292,7 +289,6 @@ def _convert_ckpt_and_cache(
             original_config_file=config_file,
             extract_ema=True,
             scan_needed=True,
-            pipeline_class=pipeline_class,
             from_safetensors=weights.suffix == ".safetensors",
             precision=torch_dtype(choose_torch_device()),
             **kwargs,

invokeai/backend/model_management/models/vae.py

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

invokeai/backend/model_management/util.py

@@ -1,75 +0,0 @@
-# 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

@@ -4,21 +4,25 @@ 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 PIL.Image
 import einops
+import PIL.Image
+import numpy as np
+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,
 )
@@ -27,20 +31,21 @@ 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 ..util import normalize_device
+from .offloading import FullyLoadedModelGroup, ModelGroup
 
 
 @dataclass
@@ -284,6 +289,8 @@ 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__(
@@ -296,7 +303,9 @@ 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,
@@ -321,6 +330,9 @@ 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):
@@ -356,6 +368,72 @@ 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,
@@ -372,7 +450,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if self.scheduler.config.get("cpu_only", False):
             scheduler_device = torch.device("cpu")
         else:
-            scheduler_device = self.unet.device
+            scheduler_device = self._model_group.device_for(self.unet)
 
         if timesteps is None:
             self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
@@ -426,7 +504,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 (batch_size,),
                 timesteps[0],
                 dtype=timesteps.dtype,
-                device=self.unet.device,
+                device=self._model_group.device_for(self.unet),
             )
             latents = self.scheduler.add_noise(latents, noise, batched_t)
 
@@ -622,6 +700,79 @@ 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
@@ -629,7 +780,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if self.scheduler.config.get("cpu_only", False):
             scheduler_device = torch.device("cpu")
         else:
-            scheduler_device = self.unet.device
+            scheduler_device = self._model_group.device_for(self.unet)
 
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
         timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
@@ -655,7 +806,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         noise_func=None,
         seed=None,
     ) -> InvokeAIStableDiffusionPipelineOutput:
-        device = self.unet.device
+        device = self._model_group.device_for(self.unet)
         latents_dtype = self.unet.dtype
 
         if isinstance(init_image, PIL.Image.Image):
@@ -726,17 +877,42 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 nsfw_content_detected=[],
                 attention_map_saver=result_attention_maps,
             )
-            return output
+            return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     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/diffusion/shared_invokeai_diffusion.py

@@ -78,9 +78,10 @@ class InvokeAIDiffuserComponent:
         self.cross_attention_control_context = None
         self.sequential_guidance = config.sequential_guidance
 
+    @classmethod
     @contextmanager
     def custom_attention_context(
-        self,
+        cls,
         unet: UNet2DConditionModel,  # note: also may futz with the text encoder depending on requested LoRAs
         extra_conditioning_info: Optional[ExtraConditioningInfo],
         step_count: int,
@@ -90,19 +91,18 @@ class InvokeAIDiffuserComponent:
             old_attn_processors = unet.attn_processors
             # Load lora conditions into the model
             if extra_conditioning_info.wants_cross_attention_control:
-                self.cross_attention_control_context = Context(
+                cross_attention_control_context = Context(
                     arguments=extra_conditioning_info.cross_attention_control_args,
                     step_count=step_count,
                 )
                 setup_cross_attention_control_attention_processors(
                     unet,
-                    self.cross_attention_control_context,
+                    cross_attention_control_context,
                 )
 
         try:
             yield None
         finally:
-            self.cross_attention_control_context = None
             if old_attn_processors is not None:
                 unet.set_attn_processor(old_attn_processors)
             # TODO resuscitate attention map saving

invokeai/backend/stable_diffusion/offloading.py

@@ -0,0 +1,253 @@
+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/backend/util/devices.py

@@ -1,8 +1,6 @@
 from __future__ import annotations
 
 from contextlib import nullcontext
-from packaging import version
-import platform
 
 import torch
 from torch import autocast
@@ -32,7 +30,7 @@ def choose_precision(device: torch.device) -> str:
         device_name = torch.cuda.get_device_name(device)
         if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):
             return "float16"
-    elif device.type == "mps" and version.parse(platform.mac_ver()[0]) < version.parse("14.0.0"):
+    elif device.type == "mps":
         return "float16"
     return "float32"
 

invokeai/frontend/install/__init__.py

@@ -1,3 +1,6 @@
 """
 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

@@ -1,795 +0,0 @@
-# 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 as invokeai_configure
+from ...backend.install.invokeai_configure import main

invokeai/frontend/install/model_install.py

@@ -28,6 +28,7 @@ from npyscreen import widget
 from invokeai.backend.util.logging import InvokeAILogger
 
 from invokeai.backend.install.model_install_backend import (
+    ModelInstallList,
     InstallSelections,
     ModelInstall,
     SchedulerPredictionType,
@@ -40,12 +41,12 @@ 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
 
@@ -155,7 +156,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
             BufferBox,
             name="Log Messages",
             editable=False,
-            max_height=6,
+            max_height=15,
         )
 
         self.nextrely += 1
@@ -692,11 +693,7 @@ def select_and_download_models(opt: Namespace):
         # needed to support the probe() method running under a subprocess
         torch.multiprocessing.set_start_method("spawn")
 
-        if not set_min_terminal_size(MIN_COLS, MIN_LINES):
+        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()
@@ -790,8 +787,6 @@ 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

@@ -21,19 +21,13 @@ MIN_COLS = 130
 MIN_LINES = 38
 
 
-class WindowTooSmallException(Exception):
-    pass
-
-
 # -------------------------------------
-def set_terminal_size(columns: int, lines: int) -> bool:
+def set_terminal_size(columns: int, lines: int):
-    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
@@ -43,18 +37,15 @@ def set_terminal_size(columns: int, lines: int) -> bool:
 
