feat(nodes): extract denoise function

.gitattributes

@@ -2,4 +2,3 @@
 # Only affects text files and ignores other file types.
 # For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
 * text=auto
-docker/** text eol=lf

.github/workflows/pyflakes.yml

@@ -0,0 +1,20 @@
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+      - development
+      - 'release-candidate-*'
+
+jobs:
+  pyflakes:
+    name: runner / pyflakes
+    if: github.event.pull_request.draft == false
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: pyflakes
+        uses: reviewdog/action-pyflakes@v1
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          reporter: github-pr-review

.github/workflows/style-checks.yml

@@ -18,7 +18,8 @@ jobs:
 
       - name: Install dependencies with pip
         run: |
-          pip install ruff
+          pip install black flake8 Flake8-pyproject isort
 
-      - run: ruff check --output-format=github .
-      - run: ruff format --check .
+      - run: isort --check-only .
+      - run: black --check .
+      - run: flake8

docker/.env.sample

@@ -11,5 +11,5 @@ INVOKEAI_ROOT=
 # HUGGING_FACE_HUB_TOKEN=
 
 ## optional variables specific to the docker setup.
-# GPU_DRIVER=cuda # or rocm
-# CONTAINER_UID=1000
+# GPU_DRIVER=cuda
+# CONTAINER_UID=1000

docker/Dockerfile

@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.1.0
-ARG TORCHVISION_VERSION=0.16
+ARG TORCH_VERSION=2.0.1
+ARG TORCHVISION_VERSION=0.15.2
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
@@ -35,7 +35,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\

docker/docker-compose.yml

@@ -15,10 +15,6 @@ services:
             - driver: nvidia
               count: 1
               capabilities: [gpu]
-    # For AMD support, comment out the deploy section above and uncomment the devices section below:
-    #devices:
-    #  - /dev/kfd:/dev/kfd
-    #  - /dev/dri:/dev/dri
     build:
       context: ..
       dockerfile: docker/Dockerfile

docker/run.sh

@@ -7,5 +7,5 @@ set -e
 SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
 cd "$SCRIPTDIR" || exit 1
 
-docker compose up -d
+docker compose up --build -d
 docker compose logs -f

docs/features/CONTROLNET.md

@@ -150,6 +150,7 @@ Start/End  - 0 represents the start of the generation, 1 represents the end. The
 
 Additionally, each  section can be expanded with the "Show Advanced" button in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in during the generation process.
 
+**Note:** T2I-Adapter models and ControlNet models cannot currently be used together.
 
 ## IP-Adapter
 

docs/index.md

@@ -198,7 +198,6 @@ The list of schedulers has been completely revamped and brought up to date:
 | **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
 | **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
 | **unipc**      | UniPCMultistepScheduler         | CPU only                    |
-| **lcm**        | LCMScheduler                    |                             |
 
 Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
 

docs/installation/040_INSTALL_DOCKER.md

@@ -99,14 +99,3 @@ If using an AMD GPU:
 Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual.
 
 Once the container starts up (and configures the InvokeAI root directory if this is a new installation), you can access InvokeAI at [http://localhost:9090](http://localhost:9090)
-
-## Troubleshooting / FAQ
-
-- Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
-- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings,
-    and you may have cloned this repository before the issue was fixed. To solve this, please change
-    the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
-    (`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL.
-    Finally, you may delete `docker-entrypoint.sh` followed by  `git pull; git checkout docker/docker-entrypoint.sh`
-    to reset the file to its most recent version.
-    For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)

docs/nodes/communityNodes.md

@@ -4,16 +4,11 @@ These are nodes that have been developed by the community, for the community. If
 
 If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md).
 
-To use a node, add the node to the `nodes` folder found in your InvokeAI install location. 
-
-The suggested method is to use `git clone` to clone the repository the node is found in. This allows for easy updates of the node in the future. 
-
-If you'd prefer, you can also just download the `.py` file from the linked repository and add it to the `nodes` folder. 
+To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations` folder in your Invoke AI install location. If you used the automated installation, this can be found inside the `.venv` folder. Along with the node, an example node graph should be provided to help you get started with the node. 
 
 To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor. 
 
 - Community Nodes
-    + [Average Images](#average-images)
     + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
     + [Film Grain](#film-grain)
     + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
@@ -38,13 +33,6 @@ To use a community workflow, download the the `.json` node graph file and load i
 - [Help](#help)
 
 
---------------------------------
-### Average Images
-
-**Description:** This node takes in a collection of images of the same size and averages them as output. It converts everything to RGB mode first.
-
-**Node Link:** https://github.com/JPPhoto/average-images-node
-
 --------------------------------
 ### Depth Map from Wavefront OBJ
 
@@ -189,8 +177,12 @@ This includes 15 Nodes:
 
 **Node Link:** https://github.com/helix4u/load_video_frame
 
+**Example Node Graph:**  https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json
+
 **Output Example:** 
-<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/main/_git_assets/testmp4_embed_converted.gif" width="500" />
+
+<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/main/testmp4_embed_converted.gif" width="500" />
+[Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4)
 
 --------------------------------
 ### Make 3D
@@ -333,9 +325,9 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
 
 **Description:** This node allows you to do super cool things with InvokeAI.
 
-**Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py
+**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
 
-**Example Workflow:**  https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json
+**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
 
 **Output Examples** 
 

docs/nodes/contributingNodes.md

@@ -4,7 +4,7 @@ To learn about the specifics of creating a new node, please visit our [Node crea
 
 Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps: 
 
-- Make sure the node is contained in a new Python (.py) file. Preferably, the node is in a repo with a README detailing the nodes usage & examples to help others more easily use your node. Including the tag "invokeai-node" in your repository's README can also help other users find it more easily. 
+- Make sure the node is contained in a new Python (.py) file. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node.
 - Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list
     - Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node.
 - A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project.

docs/nodes/exampleWorkflows.md

@@ -2,17 +2,13 @@
 
 We've curated some example workflows for you to get started with Workflows in InvokeAI
 
-To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
+To use them, right click on your desired workflow, press "Download Linked File". You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
 
 If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
 
 * [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json)
-* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_Text_to_Image.json)
-* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
-* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json)
-* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json)
-* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json)
-* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json.json)
+* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
+* [SDXL (with Refiner) Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json) 
+* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json)
 * [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json)
 * [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json)
-* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/QR_Code_Monster.json)

docs/workflows/Multi_ControlNet_Canny_and_Depth.json

@@ -1,985 +0,0 @@
-{
-  "name": "Multi ControlNet (Canny & Depth)",
-  "author": "Millu",
-  "description": "A sample workflow using canny & depth ControlNets to guide the generation process. ",
-  "version": "0.1.0",
-  "contact": "millun@invoke.ai",
-  "tags": "ControlNet, canny, depth",
-  "notes": "",
-  "exposedFields": [
-    {
-      "nodeId": "54486974-835b-4d81-8f82-05f9f32ce9e9",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "7ce68934-3419-42d4-ac70-82cfc9397306",
-      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
-      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "c4b23e64-7986-40c4-9cad-46327b12e204",
-      "fieldName": "image"
-    },
-    {
-      "nodeId": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
-      "fieldName": "image"
-    }
-  ],
-  "meta": {
-    "version": "1.0.0"
-  },
-  "nodes": [
-    {
-      "id": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
-      "type": "invocation",
-      "data": {
-        "id": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
-        "type": "image",
-        "inputs": {
-          "image": {
-            "id": "189c8adf-68cc-4774-a729-49da89f6fdf1",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": "Depth Input Image"
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "1a31cacd-9d19-4f32-b558-c5e4aa39ce73",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
-          },
-          "width": {
-            "id": "12f298fd-1d11-4cca-9426-01240f7ec7cf",
-            "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
-          },
-          "height": {
-            "id": "c47dabcb-44e8-40c9-992d-81dca59f598e",
-            "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 225,
-      "position": {
-        "x": 3617.163483500202,
-        "y": 40.5529847930888
-      }
-    },
-    {
-      "id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
-      "type": "invocation",
-      "data": {
-        "id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
-        "type": "controlnet",
-        "inputs": {
-          "image": {
-            "id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "control_model": {
-            "id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
-            "name": "control_model",
-            "type": "ControlNetModelField",
-            "fieldKind": "input",
-            "label": "",
-            "value": {
-              "model_name": "sd-controlnet-depth",
-              "base_model": "sd-1"
-            }
-          },
-          "control_weight": {
-            "id": "5e32bd8a-9dc8-42d8-9bcc-c2b0460c0b0f",
-            "name": "control_weight",
-            "type": "FloatPolymorphic",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
-          },
-          "begin_step_percent": {
-            "id": "c258a276-352a-416c-8358-152f11005c0c",
-            "name": "begin_step_percent",
-            "type": "float",
-            "fieldKind": "input",
-            "label": "",
-            "value": 0
-          },
-          "end_step_percent": {
-            "id": "43001125-0d70-4f87-8e79-da6603ad6c33",
-            "name": "end_step_percent",
-            "type": "float",
-            "fieldKind": "input",
-            "label": "",
-            "value": 1
-          },
-          "control_mode": {
-            "id": "d2f14561-9443-4374-9270-e2f05007944e",
-            "name": "control_mode",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "balanced"
-          },
-          "resize_mode": {
-            "id": "727ee7d3-8bf6-4c7d-8b8a-43546b3b59cd",
-            "name": "resize_mode",
-            "type": "enum",
-            "fieldKind": "input",
-            "label": "",
-            "value": "just_resize"
-          }
-        },
-        "outputs": {
-          "control": {
-            "id": "b034aa0f-4d0d-46e4-b5e3-e25a9588d087",
-            "name": "control",
-            "type": "ControlField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 508,
-      "position": {
-        "x": 4477.604342844504,
-        "y": -49.39005411272677
-      }
-    },
-    {
-      "id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
-      "type": "invocation",
-      "data": {
-        "id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
-        "type": "compel",
-        "inputs": {
-          "prompt": {
-            "id": "7c2c4771-2161-4d77-aced-ff8c4b3f1c15",
-            "name": "prompt",
-            "type": "string",
-            "fieldKind": "input",
-            "label": "Negative Prompt",
-            "value": ""
-          },
-          "clip": {
-            "id": "06d59e91-9cca-411d-bf05-86b099b3e8f7",
-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "input",
-            "label": ""
-          }
-        },
-        "outputs": {
-          "conditioning": {
-            "id": "858bc33c-134c-4bf6-8855-f943e1d26f14",
-            "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 261,
-      "position": {
-        "x": 4444.706437017514,
-        "y": -924.0715320874991
-      }
-    },
-    {
-      "id": "54486974-835b-4d81-8f82-05f9f32ce9e9",
-      "type": "invocation",
-      "data": {
-        "id": "54486974-835b-4d81-8f82-05f9f32ce9e9",
-        "type": "main_model_loader",
-        "inputs": {
-          "model": {
-            "id": "f4a915a5-593e-4b6d-9198-c78eb5cefaed",
-            "name": "model",
-            "type": "MainModelField",
-            "fieldKind": "input",
-            "label": "",
-            "value": {
-              "model_name": "stable-diffusion-v1-5",
-              "base_model": "sd-1",
-              "model_type": "main"
-            }
-          }
-        },
-        "outputs": {
-          "unet": {
-            "id": "ee24fb16-da38-4c66-9fbc-e8f296ed40d2",
-            "name": "unet",
-            "type": "UNetField",
-            "fieldKind": "output"
-          },
-          "clip": {
-            "id": "f3fb0524-8803-41c1-86db-a61a13ee6a33",
-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "output"
-          },
-          "vae": {
-            "id": "5c4878a8-b40f-44ab-b146-1c1f42c860b3",
-            "name": "vae",
-            "type": "VaeField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 226,
-      "position": {
-        "x": 3837.096149678291,
-        "y": -1050.015351148365
-      }
-    },
-    {
-      "id": "7ce68934-3419-42d4-ac70-82cfc9397306",
-      "type": "invocation",
-      "data": {
-        "id": "7ce68934-3419-42d4-ac70-82cfc9397306",
-        "type": "compel",
-        "inputs": {
-          "prompt": {
-            "id": "7c2c4771-2161-4d77-aced-ff8c4b3f1c15",
-            "name": "prompt",
-            "type": "string",
-            "fieldKind": "input",
-            "label": "Positive Prompt",
-            "value": ""
-          },
-          "clip": {
-            "id": "06d59e91-9cca-411d-bf05-86b099b3e8f7",
-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "input",
-            "label": ""
-          }
-        },
-        "outputs": {
-          "conditioning": {
-            "id": "858bc33c-134c-4bf6-8855-f943e1d26f14",
-            "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 261,
-      "position": {
-        "x": 4449.356038911986,
-        "y": -1201.659695420063
-      }
-    },
-    {
-      "id": "d204d184-f209-4fae-a0a1-d152800844e1",
-      "type": "invocation",
-      "data": {
-        "id": "d204d184-f209-4fae-a0a1-d152800844e1",
-        "type": "controlnet",
-        "inputs": {
-          "image": {
-            "id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "control_model": {
-            "id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
-            "name": "control_model",
-            "type": "ControlNetModelField",
-            "fieldKind": "input",
-            "label": "",
-            "value": {
-              "model_name": "sd-controlnet-canny",
-              "base_model": "sd-1"
-            }
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-      "id": "reactflow__edge-273e3f96-49ea-4dc5-9d5b-9660390f14e1conditioning-ac481b7f-08bf-4a9d-9e0c-3a82ea5243cenegative_conditioning",
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-      "source": "7ce68934-3419-42d4-ac70-82cfc9397306",
-      "sourceHandle": "conditioning",
-      "target": "ac481b7f-08bf-4a9d-9e0c-3a82ea5243ce",
-      "targetHandle": "positive_conditioning",
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-}

docs/workflows/Prompt_from_File.json

@@ -1,719 +0,0 @@
-{
-  "name": "Prompt from File",
-  "author": "InvokeAI",
-  "description": "Sample workflow using prompt from file capabilities of InvokeAI ",
-  "version": "0.1.0",
-  "contact": "millun@invoke.ai",
-  "tags": "text2image, prompt from file, default",
-  "notes": "",
-  "exposedFields": [
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-      "nodeId": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
-      "fieldName": "file_path"
-    }
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-    "version": "1.0.0"
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-      "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
-      "type": "invocation",
-      "data": {
-        "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
-        "type": "compel",
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-            "name": "clip",
-            "type": "ClipField",
-            "fieldKind": "input",
-            "label": ""
-          }
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-        "outputs": {
-          "conditioning": {
-            "id": "46205e6c-c5e2-44cb-9c82-1cd20b95674a",
-            "name": "conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "output"
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-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
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-        "type": "prompt_from_file",
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-            "name": "file_path",
-            "type": "string",
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-            "label": "Prompts File Path",
-            "value": ""
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-            "name": "pre_prompt",
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-            "id": "efee9a48-05ab-4829-8429-becfa64a0782",
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-          "max_prompts": {
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-            "name": "max_prompts",
-            "type": "integer",
-            "fieldKind": "input",
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-            "value": 1
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-        "outputs": {
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-            "name": "collection",
-            "type": "StringCollection",
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-        "label": "Prompts from File",
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docs/workflows/QR_Code_Monster.json

@@ -1,758 +0,0 @@
-{
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-  "author": "InvokeAI",
-  "description": "Sample workflow for create images with QR code Monster ControlNet",
-  "version": "1.0.1",
-  "contact": "invoke@invoke.ai",
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docs/workflows/SDXL_Text_to_Image.json

