refactor the model bases variable

fix model-not-found error
Fix model cache gc.collect() condition. (#5036 )
2024-08-30 20:32:17 +00:00 · 2023-11-04 11:25:13 -04:00 · 2023-11-04 11:12:29 -04:00 · 2023-11-04 09:09:46 -04:00 · 2023-11-04 08:52:10 -04:00 · 2023-11-03 13:50:40 -07:00
42 changed files with 7697 additions and 2725 deletions
--- a/.gitattributes
+++ b/.gitattributes
@ -2,3 +2,4 @@
 # 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
--- a/docs/features/CONTROLNET.md
+++ b/docs/features/CONTROLNET.md
@ -150,7 +150,6 @@ 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

--- a/docs/installation/040_INSTALL_DOCKER.md
+++ b/docs/installation/040_INSTALL_DOCKER.md
@ -99,3 +99,14 @@ 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)
--- a/docs/nodes/communityNodes.md
+++ b/docs/nodes/communityNodes.md
@ -4,7 +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 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 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 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. 

@ -329,9 +333,9 @@ 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/fake_node.py
+**Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py

-**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
+**Example Workflow:**  https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json

 **Output Examples** 

--- a/docs/nodes/contributingNodes.md
+++ b/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. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node.
+- 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. 
 - 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.
--- a/docs/nodes/exampleWorkflows.md
+++ b/docs/nodes/exampleWorkflows.md
@ -2,13 +2,17 @@

 We've curated some example workflows for you to get started with Workflows in InvokeAI

-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!
+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!