     # check whether it worked....
     ts = get_terminal_size()
-        if ts.columns < columns or ts.lines < lines:
+    pause = False
-            print(
+    if ts.columns < columns:
-                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"
+        print("\033[1mThis window is too narrow for the user interface.\033[0m")
-            )
+        pause = True
-            resp = input(
+    if ts.lines < lines:
-                "Maximize the window and/or decrease the font size then press any key to continue. Type [Q] to give up.."
+        print("\033[1mThis window is too short for the user interface.\033[0m")
-            )
+        pause = True
-            if resp.upper().startswith("Q"):
+    if pause:
-                break
+        input("Maximize the window then press any key to continue..")
-        else:
-            screen_ok = True
-    return screen_ok
 
 
 def _set_terminal_size_powershell(width: int, height: int):
@@ -89,14 +80,14 @@ def _set_terminal_size_unix(width: int, height: int):
     sys.stdout.flush()
 
 
-def set_min_terminal_size(min_cols: int, min_lines: int) -> bool:
+def set_min_terminal_size(min_cols: int, min_lines: int):
     # 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 True
+        return
     cols = max(term_cols, min_cols)
     lines = max(term_lines, min_lines)
-    return set_terminal_size(cols, lines)
+    set_terminal_size(cols, lines)
 
 
 class IntSlider(npyscreen.Slider):
@@ -173,7 +164,7 @@ class FloatSlider(npyscreen.Slider):
 
 
 class FloatTitleSlider(npyscreen.TitleText):
-    _entry_type = npyscreen.Slider
+    _entry_type = FloatSlider
 
 
 class SelectColumnBase:

invokeai/frontend/merge/merge_diffusers.py

@@ -382,7 +382,6 @@ 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-a4dc3f38.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-815faab3.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 $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-e9f8a36e.js";var za=String.raw,Ca=za`
+import{A as m,f$ as Je,z as y,a4 as Ka,g0 as Xa,af as va,aj as d,g1 as b,g2 as t,g3 as Ya,g4 as h,g5 as ua,g6 as Ja,g7 as Qa,aI as Za,g8 as et,ad as rt,g9 as at}from"./index-18f2f740.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"./MantineProvider-b20a2267.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-815faab3.js"></script>
+    <script type="module" crossorigin src="./assets/index-18f2f740.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -124,8 +124,7 @@
         "deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
         "deleteImagePermanent": "Deleted images cannot be restored.",
         "images": "Images",
-        "assets": "Assets",
+        "assets": "Assets"
-        "autoAssignBoardOnClick": "Auto-Assign Board on Click"
     },
     "hotkeys": {
         "keyboardShortcuts": "Keyboard Shortcuts",

invokeai/frontend/web/package.json

@@ -23,7 +23,7 @@
     "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
     "dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"",
     "build": "yarn run lint && vite build",
-    "typegen": "node scripts/typegen.js",
+    "typegen": "npx ts-node scripts/typegen.ts",
     "preview": "vite preview",
     "lint:madge": "madge --circular src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",

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

@@ -124,8 +124,7 @@
         "deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
         "deleteImagePermanent": "Deleted images cannot be restored.",
         "images": "Images",
-        "assets": "Assets",
+        "assets": "Assets"
-        "autoAssignBoardOnClick": "Auto-Assign Board on Click"
     },
     "hotkeys": {
         "keyboardShortcuts": "Keyboard Shortcuts",

invokeai/frontend/web/src/app/components/App.tsx

@@ -4,9 +4,8 @@ import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/ap
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { PartialAppConfig } from 'app/types/invokeai';
 import ImageUploader from 'common/components/ImageUploader';
-import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
-import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
 import GalleryDrawer from 'features/gallery/components/GalleryPanel';
+import DeleteImageModal from 'features/imageDeletion/components/DeleteImageModal';
 import SiteHeader from 'features/system/components/SiteHeader';
 import { configChanged } from 'features/system/store/configSlice';
 import { languageSelector } from 'features/system/store/systemSelectors';
@@ -17,6 +16,7 @@ import ParametersDrawer from 'features/ui/components/ParametersDrawer';
 import i18n from 'i18n';
 import { size } from 'lodash-es';
 import { ReactNode, memo, useEffect } from 'react';
+import UpdateImageBoardModal from '../../features/gallery/components/Boards/UpdateImageBoardModal';
 import GlobalHotkeys from './GlobalHotkeys';
 import Toaster from './Toaster';
 
@@ -84,7 +84,7 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
         </Portal>
       </Grid>
       <DeleteImageModal />
-      <ChangeBoardModal />
+      <UpdateImageBoardModal />
       <Toaster />
       <GlobalHotkeys />
     </>

invokeai/frontend/web/src/app/components/ImageDnd/DragPreview.tsx

@@ -58,7 +58,7 @@ const DragPreview = (props: OverlayDragImageProps) => {
     );
   }
 
-  if (props.dragData.payloadType === 'IMAGE_DTOS') {
+  if (props.dragData.payloadType === 'IMAGE_NAMES') {
     return (
       <Flex
         sx={{
@@ -71,7 +71,7 @@ const DragPreview = (props: OverlayDragImageProps) => {
           ...STYLES,
         }}
       >
-        <Heading>{props.dragData.payload.imageDTOs.length}</Heading>
+        <Heading>{props.dragData.payload.image_names.length}</Heading>
         <Heading size="sm">Images</Heading>
       </Flex>
     );

invokeai/frontend/web/src/app/components/ImageDnd/ImageDndContext.tsx

@@ -18,32 +18,27 @@ import {
   DragStartEvent,
   TypesafeDraggableData,
 } from './typesafeDnd';
-import { logger } from 'app/logging/logger';
 
 type ImageDndContextProps = PropsWithChildren;
 
 const ImageDndContext = (props: ImageDndContextProps) => {
   const [activeDragData, setActiveDragData] =
     useState<TypesafeDraggableData | null>(null);
-  const log = logger('images');
 
   const dispatch = useAppDispatch();
 