@@ -26,6 +26,10 @@
     {
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       "fieldName": "style"
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@@ -36,6 +40,7 @@
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       "type": "invocation",
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@@ -130,12 +135,10 @@
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@@ -145,6 +148,7 @@
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@@ -205,9 +209,7 @@
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       "height": 32,
@@ -216,10 +218,83 @@
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+            "type": "boolean",
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+            "value": true
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+            "name": "latents",
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+            "label": ""
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+          "vae": {
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+            "name": "vae",
+            "type": "VaeField",
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+        "outputs": {
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+            "fieldKind": "output"
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+          "height": {
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+            "name": "height",
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+            "fieldKind": "output"
+          }
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+        "label": "",
+        "isOpen": true,
+        "notes": "",
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+        "isIntermediate": false
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+      "height": 224,
+      "position": {
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+      }
+    },
     {
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         "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
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@@ -252,9 +327,7 @@
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-        "useCache": false,
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+        "isIntermediate": true
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@@ -267,6 +340,7 @@
       "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
       "type": "invocation",
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         "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
         "type": "sdxl_model_loader",
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@@ -277,7 +351,7 @@
             "fieldKind": "input",
             "label": "",
             "value": {
-              "model_name": "stable-diffusion-xl-base-1-0",
+              "model_name": "stable-diffusion-xl-base-1.0",
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               "model_type": "main"
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@@ -313,12 +387,10 @@
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+        "isIntermediate": true
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+      "height": 234,
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@@ -328,6 +400,7 @@
       "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
       "type": "invocation",
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         "id": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
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@@ -422,143 +495,48 @@
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           },
           "denoising_start": {
-            "id": "be219d5e-41b7-430a-8fb5-bc21a31ad219",
+            "id": "01e2f30d-0acd-4e21-98b9-a9b8e24c6db2",
             "name": "denoising_start",
             "type": "float",
             "fieldKind": "input",
@@ -566,7 +544,7 @@
             "value": 0
           },
           "denoising_end": {
-            "id": "3adfb7ae-c9f7-4a40-b6e0-4c2050bd1a99",
+            "id": "3db95479-a73b-4c75-9b44-08daec16b224",
             "name": "denoising_end",
             "type": "float",
             "fieldKind": "input",
@@ -574,71 +552,71 @@
             "value": 1
           },
           "scheduler": {
-            "id": "14423e0d-7215-4ee0-b065-f9e95eaa8d7d",
+            "id": "db8430a9-64c3-4c54-ae38-9f597cf7b6d5",
             "name": "scheduler",
             "type": "Scheduler",
             "fieldKind": "input",
             "label": "",
             "value": "euler"
           },
-          "unet": {
-            "id": "e73bbf98-6489-492b-b83c-faed215febac",
-            "name": "unet",
-            "type": "UNetField",
-            "fieldKind": "input",
-            "label": ""
-          },
           "control": {
-            "id": "dab351b3-0c86-4ea5-9782-4e8edbfb0607",
+            "id": "599b49e8-6435-4576-be41-a5155f3a17e3",
             "name": "control",
-            "type": "ControlPolymorphic",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "ip_adapter": {
-            "id": "192daea0-a90a-43cc-a2ee-0114a8e90318",
-            "name": "ip_adapter",
-            "type": "IPAdapterPolymorphic",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "t2i_adapter": {
-            "id": "ee386a55-d4c7-48c1-ac57-7bc4e3aada7a",
-            "name": "t2i_adapter",
-            "type": "T2IAdapterPolymorphic",
+            "type": "ControlField",
             "fieldKind": "input",
             "label": ""
           },
           "latents": {
-            "id": "3a922c6a-3d8c-4c9e-b3ec-2f4d81cda077",
+            "id": "226f9e91-454e-4159-9fa6-019c0cf29277",
             "name": "latents",
             "type": "LatentsField",
             "fieldKind": "input",
             "label": ""
           },
           "denoise_mask": {
-            "id": "cd7ce032-835f-495f-8b45-d57272f33132",
+            "id": "de019cb6-7fb5-45bf-a266-22e20889893f",
             "name": "denoise_mask",
             "type": "DenoiseMaskField",
             "fieldKind": "input",
             "label": ""
+          },
+          "positive_conditioning": {
+            "id": "02fc400a-110d-470e-8411-f404f966a949",
+            "name": "positive_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "negative_conditioning": {
+            "id": "4bd3bdfa-fcf4-42be-8e47-1e314255798f",
+            "name": "negative_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "unet": {
+            "id": "7c2d58a8-b5f1-4e63-8ffd-8ada52c35832",
+            "name": "unet",
+            "type": "UNetField",
+            "fieldKind": "input",
+            "label": ""
           }
         },
         "outputs": {
           "latents": {
-            "id": "6260b84f-8361-470a-98d8-5b22a45c2d8c",
+            "id": "6a6fa492-de26-4e95-b1d9-a322fe37eb13",
             "name": "latents",
             "type": "LatentsField",
             "fieldKind": "output"
           },
           "width": {
-            "id": "aede0ecf-25b6-46be-aa30-b77f79715deb",
+            "id": "a9790729-7d6c-4418-903d-4da961fccf56",
             "name": "width",
             "type": "integer",
             "fieldKind": "output"
           },
           "height": {
-            "id": "519abf62-d475-48ef-ab8f-66136bc0e499",
+            "id": "fa74efe5-7330-4a3c-b256-c82a544585b4",
             "name": "height",
             "type": "integer",
             "fieldKind": "output"
@@ -648,15 +626,13 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.4.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 646,
+      "height": 558,
       "position": {
-        "x": 1642.955772577545,
-        "y": -230.2485847594651
+        "x": 1650,
+        "y": -250
       }
     }
   ],
@@ -710,42 +686,50 @@
     {
       "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
       "sourceHandle": "vae",
-      "target": "63e91020-83b2-4f35-b174-ad9692aabb48",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
       "targetHandle": "vae",
-      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-63e91020-83b2-4f35-b174-ad9692aabb48vae",
+      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
       "type": "default"
     },
     {
-      "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
-      "sourceHandle": "unet",
-      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
-      "targetHandle": "unet",
-      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbunet",
+      "source": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "sourceHandle": "latents",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-87ee6243-fb0d-4f77-ad5f-56591659339elatents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents",
       "type": "default"
     },
     {
       "source": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
       "sourceHandle": "conditioning",
-      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
       "targetHandle": "positive_conditioning",
-      "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbpositive_conditioning",
+      "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-87ee6243-fb0d-4f77-ad5f-56591659339epositive_conditioning",
       "type": "default"
     },
     {
       "source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
       "sourceHandle": "conditioning",
-      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
       "targetHandle": "negative_conditioning",
-      "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnegative_conditioning",
+      "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-87ee6243-fb0d-4f77-ad5f-56591659339enegative_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
+      "sourceHandle": "unet",
+      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "targetHandle": "unet",
+      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-87ee6243-fb0d-4f77-ad5f-56591659339eunet",
       "type": "default"
     },
     {
       "source": "55705012-79b9-4aac-9f26-c0b10309785b",
       "sourceHandle": "noise",
-      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
       "targetHandle": "noise",
-      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnoise",
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-87ee6243-fb0d-4f77-ad5f-56591659339enoise",
       "type": "default"
     }
   ]
-}
+}

docs/workflows/Text_to_Image.json

@@ -18,6 +18,10 @@
     {
       "nodeId": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "fieldName": "prompt"
+    },
+    {
+      "nodeId": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "fieldName": "steps"
     }
   ],
   "meta": {
@@ -28,6 +32,7 @@
       "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
         "type": "compel",
         "inputs": {
@@ -59,21 +64,20 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 261,
+      "height": 235,
       "position": {
-        "x": 995.7263915923627,
-        "y": 239.67783573351227
+        "x": 1400,
+        "y": -75
       }
     },
     {
       "id": "55705012-79b9-4aac-9f26-c0b10309785b",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "55705012-79b9-4aac-9f26-c0b10309785b",
         "type": "noise",
         "inputs": {
@@ -134,21 +138,92 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 389,
+      "height": 364,
       "position": {
-        "x": 993.4442117555518,
-        "y": 605.6757415334787
+        "x": 1000,
+        "y": 350
+      }
+    },
+    {
+      "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "type": "invocation",
+      "data": {
+        "version": "1.0.0",
+        "id": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+        "type": "l2i",
+        "inputs": {
+          "tiled": {
+            "id": "24f5bc7b-f6a1-425d-8ab1-f50b4db5d0df",
+            "name": "tiled",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": false
+          },
+          "fp32": {
+            "id": "b146d873-ffb9-4767-986a-5360504841a2",
+            "name": "fp32",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": false
+          },
+          "latents": {
+            "id": "65441abd-7713-4b00-9d8d-3771404002e8",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "vae": {
+            "id": "a478b833-6e13-4611-9a10-842c89603c74",
+            "name": "vae",
+            "type": "VaeField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "image": {
+            "id": "c87ae925-f858-417a-8940-8708ba9b4b53",
+            "name": "image",
+            "type": "ImageField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "4bcb8512-b5a1-45f1-9e52-6e92849f9d6c",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "23e41c00-a354-48e8-8f59-5875679c27ab",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": true,
+        "isIntermediate": false
+      },
+      "width": 320,
+      "height": 266,
+      "position": {
+        "x": 1800,
+        "y": 200
       }
     },
     {
       "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
         "type": "main_model_loader",
         "inputs": {
@@ -186,24 +261,23 @@
           }
         },
         "label": "",
-        "isOpen": true,
+        "isOpen": false,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 226,
+      "height": 32,
       "position": {
-        "x": 163.04436745878343,
-        "y": 254.63156870373479
+        "x": 1000,
+        "y": 200
       }
     },
     {
       "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
         "type": "compel",
         "inputs": {
@@ -235,21 +309,20 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 261,
+      "height": 235,
       "position": {
-        "x": 595.7263915923627,
-        "y": 239.67783573351227
+        "x": 1000,
+        "y": -75
       }
     },
     {
       "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
       "type": "invocation",
       "data": {
+        "version": "1.0.0",
         "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
         "type": "rand_int",
         "inputs": {
@@ -279,66 +352,51 @@
           }
         },
         "label": "Random Seed",
-        "isOpen": true,
+        "isOpen": false,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": false,
-        "version": "1.0.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 218,
+      "height": 32,
       "position": {
-        "x": 541.094822888628,
-        "y": 694.5704476446829
+        "x": 1000,
+        "y": 275
       }
     },
     {
-      "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
       "type": "invocation",
       "data": {
-        "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+        "version": "1.0.0",
+        "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
         "type": "denoise_latents",
         "inputs": {
-          "positive_conditioning": {
-            "id": "90b7f4f8-ada7-4028-8100-d2e54f192052",
-            "name": "positive_conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "negative_conditioning": {
-            "id": "9393779e-796c-4f64-b740-902a1177bf53",
-            "name": "negative_conditioning",
-            "type": "ConditioningField",
-            "fieldKind": "input",
-            "label": ""
-          },
           "noise": {
-            "id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1",
+            "id": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b",
             "name": "noise",
             "type": "LatentsField",
             "fieldKind": "input",
             "label": ""
           },
           "steps": {
-            "id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88",
+            "id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c",
             "name": "steps",
             "type": "integer",
             "fieldKind": "input",
             "label": "",
-            "value": 10
+            "value": 36
           },
           "cfg_scale": {
-            "id": "87dd04d3-870e-49e1-98bf-af003a810109",
+            "id": "107267ce-4666-4cd7-94b3-7476b7973ae9",
             "name": "cfg_scale",
-            "type": "FloatPolymorphic",
+            "type": "float",
             "fieldKind": "input",
             "label": "",
             "value": 7.5
           },
           "denoising_start": {
-            "id": "f369d80f-4931-4740-9bcd-9f0620719fab",
+            "id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1",
             "name": "denoising_start",
             "type": "float",
             "fieldKind": "input",
@@ -346,7 +404,7 @@
             "value": 0
           },
           "denoising_end": {
-            "id": "747d10e5-6f02-445c-994c-0604d814de8c",
+            "id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f",
             "name": "denoising_end",
             "type": "float",
             "fieldKind": "input",
@@ -354,71 +412,71 @@
             "value": 1
           },
           "scheduler": {
-            "id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b",
+            "id": "53092874-a43b-4623-91a2-76e62fdb1f2e",
             "name": "scheduler",
             "type": "Scheduler",
             "fieldKind": "input",
             "label": "",
             "value": "euler"
           },
-          "unet": {
-            "id": "ffa6fef4-3ce2-4bdb-9296-9a834849489b",
-            "name": "unet",
-            "type": "UNetField",
-            "fieldKind": "input",
-            "label": ""
-          },
           "control": {
-            "id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
+            "id": "7abe57cc-469d-437e-ad72-a18efa28215f",
             "name": "control",
-            "type": "ControlPolymorphic",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "ip_adapter": {
-            "id": "1d6948f7-3a65-4a65-a20c-768b287251aa",
-            "name": "ip_adapter",
-            "type": "IPAdapterPolymorphic",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "t2i_adapter": {
-            "id": "75e67b09-952f-4083-aaf4-6b804d690412",
-            "name": "t2i_adapter",
-            "type": "T2IAdapterPolymorphic",
+            "type": "ControlField",
             "fieldKind": "input",
             "label": ""
           },
           "latents": {
-            "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
+            "id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129",
             "name": "latents",
             "type": "LatentsField",
             "fieldKind": "input",
             "label": ""
           },
           "denoise_mask": {
-            "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
+            "id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1",
             "name": "denoise_mask",
             "type": "DenoiseMaskField",
             "fieldKind": "input",
             "label": ""
+          },
+          "positive_conditioning": {
+            "id": "c7160303-8a23-4f15-9197-855d48802a7f",
+            "name": "positive_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "negative_conditioning": {
+            "id": "fd750efa-1dfc-4d0b-accb-828e905ba320",
+            "name": "negative_conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "unet": {
+            "id": "af1f41ba-ce2a-4314-8d7f-494bb5800381",
+            "name": "unet",
+            "type": "UNetField",
+            "fieldKind": "input",
+            "label": ""
           }
         },
         "outputs": {
           "latents": {
-            "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
+            "id": "8508d04d-f999-4a44-94d0-388ab1401d27",
             "name": "latents",
             "type": "LatentsField",
             "fieldKind": "output"
           },
           "width": {
-            "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
+            "id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e",
             "name": "width",
             "type": "integer",
             "fieldKind": "output"
           },
           "height": {
-            "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
+            "id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96",
             "name": "height",
             "type": "integer",
             "fieldKind": "output"
@@ -428,95 +486,13 @@
         "isOpen": true,
         "notes": "",
         "embedWorkflow": false,
-        "isIntermediate": true,
-        "useCache": true,
-        "version": "1.4.0"
+        "isIntermediate": true
       },
       "width": 320,
-      "height": 646,
+      "height": 558,
       "position": {
-        "x": 1476.5794704734735,
-        "y": 256.80174342731783
-      }
-    },
-    {
-      "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "type": "invocation",
-      "data": {
-        "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-        "type": "l2i",
-        "inputs": {
-          "metadata": {
-            "id": "ab375f12-0042-4410-9182-29e30db82c85",
-            "name": "metadata",
-            "type": "MetadataField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "latents": {
-            "id": "3a7e7efd-bff5-47d7-9d48-615127afee78",
-            "name": "latents",
-            "type": "LatentsField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "vae": {
-            "id": "a1f5f7a1-0795-4d58-b036-7820c0b0ef2b",
-            "name": "vae",
-            "type": "VaeField",
-            "fieldKind": "input",
-            "label": ""
-          },
-          "tiled": {
-            "id": "da52059a-0cee-4668-942f-519aa794d739",
-            "name": "tiled",
-            "type": "boolean",
-            "fieldKind": "input",
-            "label": "",
-            "value": false
-          },
-          "fp32": {
-            "id": "c4841df3-b24e-4140-be3b-ccd454c2522c",
-            "name": "fp32",
-            "type": "boolean",
-            "fieldKind": "input",
-            "label": "",
-            "value": false
-          }
-        },
-        "outputs": {
-          "image": {
-            "id": "72d667d0-cf85-459d-abf2-28bd8b823fe7",
-            "name": "image",
-            "type": "ImageField",
-            "fieldKind": "output"
-          },
-          "width": {
-            "id": "c8c907d8-1066-49d1-b9a6-83bdcd53addc",
-            "name": "width",
-            "type": "integer",
-            "fieldKind": "output"
-          },
-          "height": {
-            "id": "230f359c-b4ea-436c-b372-332d7dcdca85",
-            "name": "height",
-            "type": "integer",
-            "fieldKind": "output"
-          }
-        },
-        "label": "",
-        "isOpen": true,
-        "notes": "",
-        "embedWorkflow": false,
-        "isIntermediate": false,
-        "useCache": true,
-        "version": "1.0.0"
-      },
-      "width": 320,
-      "height": 267,
-      "position": {
-        "x": 2037.9648469717395,
-        "y": 426.10844427600136
+        "x": 1400,
+        "y": 200
       }
     }
   ],
@@ -546,52 +522,52 @@
       "type": "default"
     },
     {
-      "source": "55705012-79b9-4aac-9f26-c0b10309785b",
-      "sourceHandle": "noise",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "targetHandle": "noise",
-      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "sourceHandle": "vae",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "targetHandle": "vae",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
+      "type": "default"
+    },
+    {
+      "source": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "sourceHandle": "latents",
+      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-75899702-fa44-46d2-b2d5-3e17f234c3e7latents-dbcd2f98-d809-48c8-bf64-2635f88a2fe9latents",
       "type": "default"
     },
     {
       "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
       "sourceHandle": "conditioning",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
       "targetHandle": "positive_conditioning",
-      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning",
       "type": "default"
     },
     {
       "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
       "sourceHandle": "conditioning",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
       "targetHandle": "negative_conditioning",
-      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning",
       "type": "default"
     },
     {
       "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
       "sourceHandle": "unet",
-      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
       "targetHandle": "unet",
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet",
       "type": "default"
     },
     {
-      "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
-      "sourceHandle": "latents",
-      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "targetHandle": "latents",
-      "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
-      "type": "default"
-    },
-    {
-      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
-      "sourceHandle": "vae",
-      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
-      "targetHandle": "vae",
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
+      "source": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "sourceHandle": "noise",
+      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "targetHandle": "noise",
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-75899702-fa44-46d2-b2d5-3e17f234c3e7noise",
       "type": "default"
     }
   ]
-}
+}

installer/lib/messages.py

@@ -137,7 +137,7 @@ def dest_path(dest=None) -> Path:
         path_completer = PathCompleter(
             only_directories=True,
             expanduser=True,
-            get_paths=lambda: [browse_start],  # noqa: B023
+            get_paths=lambda: [browse_start],
             # get_paths=lambda: [".."].extend(list(browse_start.iterdir()))
         )
 
@@ -149,7 +149,7 @@ def dest_path(dest=None) -> Path:
             completer=path_completer,
             default=str(browse_start) + os.sep,
             vi_mode=True,
-            complete_while_typing=True,
+            complete_while_typing=True
             # Test that this is not needed on Windows
             # complete_style=CompleteStyle.READLINE_LIKE,
         )

invokeai/app/api/events.py

@@ -28,7 +28,7 @@ class FastAPIEventService(EventServiceBase):
         self.__queue.put(None)
 
     def dispatch(self, event_name: str, payload: Any) -> None:
-        self.__queue.put({"event_name": event_name, "payload": payload})
+        self.__queue.put(dict(event_name=event_name, payload=payload))
 
     async def __dispatch_from_queue(self, stop_event: threading.Event):
         """Get events on from the queue and dispatch them, from the correct thread"""

invokeai/app/api/routers/models.py

@@ -55,7 +55,7 @@ async def list_models(
 ) -> ModelsList:
     """Gets a list of models"""
     if base_models and len(base_models) > 0:
-        models_raw = []
+        models_raw = list()
         for base_model in base_models:
             models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
     else:

invokeai/app/api/sockets.py

@@ -34,4 +34,4 @@ class SocketIO:
 
     async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
         if "queue_id" in data:
-            await self.__sio.leave_room(sid, data["queue_id"])
+            await self.__sio.enter_room(sid, data["queue_id"])

invokeai/app/api_app.py

@@ -130,7 +130,7 @@ def custom_openapi() -> dict[str, Any]:
     # Add all outputs
     all_invocations = BaseInvocation.get_invocations()
     output_types = set()
-    output_type_titles = {}
+    output_type_titles = dict()
     for invoker in all_invocations:
         output_type = signature(invoker.invoke).return_annotation
         output_types.add(output_type)
@@ -171,12 +171,12 @@ def custom_openapi() -> dict[str, Any]:
             # print(f"Config with name {name} already defined")
             continue
 
-        openapi_schema["components"]["schemas"][name] = {
-            "title": name,
-            "description": "An enumeration.",
-            "type": "string",
-            "enum": [v.value for v in model_config_format_enum],
-        }
+        openapi_schema["components"]["schemas"][name] = dict(
+            title=name,
+            description="An enumeration.",
+            type="string",
+            enum=list(v.value for v in model_config_format_enum),
+        )
 
     app.openapi_schema = openapi_schema
     return app.openapi_schema

invokeai/app/invocations/__init__.py

@@ -25,4 +25,4 @@ spec.loader.exec_module(module)
 