 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/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)
+* [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)
 * [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)
--- a/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json
+++ b/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json
--- a/docs/workflows/FaceMask.json
+++ b/docs/workflows/FaceMask.json
--- a/docs/workflows/FaceOff_FaceScale2x.json
+++ b/docs/workflows/FaceOff_FaceScale2x.json
--- a/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json
+++ b/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json
--- a/docs/workflows/Multi_ControlNet_Canny_and_Depth.json
+++ b/docs/workflows/Multi_ControlNet_Canny_and_Depth.json
@ -0,0 +1,985 @@
+{
+  "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"
+            }
+          },
+          "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": 4479.68542130465,
+        "y": -618.4221638099414
+      }
+    },
+    {
+      "id": "c4b23e64-7986-40c4-9cad-46327b12e204",
+      "type": "invocation",
+      "data": {
+        "id": "c4b23e64-7986-40c4-9cad-46327b12e204",
+        "type": "image",
+        "inputs": {
+          "image": {
+            "id": "189c8adf-68cc-4774-a729-49da89f6fdf1",
+            "name": "image",
+            "type": "ImageField",
+            "fieldKind": "input",
+            "label": "Canny 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,
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+            "fieldKind": "input",
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+  ]
+}
--- a/docs/workflows/Prompt_from_File.json
+++ b/docs/workflows/Prompt_from_File.json
@ -0,0 +1,719 @@
+{
+  "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"
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+    {
+      "nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
+      "fieldName": "file_path"
+    }
+  ],
+  "meta": {
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+  "nodes": [
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+      "type": "invocation",
+      "data": {
+        "id": "c2eaf1ba-5708-4679-9e15-945b8b432692",
+        "type": "compel",
+        "inputs": {
+          "prompt": {
+            "id": "dcdf3f6d-9b96-4bcd-9b8d-f992fefe4f62",
+            "name": "prompt",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "",
+            "value": ""
+          },
+          "clip": {
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+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
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+      "targetHandle": "negative_conditioning",
+      "id": "reactflow__edge-c2eaf1ba-5708-4679-9e15-945b8b432692conditioning-2fb1577f-0a56-4f12-8711-8afcaaaf1d5enegative_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77",
+      "sourceHandle": "noise",
+      "target": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
+      "targetHandle": "noise",
+      "id": "reactflow__edge-0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77noise-2fb1577f-0a56-4f12-8711-8afcaaaf1d5enoise",
+      "type": "default"
+    },
+    {
+      "source": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
+      "sourceHandle": "unet",
+      "target": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
+      "targetHandle": "unet",
+      "id": "reactflow__edge-d6353b7f-b447-4e17-8f2e-80a88c91d426unet-2fb1577f-0a56-4f12-8711-8afcaaaf1d5eunet",
+      "type": "default"
+    },
+    {
+      "source": "2fb1577f-0a56-4f12-8711-8afcaaaf1d5e",
+      "sourceHandle": "latents",
+      "target": "491ec988-3c77-4c37-af8a-39a0c4e7a2a1",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-2fb1577f-0a56-4f12-8711-8afcaaaf1d5elatents-491ec988-3c77-4c37-af8a-39a0c4e7a2a1latents",
+      "type": "default"
+    },
+    {
+      "source": "d6353b7f-b447-4e17-8f2e-80a88c91d426",
+      "sourceHandle": "vae",
+      "target": "491ec988-3c77-4c37-af8a-39a0c4e7a2a1",
+      "targetHandle": "vae",
+      "id": "reactflow__edge-d6353b7f-b447-4e17-8f2e-80a88c91d426vae-491ec988-3c77-4c37-af8a-39a0c4e7a2a1vae",
+      "type": "default"
+    }
+  ]
+}
--- a/docs/workflows/QR_Code_Monster.json
+++ b/docs/workflows/QR_Code_Monster.json
@ -0,0 +1,758 @@
+{
+  "name": "QR Code Monster",
+  "author": "InvokeAI",
+  "description": "Sample workflow for create images with QR code Monster ControlNet",
+  "version": "1.0.1",
+  "contact": "invoke@invoke.ai",
+  "tags": "qrcode, controlnet, default",
+  "notes": "",
+  "exposedFields": [
+    {
+      "nodeId": "a6cc0986-f928-4a7e-8d44-ba2d4b36f54a",
+      "fieldName": "image"
+    },
+    {
+      "nodeId": "aca3b054-bfba-4392-bd20-6476f59504df",
+      "fieldName": "prompt"
+    },
+    {
+      "nodeId": "3db7cee0-31e2-4a3d-94a1-268cb16177dd",
+      "fieldName": "prompt"
+    }
+  ],
+  "meta": {
+    "version": "1.0.0"
+  },
+  "nodes": [
+    {
+      "id": "3db7cee0-31e2-4a3d-94a1-268cb16177dd",
+      "type": "invocation",
+      "data": {
+        "id": "3db7cee0-31e2-4a3d-94a1-268cb16177dd",
+        "type": "compel",
+        "inputs": {
+          "prompt": {
+            "id": "6a1fe244-5656-4f8c-91d1-1fb474e28807",
+            "name": "prompt",
+            "type": "string",
+            "fieldKind": "input",
+            "label": "Negative Prompt",
+            "value": ""
+          },
+          "clip": {
+            "id": "f24688f3-29b8-4a2d-8603-046e5a5c7250",
+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "conditioning": {
+            "id": "700528eb-3f8b-4745-b540-34f919b5b228",
+            "name": "conditioning",
+            "type": "ConditioningField",
+            "fieldKind": "output"
+          }
+        },
+        "label": "Prompt",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
+      },
+      "width": 320,
+      "height": 261,
+      "position": {
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+        "y": 1622.4836086770556
+      }
+    },
+    {
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+      "type": "invocation",
+      "data": {
+        "id": "610384f1-6f0c-4847-a9a2-37ce7f456ed1",
+        "type": "main_model_loader",
+        "inputs": {
+          "model": {
+            "id": "cb36b6d3-6c1f-4911-a200-646745b0ff74",
+            "name": "model",
+            "type": "MainModelField",
+            "fieldKind": "input",
+            "label": "",
+            "value": {
+              "model_name": "stable-diffusion-v1-5",
+              "base_model": "sd-1",
+              "model_type": "main"
+            }
+          }
+        },
+        "outputs": {
+          "unet": {
+            "id": "7246895b-b252-49bc-b952-8d801b4672f7",
+            "name": "unet",
+            "type": "UNetField",
+            "fieldKind": "output"
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+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "output"
+          },
+          "vae": {
+            "id": "b9743815-5501-4bbb-8bde-8bd6ba298a4e",
+            "name": "vae",
+            "type": "VaeField",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
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+      "height": 226,
+      "position": {
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+    {