-  const handleDragStart = useCallback(
+  const handleDragStart = useCallback((event: DragStartEvent) => {
-    (event: DragStartEvent) => {
+    console.log('dragStart', event.active.data.current);
-      log.trace({ dragData: event.active.data.current }, 'Drag started');
     const activeData = event.active.data.current;
     if (!activeData) {
       return;
     }
     setActiveDragData(activeData);
-    },
+  }, []);
-    [log]
-  );
 
   const handleDragEnd = useCallback(
     (event: DragEndEvent) => {
-      log.trace({ dragData: event.active.data.current }, 'Drag ended');
+      console.log('dragEnd', event.active.data.current);
       const overData = event.over?.data.current;
       if (!activeDragData || !overData) {
         return;
@@ -51,7 +46,7 @@ const ImageDndContext = (props: ImageDndContextProps) => {
       dispatch(dndDropped({ overData, activeData: activeDragData }));
       setActiveDragData(null);
     },
-    [activeDragData, dispatch, log]
+    [activeDragData, dispatch]
   );
 
   const mouseSensor = useSensor(MouseSensor, {

invokeai/frontend/web/src/app/components/ImageDnd/typesafeDnd.tsx

@@ -11,6 +11,7 @@ import {
   useDraggable as useOriginalDraggable,
   useDroppable as useOriginalDroppable,
 } from '@dnd-kit/core';
+import { BoardId } from 'features/gallery/store/types';
 import { ImageDTO } from 'services/api/types';
 
 type BaseDropData = {
@@ -53,13 +54,9 @@ export type AddToBatchDropData = BaseDropData & {
   actionType: 'ADD_TO_BATCH';
 };
 
-export type AddToBoardDropData = BaseDropData & {
+export type MoveBoardDropData = BaseDropData & {
-  actionType: 'ADD_TO_BOARD';
+  actionType: 'MOVE_BOARD';
-  context: { boardId: string };
+  context: { boardId: BoardId };
-};
-
-export type RemoveFromBoardDropData = BaseDropData & {
-  actionType: 'REMOVE_FROM_BOARD';
 };
 
 export type TypesafeDroppableData =
@@ -70,8 +67,7 @@ export type TypesafeDroppableData =
   | NodesImageDropData
   | AddToBatchDropData
   | NodesMultiImageDropData
-  | AddToBoardDropData
+  | MoveBoardDropData;
-  | RemoveFromBoardDropData;
 
 type BaseDragData = {
   id: string;
@@ -82,12 +78,14 @@ export type ImageDraggableData = BaseDragData & {
   payload: { imageDTO: ImageDTO };
 };
 
-export type ImageDTOsDraggableData = BaseDragData & {
+export type ImageNamesDraggableData = BaseDragData & {
-  payloadType: 'IMAGE_DTOS';
+  payloadType: 'IMAGE_NAMES';
-  payload: { imageDTOs: ImageDTO[] };
+  payload: { image_names: string[] };
 };
 
-export type TypesafeDraggableData = ImageDraggableData | ImageDTOsDraggableData;
+export type TypesafeDraggableData =
+  | ImageDraggableData
+  | ImageNamesDraggableData;
 
 interface UseDroppableTypesafeArguments
   extends Omit<UseDroppableArguments, 'data'> {
@@ -158,39 +156,14 @@ export const isValidDrop = (
     case 'SET_NODES_IMAGE':
       return payloadType === 'IMAGE_DTO';
     case 'SET_MULTI_NODES_IMAGE':
-      return payloadType === 'IMAGE_DTO' || 'IMAGE_DTOS';
+      return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
     case 'ADD_TO_BATCH':
-      return payloadType === 'IMAGE_DTO' || 'IMAGE_DTOS';
+      return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
-    case 'ADD_TO_BOARD': {
+    case 'MOVE_BOARD': {
       // If the board is the same, don't allow the drop
 
       // Check the payload types
-      const isPayloadValid = payloadType === 'IMAGE_DTO' || 'IMAGE_DTOS';
+      const isPayloadValid = payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
-      if (!isPayloadValid) {
-        return false;
-      }
-
-      // Check if the image's board is the board we are dragging onto
-      if (payloadType === 'IMAGE_DTO') {
-        const { imageDTO } = active.data.current.payload;
-        const currentBoard = imageDTO.board_id ?? 'none';
-        const destinationBoard = overData.context.boardId;
-
-        return currentBoard !== destinationBoard;
-      }
-
-      if (payloadType === 'IMAGE_DTOS') {
-        // TODO (multi-select)
-        return true;
-      }
-
-      return false;
-    }
-    case 'REMOVE_FROM_BOARD': {
-      // If the board is the same, don't allow the drop
-
-      // Check the payload types
-      const isPayloadValid = payloadType === 'IMAGE_DTO' || 'IMAGE_DTOS';
       if (!isPayloadValid) {
         return false;
       }
@@ -199,16 +172,20 @@ export const isValidDrop = (
       if (payloadType === 'IMAGE_DTO') {
         const { imageDTO } = active.data.current.payload;
         const currentBoard = imageDTO.board_id;
+        const destinationBoard = overData.context.boardId;
 
-        return currentBoard !== 'none';
+        const isSameBoard = currentBoard === destinationBoard;
+        const isDestinationValid = !currentBoard ? destinationBoard : true;
+
+        return !isSameBoard && isDestinationValid;
       }
 
-      if (payloadType === 'IMAGE_DTOS') {
+      if (payloadType === 'IMAGE_NAMES') {
         // TODO (multi-select)
-        return true;
+        return false;
       }
 
-      return false;
+      return true;
     }
     default:
       return false;

invokeai/frontend/web/src/app/components/InvokeAIUI.tsx

@@ -1,6 +1,4 @@
-import { Middleware } from '@reduxjs/toolkit';
 import { store } from 'app/store/store';
-import { PartialAppConfig } from 'app/types/invokeai';
 import React, {
   lazy,
   memo,
@@ -9,11 +7,16 @@ import React, {
   useEffect,
 } from 'react';
 import { Provider } from 'react-redux';
+
+import { PartialAppConfig } from 'app/types/invokeai';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
-import { $authToken, $baseUrl, $projectId } from 'services/api/client';
-import { socketMiddleware } from 'services/events/middleware';
 import Loading from '../../common/components/Loading/Loading';
+
+import { Middleware } from '@reduxjs/toolkit';
+import { $authToken, $baseUrl } from 'services/api/client';
+import { socketMiddleware } from 'services/events/middleware';
 import '../../i18n';
+import { AddImageToBoardContextProvider } from '../contexts/AddImageToBoardContext';
 import ImageDndContext from './ImageDnd/ImageDndContext';
 
 const App = lazy(() => import('./App'));
@@ -34,7 +37,6 @@ const InvokeAIUI = ({
   config,
   headerComponent,
   middleware,
-  projectId,
 }: Props) => {
   useEffect(() => {
     // configure API client token
@@ -47,11 +49,6 @@ const InvokeAIUI = ({
       $baseUrl.set(apiUrl);
     }
 