 # add core nodes to __all__
 python_files = filter(lambda f: not f.name.startswith("_"), Path(__file__).parent.glob("*.py"))
-__all__ = [f.stem for f in python_files]  # type: ignore
+__all__ = list(f.stem for f in python_files)  # type: ignore

invokeai/app/invocations/baseinvocation.py

@@ -16,7 +16,6 @@ from pydantic.fields import FieldInfo, _Unset
 from pydantic_core import PydanticUndefined
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.misc import uuid_string
 
 if TYPE_CHECKING:
@@ -31,6 +30,70 @@ class InvalidFieldError(TypeError):
     pass
 
 
+class FieldDescriptions:
+    denoising_start = "When to start denoising, expressed a percentage of total steps"
+    denoising_end = "When to stop denoising, expressed a percentage of total steps"
+    cfg_scale = "Classifier-Free Guidance scale"
+    scheduler = "Scheduler to use during inference"
+    positive_cond = "Positive conditioning tensor"
+    negative_cond = "Negative conditioning tensor"
+    noise = "Noise tensor"
+    clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
+    unet = "UNet (scheduler, LoRAs)"
+    vae = "VAE"
+    cond = "Conditioning tensor"
+    controlnet_model = "ControlNet model to load"
+    vae_model = "VAE model to load"
+    lora_model = "LoRA model to load"
+    main_model = "Main model (UNet, VAE, CLIP) to load"
+    sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
+    sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
+    onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
+    lora_weight = "The weight at which the LoRA is applied to each model"
+    compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
+    raw_prompt = "Raw prompt text (no parsing)"
+    sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
+    skipped_layers = "Number of layers to skip in text encoder"
+    seed = "Seed for random number generation"
+    steps = "Number of steps to run"
+    width = "Width of output (px)"
+    height = "Height of output (px)"
+    control = "ControlNet(s) to apply"
+    ip_adapter = "IP-Adapter to apply"
+    t2i_adapter = "T2I-Adapter(s) to apply"
+    denoised_latents = "Denoised latents tensor"
+    latents = "Latents tensor"
+    strength = "Strength of denoising (proportional to steps)"
+    metadata = "Optional metadata to be saved with the image"
+    metadata_collection = "Collection of Metadata"
+    metadata_item_polymorphic = "A single metadata item or collection of metadata items"
+    metadata_item_label = "Label for this metadata item"
+    metadata_item_value = "The value for this metadata item (may be any type)"
+    workflow = "Optional workflow to be saved with the image"
+    interp_mode = "Interpolation mode"
+    torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
+    fp32 = "Whether or not to use full float32 precision"
+    precision = "Precision to use"
+    tiled = "Processing using overlapping tiles (reduce memory consumption)"
+    detect_res = "Pixel resolution for detection"
+    image_res = "Pixel resolution for output image"
+    safe_mode = "Whether or not to use safe mode"
+    scribble_mode = "Whether or not to use scribble mode"
+    scale_factor = "The factor by which to scale"
+    blend_alpha = (
+        "Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
+    )
+    num_1 = "The first number"
+    num_2 = "The second number"
+    mask = "The mask to use for the operation"
+    board = "The board to save the image to"
+    image = "The image to process"
+    tile_size = "Tile size"
+    inclusive_low = "The inclusive low value"
+    exclusive_high = "The exclusive high value"
+    decimal_places = "The number of decimal places to round to"
+
+
 class Input(str, Enum):
     """
     The type of input a field accepts.
@@ -236,35 +299,35 @@ def InputField(
       Ignored for non-collection fields.
     """
 
-    json_schema_extra_: dict[str, Any] = {
-        "input": input,
-        "ui_type": ui_type,
-        "ui_component": ui_component,
-        "ui_hidden": ui_hidden,
-        "ui_order": ui_order,
-        "item_default": item_default,
-        "ui_choice_labels": ui_choice_labels,
-        "_field_kind": "input",
-    }
+    json_schema_extra_: dict[str, Any] = dict(
+        input=input,
+        ui_type=ui_type,
+        ui_component=ui_component,
+        ui_hidden=ui_hidden,
+        ui_order=ui_order,
+        item_default=item_default,
+        ui_choice_labels=ui_choice_labels,
+        _field_kind="input",
+    )
 
-    field_args = {
-        "default": default,
-        "default_factory": default_factory,
-        "title": title,
-        "description": description,
-        "pattern": pattern,
-        "strict": strict,
-        "gt": gt,
-        "ge": ge,
-        "lt": lt,
-        "le": le,
-        "multiple_of": multiple_of,
-        "allow_inf_nan": allow_inf_nan,
-        "max_digits": max_digits,
-        "decimal_places": decimal_places,
-        "min_length": min_length,
-        "max_length": max_length,
-    }
+    field_args = dict(
+        default=default,
+        default_factory=default_factory,
+        title=title,
+        description=description,
+        pattern=pattern,
+        strict=strict,
+        gt=gt,
+        ge=ge,
+        lt=lt,
+        le=le,
+        multiple_of=multiple_of,
+        allow_inf_nan=allow_inf_nan,
+        max_digits=max_digits,
+        decimal_places=decimal_places,
+        min_length=min_length,
+        max_length=max_length,
+    )
 
     """
     Invocation definitions have their fields typed correctly for their `invoke()` functions.
@@ -299,24 +362,24 @@ def InputField(
 
     # because we are manually making fields optional, we need to store the original required bool for reference later
     if default is PydanticUndefined and default_factory is PydanticUndefined:
-        json_schema_extra_.update({"orig_required": True})
+        json_schema_extra_.update(dict(orig_required=True))
     else:
-        json_schema_extra_.update({"orig_required": False})
+        json_schema_extra_.update(dict(orig_required=False))
 
     # make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
     if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined:
         default_ = None if default is PydanticUndefined else default
-        provided_args.update({"default": default_})
+        provided_args.update(dict(default=default_))
         if default is not PydanticUndefined:
             # before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value
-            json_schema_extra_.update({"default": default})
-            json_schema_extra_.update({"orig_default": default})
+            json_schema_extra_.update(dict(default=default))
+            json_schema_extra_.update(dict(orig_default=default))
     elif default is not PydanticUndefined and default_factory is PydanticUndefined:
         default_ = default
-        provided_args.update({"default": default_})
-        json_schema_extra_.update({"orig_default": default_})
+        provided_args.update(dict(default=default_))
+        json_schema_extra_.update(dict(orig_default=default_))
     elif default_factory is not PydanticUndefined:
-        provided_args.update({"default_factory": default_factory})
+        provided_args.update(dict(default_factory=default_factory))
         # TODO: cannot serialize default_factory...
         # json_schema_extra_.update(dict(orig_default_factory=default_factory))
 
@@ -383,12 +446,12 @@ def OutputField(
         decimal_places=decimal_places,
         min_length=min_length,
         max_length=max_length,
-        json_schema_extra={
-            "ui_type": ui_type,
-            "ui_hidden": ui_hidden,
-            "ui_order": ui_order,
-            "_field_kind": "output",
-        },
+        json_schema_extra=dict(
+            ui_type=ui_type,
+            ui_hidden=ui_hidden,
+            ui_order=ui_order,
+            _field_kind="output",
+        ),
     )
 
 
@@ -460,14 +523,14 @@ class BaseInvocationOutput(BaseModel):
 
     @classmethod
     def get_output_types(cls) -> Iterable[str]:
-        return (get_type(i) for i in BaseInvocationOutput.get_outputs())
+        return map(lambda i: get_type(i), BaseInvocationOutput.get_outputs())
 
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
         # Because we use a pydantic Literal field with default value for the invocation type,
         # it will be typed as optional in the OpenAPI schema. Make it required manually.
         if "required" not in schema or not isinstance(schema["required"], list):
-            schema["required"] = []
+            schema["required"] = list()
         schema["required"].extend(["type"])
 
     model_config = ConfigDict(
@@ -527,11 +590,16 @@ class BaseInvocation(ABC, BaseModel):
     @classmethod
     def get_invocations_map(cls) -> dict[str, BaseInvocation]:
         # Get the type strings out of the literals and into a dictionary
-        return {get_type(i): i for i in BaseInvocation.get_invocations()}
+        return dict(
+            map(
+                lambda i: (get_type(i), i),
+                BaseInvocation.get_invocations(),
+            )
+        )
 
     @classmethod
     def get_invocation_types(cls) -> Iterable[str]:
-        return (get_type(i) for i in BaseInvocation.get_invocations())
+        return map(lambda i: get_type(i), BaseInvocation.get_invocations())
 
     @classmethod
     def get_output_type(cls) -> BaseInvocationOutput:
@@ -550,7 +618,7 @@ class BaseInvocation(ABC, BaseModel):
         if uiconfig and hasattr(uiconfig, "version"):
             schema["version"] = uiconfig.version
         if "required" not in schema or not isinstance(schema["required"], list):
-            schema["required"] = []
+            schema["required"] = list()
         schema["required"].extend(["type", "id"])
 
     @abstractmethod
@@ -604,15 +672,15 @@ class BaseInvocation(ABC, BaseModel):
     id: str = Field(
         default_factory=uuid_string,
         description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
-        json_schema_extra={"_field_kind": "internal"},
+        json_schema_extra=dict(_field_kind="internal"),
     )
     is_intermediate: bool = Field(
         default=False,
         description="Whether or not this is an intermediate invocation.",
-        json_schema_extra={"ui_type": UIType.IsIntermediate, "_field_kind": "internal"},
+        json_schema_extra=dict(ui_type=UIType.IsIntermediate, _field_kind="internal"),
     )
     use_cache: bool = Field(
-        default=True, description="Whether or not to use the cache", json_schema_extra={"_field_kind": "internal"}
+        default=True, description="Whether or not to use the cache", json_schema_extra=dict(_field_kind="internal")
     )
 
     UIConfig: ClassVar[Type[UIConfigBase]]
@@ -646,7 +714,7 @@ class _Model(BaseModel):
 
 
 # Get all pydantic model attrs, methods, etc
-RESERVED_PYDANTIC_FIELD_NAMES = {m[0] for m in inspect.getmembers(_Model())}
+RESERVED_PYDANTIC_FIELD_NAMES = set(map(lambda m: m[0], inspect.getmembers(_Model())))
 
 
 def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None:
@@ -661,7 +729,9 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
 
         field_kind = (
             # _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
-            field.json_schema_extra.get("_field_kind", None) if field.json_schema_extra else None
+            field.json_schema_extra.get("_field_kind", None)
+            if field.json_schema_extra
+            else None
         )
 
         # must have a field_kind
@@ -722,7 +792,7 @@ def invocation(
         # Add OpenAPI schema extras
         uiconf_name = cls.__qualname__ + ".UIConfig"
         if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
-            cls.UIConfig = type(uiconf_name, (UIConfigBase,), {})
+            cls.UIConfig = type(uiconf_name, (UIConfigBase,), dict())
         if title is not None:
             cls.UIConfig.title = title
         if tags is not None:
@@ -749,7 +819,7 @@ def invocation(
 
         invocation_type_annotation = Literal[invocation_type]  # type: ignore
         invocation_type_field = Field(
-            title="type", default=invocation_type, json_schema_extra={"_field_kind": "internal"}
+            title="type", default=invocation_type, json_schema_extra=dict(_field_kind="internal")
         )
 
         docstring = cls.__doc__
@@ -795,7 +865,7 @@ def invocation_output(
         # Add the output type to the model.
 
         output_type_annotation = Literal[output_type]  # type: ignore
-        output_type_field = Field(title="type", default=output_type, json_schema_extra={"_field_kind": "internal"})
+        output_type_field = Field(title="type", default=output_type, json_schema_extra=dict(_field_kind="internal"))
 
         docstring = cls.__doc__
         cls = create_model(
@@ -827,7 +897,7 @@ WorkflowFieldValidator = TypeAdapter(WorkflowField)
 
 class WithWorkflow(BaseModel):
     workflow: Optional[WorkflowField] = Field(
-        default=None, description=FieldDescriptions.workflow, json_schema_extra={"_field_kind": "internal"}
+        default=None, description=FieldDescriptions.workflow, json_schema_extra=dict(_field_kind="internal")
     )
 
 
@@ -845,5 +915,5 @@ MetadataFieldValidator = TypeAdapter(MetadataField)
 
 class WithMetadata(BaseModel):
     metadata: Optional[MetadataField] = Field(
-        default=None, description=FieldDescriptions.metadata, json_schema_extra={"_field_kind": "internal"}
+        default=None, description=FieldDescriptions.metadata, json_schema_extra=dict(_field_kind="internal")
     )

invokeai/app/invocations/compel.py

@@ -7,7 +7,6 @@ from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ExtraConditioningInfo,
@@ -20,6 +19,7 @@ from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -108,14 +108,13 @@ class CompelInvocation(BaseInvocation):
                 print(f'Warn: trigger: "{trigger}" not found')
 
         with (
+            ModelPatcher.apply_lora_text_encoder(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,
             ),
             ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
             text_encoder_info as text_encoder,
-            # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
-            ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
         ):
             compel = Compel(
                 tokenizer=tokenizer,
@@ -230,14 +229,13 @@ class SDXLPromptInvocationBase:
                 print(f'Warn: trigger: "{trigger}" not found')
 
         with (
+            ModelPatcher.apply_lora(text_encoder_info.context.model, _lora_loader(), lora_prefix),
             ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
             ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
             text_encoder_info as text_encoder,
-            # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
-            ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
         ):
             compel = Compel(
                 tokenizer=tokenizer,

invokeai/app/invocations/controlnet_image_processors.py

@@ -28,12 +28,12 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 
 from ...backend.model_management import BaseModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,

invokeai/app/invocations/facetools.py

@@ -131,7 +131,7 @@ def prepare_faces_list(
     deduped_faces: list[FaceResultData] = []
 
     if len(face_result_list) == 0:
-        return []
+        return list()
 
     for candidate in face_result_list:
         should_add = True
@@ -210,7 +210,7 @@ def generate_face_box_mask(
     # Check if any face is detected.
     if results.multi_face_landmarks:  # type: ignore # this are via protobuf and not typed
         # Search for the face_id in the detected faces.
-        for _face_id, face_landmarks in enumerate(results.multi_face_landmarks):  # type: ignore #this are via protobuf and not typed
+        for face_id, face_landmarks in enumerate(results.multi_face_landmarks):  # type: ignore #this are via protobuf and not typed
             # Get the bounding box of the face mesh.
             x_coordinates = [landmark.x for landmark in face_landmarks.landmark]
             y_coordinates = [landmark.y for landmark in face_landmarks.landmark]

invokeai/app/invocations/image.py

@@ -9,11 +9,19 @@ from PIL import Image, ImageChops, ImageFilter, ImageOps
 
 from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
-from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
+from .baseinvocation import (
+    BaseInvocation,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    WithMetadata,
+    WithWorkflow,
+    invocation,
+)
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")

invokeai/app/invocations/ip_adapter.py

@@ -7,6 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -16,7 +17,6 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType, ModelType
 from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
 
@@ -67,7 +67,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
     weight: Union[float, List[float]] = InputField(
-        default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
+        default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
     )
 
     begin_step_percent: float = InputField(

invokeai/app/invocations/latent.py

@@ -2,7 +2,7 @@
 
 from contextlib import ExitStack
 from functools import singledispatchmethod
-from typing import List, Literal, Optional, Union
+from typing import Callable, List, Literal, Optional, Union
 
 import einops
 import numpy as np
@@ -10,7 +10,7 @@ import torch
 import torchvision.transforms as T
 from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.models.adapter import T2IAdapter
+from diffusers.models.adapter import FullAdapterXL, T2IAdapter
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -34,7 +34,6 @@ from invokeai.app.invocations.primitives import (
 )
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
@@ -58,6 +57,7 @@ from ...backend.util.devices import choose_precision, choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -77,7 +77,7 @@ if choose_torch_device() == torch.device("mps"):
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
-SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
+SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
 @invocation_output("scheduler_output")
@@ -562,6 +562,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
             t2i_adapter_model: T2IAdapter
             with t2i_adapter_model_info as t2i_adapter_model:
                 total_downscale_factor = t2i_adapter_model.total_downscale_factor
+                if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
+                    # HACK(ryand): Work around a bug in FullAdapterXL. This is being addressed upstream in diffusers by
+                    # this PR: https://github.com/huggingface/diffusers/pull/5134.
+                    total_downscale_factor = total_downscale_factor // 2
 
                 # Resize the T2I-Adapter input image.
                 # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
@@ -647,8 +651,20 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         return 1 - mask, masked_latents
 
-    @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
+        # Get the source node id (we are invoking the prepared node)
+        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+
+        def step_callback(state: PipelineIntermediateState):
+            self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
+
+        return self.denoise(context, step_callback)
+
+    @torch.no_grad()
+    def denoise(
+        self, context: InvocationContext, step_callback: Callable[[PipelineIntermediateState], None]
+    ) -> LatentsOutput:
         with SilenceWarnings():  # this quenches NSFW nag from diffusers
             seed = None
             noise = None
@@ -683,13 +699,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 do_classifier_free_guidance=True,
             )
 
-            # Get the source node id (we are invoking the prepared node)
-            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-            def step_callback(state: PipelineIntermediateState):
-                self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
-
             def _lora_loader():
                 for lora in self.unet.loras:
                     lora_info = context.services.model_manager.get_model(
@@ -707,11 +716,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
             with (
                 ExitStack() as exit_stack,
                 ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
-                ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
                 set_seamless(unet_info.context.model, self.unet.seamless_axes),
                 unet_info as unet,
-                # Apply the LoRA after unet has been moved to its target device for faster patching.
-                ModelPatcher.apply_lora_unet(unet, _lora_loader()),
             ):
                 latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
@@ -1105,7 +1111,7 @@ class BlendLatentsInvocation(BaseInvocation):
         latents_b = context.services.latents.get(self.latents_b.latents_name)
 
         if latents_a.shape != latents_b.shape:
-            raise Exception("Latents to blend must be the same size.")
+            raise "Latents to blend must be the same size."
 