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+        "type": "compel",
+        "inputs": {
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+            "name": "prompt",
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+            "name": "clip",
+            "type": "ClipField",
+            "fieldKind": "input",
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+          }
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+        "outputs": {
+          "conditioning": {
+            "id": "700528eb-3f8b-4745-b540-34f919b5b228",
+            "name": "conditioning",
+            "type": "ConditioningField",
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+            "name": "image",
+            "type": "ImageField",
+            "fieldKind": "input",
+            "label": "QR Code / Hidden Image"
+          }
+        },
+        "outputs": {
+          "image": {
+            "id": "54335653-0e17-42da-b9e8-83c5fb5af670",
+            "name": "image",
+            "type": "ImageField",
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+          },
+          "use_cpu": {
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+            "name": "use_cpu",
+            "type": "boolean",
+            "fieldKind": "input",
+            "label": "",
+            "value": true
+          }
+        },
+        "outputs": {
+          "noise": {
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+            "name": "noise",
+            "type": "LatentsField",
+            "fieldKind": "output"
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+        "useCache": true,
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+        "type": "controlnet",
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+          "resize_mode": {
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+            "name": "resize_mode",
+            "type": "enum",
+            "fieldKind": "input",
+            "label": "",
+            "value": "just_resize"
+          }
+        },
+        "outputs": {
+          "control": {
+            "id": "c9a1fc7e-cb25-45a9-adff-1a97c9ff04d6",
+            "name": "control",
+            "type": "ControlField",
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+        "useCache": true,
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+          }
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+        "outputs": {
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+          "ip_adapter": {
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+            "name": "denoise_mask",
+            "type": "DenoiseMaskField",
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+        "outputs": {
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+        "notes": "",
+        "embedWorkflow": false,
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+            "name": "high",
+            "type": "integer",
+            "fieldKind": "input",
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+            "value": 2147483647
+          }
+        },
+        "outputs": {
+          "value": {
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+            "name": "value",
+            "type": "integer",
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+        "notes": "",
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+      "height": 32,
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+      "sourceHandle": "clip",
+      "target": "aca3b054-bfba-4392-bd20-6476f59504df",
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+      "target": "2ac03cf6-0326-454a-bed0-d8baef2bf30d",
+      "targetHandle": "image",
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+      "sourceHandle": "vae",
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+      "sourceHandle": "conditioning",
+      "target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
+      "targetHandle": "negative_conditioning",
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+      "type": "default"
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+    {
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+      "sourceHandle": "conditioning",
+      "target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
+      "targetHandle": "positive_conditioning",
+      "id": "reactflow__edge-aca3b054-bfba-4392-bd20-6476f59504dfconditioning-9755ae4c-ef30-4db3-80f6-a31f98979a11positive_conditioning",
+      "type": "default"
+    },
+    {
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+      "sourceHandle": "unet",
+      "target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
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+      "type": "default"
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+      "sourceHandle": "control",
+      "target": "9755ae4c-ef30-4db3-80f6-a31f98979a11",
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+      "type": "default"
+    },
+    {
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+      "sourceHandle": "latents",
+      "target": "28542b66-5a00-4780-a318-0a036d2df914",
+      "targetHandle": "latents",
+      "id": "reactflow__edge-9755ae4c-ef30-4db3-80f6-a31f98979a11latents-28542b66-5a00-4780-a318-0a036d2df914latents",
+      "type": "default"
+    },
+    {
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+      "sourceHandle": "value",
+      "target": "280fd8a7-3b0c-49fe-8be4-6246e08b6c9a",
+      "targetHandle": "seed",
+      "id": "reactflow__edge-59349822-af20-4e0e-a53f-3ba135d00c3fvalue-280fd8a7-3b0c-49fe-8be4-6246e08b6c9aseed",
+      "type": "default"
+    }
+  ]
+}
--- a/docs/workflows/SDXL_Text_to_Image.json
+++ b/docs/workflows/SDXL_Text_to_Image.json
@ -26,10 +26,6 @@
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@ -40,7 +36,6 @@
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        "inputs": {
@ -135,10 +130,12 @@
        "isOpen": true,
        "notes": "",
        "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
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+      "height": 793,
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@ -148,7 +145,6 @@
      "id": "55705012-79b9-4aac-9f26-c0b10309785b",
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        "inputs": {
@ -209,7 +205,9 @@
        "isOpen": false,
        "notes": "",
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+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
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      "height": 32,
@ -218,83 +216,10 @@
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        "inputs": {
@ -327,7 +252,9 @@
        "isOpen": false,
        "notes": "",
        "embedWorkflow": false,
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+        "isIntermediate": true,
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@ -340,7 +267,6 @@
      "id": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
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@ -351,7 +277,7 @@
            "fieldKind": "input",
            "label": "",
            "value": {
-              "model_name": "stable-diffusion-xl-base-1.0",
+              "model_name": "stable-diffusion-xl-base-1-0",
              "base_model": "sdxl",
              "model_type": "main"