-    // configure API client project header
-    if (projectId) {
-      $projectId.set(projectId);
-    }
-
     // reset dynamically added middlewares
     resetMiddlewares();
 
@@ -71,9 +68,8 @@ const InvokeAIUI = ({
       // Reset the API client token and base url on unmount
       $baseUrl.set(undefined);
       $authToken.set(undefined);
-      $projectId.set(undefined);
     };
-  }, [apiUrl, token, middleware, projectId]);
+  }, [apiUrl, token, middleware]);
 
   return (
     <React.StrictMode>
@@ -81,7 +77,9 @@ const InvokeAIUI = ({
         <React.Suspense fallback={<Loading />}>
           <ThemeLocaleProvider>
             <ImageDndContext>
+              <AddImageToBoardContextProvider>
                 <App config={config} headerComponent={headerComponent} />
+              </AddImageToBoardContextProvider>
             </ImageDndContext>
           </ThemeLocaleProvider>
         </React.Suspense>

invokeai/frontend/web/src/app/contexts/AddImageToBoardContext.tsx

@@ -0,0 +1,91 @@
+import { useDisclosure } from '@chakra-ui/react';
+import { PropsWithChildren, createContext, useCallback, useState } from 'react';
+import { ImageDTO } from 'services/api/types';
+import { imagesApi } from 'services/api/endpoints/images';
+import { useAppDispatch } from '../store/storeHooks';
+
+export type ImageUsage = {
+  isInitialImage: boolean;
+  isCanvasImage: boolean;
+  isNodesImage: boolean;
+  isControlNetImage: boolean;
+};
+
+type AddImageToBoardContextValue = {
+  /**
+   * Whether the move image dialog is open.
+   */
+  isOpen: boolean;
+  /**
+   * Closes the move image dialog.
+   */
+  onClose: () => void;
+  /**
+   * The image pending movement
+   */
+  image?: ImageDTO;
+  onClickAddToBoard: (image: ImageDTO) => void;
+  handleAddToBoard: (boardId: string) => void;
+};
+
+export const AddImageToBoardContext =
+  createContext<AddImageToBoardContextValue>({
+    isOpen: false,
+    onClose: () => undefined,
+    onClickAddToBoard: () => undefined,
+    handleAddToBoard: () => undefined,
+  });
+
+type Props = PropsWithChildren;
+
+export const AddImageToBoardContextProvider = (props: Props) => {
+  const [imageToMove, setImageToMove] = useState<ImageDTO>();
+  const { isOpen, onOpen, onClose } = useDisclosure();
+  const dispatch = useAppDispatch();
+
+  // Clean up after deleting or dismissing the modal
+  const closeAndClearImageToDelete = useCallback(() => {
+    setImageToMove(undefined);
+    onClose();
+  }, [onClose]);
+
+  const onClickAddToBoard = useCallback(
+    (image?: ImageDTO) => {
+      if (!image) {
+        return;
+      }
+      setImageToMove(image);
+      onOpen();
+    },
+    [setImageToMove, onOpen]
+  );
+
+  const handleAddToBoard = useCallback(
+    (boardId: string) => {
+      if (imageToMove) {
+        dispatch(
+          imagesApi.endpoints.addImageToBoard.initiate({
+            imageDTO: imageToMove,
+            board_id: boardId,
+          })
+        );
+        closeAndClearImageToDelete();
+      }
+    },
+    [dispatch, closeAndClearImageToDelete, imageToMove]
+  );
+
+  return (
+    <AddImageToBoardContext.Provider
+      value={{
+        isOpen,
+        image: imageToMove,
+        onClose: closeAndClearImageToDelete,
+        onClickAddToBoard,
+        handleAddToBoard,
+      }}
+    >
+      {props.children}
+    </AddImageToBoardContext.Provider>
+  );
+};

invokeai/frontend/web/src/app/contexts/ImageUploaderTriggerContext.ts

@@ -0,0 +1,8 @@
+import { createContext } from 'react';
+
+type VoidFunc = () => void;
+
+type ImageUploaderTriggerContextType = VoidFunc | null;
+
+export const ImageUploaderTriggerContext =
+  createContext<ImageUploaderTriggerContextType>(null);

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/serialize.ts

@@ -23,6 +23,6 @@ const serializationDenylist: {
 };
 
 export const serialize: SerializeFunction = (data, key) => {
-  const result = omit(data, serializationDenylist[key] ?? []);
+  const result = omit(data, serializationDenylist[key]);
   return JSON.stringify(result);
 };

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

@@ -27,8 +27,7 @@ import {
   addImageDeletedFulfilledListener,
   addImageDeletedPendingListener,
   addImageDeletedRejectedListener,
-  addRequestedSingleImageDeletionListener,
+  addRequestedImageDeletionListener,
-  addRequestedMultipleImageDeletionListener,
 } from './listeners/imageDeleted';
 import { addImageDroppedListener } from './listeners/imageDropped';
 import {
@@ -112,8 +111,7 @@ addImageUploadedRejectedListener();
 addInitialImageSelectedListener();
 
 // Image deleted
-addRequestedSingleImageDeletionListener();
+addRequestedImageDeletionListener();
-addRequestedMultipleImageDeletionListener();
 addImageDeletedPendingListener();
 addImageDeletedFulfilledListener();
 addImageDeletedRejectedListener();

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

@@ -1,10 +1,12 @@
 import { createAction } from '@reduxjs/toolkit';
 import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
-import { imagesApi } from 'services/api/endpoints/images';
+import {
+  ImageCache,
+  getListImagesUrl,
+  imagesApi,
+} from 'services/api/endpoints/images';
 import { startAppListening } from '..';
-import { getListImagesUrl, imagesAdapter } from 'services/api/util';
-import { ImageCache } from 'services/api/types';
 
 export const appStarted = createAction('app/appStarted');
 