         # TODO:
         device = choose_torch_device()

invokeai/app/invocations/math.py

@@ -6,9 +6,8 @@ import numpy as np
 from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
-from invokeai.app.shared.fields import FieldDescriptions
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
 
 
 @invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
@@ -145,17 +144,17 @@ INTEGER_OPERATIONS = Literal[
 ]
 
 
-INTEGER_OPERATIONS_LABELS = {
-    "ADD": "Add A+B",
-    "SUB": "Subtract A-B",
-    "MUL": "Multiply A*B",
-    "DIV": "Divide A/B",
-    "EXP": "Exponentiate A^B",
-    "MOD": "Modulus A%B",
-    "ABS": "Absolute Value of A",
-    "MIN": "Minimum(A,B)",
-    "MAX": "Maximum(A,B)",
-}
+INTEGER_OPERATIONS_LABELS = dict(
+    ADD="Add A+B",
+    SUB="Subtract A-B",
+    MUL="Multiply A*B",
+    DIV="Divide A/B",
+    EXP="Exponentiate A^B",
+    MOD="Modulus A%B",
+    ABS="Absolute Value of A",
+    MIN="Minimum(A,B)",
+    MAX="Maximum(A,B)",
+)
 
 
 @invocation(
@@ -183,8 +182,8 @@ class IntegerMathInvocation(BaseInvocation):
     operation: INTEGER_OPERATIONS = InputField(
         default="ADD", description="The operation to perform", ui_choice_labels=INTEGER_OPERATIONS_LABELS
     )
-    a: int = InputField(default=1, description=FieldDescriptions.num_1)
-    b: int = InputField(default=1, description=FieldDescriptions.num_2)
+    a: int = InputField(default=0, description=FieldDescriptions.num_1)
+    b: int = InputField(default=0, description=FieldDescriptions.num_2)
 
     @field_validator("b")
     def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
@@ -231,17 +230,17 @@ FLOAT_OPERATIONS = Literal[
 ]
 
 
-FLOAT_OPERATIONS_LABELS = {
-    "ADD": "Add A+B",
-    "SUB": "Subtract A-B",
-    "MUL": "Multiply A*B",
-    "DIV": "Divide A/B",
-    "EXP": "Exponentiate A^B",
-    "ABS": "Absolute Value of A",
-    "SQRT": "Square Root of A",
-    "MIN": "Minimum(A,B)",
-    "MAX": "Maximum(A,B)",
-}
+FLOAT_OPERATIONS_LABELS = dict(
+    ADD="Add A+B",
+    SUB="Subtract A-B",
+    MUL="Multiply A*B",
+    DIV="Divide A/B",
+    EXP="Exponentiate A^B",
+    ABS="Absolute Value of A",
+    SQRT="Square Root of A",
+    MIN="Minimum(A,B)",
+    MAX="Maximum(A,B)",
+)
 
 
 @invocation(
@@ -257,8 +256,8 @@ class FloatMathInvocation(BaseInvocation):
     operation: FLOAT_OPERATIONS = InputField(
         default="ADD", description="The operation to perform", ui_choice_labels=FLOAT_OPERATIONS_LABELS
     )
-    a: float = InputField(default=1, description=FieldDescriptions.num_1)
-    b: float = InputField(default=1, description=FieldDescriptions.num_2)
+    a: float = InputField(default=0, description=FieldDescriptions.num_1)
+    b: float = InputField(default=0, description=FieldDescriptions.num_2)
 
     @field_validator("b")
     def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
@@ -266,7 +265,7 @@ class FloatMathInvocation(BaseInvocation):
             raise ValueError("Cannot divide by zero")
         elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0:
             raise ValueError("Cannot raise zero to a negative power")
-        elif info.data["operation"] == "EXP" and isinstance(info.data["a"] ** v, complex):
+        elif info.data["operation"] == "EXP" and type(info.data["a"] ** v) is complex:
             raise ValueError("Root operation resulted in a complex number")
         return v
 

invokeai/app/invocations/metadata.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     InputField,
     InvocationContext,
     MetadataField,
@@ -18,7 +19,6 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField
 from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
-from invokeai.app.shared.fields import FieldDescriptions
 
 from ...version import __version__
 
@@ -107,16 +107,11 @@ class MergeMetadataInvocation(BaseInvocation):
         return MetadataOutput(metadata=MetadataField.model_validate(data))
 
 
-GENERATION_MODES = Literal[
-    "txt2img", "img2img", "inpaint", "outpaint", "sdxl_txt2img", "sdxl_img2img", "sdxl_inpaint", "sdxl_outpaint"
-]
-
-
 @invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0")
 class CoreMetadataInvocation(BaseInvocation):
     """Collects core generation metadata into a MetadataField"""
 
-    generation_mode: Optional[GENERATION_MODES] = InputField(
+    generation_mode: Literal["txt2img", "img2img", "inpaint", "outpaint"] = InputField(
         default=None,
         description="The generation mode that output this image",
     )
@@ -160,14 +155,13 @@ class CoreMetadataInvocation(BaseInvocation):
     )
 
     # High resolution fix metadata.
-    hrf_enabled: Optional[float] = InputField(
+    hrf_width: Optional[int] = InputField(
         default=None,
-        description="Whether or not high resolution fix was enabled.",
+        description="The high resolution fix height and width multipler.",
     )
-    # TODO: should this be stricter or do we just let the UI handle it?
-    hrf_method: Optional[str] = InputField(
+    hrf_height: Optional[int] = InputField(
         default=None,
-        description="The high resolution fix upscale method.",
+        description="The high resolution fix height and width multipler.",
     )
     hrf_strength: Optional[float] = InputField(
         default=None,

invokeai/app/invocations/model.py

@@ -3,13 +3,11 @@ from typing import List, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
-from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.app.shared.models import FreeUConfig
-
 from ...backend.model_management import BaseModelType, ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -38,7 +36,6 @@ class UNetField(BaseModel):
     scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
     loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
-    freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
 
 
 class ClipField(BaseModel):
@@ -54,32 +51,13 @@ class VaeField(BaseModel):
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
-@invocation_output("unet_output")
-class UNetOutput(BaseInvocationOutput):
-    """Base class for invocations that output a UNet field"""
-
-    unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
-
-
-@invocation_output("vae_output")
-class VAEOutput(BaseInvocationOutput):
-    """Base class for invocations that output a VAE field"""
-
-    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
-
-
-@invocation_output("clip_output")
-class CLIPOutput(BaseInvocationOutput):
-    """Base class for invocations that output a CLIP field"""
-
-    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
-
-
 @invocation_output("model_loader_output")
-class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
+class ModelLoaderOutput(BaseInvocationOutput):
     """Model loader output"""
 
-    pass
+    unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
+    clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
 class MainModelField(BaseModel):
@@ -388,6 +366,13 @@ class VAEModelField(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
 
+@invocation_output("vae_loader_output")
+class VaeLoaderOutput(BaseInvocationOutput):
+    """VAE output"""
+
+    vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
+
+
 @invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
 class VaeLoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
@@ -399,7 +384,7 @@ class VaeLoaderInvocation(BaseInvocation):
         title="VAE",
     )
 
-    def invoke(self, context: InvocationContext) -> VAEOutput:
+    def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
         base_model = self.vae_model.base_model
         model_name = self.vae_model.model_name
         model_type = ModelType.Vae
@@ -410,7 +395,7 @@ class VaeLoaderInvocation(BaseInvocation):
             model_type=model_type,
         ):
             raise Exception(f"Unkown vae name: {model_name}!")
-        return VAEOutput(
+        return VaeLoaderOutput(
             vae=VaeField(
                 vae=ModelInfo(
                     model_name=model_name,
@@ -472,24 +457,3 @@ class SeamlessModeInvocation(BaseInvocation):
             vae.seamless_axes = seamless_axes_list
 
         return SeamlessModeOutput(unet=unet, vae=vae)
-
-
-@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.0")
-class FreeUInvocation(BaseInvocation):
-    """
-    Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2):
-
-    SD1.5: 1.2/1.4/0.9/0.2,
-    SD2: 1.1/1.2/0.9/0.2,
-    SDXL: 1.1/1.2/0.6/0.4,
-    """
-
-    unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet")
-    b1: float = InputField(default=1.2, ge=-1, le=3, description=FieldDescriptions.freeu_b1)
-    b2: float = InputField(default=1.4, ge=-1, le=3, description=FieldDescriptions.freeu_b2)
-    s1: float = InputField(default=0.9, ge=-1, le=3, description=FieldDescriptions.freeu_s1)
-    s2: float = InputField(default=0.2, ge=-1, le=3, description=FieldDescriptions.freeu_s2)
-
-    def invoke(self, context: InvocationContext) -> UNetOutput:
-        self.unet.freeu_config = FreeUConfig(s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2)
-        return UNetOutput(unet=self.unet)

invokeai/app/invocations/noise.py

@@ -5,13 +5,13 @@ import torch
 from pydantic import field_validator
 
 from invokeai.app.invocations.latent import LatentsField
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     InputField,
     InvocationContext,
     OutputField,

invokeai/app/invocations/onnx.py

@@ -14,7 +14,6 @@ from tqdm import tqdm
 
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend import BaseModelType, ModelType, SubModelType
 
@@ -24,6 +23,7 @@ from ...backend.util import choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -54,7 +54,7 @@ ORT_TO_NP_TYPE = {
     "tensor(double)": np.float64,
 }
 
-PRECISION_VALUES = Literal[tuple(ORT_TO_NP_TYPE.keys())]
+PRECISION_VALUES = Literal[tuple(list(ORT_TO_NP_TYPE.keys()))]
 
 
 @invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning", version="1.0.0")
@@ -252,7 +252,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
         scheduler.set_timesteps(self.steps)
         latents = latents * np.float64(scheduler.init_noise_sigma)
 
-        extra_step_kwargs = {}
+        extra_step_kwargs = dict()
         if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
             extra_step_kwargs.update(
                 eta=0.0,

invokeai/app/invocations/param_easing.py

@@ -100,7 +100,7 @@ EASING_FUNCTIONS_MAP = {
     "BounceInOut": BounceEaseInOut,
 }
 
-EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
+EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
 
 
 # actually I think for now could just use CollectionOutput (which is list[Any]
@@ -161,7 +161,7 @@ class StepParamEasingInvocation(BaseInvocation):
         easing_class = EASING_FUNCTIONS_MAP[self.easing]
         if log_diagnostics:
             context.services.logger.debug("easing class: " + str(easing_class))
-        easing_list = []
+        easing_list = list()
         if self.mirror:  # "expected" mirroring
             # if number of steps is even, squeeze duration down to (number_of_steps)/2
             # and create reverse copy of list to append
@@ -178,7 +178,7 @@ class StepParamEasingInvocation(BaseInvocation):
                 end=self.end_value,
                 duration=base_easing_duration - 1,
             )
-            base_easing_vals = []
+            base_easing_vals = list()
             for step_index in range(base_easing_duration):
                 easing_val = easing_function.ease(step_index)
                 base_easing_vals.append(easing_val)

invokeai/app/invocations/primitives.py

@@ -5,11 +5,10 @@ from typing import Optional, Tuple
 import torch
 from pydantic import BaseModel, Field
 
-from invokeai.app.shared.fields import FieldDescriptions
-
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -294,7 +293,7 @@ class DenoiseMaskField(BaseModel):
     """An inpaint mask field"""
 
     mask_name: str = Field(description="The name of the mask image")
-    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
+    masked_latents_name: Optional[str] = Field(description="The name of the masked image latents")
 
 
 @invocation_output("denoise_mask_output")

invokeai/app/invocations/sdxl.py

@@ -1,9 +1,8 @@
-from invokeai.app.shared.fields import FieldDescriptions
-
 from ...backend.model_management import ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,

invokeai/app/invocations/t2i_adapter.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -15,7 +16,6 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType
 
 

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -139,7 +139,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 (board_id,),
             )
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            images = [deserialize_image_record(dict(r)) for r in result]
+            images = list(map(lambda r: deserialize_image_record(dict(r)), result))
 
             self._cursor.execute(
                 """--sql
@@ -167,7 +167,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 (board_id,),
             )
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            image_names = [r[0] for r in result]
+            image_names = list(map(lambda r: r[0], result))
             return image_names
         except sqlite3.Error as e:
             self._conn.rollback()

invokeai/app/services/board_records/board_records_sqlite.py

@@ -199,7 +199,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
             )
 
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            boards = [deserialize_board_record(dict(r)) for r in result]
+            boards = list(map(lambda r: deserialize_board_record(dict(r)), result))
 
             # Get the total number of boards
             self._cursor.execute(
@@ -236,7 +236,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
             )
 
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            boards = [deserialize_board_record(dict(r)) for r in result]
+            boards = list(map(lambda r: deserialize_board_record(dict(r)), result))
 
             return boards
 

invokeai/app/services/config/config_base.py

@@ -55,7 +55,7 @@ class InvokeAISettings(BaseSettings):
         """
         cls = self.__class__
         type = get_args(get_type_hints(cls)["type"])[0]
-        field_dict = {type: {}}
+        field_dict = dict({type: dict()})
         for name, field in self.model_fields.items():
             if name in cls._excluded_from_yaml():
                 continue
@@ -64,7 +64,7 @@ class InvokeAISettings(BaseSettings):
             )
             value = getattr(self, name)
             if category not in field_dict[type]:
-                field_dict[type][category] = {}
+                field_dict[type][category] = dict()
             # keep paths as strings to make it easier to read
             field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
         conf = OmegaConf.create(field_dict)
@@ -89,7 +89,7 @@ class InvokeAISettings(BaseSettings):
         # create an upcase version of the environment in
         # order to achieve case-insensitive environment
         # variables (the way Windows does)
-        upcase_environ = {}
+        upcase_environ = dict()
         for key, value in os.environ.items():
             upcase_environ[key.upper()] = value
 

invokeai/app/services/config/config_default.py

@@ -45,7 +45,6 @@ InvokeAI:
     ram: 13.5
     vram: 0.25
     lazy_offload: true
-    log_memory_usage: false
   Device:
     device: auto
     precision: auto
@@ -188,18 +187,18 @@ DEFAULT_MAX_VRAM = 0.5
 
 
 class Categories(object):
-    WebServer = {"category": "Web Server"}
-    Features = {"category": "Features"}
-    Paths = {"category": "Paths"}
-    Logging = {"category": "Logging"}
-    Development = {"category": "Development"}
-    Other = {"category": "Other"}
-    ModelCache = {"category": "Model Cache"}
-    Device = {"category": "Device"}
-    Generation = {"category": "Generation"}
-    Queue = {"category": "Queue"}
-    Nodes = {"category": "Nodes"}
-    MemoryPerformance = {"category": "Memory/Performance"}
+    WebServer = dict(category="Web Server")
+    Features = dict(category="Features")
+    Paths = dict(category="Paths")
+    Logging = dict(category="Logging")
+    Development = dict(category="Development")
+    Other = dict(category="Other")
+    ModelCache = dict(category="Model Cache")
+    Device = dict(category="Device")
+    Generation = dict(category="Generation")
+    Queue = dict(category="Queue")
+    Nodes = dict(category="Nodes")
+    MemoryPerformance = dict(category="Memory/Performance")
 
 
 class InvokeAIAppConfig(InvokeAISettings):
@@ -262,7 +261,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     ram                 : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
     vram                : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
     lazy_offload        : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
-    log_memory_usage    : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
 
     # DEVICE
     device              : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
@@ -482,7 +480,7 @@ def _find_root() -> Path:
     venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
     if os.environ.get("INVOKEAI_ROOT"):
         root = Path(os.environ["INVOKEAI_ROOT"])
-    elif any((venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]):
+    elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
         root = (venv.parent).resolve()
     else:
         root = Path("~/invokeai").expanduser().resolve()

invokeai/app/services/events/events_base.py

@@ -27,7 +27,7 @@ class EventServiceBase:
         payload["timestamp"] = get_timestamp()
         self.dispatch(
             event_name=EventServiceBase.queue_event,
-            payload={"event": event_name, "data": payload},
+            payload=dict(event=event_name, data=payload),
         )
 
     # Define events here for every event in the system.
@@ -48,18 +48,18 @@ class EventServiceBase:
         """Emitted when there is generation progress"""
         self.__emit_queue_event(
             event_name="generator_progress",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node_id": node.get("id"),
-                "source_node_id": source_node_id,
-                "progress_image": progress_image.model_dump() if progress_image is not None else None,
-                "step": step,
-                "order": order,
-                "total_steps": total_steps,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node_id=node.get("id"),
+                source_node_id=source_node_id,
+                progress_image=progress_image.model_dump() if progress_image is not None else None,
+                step=step,
+                order=order,
+                total_steps=total_steps,
+            ),
         )
 
     def emit_invocation_complete(
@@ -75,15 +75,15 @@ class EventServiceBase:
         """Emitted when an invocation has completed"""
         self.__emit_queue_event(
             event_name="invocation_complete",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node": node,
-                "source_node_id": source_node_id,
-                "result": result,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node=node,
+                source_node_id=source_node_id,
+                result=result,
+            ),
         )
 
     def emit_invocation_error(
@@ -100,16 +100,16 @@ class EventServiceBase:
         """Emitted when an invocation has completed"""
         self.__emit_queue_event(
             event_name="invocation_error",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node": node,
-                "source_node_id": source_node_id,
-                "error_type": error_type,
-                "error": error,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node=node,
+                source_node_id=source_node_id,
+                error_type=error_type,
+                error=error,
+            ),
         )
 
     def emit_invocation_started(
@@ -124,14 +124,14 @@ class EventServiceBase:
         """Emitted when an invocation has started"""
         self.__emit_queue_event(
             event_name="invocation_started",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node": node,
-                "source_node_id": source_node_id,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node=node,
+                source_node_id=source_node_id,
+            ),
         )
 
     def emit_graph_execution_complete(
@@ -140,12 +140,12 @@ class EventServiceBase:
         """Emitted when a session has completed all invocations"""
         self.__emit_queue_event(
             event_name="graph_execution_state_complete",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+            ),
         )
 
     def emit_model_load_started(
@@ -162,16 +162,16 @@ class EventServiceBase:
         """Emitted when a model is requested"""
         self.__emit_queue_event(
             event_name="model_load_started",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "model_name": model_name,
-                "base_model": base_model,
-                "model_type": model_type,
-                "submodel": submodel,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+                submodel=submodel,
+            ),
         )
 
     def emit_model_load_completed(
@@ -189,19 +189,19 @@ class EventServiceBase:
         """Emitted when a model is correctly loaded (returns model info)"""
         self.__emit_queue_event(
             event_name="model_load_completed",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "model_name": model_name,
-                "base_model": base_model,
-                "model_type": model_type,
-                "submodel": submodel,
-                "hash": model_info.hash,
-                "location": str(model_info.location),
-                "precision": str(model_info.precision),
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+                submodel=submodel,
+                hash=model_info.hash,
+                location=str(model_info.location),
+                precision=str(model_info.precision),
+            ),
         )
 
     def emit_session_retrieval_error(
@@ -216,14 +216,14 @@ class EventServiceBase:
         """Emitted when session retrieval fails"""
         self.__emit_queue_event(
             event_name="session_retrieval_error",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "error_type": error_type,
-                "error": error,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                error_type=error_type,
+                error=error,
+            ),
         )
 
     def emit_invocation_retrieval_error(
@@ -239,15 +239,15 @@ class EventServiceBase:
         """Emitted when invocation retrieval fails"""
         self.__emit_queue_event(
             event_name="invocation_retrieval_error",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-                "node_id": node_id,
-                "error_type": error_type,
-                "error": error,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+                node_id=node_id,
+                error_type=error_type,
+                error=error,
+            ),
         )
 
     def emit_session_canceled(
@@ -260,12 +260,12 @@ class EventServiceBase:
         """Emitted when a session is canceled"""
         self.__emit_queue_event(
             event_name="session_canceled",
-            payload={
-                "queue_id": queue_id,
-                "queue_item_id": queue_item_id,
-                "queue_batch_id": queue_batch_id,
-                "graph_execution_state_id": graph_execution_state_id,
-            },
+            payload=dict(
+                queue_id=queue_id,
+                queue_item_id=queue_item_id,
+                queue_batch_id=queue_batch_id,
+                graph_execution_state_id=graph_execution_state_id,
+            ),
         )
 
     def emit_queue_item_status_changed(
@@ -277,39 +277,39 @@ class EventServiceBase:
         """Emitted when a queue item's status changes"""
         self.__emit_queue_event(
             event_name="queue_item_status_changed",
-            payload={
-                "queue_id": queue_status.queue_id,
-                "queue_item": {
-                    "queue_id": session_queue_item.queue_id,
-                    "item_id": session_queue_item.item_id,
-                    "status": session_queue_item.status,
-                    "batch_id": session_queue_item.batch_id,
-                    "session_id": session_queue_item.session_id,
-                    "error": session_queue_item.error,
-                    "created_at": str(session_queue_item.created_at) if session_queue_item.created_at else None,
-                    "updated_at": str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
-                    "started_at": str(session_queue_item.started_at) if session_queue_item.started_at else None,
-                    "completed_at": str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
-                },
-                "batch_status": batch_status.model_dump(),
-                "queue_status": queue_status.model_dump(),
-            },
+            payload=dict(
+                queue_id=queue_status.queue_id,
+                queue_item=dict(
+                    queue_id=session_queue_item.queue_id,
+                    item_id=session_queue_item.item_id,
+                    status=session_queue_item.status,
+                    batch_id=session_queue_item.batch_id,
+                    session_id=session_queue_item.session_id,
+                    error=session_queue_item.error,
+                    created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
+                    updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
+                    started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
+                    completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
+                ),
+                batch_status=batch_status.model_dump(),
+                queue_status=queue_status.model_dump(),
+            ),
         )
 
     def emit_batch_enqueued(self, enqueue_result: EnqueueBatchResult) -> None:
         """Emitted when a batch is enqueued"""
         self.__emit_queue_event(
             event_name="batch_enqueued",
-            payload={
-                "queue_id": enqueue_result.queue_id,
-                "batch_id": enqueue_result.batch.batch_id,
-                "enqueued": enqueue_result.enqueued,
-            },
+            payload=dict(
+                queue_id=enqueue_result.queue_id,
+                batch_id=enqueue_result.batch.batch_id,
+                enqueued=enqueue_result.enqueued,
+            ),
         )
 
     def emit_queue_cleared(self, queue_id: str) -> None:
         """Emitted when the queue is cleared"""
         self.__emit_queue_event(
             event_name="queue_cleared",
-            payload={"queue_id": queue_id},
+            payload=dict(queue_id=queue_id),
         )

invokeai/app/services/image_files/image_files_disk.py

@@ -25,7 +25,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
     __invoker: Invoker
 
     def __init__(self, output_folder: Union[str, Path]):
-        self.__cache = {}
+        self.__cache = dict()
         self.__cache_ids = Queue()
         self.__max_cache_size = 10  # TODO: get this from config
 

invokeai/app/services/image_records/image_records_common.py

@@ -90,23 +90,25 @@ class ImageRecordDeleteException(Exception):
 
 
 IMAGE_DTO_COLS = ", ".join(
-    [
-        "images." + c
-        for c in [
-            "image_name",
-            "image_origin",
-            "image_category",
-            "width",
-            "height",
-            "session_id",
-            "node_id",
-            "is_intermediate",
-            "created_at",
-            "updated_at",
-            "deleted_at",
-            "starred",
-        ]
-    ]
+    list(
+        map(
+            lambda c: "images." + c,
+            [
+                "image_name",
+                "image_origin",
+                "image_category",
+                "width",
+                "height",
+                "session_id",
+                "node_id",
+                "is_intermediate",
+                "created_at",
+                "updated_at",
+                "deleted_at",
+                "starred",
+            ],
+        )
+    )
 )
 
 

invokeai/app/services/image_records/image_records_sqlite.py

@@ -263,7 +263,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
             if categories is not None:
                 # Convert the enum values to unique list of strings
-                category_strings = [c.value for c in set(categories)]
+                category_strings = list(map(lambda c: c.value, set(categories)))
                 # Create the correct length of placeholders
                 placeholders = ",".join("?" * len(category_strings))
 
@@ -307,7 +307,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             # Build the list of images, deserializing each row
             self._cursor.execute(images_query, images_params)
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            images = [deserialize_image_record(dict(r)) for r in result]
+            images = list(map(lambda r: deserialize_image_record(dict(r)), result))
 
             # Set up and execute the count query, without pagination
             count_query += query_conditions + ";"
@@ -386,7 +386,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                 """
             )
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            image_names = [r[0] for r in result]
+            image_names = list(map(lambda r: r[0], result))
             self._cursor.execute(
                 """--sql
                 DELETE FROM images

invokeai/app/services/images/images_base.py

@@ -21,8 +21,8 @@ class ImageServiceABC(ABC):
     _on_deleted_callbacks: list[Callable[[str], None]]
 
     def __init__(self) -> None:
-        self._on_changed_callbacks = []
-        self._on_deleted_callbacks = []
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
 
     def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
         """Register a callback for when an image is changed"""

invokeai/app/services/images/images_default.py

@@ -217,16 +217,18 @@ class ImageService(ImageServiceABC):
                 board_id,
             )
 
-            image_dtos = [
-                image_record_to_dto(
-                    image_record=r,
-                    image_url=self.__invoker.services.urls.get_image_url(r.image_name),
-                    thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
-                    board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
-                    workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
+            image_dtos = list(
+                map(
+                    lambda r: image_record_to_dto(
+                        image_record=r,
+                        image_url=self.__invoker.services.urls.get_image_url(r.image_name),
+                        thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
+                        board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
+                        workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
+                    ),
+                    results.items,
                 )
-                for r in results.items
-            ]
+            )
 
             return OffsetPaginatedResults[ImageDTO](
                 items=image_dtos,

invokeai/app/services/invocation_processor/invocation_processor_base.py

@@ -1,5 +1,5 @@
 from abc import ABC
 
 
-class InvocationProcessorABC(ABC):  # noqa: B024
+class InvocationProcessorABC(ABC):
     pass

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -26,7 +26,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
         self.__invoker_thread = Thread(
             name="invoker_processor",
             target=self.__process,
-            kwargs={"stop_event": self.__stop_event},
+            kwargs=dict(stop_event=self.__stop_event),
         )
         self.__invoker_thread.daemon = True  # TODO: make async and do not use threads
         self.__invoker_thread.start()

invokeai/app/services/invocation_queue/invocation_queue_memory.py

@@ -14,7 +14,7 @@ class MemoryInvocationQueue(InvocationQueueABC):
 
     def __init__(self):
         self.__queue = Queue()
-        self.__cancellations = {}
+        self.__cancellations = dict()
 
     def get(self) -> InvocationQueueItem:
         item = self.__queue.get()

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -122,7 +122,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
     def log_stats(self):
         completed = set()
         errored = set()
-        for graph_id, _node_log in self._stats.items():
+        for graph_id, node_log in self._stats.items():
             try:
                 current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
             except Exception:
@@ -142,7 +142,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
             cache_stats = self._cache_stats[graph_id]
             hwm = cache_stats.high_watermark / GIG
             tot = cache_stats.cache_size / GIG
-            loaded = sum(list(cache_stats.loaded_model_sizes.values())) / GIG
+            loaded = sum([v for v in cache_stats.loaded_model_sizes.values()]) / GIG
 
             logger.info(f"TOTAL GRAPH EXECUTION TIME:  {total_time:7.3f}s")
             logger.info("RAM used by InvokeAI process: " + "%4.2fG" % self.ram_used + f" ({self.ram_changed:+5.3f}G)")

invokeai/app/services/item_storage/item_storage_base.py

@@ -15,8 +15,8 @@ class ItemStorageABC(ABC, Generic[T]):
     _on_deleted_callbacks: list[Callable[[str], None]]
 
     def __init__(self) -> None:
-        self._on_changed_callbacks = []
-        self._on_deleted_callbacks = []
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
 
     """Base item storage class"""
 

invokeai/app/services/item_storage/item_storage_sqlite.py

@@ -112,7 +112,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
             )
             result = self._cursor.fetchall()
 
-            items = [self._parse_item(r[0]) for r in result]
+            items = list(map(lambda r: self._parse_item(r[0]), result))
 
             self._cursor.execute(f"""SELECT count(*) FROM {self._table_name};""")
             count = self._cursor.fetchone()[0]
@@ -132,7 +132,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
             )
             result = self._cursor.fetchall()
 
-            items = [self._parse_item(r[0]) for r in result]
+            items = list(map(lambda r: self._parse_item(r[0]), result))
 
             self._cursor.execute(
                 f"""SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;""",

invokeai/app/services/latents_storage/latents_storage_base.py

@@ -13,8 +13,8 @@ class LatentsStorageBase(ABC):
     _on_deleted_callbacks: list[Callable[[str], None]]
 
     def __init__(self) -> None:
-        self._on_changed_callbacks = []
-        self._on_deleted_callbacks = []
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
 
     @abstractmethod
     def get(self, name: str) -> torch.Tensor:

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

@@ -19,7 +19,7 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
     def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
         super().__init__()
         self.__underlying_storage = underlying_storage
-        self.__cache = {}
+        self.__cache = dict()
         self.__cache_ids = Queue()
         self.__max_cache_size = max_cache_size
 

invokeai/app/services/model_manager/__init__.py

@@ -1 +0,0 @@
-from .model_manager_default import ModelManagerService  # noqa F401

invokeai/app/services/session_processor/session_processor_default.py

@@ -33,11 +33,9 @@ class DefaultSessionProcessor(SessionProcessorBase):
         self.__thread = Thread(
             name="session_processor",
             target=self.__process,
-            kwargs={
-                "stop_event": self.__stop_event,
-                "poll_now_event": self.__poll_now_event,
-                "resume_event": self.__resume_event,
-            },
+            kwargs=dict(
+                stop_event=self.__stop_event, poll_now_event=self.__poll_now_event, resume_event=self.__resume_event
+            ),
         )
         self.__thread.start()
 

invokeai/app/services/session_queue/session_queue_common.py

@@ -129,12 +129,12 @@ class Batch(BaseModel):
         return v
 
     model_config = ConfigDict(
-        json_schema_extra={
-            "required": [
+        json_schema_extra=dict(
+            required=[
                 "graph",
                 "runs",
             ]
-        }
+        )
     )
 
 
@@ -191,8 +191,8 @@ class SessionQueueItemWithoutGraph(BaseModel):
         return SessionQueueItemDTO(**queue_item_dict)
 
     model_config = ConfigDict(
-        json_schema_extra={
-            "required": [
+        json_schema_extra=dict(
+            required=[
                 "item_id",
                 "status",
                 "batch_id",
@@ -203,7 +203,7 @@ class SessionQueueItemWithoutGraph(BaseModel):
                 "created_at",
                 "updated_at",
             ]
-        }
+        )
     )
 
 
@@ -222,8 +222,8 @@ class SessionQueueItem(SessionQueueItemWithoutGraph):
         return SessionQueueItem(**queue_item_dict)
 
     model_config = ConfigDict(
-        json_schema_extra={
-            "required": [
+        json_schema_extra=dict(
+            required=[
                 "item_id",
                 "status",
                 "batch_id",
@@ -235,7 +235,7 @@ class SessionQueueItem(SessionQueueItemWithoutGraph):
                 "created_at",
                 "updated_at",
             ]
-        }
+        )
     )
 
 
@@ -355,7 +355,7 @@ def create_session_nfv_tuples(
                 for item in batch_datum.items
             ]
             node_field_values_to_zip.append(node_field_values)
-        data.append(list(zip(*node_field_values_to_zip, strict=True)))  # type: ignore [arg-type]
+        data.append(list(zip(*node_field_values_to_zip)))  # type: ignore [arg-type]
 
     # create generator to yield session,nfv tuples
     count = 0
@@ -383,7 +383,7 @@ def calc_session_count(batch: Batch) -> int:
         for batch_datum in batch_datum_list:
             batch_data_items = range(len(batch_datum.items))
             to_zip.append(batch_data_items)
-        data.append(list(zip(*to_zip, strict=True)))
+        data.append(list(zip(*to_zip)))
     data_product = list(product(*data))
     return len(data_product) * batch.runs
 

invokeai/app/services/shared/default_graphs.py

@@ -78,7 +78,7 @@ def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[Li
     """Creates the default system graphs, or adds new versions if the old ones don't match"""
 
     # TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
-    graphs: list[LibraryGraph] = []
+    graphs: list[LibraryGraph] = list()
 
     text_to_image = graph_library.get(default_text_to_image_graph_id)
 

invokeai/app/services/shared/graph.py

@@ -352,7 +352,7 @@ class Graph(BaseModel):
 
         # Validate that all node ids are unique
         node_ids = [n.id for n in self.nodes.values()]
-        duplicate_node_ids = {node_id for node_id in node_ids if node_ids.count(node_id) >= 2}
+        duplicate_node_ids = set([node_id for node_id in node_ids if node_ids.count(node_id) >= 2])
         if duplicate_node_ids:
             raise DuplicateNodeIdError(f"Node ids must be unique, found duplicates {duplicate_node_ids}")
 
@@ -616,7 +616,7 @@ class Graph(BaseModel):
         self, node_path: str, prefix: Optional[str] = None
     ) -> list[tuple["Graph", Union[str, None], Edge]]:
         """Gets all input edges for a node along with the graph they are in and the graph's path"""
-        edges = []
+        edges = list()
 
         # Return any input edges that appear in this graph
         edges.extend([(self, prefix, e) for e in self.edges if e.destination.node_id == node_path])
@@ -658,7 +658,7 @@ class Graph(BaseModel):
         self, node_path: str, prefix: Optional[str] = None
     ) -> list[tuple["Graph", Union[str, None], Edge]]:
         """Gets all output edges for a node along with the graph they are in and the graph's path"""
-        edges = []
+        edges = list()
 
         # Return any input edges that appear in this graph
         edges.extend([(self, prefix, e) for e in self.edges if e.source.node_id == node_path])
@@ -680,8 +680,8 @@ class Graph(BaseModel):
         new_input: Optional[EdgeConnection] = None,
         new_output: Optional[EdgeConnection] = None,
     ) -> bool:
-        inputs = [e.source for e in self._get_input_edges(node_path, "collection")]
-        outputs = [e.destination for e in self._get_output_edges(node_path, "item")]
+        inputs = list([e.source for e in self._get_input_edges(node_path, "collection")])
+        outputs = list([e.destination for e in self._get_output_edges(node_path, "item")])
 
         if new_input is not None:
             inputs.append(new_input)
@@ -694,7 +694,7 @@ class Graph(BaseModel):
 
         # Get input and output fields (the fields linked to the iterator's input/output)
         input_field = get_output_field(self.get_node(inputs[0].node_id), inputs[0].field)
-        output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
+        output_fields = list([get_input_field(self.get_node(e.node_id), e.field) for e in outputs])
 
         # Input type must be a list
         if get_origin(input_field) != list:
@@ -713,8 +713,8 @@ class Graph(BaseModel):
         new_input: Optional[EdgeConnection] = None,
         new_output: Optional[EdgeConnection] = None,
     ) -> bool:
-        inputs = [e.source for e in self._get_input_edges(node_path, "item")]
-        outputs = [e.destination for e in self._get_output_edges(node_path, "collection")]
+        inputs = list([e.source for e in self._get_input_edges(node_path, "item")])
+        outputs = list([e.destination for e in self._get_output_edges(node_path, "collection")])
 
         if new_input is not None:
             inputs.append(new_input)
@@ -722,16 +722,18 @@ class Graph(BaseModel):
             outputs.append(new_output)
 
         # Get input and output fields (the fields linked to the iterator's input/output)
-        input_fields = [get_output_field(self.get_node(e.node_id), e.field) for e in inputs]
-        output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
+        input_fields = list([get_output_field(self.get_node(e.node_id), e.field) for e in inputs])
+        output_fields = list([get_input_field(self.get_node(e.node_id), e.field) for e in outputs])
 