@@ -32,8 +34,7 @@ export const addFirstListImagesListener = () => {
 
       if (data.ids.length > 0) {
         // Select the first image
-        const firstImage = imagesAdapter.getSelectors().selectAll(data)[0];
+        dispatch(imageSelected(data.ids[0] as string));
-        dispatch(imageSelected(firstImage ?? null));
       }
     },
   });

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

@@ -18,9 +18,7 @@ export const addAppConfigReceivedListener = () => {
       const infillMethod = getState().generation.infillMethod;
 
       if (!infill_methods.includes(infillMethod)) {
-        // if there is no infill method, set it to the first one
+        dispatch(setInfillMethod(infill_methods[0]));
-        // if there is no first one... god help us
-        dispatch(setInfillMethod(infill_methods[0] as string));
       }
 
       if (!nsfw_methods.includes('nsfw_checker')) {

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

@@ -1,14 +1,14 @@
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import { controlNetReset } from 'features/controlNet/store/controlNetSlice';
-import { getImageUsage } from 'features/deleteImageModal/store/selectors';
+import { getImageUsage } from 'features/imageDeletion/store/imageDeletionSelectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
 import { clearInitialImage } from 'features/parameters/store/generationSlice';
-import { imagesApi } from 'services/api/endpoints/images';
 import { startAppListening } from '..';
+import { boardsApi } from '../../../../../services/api/endpoints/boards';
 
 export const addDeleteBoardAndImagesFulfilledListener = () => {
   startAppListening({
-    matcher: imagesApi.endpoints.deleteBoardAndImages.matchFulfilled,
+    matcher: boardsApi.endpoints.deleteBoardAndImages.matchFulfilled,
     effect: async (action, { dispatch, getState }) => {
       const { deleted_images } = action.payload;
 

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

@@ -10,7 +10,6 @@ import {
 } from 'features/gallery/store/types';
 import { imagesApi } from 'services/api/endpoints/images';
 import { startAppListening } from '..';
-import { imagesSelectors } from 'services/api/util';
 
 export const addBoardIdSelectedListener = () => {
   startAppListening({
@@ -53,9 +52,8 @@ export const addBoardIdSelectedListener = () => {
           queryArgs
         )(getState());
 
-        if (boardImagesData) {
+        if (boardImagesData?.ids.length) {
-          const firstImage = imagesSelectors.selectAll(boardImagesData)[0];
+          dispatch(imageSelected((boardImagesData.ids[0] as string) ?? null));
-          dispatch(imageSelected(firstImage ?? null));
         } else {
           // board has no images - deselect
           dispatch(imageSelected(null));

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

@@ -26,8 +26,6 @@ export const addCanvasSavedToGalleryListener = () => {
         return;
       }
 
-      const { autoAddBoardId } = state.gallery;
-
       dispatch(
         imagesApi.endpoints.uploadImage.initiate({
           file: new File([blob], 'savedCanvas.png', {
@@ -35,7 +33,7 @@ export const addCanvasSavedToGalleryListener = () => {
           }),
           image_category: 'general',
           is_intermediate: false,
-          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          board_id: state.gallery.autoAddBoardId,
           crop_visible: true,
           postUploadAction: {
             type: 'TOAST',

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

@@ -31,20 +31,15 @@ const predicate: AnyListenerPredicate<RootState> = (
     // do not process if the user just disabled auto-config
     if (
       prevState.controlNet.controlNets[action.payload.controlNetId]
-        ?.shouldAutoConfig === true
+        .shouldAutoConfig === true
     ) {
       return false;
     }
   }
 
-  const cn = state.controlNet.controlNets[action.payload.controlNetId];
+  const { controlImage, processorType, shouldAutoConfig } =
+    state.controlNet.controlNets[action.payload.controlNetId];
 
-  if (!cn) {
-    // something is wrong, the controlNet should exist
-    return false;
-  }
-
-  const { controlImage, processorType, shouldAutoConfig } = cn;
   if (controlNetModelChanged.match(action) && !shouldAutoConfig) {
     // do not process if the action is a model change but the processor settings are dirty
     return false;

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

@@ -17,7 +17,7 @@ export const addControlNetImageProcessedListener = () => {
       const { controlNetId } = action.payload;
       const controlNet = getState().controlNet.controlNets[controlNetId];
 
-      if (!controlNet?.controlImage) {
+      if (!controlNet.controlImage) {
         log.error('Unable to process ControlNet image');
         return;
       }

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

@@ -1,72 +1,57 @@
 import { logger } from 'app/logging/logger';
 import { resetCanvas } from 'features/canvas/store/canvasSlice';
 import { controlNetReset } from 'features/controlNet/store/controlNetSlice';
-import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
-import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
 import { selectListImagesBaseQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { imageDeletionConfirmed } from 'features/imageDeletion/store/actions';
+import { isModalOpenChanged } from 'features/imageDeletion/store/imageDeletionSlice';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
 import { clearInitialImage } from 'features/parameters/store/generationSlice';
 import { clamp } from 'lodash-es';
 import { api } from 'services/api';
 import { imagesApi } from 'services/api/endpoints/images';
-import { imagesAdapter } from 'services/api/util';
 import { startAppListening } from '..';
 
-export const addRequestedSingleImageDeletionListener = () => {
+/**
+ * Called when the user requests an image deletion
+ */
+export const addRequestedImageDeletionListener = () => {
   startAppListening({
     actionCreator: imageDeletionConfirmed,
     effect: async (action, { dispatch, getState, condition }) => {
-      const { imageDTOs, imagesUsage } = action.payload;
+      const { imageDTO, imageUsage } = action.payload;
-
-      if (imageDTOs.length !== 1 || imagesUsage.length !== 1) {
-        // handle multiples in separate listener
-        return;
-      }
-
-      const imageDTO = imageDTOs[0];
-      const imageUsage = imagesUsage[0];
-
-      if (!imageDTO || !imageUsage) {
-        // satisfy noUncheckedIndexedAccess
-        return;
-      }
 
       dispatch(isModalOpenChanged(false));
 
-      const state = getState();
-      const lastSelectedImage =
-        state.gallery.selection[state.gallery.selection.length - 1]?.image_name;
-
-      if (imageDTO && imageDTO?.image_name === lastSelectedImage) {
       const { image_name } = imageDTO;
 
+      const state = getState();
+      const lastSelectedImage =
+        state.gallery.selection[state.gallery.selection.length - 1];
+
+      if (lastSelectedImage === image_name) {
         const baseQueryArgs = selectListImagesBaseQueryArgs(state);
         const { data } =
           imagesApi.endpoints.listImages.select(baseQueryArgs)(state);
 
-        const cachedImageDTOs = data
+        const ids = data?.ids ?? [];
-          ? imagesAdapter.getSelectors().selectAll(data)
-          : [];
 
-        const deletedImageIndex = cachedImageDTOs.findIndex(
+        const deletedImageIndex = ids.findIndex(
-          (i) => i.image_name === image_name
+          (result) => result.toString() === image_name
         );
 
-        const filteredImageDTOs = cachedImageDTOs.filter(
+        const filteredIds = ids.filter((id) => id.toString() !== image_name);
-          (i) => i.image_name !== image_name
-        );
 
         const newSelectedImageIndex = clamp(
           deletedImageIndex,
           0,
-          filteredImageDTOs.length - 1
+          filteredIds.length - 1
         );
 
-        const newSelectedImageDTO = filteredImageDTOs[newSelectedImageIndex];
+        const newSelectedImageId = filteredIds[newSelectedImageIndex];
 
-        if (newSelectedImageDTO) {
+        if (newSelectedImageId) {
-          dispatch(imageSelected(newSelectedImageDTO));
+          dispatch(imageSelected(newSelectedImageId as string));
         } else {
           dispatch(imageSelected(null));
         }
@@ -112,66 +97,6 @@ export const addRequestedSingleImageDeletionListener = () => {
   });
 };
 
-/**
- * Called when the user requests an image deletion
- */
-export const addRequestedMultipleImageDeletionListener = () => {
-  startAppListening({
-    actionCreator: imageDeletionConfirmed,
-    effect: async (action, { dispatch, getState }) => {
-      const { imageDTOs, imagesUsage } = action.payload;
-
-      if (imageDTOs.length < 1 || imagesUsage.length < 1) {
-        // handle singles in separate listener
-        return;
-      }
-
-      try {
-        // Delete from server
-        await dispatch(
-          imagesApi.endpoints.deleteImages.initiate({ imageDTOs })
-        ).unwrap();
-        const state = getState();
-        const baseQueryArgs = selectListImagesBaseQueryArgs(state);
-        const { data } =
-          imagesApi.endpoints.listImages.select(baseQueryArgs)(state);
-
-        const newSelectedImageDTO = data
-          ? imagesAdapter.getSelectors().selectAll(data)[0]
-          : undefined;
-
-        if (newSelectedImageDTO) {
-          dispatch(imageSelected(newSelectedImageDTO));
-        } else {
-          dispatch(imageSelected(null));
-        }
-
-        dispatch(isModalOpenChanged(false));
-
-        // We need to reset the features where the image is in use - none of these work if their image(s) don't exist
-
-        if (imagesUsage.some((i) => i.isCanvasImage)) {
-          dispatch(resetCanvas());
-        }
-
-        if (imagesUsage.some((i) => i.isControlNetImage)) {
-          dispatch(controlNetReset());
-        }
-
-        if (imagesUsage.some((i) => i.isInitialImage)) {
-          dispatch(clearInitialImage());
-        }
-
-        if (imagesUsage.some((i) => i.isNodesImage)) {
-          dispatch(nodeEditorReset());
-        }
-      } catch {
-        // no-op
-      }
-    },
-  });
-};
-
 /**
  * Called when the actual delete request is sent to the server
  */

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

@@ -6,7 +6,10 @@ import {
 import { logger } from 'app/logging/logger';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
 import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
+import {
+  imageSelected,
+  imagesAddedToBatch,
+} from 'features/gallery/store/gallerySlice';
 import { fieldValueChanged } from 'features/nodes/store/nodesSlice';
 import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { imagesApi } from 'services/api/endpoints/images';
@@ -24,32 +27,19 @@ export const addImageDroppedListener = () => {
       const log = logger('images');
       const { activeData, overData } = action.payload;
 
-      if (activeData.payloadType === 'IMAGE_DTO') {
+      log.debug({ activeData, overData }, 'Image or selection dropped');
-        log.debug({ activeData, overData }, 'Image dropped');
-      } else if (activeData.payloadType === 'IMAGE_DTOS') {
-        log.debug(
-          { activeData, overData },
-          `Images (${activeData.payload.imageDTOs.length}) dropped`
-        );
-      } else {
-        log.debug({ activeData, overData }, `Unknown payload dropped`);
-      }
 
-      /**
+      // set current image
-       * Image dropped on current image
-       */
       if (
         overData.actionType === 'SET_CURRENT_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        dispatch(imageSelected(activeData.payload.imageDTO));
+        dispatch(imageSelected(activeData.payload.imageDTO.image_name));
         return;
       }
 
-      /**
+      // set initial image
-       * Image dropped on initial image
-       */
       if (
         overData.actionType === 'SET_INITIAL_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
@@ -59,9 +49,27 @@ export const addImageDroppedListener = () => {
         return;
       }
 
-      /**
+      // add image to batch
-       * Image dropped on ControlNet
+      if (
-       */
+        overData.actionType === 'ADD_TO_BATCH' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        dispatch(imagesAddedToBatch([activeData.payload.imageDTO.image_name]));
+        return;
+      }
+
+      // add multiple images to batch
+      if (
+        overData.actionType === 'ADD_TO_BATCH' &&
+        activeData.payloadType === 'IMAGE_NAMES'
+      ) {
+        dispatch(imagesAddedToBatch(activeData.payload.image_names));
+
+        return;
+      }
+
+      // set control image
       if (
         overData.actionType === 'SET_CONTROLNET_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
@@ -77,9 +85,7 @@ export const addImageDroppedListener = () => {
         return;
       }
 
-      /**
+      // set canvas image
-       * Image dropped on Canvas
-       */
       if (
         overData.actionType === 'SET_CANVAS_INITIAL_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
@@ -89,9 +95,7 @@ export const addImageDroppedListener = () => {
         return;
       }
 
-      /**
+      // set nodes image
-       * Image dropped on node image field
-       */
       if (
         overData.actionType === 'SET_NODES_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
@@ -108,36 +112,61 @@ export const addImageDroppedListener = () => {
         return;
       }
 