         # Validate that all inputs are derived from or match a single type
-        input_field_types = {
-            t
-            for input_field in input_fields
-            for t in ([input_field] if get_origin(input_field) is None else get_args(input_field))
-            if t != NoneType
-        }  # Get unique types
+        input_field_types = set(
+            [
+                t
+                for input_field in input_fields
+                for t in ([input_field] if get_origin(input_field) is None else get_args(input_field))
+                if t != NoneType
+            ]
+        )  # Get unique types
         type_tree = nx.DiGraph()
         type_tree.add_nodes_from(input_field_types)
         type_tree.add_edges_from([e for e in itertools.permutations(input_field_types, 2) if issubclass(e[1], e[0])])
@@ -759,15 +761,15 @@ class Graph(BaseModel):
         """Returns a NetworkX DiGraph representing the layout of this graph"""
         # TODO: Cache this?
         g = nx.DiGraph()
-        g.add_nodes_from(list(self.nodes.keys()))
-        g.add_edges_from({(e.source.node_id, e.destination.node_id) for e in self.edges})
+        g.add_nodes_from([n for n in self.nodes.keys()])
+        g.add_edges_from(set([(e.source.node_id, e.destination.node_id) for e in self.edges]))
         return g
 
     def nx_graph_with_data(self) -> nx.DiGraph:
         """Returns a NetworkX DiGraph representing the data and layout of this graph"""
         g = nx.DiGraph()
-        g.add_nodes_from(list(self.nodes.items()))
-        g.add_edges_from({(e.source.node_id, e.destination.node_id) for e in self.edges})
+        g.add_nodes_from([n for n in self.nodes.items()])
+        g.add_edges_from(set([(e.source.node_id, e.destination.node_id) for e in self.edges]))
         return g
 
     def nx_graph_flat(self, nx_graph: Optional[nx.DiGraph] = None, prefix: Optional[str] = None) -> nx.DiGraph:
@@ -789,7 +791,7 @@ class Graph(BaseModel):
 
         # TODO: figure out if iteration nodes need to be expanded
 
-        unique_edges = {(e.source.node_id, e.destination.node_id) for e in self.edges}
+        unique_edges = set([(e.source.node_id, e.destination.node_id) for e in self.edges])
         g.add_edges_from([(self._get_node_path(e[0], prefix), self._get_node_path(e[1], prefix)) for e in unique_edges])
         return g
 
@@ -841,8 +843,8 @@ class GraphExecutionState(BaseModel):
         return v
 
     model_config = ConfigDict(
-        json_schema_extra={
-            "required": [
+        json_schema_extra=dict(
+            required=[
                 "id",
                 "graph",
                 "execution_graph",
@@ -853,7 +855,7 @@ class GraphExecutionState(BaseModel):
                 "prepared_source_mapping",
                 "source_prepared_mapping",
             ]
-        }
+        )
     )
 
     def next(self) -> Optional[BaseInvocation]:
@@ -893,7 +895,7 @@ class GraphExecutionState(BaseModel):
         source_node = self.prepared_source_mapping[node_id]
         prepared_nodes = self.source_prepared_mapping[source_node]
 
-        if all(n in self.executed for n in prepared_nodes):
+        if all([n in self.executed for n in prepared_nodes]):
             self.executed.add(source_node)
             self.executed_history.append(source_node)
 
@@ -928,7 +930,7 @@ class GraphExecutionState(BaseModel):
             input_collection = getattr(input_collection_prepared_node_output, input_collection_edge.source.field)
             self_iteration_count = len(input_collection)
 
-        new_nodes: list[str] = []
+        new_nodes: list[str] = list()
         if self_iteration_count == 0:
             # TODO: should this raise a warning? It might just happen if an empty collection is input, and should be valid.
             return new_nodes
@@ -938,7 +940,7 @@ class GraphExecutionState(BaseModel):
 
         # Create new edges for this iteration
         # For collect nodes, this may contain multiple inputs to the same field
-        new_edges: list[Edge] = []
+        new_edges: list[Edge] = list()
         for edge in input_edges:
             for input_node_id in (n[1] for n in iteration_node_map if n[0] == edge.source.node_id):
                 new_edge = Edge(
@@ -1032,7 +1034,7 @@ class GraphExecutionState(BaseModel):
 
         # Create execution nodes
         next_node = self.graph.get_node(next_node_id)
-        new_node_ids = []
+        new_node_ids = list()
         if isinstance(next_node, CollectInvocation):
             # Collapse all iterator input mappings and create a single execution node for the collect invocation
             all_iteration_mappings = list(
@@ -1053,10 +1055,7 @@ class GraphExecutionState(BaseModel):
             # For every iterator, the parent must either not be a child of that iterator, or must match the prepared iteration for that iterator
             # TODO: Handle a node mapping to none
             eg = self.execution_graph.nx_graph_flat()
-            prepared_parent_mappings = [
-                [(n, self._get_iteration_node(n, g, eg, it)) for n in next_node_parents]
-                for it in iterator_node_prepared_combinations
-            ]  # type: ignore
+            prepared_parent_mappings = [[(n, self._get_iteration_node(n, g, eg, it)) for n in next_node_parents] for it in iterator_node_prepared_combinations]  # type: ignore
 
             # Create execution node for each iteration
             for iteration_mappings in prepared_parent_mappings:
@@ -1122,7 +1121,7 @@ class GraphExecutionState(BaseModel):
                 for edge in input_edges
                 if edge.destination.field == "item"
             ]
-            node.collection = output_collection
+            setattr(node, "collection", output_collection)
         else:
             for edge in input_edges:
                 output_value = getattr(self.results[edge.source.node_id], edge.source.field)
@@ -1202,7 +1201,7 @@ class LibraryGraph(BaseModel):
 
     @field_validator("exposed_inputs", "exposed_outputs")
     def validate_exposed_aliases(cls, v: list[Union[ExposedNodeInput, ExposedNodeOutput]]):
-        if len(v) != len({i.alias for i in v}):
+        if len(v) != len(set(i.alias for i in v)):
             raise ValueError("Duplicate exposed alias")
         return v
 

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -57,7 +57,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 INSERT INTO workflows(workflow)
                 VALUES (?);
                 """,
-                (workflow.model_dump_json(),),
+                (workflow.json(),),
             )
             self._conn.commit()
         except Exception:

invokeai/app/shared/__init__.py

@@ -1,5 +0,0 @@
-"""
-This module contains various classes, functions and models which are shared across the app, particularly by invocations.
-
-Lifting these classes, functions and models into this shared module helps to reduce circular imports.
-"""

invokeai/app/shared/fields.py

@@ -1,66 +0,0 @@
-class FieldDescriptions:
-    denoising_start = "When to start denoising, expressed a percentage of total steps"
-    denoising_end = "When to stop denoising, expressed a percentage of total steps"
-    cfg_scale = "Classifier-Free Guidance scale"
-    scheduler = "Scheduler to use during inference"
-    positive_cond = "Positive conditioning tensor"
-    negative_cond = "Negative conditioning tensor"
-    noise = "Noise tensor"
-    clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
-    unet = "UNet (scheduler, LoRAs)"
-    vae = "VAE"
-    cond = "Conditioning tensor"
-    controlnet_model = "ControlNet model to load"
-    vae_model = "VAE model to load"
-    lora_model = "LoRA model to load"
-    main_model = "Main model (UNet, VAE, CLIP) to load"
-    sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
-    sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
-    onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
-    lora_weight = "The weight at which the LoRA is applied to each model"
-    compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
-    raw_prompt = "Raw prompt text (no parsing)"
-    sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
-    skipped_layers = "Number of layers to skip in text encoder"
-    seed = "Seed for random number generation"
-    steps = "Number of steps to run"
-    width = "Width of output (px)"
-    height = "Height of output (px)"
-    control = "ControlNet(s) to apply"
-    ip_adapter = "IP-Adapter to apply"
-    t2i_adapter = "T2I-Adapter(s) to apply"
-    denoised_latents = "Denoised latents tensor"
-    latents = "Latents tensor"
-    strength = "Strength of denoising (proportional to steps)"
-    metadata = "Optional metadata to be saved with the image"
-    metadata_collection = "Collection of Metadata"
-    metadata_item_polymorphic = "A single metadata item or collection of metadata items"
-    metadata_item_label = "Label for this metadata item"
-    metadata_item_value = "The value for this metadata item (may be any type)"
-    workflow = "Optional workflow to be saved with the image"
-    interp_mode = "Interpolation mode"
-    torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
-    fp32 = "Whether or not to use full float32 precision"
-    precision = "Precision to use"
-    tiled = "Processing using overlapping tiles (reduce memory consumption)"
-    detect_res = "Pixel resolution for detection"
-    image_res = "Pixel resolution for output image"
-    safe_mode = "Whether or not to use safe mode"
-    scribble_mode = "Whether or not to use scribble mode"
-    scale_factor = "The factor by which to scale"
-    blend_alpha = (
-        "Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
-    )
-    num_1 = "The first number"
-    num_2 = "The second number"
-    mask = "The mask to use for the operation"
-    board = "The board to save the image to"
-    image = "The image to process"
-    tile_size = "Tile size"
-    inclusive_low = "The inclusive low value"
-    exclusive_high = "The exclusive high value"
-    decimal_places = "The number of decimal places to round to"
-    freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
-    freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
-    freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
-    freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."

invokeai/app/shared/models.py

@@ -1,16 +0,0 @@
-from pydantic import BaseModel, Field
-
-from invokeai.app.shared.fields import FieldDescriptions
-
-
-class FreeUConfig(BaseModel):
-    """
-    Configuration for the FreeU hyperparameters.
-    - https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu
-    - https://github.com/ChenyangSi/FreeU
-    """
-
-    s1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s1)
-    s2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s2)
-    b1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b1)
-    b2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b2)

invokeai/app/util/controlnet_utils.py

@@ -59,7 +59,7 @@ def thin_one_time(x, kernels):
 
 def lvmin_thin(x, prunings=True):
     y = x
-    for _i in range(32):
+    for i in range(32):
         y, is_done = thin_one_time(y, lvmin_kernels)
         if is_done:
             break

invokeai/app/util/metadata.py

@@ -21,11 +21,11 @@ def get_metadata_graph_from_raw_session(session_raw: str) -> Optional[dict]:
 
     # sanity check make sure the graph is at least reasonably shaped
     if (
-        not isinstance(graph, dict)
+        type(graph) is not dict
         or "nodes" not in graph
-        or not isinstance(graph["nodes"], dict)
+        or type(graph["nodes"]) is not dict
         or "edges" not in graph
-        or not isinstance(graph["edges"], list)
+        or type(graph["edges"]) is not list
     ):
         # something has gone terribly awry, return an empty dict
         return None

invokeai/backend/image_util/pngwriter.py

@@ -88,7 +88,7 @@ class PromptFormatter:
         t2i = self.t2i
         opt = self.opt
 
-        switches = []
+        switches = list()
         switches.append(f'"{opt.prompt}"')
         switches.append(f"-s{opt.steps        or t2i.steps}")
         switches.append(f"-W{opt.width        or t2i.width}")

invokeai/backend/image_util/txt2mask.py

@@ -88,7 +88,7 @@ class Txt2Mask(object):
         provided image and returns a SegmentedGrayscale object in which the brighter
         pixels indicate where the object is inferred to be.
         """
-        if isinstance(image, str):
+        if type(image) is str:
             image = Image.open(image).convert("RGB")
 
         image = ImageOps.exif_transpose(image)

invokeai/backend/image_util/util.py

@@ -40,7 +40,7 @@ class InitImageResizer:
         (rw, rh) = (int(scale * im.width), int(scale * im.height))
 
         # round everything to multiples of 64
-        width, height, rw, rh = (x - x % 64 for x in (width, height, rw, rh))
+        width, height, rw, rh = map(lambda x: x - x % 64, (width, height, rw, rh))
 
         # no resize necessary, but return a copy
         if im.width == width and im.height == height:

invokeai/backend/install/invokeai_configure.py

@@ -32,7 +32,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
 from huggingface_hub import HfFolder
 from huggingface_hub import login as hf_hub_login
 from omegaconf import OmegaConf
-from pydantic import ValidationError
+from pydantic.error_wrappers import ValidationError
 from tqdm import tqdm
 from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
@@ -197,7 +197,7 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
 
 def download_conversion_models():
     target_dir = config.models_path / "core/convert"
-    kwargs = {}  # for future use
+    kwargs = dict()  # for future use
     try:
         logger.info("Downloading core tokenizers and text encoders")
 
@@ -252,26 +252,26 @@ def download_conversion_models():
 def download_realesrgan():
     logger.info("Installing ESRGAN Upscaling models...")
     URLs = [
-        {
-            "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
-            "dest": "core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
-            "description": "RealESRGAN_x4plus.pth",
-        },
-        {
-            "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-            "dest": "core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
-            "description": "RealESRGAN_x4plus_anime_6B.pth",
-        },
-        {
-            "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
-            "dest": "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
-            "description": "ESRGAN_SRx4_DF2KOST_official.pth",
-        },
-        {
-            "url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-            "dest": "core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
-            "description": "RealESRGAN_x2plus.pth",
-        },
+        dict(
+            url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+            dest="core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
+            description="RealESRGAN_x4plus.pth",
+        ),
+        dict(
+            url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+            dest="core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
+            description="RealESRGAN_x4plus_anime_6B.pth",
+        ),
+        dict(
+            url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
+            dest="core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
+            description="ESRGAN_SRx4_DF2KOST_official.pth",
+        ),
+        dict(
+            url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+            dest="core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
+            description="RealESRGAN_x2plus.pth",
+        ),
     ]
     for model in URLs:
         download_with_progress_bar(model["url"], config.models_path / model["dest"], model["description"])
@@ -680,7 +680,7 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections:
         if program_opts.default_only
         else [models[x].path or models[x].repo_id for x in installer.recommended_models()]
         if program_opts.yes_to_all
-        else [],
+        else list(),
     )
 
 

invokeai/backend/install/legacy_arg_parsing.py

@@ -38,7 +38,6 @@ SAMPLER_CHOICES = [
     "k_heun",
     "k_lms",
     "plms",
-    "lcm",
 ]
 
 PRECISION_CHOICES = [

invokeai/backend/install/migrate_to_3.py

@@ -123,6 +123,8 @@ class MigrateTo3(object):
                     logger.error(str(e))
                 except KeyboardInterrupt:
                     raise
+                except Exception as e:
+                    logger.error(str(e))
             for f in files:
                 # don't copy raw learned_embeds.bin or pytorch_lora_weights.bin
                 # let them be copied as part of a tree copy operation
@@ -141,6 +143,8 @@ class MigrateTo3(object):
                     logger.error(str(e))
                 except KeyboardInterrupt:
                     raise
+                except Exception as e:
+                    logger.error(str(e))
 
     def migrate_support_models(self):
         """
@@ -178,10 +182,10 @@ class MigrateTo3(object):
         """
 
         dest_directory = self.dest_models
-        kwargs = {
-            "cache_dir": self.root_directory / "models/hub",
+        kwargs = dict(
+            cache_dir=self.root_directory / "models/hub",
             # local_files_only = True
-        }
+        )
         try:
             logger.info("Migrating core tokenizers and text encoders")
             target_dir = dest_directory / "core" / "convert"
@@ -312,11 +316,11 @@ class MigrateTo3(object):
         dest_dir = self.dest_models
 
         cache = self.root_directory / "models/hub"
-        kwargs = {
-            "cache_dir": cache,
-            "safety_checker": None,
+        kwargs = dict(
+            cache_dir=cache,
+            safety_checker=None,
             # local_files_only = True,
-        }
+        )
 
         owner, repo_name = repo_id.split("/")
         model_name = model_name or repo_name

invokeai/backend/install/model_install_backend.py

@@ -120,7 +120,7 @@ class ModelInstall(object):
         be treated uniformly. It also sorts the models alphabetically
         by their name, to improve the display somewhat.
         """
-        model_dict = {}
+        model_dict = dict()
 
         # first populate with the entries in INITIAL_MODELS.yaml
         for key, value in self.datasets.items():
@@ -134,7 +134,7 @@ class ModelInstall(object):
             model_dict[key] = model_info
 
         # supplement with entries in models.yaml
-        installed_models = list(self.mgr.list_models())
+        installed_models = [x for x in self.mgr.list_models()]
 
         for md in installed_models:
             base = md["base_model"]
@@ -176,7 +176,7 @@ class ModelInstall(object):
     # logic here a little reversed to maintain backward compatibility
     def starter_models(self, all_models: bool = False) -> Set[str]:
         models = set()
-        for key, _value in self.datasets.items():
+        for key, value in self.datasets.items():
             name, base, model_type = ModelManager.parse_key(key)
             if all_models or model_type in [ModelType.Main, ModelType.Vae]:
                 models.add(key)
@@ -184,7 +184,7 @@ class ModelInstall(object):
 
     def recommended_models(self) -> Set[str]:
         starters = self.starter_models(all_models=True)
-        return {x for x in starters if self.datasets[x].get("recommended", False)}
+        return set([x for x in starters if self.datasets[x].get("recommended", False)])
 
     def default_model(self) -> str:
         starters = self.starter_models()
@@ -234,7 +234,7 @@ class ModelInstall(object):
         """
 
         if not models_installed:
-            models_installed = {}
+            models_installed = dict()
 
         model_path_id_or_url = str(model_path_id_or_url).strip("\"' ")
 
@@ -252,14 +252,10 @@ class ModelInstall(object):
 
         # folders style or similar
         elif path.is_dir() and any(
-            (path / x).exists()
-            for x in {
-                "config.json",
-                "model_index.json",
-                "learned_embeds.bin",
-                "pytorch_lora_weights.bin",
-                "pytorch_lora_weights.safetensors",
-            }
+            [
+                (path / x).exists()
+                for x in {"config.json", "model_index.json", "learned_embeds.bin", "pytorch_lora_weights.bin"}
+            ]
         ):
             models_installed.update({str(model_path_id_or_url): self._install_path(path)})
 
@@ -361,7 +357,7 @@ class ModelInstall(object):
                 for suffix in ["safetensors", "bin"]:
                     if f"{prefix}pytorch_lora_weights.{suffix}" in files:
                         location = self._download_hf_model(
-                            repo_id, [f"pytorch_lora_weights.{suffix}"], staging, subfolder=subfolder
+                            repo_id, ["pytorch_lora_weights.bin"], staging, subfolder=subfolder
                         )  # LoRA
                         break
                     elif (
@@ -431,17 +427,17 @@ class ModelInstall(object):
 
         rel_path = self.relative_to_root(path, self.config.models_path)
 