-      /**
+      // set multiple nodes images (single image handler)
-       * TODO
+      if (
-       * Image selection dropped on node image collection field
+        overData.actionType === 'SET_MULTI_NODES_IMAGE' &&
-       */
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { fieldName, nodeId } = overData.context;
+        dispatch(
+          fieldValueChanged({
+            nodeId,
+            fieldName,
+            value: [activeData.payload.imageDTO],
+          })
+        );
+        return;
+      }
+
+      // // set multiple nodes images (multiple images handler)
       // if (
       //   overData.actionType === 'SET_MULTI_NODES_IMAGE' &&
-      //   activeData.payloadType === 'IMAGE_DTO' &&
+      //   activeData.payloadType === 'IMAGE_NAMES'
-      //   activeData.payload.imageDTO
       // ) {
       //   const { fieldName, nodeId } = overData.context;
       //   dispatch(
-      //     fieldValueChanged({
+      //     imageCollectionFieldValueChanged({
       //       nodeId,
       //       fieldName,
-      //       value: [activeData.payload.imageDTO],
+      //       value: activeData.payload.image_names.map((image_name) => ({
+      //         image_name,
+      //       })),
       //     })
       //   );
       //   return;
       // }
 
-      /**
+      // add image to board
-       * Image dropped on user board
-       */
       if (
-        overData.actionType === 'ADD_TO_BOARD' &&
+        overData.actionType === 'MOVE_BOARD' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
         const { imageDTO } = activeData.payload;
         const { boardId } = overData.context;
+
+        // image was droppe on the "NoBoardBoard"
+        if (!boardId) {
+          dispatch(
+            imagesApi.endpoints.removeImageFromBoard.initiate({
+              imageDTO,
+            })
+          );
+          return;
+        }
+
+        // image was dropped on a user board
         dispatch(
           imagesApi.endpoints.addImageToBoard.initiate({
             imageDTO,
@@ -147,58 +176,67 @@ export const addImageDroppedListener = () => {
         return;
       }
 
-      /**
+      // // add gallery selection to board
-       * Image dropped on 'none' board
+      // if (
-       */
+      //   overData.actionType === 'MOVE_BOARD' &&
-      if (
+      //   activeData.payloadType === 'IMAGE_NAMES' &&
-        overData.actionType === 'REMOVE_FROM_BOARD' &&
+      //   overData.context.boardId
-        activeData.payloadType === 'IMAGE_DTO' &&
+      // ) {
-        activeData.payload.imageDTO
+      //   console.log('adding gallery selection to board');
-      ) {
+      //   const board_id = overData.context.boardId;
-        const { imageDTO } = activeData.payload;
+      //   dispatch(
-        dispatch(
+      //     boardImagesApi.endpoints.addManyBoardImages.initiate({
-          imagesApi.endpoints.removeImageFromBoard.initiate({
+      //       board_id,
-            imageDTO,
+      //       image_names: activeData.payload.image_names,
-          })
+      //     })
-        );
+      //   );
-        return;
+      //   return;
-      }
+      // }
 
-      /**
+      // // remove gallery selection from board
-       * Multiple images dropped on user board
+      // if (
-       */
+      //   overData.actionType === 'MOVE_BOARD' &&
-      if (
+      //   activeData.payloadType === 'IMAGE_NAMES' &&
-        overData.actionType === 'ADD_TO_BOARD' &&
+      //   overData.context.boardId === null
-        activeData.payloadType === 'IMAGE_DTOS' &&
+      // ) {
-        activeData.payload.imageDTOs
+      //   console.log('removing gallery selection to board');
-      ) {
+      //   dispatch(
-        const { imageDTOs } = activeData.payload;
+      //     boardImagesApi.endpoints.deleteManyBoardImages.initiate({
-        const { boardId } = overData.context;
+      //       image_names: activeData.payload.image_names,
-        dispatch(
+      //     })
-          imagesApi.endpoints.addImagesToBoard.initiate({
+      //   );
-            imageDTOs,
+      //   return;
-            board_id: boardId,
+      // }
-          })
-        );
-        return;
-      }
 
-      /**
+      // // add batch selection to board
-       * Multiple images dropped on 'none' board
+      // if (
-       */
+      //   overData.actionType === 'MOVE_BOARD' &&
-      if (
+      //   activeData.payloadType === 'IMAGE_NAMES' &&
-        overData.actionType === 'REMOVE_FROM_BOARD' &&
+      //   overData.context.boardId
-        activeData.payloadType === 'IMAGE_DTOS' &&
+      // ) {
-        activeData.payload.imageDTOs
+      //   const board_id = overData.context.boardId;
-      ) {
+      //   dispatch(
-        const { imageDTOs } = activeData.payload;
+      //     boardImagesApi.endpoints.addManyBoardImages.initiate({
-        dispatch(
+      //       board_id,
-          imagesApi.endpoints.removeImagesFromBoard.initiate({
+      //       image_names: activeData.payload.image_names,
-            imageDTOs,
+      //     })
-          })
+      //   );
-        );
+      //   return;
-        return;
+      // }
-      }
+
+      // // remove batch selection from board
+      // if (
+      //   overData.actionType === 'MOVE_BOARD' &&
+      //   activeData.payloadType === 'IMAGE_NAMES' &&
+      //   overData.context.boardId === null
+      // ) {
+      //   dispatch(
+      //     boardImagesApi.endpoints.deleteManyBoardImages.initiate({
+      //       image_names: activeData.payload.image_names,
+      //     })
+      //   );
+      //   return;
+      // }
     },
   });
 };

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

@@ -1,32 +1,37 @@
-import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
+import { imageDeletionConfirmed } from 'features/imageDeletion/store/actions';
-import { selectImageUsage } from 'features/deleteImageModal/store/selectors';
+import { selectImageUsage } from 'features/imageDeletion/store/imageDeletionSelectors';
 import {
-  imagesToDeleteSelected,
+  imageToDeleteSelected,
   isModalOpenChanged,
-} from 'features/deleteImageModal/store/slice';
+} from 'features/imageDeletion/store/imageDeletionSlice';
 import { startAppListening } from '..';
 
 export const addImageToDeleteSelectedListener = () => {
   startAppListening({
-    actionCreator: imagesToDeleteSelected,
+    actionCreator: imageToDeleteSelected,
     effect: async (action, { dispatch, getState }) => {
-      const imageDTOs = action.payload;
+      const imageDTO = action.payload;
       const state = getState();
       const { shouldConfirmOnDelete } = state.system;
-      const imagesUsage = selectImageUsage(getState());
+      const imageUsage = selectImageUsage(getState());
+
+      if (!imageUsage) {
+        // should never happen
+        return;
+      }
 
       const isImageInUse =
-        imagesUsage.some((i) => i.isCanvasImage) ||
+        imageUsage.isCanvasImage ||
-        imagesUsage.some((i) => i.isInitialImage) ||
+        imageUsage.isInitialImage ||
-        imagesUsage.some((i) => i.isControlNetImage) ||
+        imageUsage.isControlNetImage ||
-        imagesUsage.some((i) => i.isNodesImage);
+        imageUsage.isNodesImage;
 
       if (shouldConfirmOnDelete || isImageInUse) {
         dispatch(isModalOpenChanged(true));
         return;
       }
 
-      dispatch(imageDeletionConfirmed({ imageDTOs, imagesUsage }));
+      dispatch(imageDeletionConfirmed({ imageDTO, imageUsage }));
     },
   });
 };

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

@@ -2,13 +2,14 @@ import { UseToastOptions } from '@chakra-ui/react';
 import { logger } from 'app/logging/logger';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
 import { controlNetImageChanged } from 'features/controlNet/store/controlNetSlice';
+import { imagesAddedToBatch } from 'features/gallery/store/gallerySlice';
 import { fieldValueChanged } from 'features/nodes/store/nodesSlice';
 import { initialImageChanged } from 'features/parameters/store/generationSlice';
 import { addToast } from 'features/system/store/systemSlice';
-import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { startAppListening } from '..';
 import { imagesApi } from '../../../../../services/api/endpoints/images';
+import { omit } from 'lodash-es';
 
 const DEFAULT_UPLOADED_TOAST: UseToastOptions = {
   title: 'Image Uploaded',
@@ -40,7 +41,7 @@ export const addImageUploadedFulfilledListener = () => {
       // default action - just upload and alert user
       if (postUploadAction?.type === 'TOAST') {
         const { toastOptions } = postUploadAction;
-        if (!autoAddBoardId || autoAddBoardId === 'none') {
+        if (!autoAddBoardId) {
           dispatch(addToast({ ...DEFAULT_UPLOADED_TOAST, ...toastOptions }));
         } else {
           // Add this image to the board
@@ -120,6 +121,17 @@ export const addImageUploadedFulfilledListener = () => {
         );
         return;
       }
+
+      if (postUploadAction?.type === 'ADD_TO_BATCH') {
+        dispatch(imagesAddedToBatch([imageDTO.image_name]));
+        dispatch(
+          addToast({
+            ...DEFAULT_UPLOADED_TOAST,
+            description: 'Added to batch',
+          })
+        );
+        return;
+      }
     },
   });
 };

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

@@ -15,7 +15,7 @@ import {
   setShouldUseSDXLRefiner,
 } from 'features/sdxl/store/sdxlSlice';
 import { forEach, some } from 'lodash-es';
-import { modelsApi, vaeModelsAdapter } from 'services/api/endpoints/models';
+import { modelsApi } from 'services/api/endpoints/models';
 import { startAppListening } from '..';
 
 export const addModelsLoadedListener = () => {
@@ -144,9 +144,8 @@ export const addModelsLoadedListener = () => {
         return;
       }
 
-      const firstModel = vaeModelsAdapter
+      const firstModelId = action.payload.ids[0];
-        .getSelectors()
+      const firstModel = action.payload.entities[firstModelId];
-        .selectAll(action.payload)[0];
 
       if (!firstModel) {
         // No custom VAEs loaded at all; use the default

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

@@ -8,10 +8,9 @@ import {
 } from 'features/gallery/store/gallerySlice';
 import { IMAGE_CATEGORIES } from 'features/gallery/store/types';
 import { progressImageSet } from 'features/system/store/systemSlice';
-import { imagesApi } from 'services/api/endpoints/images';
+import { imagesAdapter, imagesApi } from 'services/api/endpoints/images';
 import { isImageOutput } from 'services/api/guards';
 import { sessionCanceled } from 'services/api/thunks/session';
-import { imagesAdapter } from 'services/api/util';
 import {
   appSocketInvocationComplete,
   socketInvocationComplete,
@@ -68,7 +67,7 @@ export const addInvocationCompleteEventListener = () => {
            */
 
           const { autoAddBoardId } = gallery;
-          if (autoAddBoardId && autoAddBoardId !== 'none') {
+          if (autoAddBoardId) {
             dispatch(
               imagesApi.endpoints.addImageToBoard.initiate({
                 board_id: autoAddBoardId,
@@ -84,7 +83,10 @@ export const addInvocationCompleteEventListener = () => {
                   categories: IMAGE_CATEGORIES,
                 },
                 (draft) => {
-                  imagesAdapter.addOne(draft, imageDTO);
+                  const oldTotal = draft.total;
+                  const newState = imagesAdapter.addOne(draft, imageDTO);
+                  const delta = newState.total - oldTotal;
+                  draft.total = draft.total + delta;
                 }
               )
             );
@@ -92,8 +94,8 @@ export const addInvocationCompleteEventListener = () => {
 
           dispatch(
             imagesApi.util.invalidateTags([
-              { type: 'BoardImagesTotal', id: autoAddBoardId },
+              { type: 'BoardImagesTotal', id: autoAddBoardId ?? 'none' },
-              { type: 'BoardAssetsTotal', id: autoAddBoardId },
+              { type: 'BoardAssetsTotal', id: autoAddBoardId ?? 'none' },
             ])
           );
 
@@ -108,7 +110,7 @@ export const addInvocationCompleteEventListener = () => {
             } else if (!autoAddBoardId) {
               dispatch(galleryViewChanged('images'));
             }
-            dispatch(imageSelected(imageDTO));
+            dispatch(imageSelected(imageDTO.image_name));
           }
         }