-        attributes = {
-            "path": str(rel_path),
-            "description": str(description),
-            "model_format": info.format,
-        }
+        attributes = dict(
+            path=str(rel_path),
+            description=str(description),
+            model_format=info.format,
+        )
         legacy_conf = None
         if info.model_type == ModelType.Main or info.model_type == ModelType.ONNX:
             attributes.update(
-                {
-                    "variant": info.variant_type,
-                }
+                dict(
+                    variant=info.variant_type,
+                )
             )
             if info.format == "checkpoint":
                 try:
@@ -464,15 +460,9 @@ class ModelInstall(object):
             possible_conf = path.with_suffix(".yaml")
             if possible_conf.exists():
                 legacy_conf = str(self.relative_to_root(possible_conf))
-            else:
-                legacy_conf = Path(
-                    self.config.root_path,
-                    "configs/controlnet",
-                    ("cldm_v15.yaml" if info.base_type == BaseModelType("sd-1") else "cldm_v21.yaml"),
-                )
 
         if legacy_conf:
-            attributes.update({"config": str(legacy_conf)})
+            attributes.update(dict(config=str(legacy_conf)))
         return attributes
 
     def relative_to_root(self, path: Path, root: Optional[Path] = None) -> Path:
@@ -517,7 +507,7 @@ class ModelInstall(object):
     def _download_hf_model(self, repo_id: str, files: List[str], staging: Path, subfolder: None) -> Path:
         _, name = repo_id.split("/")
         location = staging / name
-        paths = []
+        paths = list()
         for filename in files:
             filePath = Path(filename)
             p = hf_download_with_resume(

invokeai/backend/ip_adapter/attention_processor.py

@@ -130,9 +130,7 @@ class IPAttnProcessor2_0(torch.nn.Module):
             assert ip_adapter_image_prompt_embeds is not None
             assert len(ip_adapter_image_prompt_embeds) == len(self._weights)
 
-            for ipa_embed, ipa_weights, scale in zip(
-                ip_adapter_image_prompt_embeds, self._weights, self._scales, strict=True
-            ):
+            for ipa_embed, ipa_weights, scale in zip(ip_adapter_image_prompt_embeds, self._weights, self._scales):
                 # The batch dimensions should match.
                 assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
                 # The token_len dimensions should match.

invokeai/backend/ip_adapter/resampler.py

@@ -56,7 +56,7 @@ class PerceiverAttention(nn.Module):
         x = self.norm1(x)
         latents = self.norm2(latents)
 
-        b, L, _ = latents.shape
+        b, l, _ = latents.shape
 
         q = self.to_q(latents)
         kv_input = torch.cat((x, latents), dim=-2)
@@ -72,7 +72,7 @@ class PerceiverAttention(nn.Module):
         weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         out = weight @ v
 
-        out = out.permute(0, 2, 1, 3).reshape(b, L, -1)
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
 
         return self.to_out(out)
 

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -269,7 +269,7 @@ def create_unet_diffusers_config(original_config, image_size: int, controlnet=Fa
             resolution *= 2
 
     up_block_types = []
-    for _i in range(len(block_out_channels)):
+    for i in range(len(block_out_channels)):
         block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
         up_block_types.append(block_type)
         resolution //= 2
@@ -1223,7 +1223,7 @@ def download_from_original_stable_diffusion_ckpt(
             # scan model
             scan_result = scan_file_path(checkpoint_path)
             if scan_result.infected_files != 0:
-                raise Exception("The model {checkpoint_path} is potentially infected by malware. Aborting import.")
+                raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
         if device is None:
             device = "cuda" if torch.cuda.is_available() else "cpu"
             checkpoint = torch.load(checkpoint_path, map_location=device)
@@ -1664,7 +1664,7 @@ def download_controlnet_from_original_ckpt(
             # scan model
             scan_result = scan_file_path(checkpoint_path)
             if scan_result.infected_files != 0:
-                raise Exception("The model {checkpoint_path} is potentially infected by malware. Aborting import.")
+                raise "The model {checkpoint_path} is potentially infected by malware. Aborting import."
         if device is None:
             device = "cuda" if torch.cuda.is_available() else "cpu"
             checkpoint = torch.load(checkpoint_path, map_location=device)

invokeai/backend/model_management/lora.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pickle
+import copy
 from contextlib import contextmanager
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
@@ -12,8 +12,6 @@ from diffusers.models import UNet2DConditionModel
 from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
-from invokeai.app.shared.models import FreeUConfig
-
 from .models.lora import LoRAModel
 
 """
@@ -56,6 +54,24 @@ class ModelPatcher:
 
         return (module_key, module)
 
+    @staticmethod
+    def _lora_forward_hook(
+        applied_loras: List[Tuple[LoRAModel, float]],
+        layer_name: str,
+    ):
+        def lora_forward(module, input_h, output):
+            if len(applied_loras) == 0:
+                return output
+
+            for lora, weight in applied_loras:
+                layer = lora.layers.get(layer_name, None)
+                if layer is None:
+                    continue
+                output += layer.forward(module, input_h, weight)
+            return output
+
+        return lora_forward
+
     @classmethod
     @contextmanager
     def apply_lora_unet(
@@ -104,7 +120,7 @@ class ModelPatcher:
         loras: List[Tuple[LoRAModel, float]],
         prefix: str,
     ):
-        original_weights = {}
+        original_weights = dict()
         try:
             with torch.no_grad():
                 for lora, lora_weight in loras:
@@ -113,40 +129,21 @@ class ModelPatcher:
                         if not layer_key.startswith(prefix):
                             continue
 
-                        # TODO(ryand): A non-negligible amount of time is currently spent resolving LoRA keys. This
-                        # should be improved in the following ways:
-                        # 1. The key mapping could be more-efficiently pre-computed. This would save time every time a
-                        #    LoRA model is applied.
-                        # 2. From an API perspective, there's no reason that the `ModelPatcher` should be aware of the
-                        #    intricacies of Stable Diffusion key resolution. It should just expect the input LoRA
-                        #    weights to have valid keys.
                         module_key, module = cls._resolve_lora_key(model, layer_key, prefix)
-
-                        # All of the LoRA weight calculations will be done on the same device as the module weight.
-                        # (Performance will be best if this is a CUDA device.)
-                        device = module.weight.device
-                        dtype = module.weight.dtype
-
                         if module_key not in original_weights:
                             original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
 
-                        layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
-
-                        # We intentionally move to the target device first, then cast. Experimentally, this was found to
-                        # be significantly faster for 16-bit CPU tensors being moved to a CUDA device than doing the
-                        # same thing in a single call to '.to(...)'.
-                        layer.to(device=device)
+                        # enable autocast to calc fp16 loras on cpu
+                        # with torch.autocast(device_type="cpu"):
                         layer.to(dtype=torch.float32)
-                        # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
-                        # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
-                        layer_weight = layer.get_weight(module.weight) * (lora_weight * layer_scale)
-                        layer.to(device="cpu")
+                        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
 
                         if module.weight.shape != layer_weight.shape:
                             # TODO: debug on lycoris
                             layer_weight = layer_weight.reshape(module.weight.shape)
 
-                        module.weight += layer_weight.to(dtype=dtype)
+                        module.weight += layer_weight.to(device=module.weight.device, dtype=module.weight.dtype)
 
             yield  # wait for context manager exit
 
@@ -166,25 +163,10 @@ class ModelPatcher:
         init_tokens_count = None
         new_tokens_added = None
 
-        # TODO: This is required since Transformers 4.32 see
-        # https://github.com/huggingface/transformers/pull/25088
-        # More information by NVIDIA:
-        # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
-        # This value might need to be changed in the future and take the GPUs model into account as there seem
-        # to be ideal values for different GPUS. This value is temporary!
-        # For references to the current discussion please see https://github.com/invoke-ai/InvokeAI/pull/4817
-        pad_to_multiple_of = 8
-
         try:
-            # HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
-            # workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
-            # exiting this `apply_ti(...)` context manager.
-            #
-            # In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`,
-            # but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
-            ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
+            ti_tokenizer = copy.deepcopy(tokenizer)
             ti_manager = TextualInversionManager(ti_tokenizer)
-            init_tokens_count = text_encoder.resize_token_embeddings(None, pad_to_multiple_of).num_embeddings
+            init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings
 
             def _get_trigger(ti_name, index):
                 trigger = ti_name
@@ -199,7 +181,7 @@ class ModelPatcher:
                     new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
             # modify text_encoder
-            text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
+            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:
@@ -214,9 +196,7 @@ class ModelPatcher:
 
                     if model_embeddings.weight.data[token_id].shape != embedding.shape:
                         raise ValueError(
-                            f"Cannot load embedding for {trigger}. It was trained on a model with token dimension"
-                            f" {embedding.shape[0]}, but the current model has token dimension"
-                            f" {model_embeddings.weight.data[token_id].shape[0]}."
+                            f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {model_embeddings.weight.data[token_id].shape[0]}."
                         )
 
                     model_embeddings.weight.data[token_id] = embedding.to(
@@ -231,7 +211,7 @@ class ModelPatcher:
 
         finally:
             if init_tokens_count and new_tokens_added:
-                text_encoder.resize_token_embeddings(init_tokens_count, pad_to_multiple_of)
+                text_encoder.resize_token_embeddings(init_tokens_count)
 
     @classmethod
     @contextmanager
@@ -242,7 +222,7 @@ class ModelPatcher:
     ):
         skipped_layers = []
         try:
-            for _i in range(clip_skip):
+            for i in range(clip_skip):
                 skipped_layers.append(text_encoder.text_model.encoder.layers.pop(-1))
 
             yield
@@ -251,25 +231,6 @@ class ModelPatcher:
             while len(skipped_layers) > 0:
                 text_encoder.text_model.encoder.layers.append(skipped_layers.pop())
 
-    @classmethod
-    @contextmanager
-    def apply_freeu(
-        cls,
-        unet: UNet2DConditionModel,
-        freeu_config: Optional[FreeUConfig] = None,
-    ):
-        did_apply_freeu = False
-        try:
-            if freeu_config is not None:
-                unet.enable_freeu(b1=freeu_config.b1, b2=freeu_config.b2, s1=freeu_config.s1, s2=freeu_config.s2)
-                did_apply_freeu = True
-
-            yield
-
-        finally:
-            if did_apply_freeu:
-                unet.disable_freeu()
-
 
 class TextualInversionModel:
     embedding: torch.Tensor  # [n, 768]|[n, 1280]
@@ -296,8 +257,7 @@ class TextualInversionModel:
         if "string_to_param" in state_dict:
             if len(state_dict["string_to_param"]) > 1:
                 print(
-                    f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first'
-                    " token will be used."
+                    f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first token will be used.'
                 )
 
             result.embedding = next(iter(state_dict["string_to_param"].values()))
@@ -324,7 +284,7 @@ class TextualInversionManager(BaseTextualInversionManager):
     tokenizer: CLIPTokenizer
 
     def __init__(self, tokenizer: CLIPTokenizer):
-        self.pad_tokens = {}
+        self.pad_tokens = dict()
         self.tokenizer = tokenizer
 
     def expand_textual_inversion_token_ids_if_necessary(self, token_ids: list[int]) -> list[int]:
@@ -385,10 +345,10 @@ class ONNXModelPatcher:
         if not isinstance(model, IAIOnnxRuntimeModel):
             raise Exception("Only IAIOnnxRuntimeModel models supported")
 
-        orig_weights = {}
+        orig_weights = dict()
 
         try:
-            blended_loras = {}
+            blended_loras = dict()
 
             for lora, lora_weight in loras:
                 for layer_key, layer in lora.layers.items():
@@ -404,7 +364,7 @@ class ONNXModelPatcher:
                     else:
                         blended_loras[layer_key] = layer_weight
 
-            node_names = {}
+            node_names = dict()
             for node in model.nodes.values():
                 node_names[node.name.replace("/", "_").replace(".", "_").lstrip("_")] = node.name
 
@@ -475,13 +435,7 @@ class ONNXModelPatcher:
         orig_embeddings = None
 
         try:
-            # HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
-            # workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
-            # exiting this `apply_ti(...)` context manager.
-            #
-            # In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`,
-            # but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
-            ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
+            ti_tokenizer = copy.deepcopy(tokenizer)
             ti_manager = TextualInversionManager(ti_tokenizer)
 
             def _get_trigger(ti_name, index):
@@ -516,9 +470,7 @@ class ONNXModelPatcher:
 
                     if embeddings[token_id].shape != embedding.shape:
                         raise ValueError(
-                            f"Cannot load embedding for {trigger}. It was trained on a model with token dimension"
-                            f" {embedding.shape[0]}, but the current model has token dimension"
-                            f" {embeddings[token_id].shape[0]}."
+                            f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {embeddings[token_id].shape[0]}."
                         )
 
                     embeddings[token_id] = embedding

invokeai/backend/model_management/memory_snapshot.py

@@ -64,7 +64,7 @@ class MemorySnapshot:
         return cls(process_ram, vram, malloc_info)
 
 
-def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: Optional[MemorySnapshot]) -> str:
+def get_pretty_snapshot_diff(snapshot_1: MemorySnapshot, snapshot_2: MemorySnapshot) -> str:
     """Get a pretty string describing the difference between two `MemorySnapshot`s."""
 
     def get_msg_line(prefix: str, val1: int, val2: int):
@@ -73,9 +73,6 @@ def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: O
 
     msg = ""
 
-    if snapshot_1 is None or snapshot_2 is None:
-        return msg
-
     msg += get_msg_line("Process RAM", snapshot_1.process_ram, snapshot_2.process_ram)
 
     if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None:

invokeai/backend/model_management/model_cache.py

@@ -66,13 +66,11 @@ class CacheStats(object):
 
 class ModelLocker(object):
     "Forward declaration"
-
     pass
 
 
 class ModelCache(object):
     "Forward declaration"
-
     pass
 
 
@@ -119,7 +117,6 @@ class ModelCache(object):
         lazy_offloading: bool = True,
         sha_chunksize: int = 16777216,
         logger: types.ModuleType = logger,
-        log_memory_usage: bool = False,
     ):
         """
         :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
@@ -129,12 +126,8 @@ class ModelCache(object):
         :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
         :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
         :param sha_chunksize: Chunksize to use when calculating sha256 model hash
-        :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
-            operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
-            snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
-            behaviour.
         """
-        self.model_infos: Dict[str, ModelBase] = {}
+        self.model_infos: Dict[str, ModelBase] = dict()
         # allow lazy offloading only when vram cache enabled
         self.lazy_offloading = lazy_offloading and max_vram_cache_size > 0
         self.precision: torch.dtype = precision
@@ -144,18 +137,12 @@ class ModelCache(object):
         self.storage_device: torch.device = storage_device
         self.sha_chunksize = sha_chunksize
         self.logger = logger
-        self._log_memory_usage = log_memory_usage
 
         # used for stats collection
         self.stats = None
 
-        self._cached_models = {}
-        self._cache_stack = []
-
-    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
-        if self._log_memory_usage:
-            return MemorySnapshot.capture()
-        return None
+        self._cached_models = dict()
+        self._cache_stack = list()
 
     def get_key(
         self,
@@ -236,10 +223,10 @@ class ModelCache(object):
 
             # Load the model from disk and capture a memory snapshot before/after.
             start_load_time = time.time()
-            snapshot_before = self._capture_memory_snapshot()
+            snapshot_before = MemorySnapshot.capture()
             with skip_torch_weight_init():
                 model = model_info.get_model(child_type=submodel, torch_dtype=self.precision)
-            snapshot_after = self._capture_memory_snapshot()
+            snapshot_after = MemorySnapshot.capture()
             end_load_time = time.time()
 
             self_reported_model_size_after_load = model_info.get_size(submodel)
@@ -288,9 +275,9 @@ class ModelCache(object):
             return
 
         start_model_to_time = time.time()
-        snapshot_before = self._capture_memory_snapshot()
+        snapshot_before = MemorySnapshot.capture()
         cache_entry.model.to(target_device)
-        snapshot_after = self._capture_memory_snapshot()
+        snapshot_after = MemorySnapshot.capture()
         end_model_to_time = time.time()
         self.logger.debug(
             f"Moved model '{key}' from {source_device} to"
@@ -299,12 +286,7 @@ class ModelCache(object):
             f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
         )
 
-        if (
-            snapshot_before is not None
-            and snapshot_after is not None
-            and snapshot_before.vram is not None
-            and snapshot_after.vram is not None
-        ):
+        if snapshot_before.vram is not None and snapshot_after.vram is not None:
             vram_change = abs(snapshot_before.vram - snapshot_after.vram)
 
             # If the estimated model size does not match the change in VRAM, log a warning.
@@ -440,17 +422,12 @@ class ModelCache(object):
         self.logger.debug(f"Before unloading: cached_models={len(self._cached_models)}")
 
         pos = 0
-        models_cleared = 0
         while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack):
             model_key = self._cache_stack[pos]
             cache_entry = self._cached_models[model_key]
 
             refs = sys.getrefcount(cache_entry.model)
 
-            # HACK: This is a workaround for a memory-management issue that we haven't tracked down yet. We are directly
-            # going against the advice in the Python docs by using `gc.get_referrers(...)` in this way:
-            # https://docs.python.org/3/library/gc.html#gc.get_referrers
-
             # manualy clear local variable references of just finished function calls
             # for some reason python don't want to collect it even by gc.collect() immidiately
             if refs > 2:
@@ -476,16 +453,15 @@ class ModelCache(object):
                 f" refs: {refs}"
             )
 
-            # Expected refs:
+            # 2 refs:
             # 1 from cache_entry
             # 1 from getrefcount function
             # 1 from onnx runtime object
-            if not cache_entry.locked and refs <= (3 if "onnx" in model_key else 2):
+            if not cache_entry.locked and refs <= 3 if "onnx" in model_key else 2:
                 self.logger.debug(
                     f"Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
                 )
                 current_size -= cache_entry.size
-                models_cleared += 1
                 if self.stats:
                     self.stats.cleared += 1
                 del self._cache_stack[pos]
@@ -495,20 +471,7 @@ class ModelCache(object):
             else:
                 pos += 1
 
-        if models_cleared > 0:
-            # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
-            # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
-            # is high even if no garbage gets collected.)
-            #
-            # Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
-            # - If models had to be cleared, it's a signal that we are close to our memory limit.
-            # - If models were cleared, there's a good chance that there's a significant amount of garbage to be
-            #   collected.
-            #
-            # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
-            # immediately when their reference count hits 0.
-            gc.collect()
-
+        gc.collect()
         torch.cuda.empty_cache()
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()
@@ -528,6 +491,7 @@ class ModelCache(object):
                 vram_in_use = torch.cuda.memory_allocated()
                 self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
 
+        gc.collect()
         torch.cuda.empty_cache()
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()

invokeai/backend/model_management/model_load_optimizations.py

@@ -17,7 +17,7 @@ def skip_torch_weight_init():
     completely unnecessary if the intent is to load checkpoint weights from disk for the layer. This context manager
     monkey-patches common torch layers to skip the weight initialization step.
     """
-    torch_modules = [torch.nn.Linear, torch.nn.modules.conv._ConvNd, torch.nn.Embedding]
+    torch_modules = [torch.nn.Linear, torch.nn.modules.conv._ConvNd]
     saved_functions = [m.reset_parameters for m in torch_modules]
 
     try:
@@ -26,5 +26,5 @@ def skip_torch_weight_init():
 
         yield None
     finally:
-        for torch_module, saved_function in zip(torch_modules, saved_functions, strict=True):
+        for torch_module, saved_function in zip(torch_modules, saved_functions):
             torch_module.reset_parameters = saved_function

invokeai/backend/model_management/model_manager.py

@@ -351,7 +351,6 @@ class ModelManager(object):
             precision=precision,
             sequential_offload=sequential_offload,
             logger=logger,
-            log_memory_usage=self.app_config.log_memory_usage,
         )
 
         self._read_models(config)
@@ -363,7 +362,7 @@ class ModelManager(object):
             else:
                 return
 
-        self.models = {}
+        self.models = dict()
         for model_key, model_config in config.items():
             if model_key.startswith("_"):
                 continue
@@ -374,7 +373,7 @@ class ModelManager(object):
             self.models[model_key] = model_class.create_config(**model_config)
 
         # check config version number and update on disk/RAM if necessary
-        self.cache_keys = {}
+        self.cache_keys = dict()
 
         # add controlnet, lora and textual_inversion models from disk
         self.scan_models_directory()
@@ -655,7 +654,7 @@ class ModelManager(object):
         """
         # TODO: redo
         for model_dict in self.list_models():
-            for _model_name, model_info in model_dict.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)
 
@@ -902,7 +901,7 @@ class ModelManager(object):
         """
         Write current configuration out to the indicated file.
         """
-        data_to_save = {}
+        data_to_save = dict()
         data_to_save["__metadata__"] = self.config_meta.model_dump()
 
         for model_key, model_config in self.models.items():
@@ -1034,7 +1033,7 @@ class ModelManager(object):
                 self.ignore = ignore
 
             def on_search_started(self):
-                self.new_models_found = {}
+                self.new_models_found = dict()
 
             def on_model_found(self, model: Path):
                 if model not in self.ignore:
@@ -1106,7 +1105,7 @@ class ModelManager(object):
         # avoid circular import here
         from invokeai.backend.install.model_install_backend import ModelInstall
 
-        successfully_installed = {}
+        successfully_installed = dict()
 
         installer = ModelInstall(
             config=self.app_config, prediction_type_helper=prediction_type_helper, model_manager=self

invokeai/backend/model_management/model_merge.py

@@ -92,7 +92,7 @@ class ModelMerger(object):
         **kwargs - the default DiffusionPipeline.get_config_dict kwargs:
              cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
         """
-        model_paths = []
+        model_paths = list()
         config = self.manager.app_config
         base_model = BaseModelType(base_model)
         vae = None
@@ -124,13 +124,13 @@ class ModelMerger(object):
         dump_path = (dump_path / merged_model_name).as_posix()
 
         merged_pipe.save_pretrained(dump_path, safe_serialization=True)
-        attributes = {
-            "path": dump_path,
-            "description": f"Merge of models {', '.join(model_names)}",
-            "model_format": "diffusers",
-            "variant": ModelVariantType.Normal.value,
-            "vae": vae,
-        }
+        attributes = dict(
+            path=dump_path,
+            description=f"Merge of models {', '.join(model_names)}",
+            model_format="diffusers",
+            variant=ModelVariantType.Normal.value,
+            vae=vae,
+        )
         return self.manager.add_model(
             merged_model_name,
             base_model=base_model,

invokeai/backend/model_management/model_probe.py

@@ -183,13 +183,12 @@ class ModelProbe(object):
         if model:
             class_name = model.__class__.__name__
         else:
-            for suffix in ["bin", "safetensors"]:
-                if (folder_path / f"learned_embeds.{suffix}").exists():
-                    return ModelType.TextualInversion
-                if (folder_path / f"pytorch_lora_weights.{suffix}").exists():
-                    return ModelType.Lora
             if (folder_path / "unet/model.onnx").exists():
                 return ModelType.ONNX
+            if (folder_path / "learned_embeds.bin").exists():
+                return ModelType.TextualInversion
+            if (folder_path / "pytorch_lora_weights.bin").exists():
+                return ModelType.Lora
             if (folder_path / "image_encoder.txt").exists():
                 return ModelType.IPAdapter
 
@@ -237,7 +236,7 @@ class ModelProbe(object):
         # scan model
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
-            raise Exception("The model {model_name} is potentially infected by malware. Aborting import.")
+            raise "The model {model_name} is potentially infected by malware. Aborting import."
 
 
 # ##################################################3

invokeai/backend/model_management/model_search.py

@@ -59,7 +59,7 @@ class ModelSearch(ABC):
         for root, dirs, files in os.walk(path, followlinks=True):
             if str(Path(root).name).startswith("."):
                 self._pruned_paths.add(root)
-            if any(Path(root).is_relative_to(x) for x in self._pruned_paths):
+            if any([Path(root).is_relative_to(x) for x in self._pruned_paths]):
                 continue
 
             self._items_scanned += len(dirs) + len(files)
@@ -69,14 +69,16 @@ class ModelSearch(ABC):
                     self._scanned_dirs.add(path)
                     continue
                 if any(
-                    (path / x).exists()
-                    for x in {
-                        "config.json",
-                        "model_index.json",
-                        "learned_embeds.bin",
-                        "pytorch_lora_weights.bin",
-                        "image_encoder.txt",
-                    }
+                    [
+                        (path / x).exists()
+                        for x in {
+                            "config.json",
+                            "model_index.json",
+                            "learned_embeds.bin",
+                            "pytorch_lora_weights.bin",
+                            "image_encoder.txt",
+                        }
+                    ]
                 ):
                     try:
                         self.on_model_found(path)

invokeai/backend/model_management/models/__init__.py

@@ -97,8 +97,8 @@ MODEL_CLASSES = {
     # },
 }
 
-MODEL_CONFIGS = []
-OPENAPI_MODEL_CONFIGS = []
+MODEL_CONFIGS = list()
+OPENAPI_MODEL_CONFIGS = list()
 
 
 class OpenAPIModelInfoBase(BaseModel):
@@ -109,7 +109,7 @@ class OpenAPIModelInfoBase(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
 
-for _base_model, models in MODEL_CLASSES.items():
+for base_model, models in MODEL_CLASSES.items():
     for model_type, model_class in models.items():
         model_configs = set(model_class._get_configs().values())
         model_configs.discard(None)
@@ -133,7 +133,7 @@ for _base_model, models in MODEL_CLASSES.items():
 
 
 def get_model_config_enums():
-    enums = []
+    enums = list()
 
     for model_config in MODEL_CONFIGS:
         if hasattr(inspect, "get_annotations"):

invokeai/backend/model_management/models/base.py

@@ -153,7 +153,7 @@ class ModelBase(metaclass=ABCMeta):
 
         else:
             res_type = sys.modules["diffusers"]
-            res_type = res_type.pipelines
+            res_type = getattr(res_type, "pipelines")
 
         for subtype in subtypes:
             res_type = getattr(res_type, subtype)
@@ -164,7 +164,7 @@ class ModelBase(metaclass=ABCMeta):
         with suppress(Exception):
             return cls.__configs
 
-        configs = {}
+        configs = dict()
         for name in dir(cls):
             if name.startswith("__"):
                 continue
@@ -246,8 +246,8 @@ class DiffusersModel(ModelBase):
     def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
         super().__init__(model_path, base_model, model_type)
 
-        self.child_types: Dict[str, Type] = {}
-        self.child_sizes: Dict[str, int] = {}
+        self.child_types: Dict[str, Type] = dict()
+        self.child_sizes: Dict[str, int] = dict()
 
         try:
             config_data = DiffusionPipeline.load_config(self.model_path)
@@ -326,8 +326,8 @@ def calc_model_size_by_fs(model_path: str, subfolder: Optional[str] = None, vari
     all_files = os.listdir(model_path)
     all_files = [f for f in all_files if os.path.isfile(os.path.join(model_path, f))]
 
-    fp16_files = {f for f in all_files if ".fp16." in f or ".fp16-" in f}
-    bit8_files = {f for f in all_files if ".8bit." in f or ".8bit-" in f}
+    fp16_files = set([f for f in all_files if ".fp16." in f or ".fp16-" in f])
+    bit8_files = set([f for f in all_files if ".8bit." in f or ".8bit-" in f])
     other_files = set(all_files) - fp16_files - bit8_files
 
     if variant is None:
@@ -413,7 +413,7 @@ def _calc_onnx_model_by_data(model) -> int:
 
 
 def _fast_safetensors_reader(path: str):
-    checkpoint = {}
+    checkpoint = dict()
     device = torch.device("meta")
     with open(path, "rb") as f:
         definition_len = int.from_bytes(f.read(8), "little")
@@ -483,7 +483,7 @@ class IAIOnnxRuntimeModel:
     class _tensor_access:
         def __init__(self, model):
             self.model = model
-            self.indexes = {}
+            self.indexes = dict()
             for idx, obj in enumerate(self.model.proto.graph.initializer):
                 self.indexes[obj.name] = idx
 
@@ -524,7 +524,7 @@ class IAIOnnxRuntimeModel:
 
     class _access_helper:
         def __init__(self, raw_proto):
-            self.indexes = {}
+            self.indexes = dict()
             self.raw_proto = raw_proto
             for idx, obj in enumerate(raw_proto):
                 self.indexes[obj.name] = idx
@@ -549,7 +549,7 @@ class IAIOnnxRuntimeModel:
             return self.indexes.keys()
 
         def values(self):
-            return list(self.raw_proto)
+            return [obj for obj in self.raw_proto]
 
     def __init__(self, model_path: str, provider: Optional[str]):
         self.path = model_path

invokeai/backend/model_management/models/controlnet.py

@@ -104,7 +104,7 @@ class ControlNetModel(ModelBase):
                 return ControlNetModelFormat.Diffusers
 
         if os.path.isfile(path):
-            if any(path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "pth"]):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "pth"]]):
                 return ControlNetModelFormat.Checkpoint
 
         raise InvalidModelException(f"Not a valid model: {path}")
@@ -132,14 +132,13 @@ def _convert_controlnet_ckpt_and_cache(
     model_path: str,
     output_path: str,
     base_model: BaseModelType,
-    model_config: str,
+    model_config: ControlNetModel.CheckpointConfig,
 ) -> str:
     """
     Convert the controlnet from checkpoint format to diffusers format,
     cache it to disk, and return Path to converted
     file. If already on disk then just returns Path.
     """
-    print(f"DEBUG: controlnet config = {model_config}")
     app_config = InvokeAIAppConfig.get_config()
     weights = app_config.root_path / model_path
     output_path = Path(output_path)

invokeai/backend/model_management/models/lora.py

@@ -68,12 +68,11 @@ class LoRAModel(ModelBase):
             raise ModelNotFoundException()
 
         if os.path.isdir(path):
-            for ext in ["safetensors", "bin"]:
-                if os.path.exists(os.path.join(path, f"pytorch_lora_weights.{ext}")):
-                    return LoRAModelFormat.Diffusers
+            if os.path.exists(os.path.join(path, "pytorch_lora_weights.bin")):
+                return LoRAModelFormat.Diffusers
 
         if os.path.isfile(path):
-            if any(path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
                 return LoRAModelFormat.LyCORIS
 
         raise InvalidModelException(f"Not a valid model: {path}")
@@ -87,10 +86,8 @@ class LoRAModel(ModelBase):
         base_model: BaseModelType,
     ) -> str:
         if cls.detect_format(model_path) == LoRAModelFormat.Diffusers:
-            for ext in ["safetensors", "bin"]:  # return path to the safetensors file inside the folder
-                path = Path(model_path, f"pytorch_lora_weights.{ext}")
-                if path.exists():
-                    return path
+            # TODO: add diffusers lora when it stabilizes a bit
+            raise NotImplementedError("Diffusers lora not supported")
         else:
             return model_path
 
@@ -443,27 +440,43 @@ class IA3Layer(LoRALayerBase):
 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():
+        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
@@ -499,7 +512,7 @@ class LoRAModelRaw:  # (torch.nn.Module):
         stability_unet_keys = list(SDXL_UNET_STABILITY_TO_DIFFUSERS_MAP)
         stability_unet_keys.sort()
 
-        new_state_dict = {}
+        new_state_dict = dict()
         for full_key, value in state_dict.items():
             if full_key.startswith("lora_unet_"):
                 search_key = full_key.replace("lora_unet_", "")
@@ -544,8 +557,10 @@ class LoRAModelRaw:  # (torch.nn.Module):
             file_path = Path(file_path)
 
         model = cls(
+            device=device,
+            dtype=dtype,
             name=file_path.stem,  # TODO:
-            layers={},
+            layers=dict(),
         )
 
         if file_path.suffix == ".safetensors":
@@ -593,12 +608,12 @@ class LoRAModelRaw:  # (torch.nn.Module):
 
     @staticmethod
     def _group_state(state_dict: dict):
-        state_dict_groupped = {}
+        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] = {}
+                state_dict_groupped[stem] = dict()
             state_dict_groupped[stem][leaf] = value
 
         return state_dict_groupped

invokeai/backend/model_management/models/stable_diffusion.py

@@ -110,7 +110,7 @@ class StableDiffusion1Model(DiffusersModel):
                 return StableDiffusion1ModelFormat.Diffusers
 
         if os.path.isfile(model_path):
-            if any(model_path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]):
+            if any([model_path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
                 return StableDiffusion1ModelFormat.Checkpoint
 
         raise InvalidModelException(f"Not a valid model: {model_path}")
@@ -221,7 +221,7 @@ class StableDiffusion2Model(DiffusersModel):
                 return StableDiffusion2ModelFormat.Diffusers
 
         if os.path.isfile(model_path):
-            if any(model_path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]):
+            if any([model_path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
                 return StableDiffusion2ModelFormat.Checkpoint
 
         raise InvalidModelException(f"Not a valid model: {model_path}")

invokeai/backend/model_management/models/textual_inversion.py

@@ -71,7 +71,7 @@ class TextualInversionModel(ModelBase):
                 return None  # diffusers-ti
 
         if os.path.isfile(path):
-            if any(path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "bin"]):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "bin"]]):
                 return None
 
         raise InvalidModelException(f"Not a valid model: {path}")

invokeai/backend/model_management/models/vae.py

@@ -89,7 +89,7 @@ class VaeModel(ModelBase):
                 return VaeModelFormat.Diffusers
 
         if os.path.isfile(path):
-            if any(path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]):
+            if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
                 return VaeModelFormat.Checkpoint
 
         raise InvalidModelException(f"Not a valid model: {path}")

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -193,7 +193,6 @@ class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
         attention_map_saver (`AttentionMapSaver`): Object containing attention maps that can be displayed to the user
          after generation completes. Optional.
     """
-
     attention_map_saver: Optional[AttentionMapSaver]
 
 
@@ -547,13 +546,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         # Handle ControlNet(s) and T2I-Adapter(s)
         down_block_additional_residuals = None
         mid_block_additional_residual = None
-        down_intrablock_additional_residuals = None
-        # if control_data is not None and t2i_adapter_data is not None:
-        # TODO(ryand): This is a limitation of the UNet2DConditionModel API, not a fundamental incompatibility
-        # between ControlNets and T2I-Adapters. We will try to fix this upstream in diffusers.
-        #    raise Exception("ControlNet(s) and T2I-Adapter(s) cannot be used simultaneously (yet).")
-        # elif control_data is not None:
-        if control_data is not None:
+        if control_data is not None and t2i_adapter_data is not None:
+            # TODO(ryand): This is a limitation of the UNet2DConditionModel API, not a fundamental incompatibility
+            # between ControlNets and T2I-Adapters. We will try to fix this upstream in diffusers.
+            raise Exception("ControlNet(s) and T2I-Adapter(s) cannot be used simultaneously (yet).")
+        elif control_data is not None:
             down_block_additional_residuals, mid_block_additional_residual = self.invokeai_diffuser.do_controlnet_step(
                 control_data=control_data,
                 sample=latent_model_input,
@@ -562,8 +559,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 total_step_count=total_step_count,
                 conditioning_data=conditioning_data,
             )
-        # elif t2i_adapter_data is not None:
-        if t2i_adapter_data is not None:
+        elif t2i_adapter_data is not None:
             accum_adapter_state = None
             for single_t2i_adapter_data in t2i_adapter_data:
                 # Determine the T2I-Adapter weights for the current denoising step.
@@ -588,8 +584,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     for idx, value in enumerate(single_t2i_adapter_data.adapter_state):
                         accum_adapter_state[idx] += value * t2i_adapter_weight
 
-            # down_block_additional_residuals = accum_adapter_state
-            down_intrablock_additional_residuals = accum_adapter_state
+            down_block_additional_residuals = accum_adapter_state
 
         uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step(
             sample=latent_model_input,
@@ -598,9 +593,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             total_step_count=total_step_count,
             conditioning_data=conditioning_data,
             # extra:
-            down_block_additional_residuals=down_block_additional_residuals,  # for ControlNet
-            mid_block_additional_residual=mid_block_additional_residual,  # for ControlNet
-            down_intrablock_additional_residuals=down_intrablock_additional_residuals,  # for T2I-Adapter
+            down_block_additional_residuals=down_block_additional_residuals,
+            mid_block_additional_residual=mid_block_additional_residual,
         )
 
         guidance_scale = conditioning_data.guidance_scale