            }
@ -387,10 +313,12 @@
        "isOpen": true,
        "notes": "",
        "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
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+        "version": "1.0.0"
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@ -400,7 +328,6 @@
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@ -495,128 +422,77 @@
        "isOpen": true,
        "notes": "",
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-        "isIntermediate": true
+        "isIntermediate": true,
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    },
    {
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-            "value": "euler"
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-          "control": {
-            "id": "599b49e8-6435-4576-be41-a5155f3a17e3",
-            "name": "control",
-            "type": "ControlField",
+          "metadata": {
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+            "name": "metadata",
+            "type": "MetadataField",
            "fieldKind": "input",
            "label": ""
          },
          "latents": {
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        "outputs": {
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-            "name": "latents",
-            "type": "LatentsField",
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            "fieldKind": "output"
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          "width": {
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          "height": {
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@ -626,13 +502,161 @@
        "isOpen": true,
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-        "isIntermediate": true
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+            "name": "denoising_end",
+            "type": "float",
+            "fieldKind": "input",
+            "label": "",
+            "value": 1
+          },
+          "scheduler": {
+            "id": "14423e0d-7215-4ee0-b065-f9e95eaa8d7d",
+            "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",
+            "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",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "latents": {
+            "id": "3a922c6a-3d8c-4c9e-b3ec-2f4d81cda077",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "input",
+            "label": ""
+          },
+          "denoise_mask": {
+            "id": "cd7ce032-835f-495f-8b45-d57272f33132",
+            "name": "denoise_mask",
+            "type": "DenoiseMaskField",
+            "fieldKind": "input",
+            "label": ""
+          }
+        },
+        "outputs": {
+          "latents": {
+            "id": "6260b84f-8361-470a-98d8-5b22a45c2d8c",
+            "name": "latents",
+            "type": "LatentsField",
+            "fieldKind": "output"
+          },
+          "width": {
+            "id": "aede0ecf-25b6-46be-aa30-b77f79715deb",
+            "name": "width",
+            "type": "integer",
+            "fieldKind": "output"
+          },
+          "height": {
+            "id": "519abf62-d475-48ef-ab8f-66136bc0e499",
+            "name": "height",
+            "type": "integer",
+            "fieldKind": "output"
+          }
+        },
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "embedWorkflow": false,
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.4.0"
+      },
+      "width": 320,
+      "height": 646,
+      "position": {
+        "x": 1642.955772577545,
+        "y": -230.2485847594651
      }
    }
  ],
@ -686,50 +710,42 @@
    {
      "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
      "sourceHandle": "vae",
-      "target": "dbcd2f98-d809-48c8-bf64-2635f88a2fe9",
+      "target": "63e91020-83b2-4f35-b174-ad9692aabb48",
      "targetHandle": "vae",
-      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-dbcd2f98-d809-48c8-bf64-2635f88a2fe9vae",
-      "type": "default"
-    },
-    {
-      "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": "87ee6243-fb0d-4f77-ad5f-56591659339e",
-      "targetHandle": "positive_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": "87ee6243-fb0d-4f77-ad5f-56591659339e",
-      "targetHandle": "negative_conditioning",
-      "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-87ee6243-fb0d-4f77-ad5f-56591659339enegative_conditioning",
+      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22vae-63e91020-83b2-4f35-b174-ad9692aabb48vae",
      "type": "default"
    },
    {
      "source": "30d3289c-773c-4152-a9d2-bd8a99c8fd22",
      "sourceHandle": "unet",
-      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
      "targetHandle": "unet",
-      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-87ee6243-fb0d-4f77-ad5f-56591659339eunet",
+      "id": "reactflow__edge-30d3289c-773c-4152-a9d2-bd8a99c8fd22unet-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbunet",
+      "type": "default"
+    },
+    {
+      "source": "faf965a4-7530-427b-b1f3-4ba6505c2a08",
+      "sourceHandle": "conditioning",
+      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "targetHandle": "positive_conditioning",
+      "id": "reactflow__edge-faf965a4-7530-427b-b1f3-4ba6505c2a08conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbpositive_conditioning",
+      "type": "default"
+    },
+    {
+      "source": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204",
+      "sourceHandle": "conditioning",
+      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
+      "targetHandle": "negative_conditioning",
+      "id": "reactflow__edge-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204conditioning-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnegative_conditioning",
      "type": "default"
    },
    {
      "source": "55705012-79b9-4aac-9f26-c0b10309785b",
      "sourceHandle": "noise",
-      "target": "87ee6243-fb0d-4f77-ad5f-56591659339e",
+      "target": "50a36525-3c0a-4cc5-977c-e4bfc3fd6dfb",
      "targetHandle": "noise",
-      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-87ee6243-fb0d-4f77-ad5f-56591659339enoise",
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-50a36525-3c0a-4cc5-977c-e4bfc3fd6dfbnoise",
      "type": "default"
    }
  ]
-}
+}
--- a/docs/workflows/SDXL_w_Refiner_Text_to_Image.json
+++ b/docs/workflows/SDXL_w_Refiner_Text_to_Image.json
--- a/docs/workflows/Text_to_Image.json
+++ b/docs/workflows/Text_to_Image.json
@ -18,10 +18,6 @@
    {
      "nodeId": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
-      "fieldName": "steps"
    }
  ],
  "meta": {
@ -32,7 +28,6 @@
      "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
      "type": "invocation",
      "data": {
-        "version": "1.0.0",
        "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
        "type": "compel",
        "inputs": {
@ -64,20 +59,21 @@
        "isOpen": true,
        "notes": "",
        "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
      },
      "width": 320,
-      "height": 235,
+      "height": 261,
      "position": {
-        "x": 1400,
-        "y": -75
+        "x": 995.7263915923627,
+        "y": 239.67783573351227
      }
    },
    {
      "id": "55705012-79b9-4aac-9f26-c0b10309785b",
      "type": "invocation",
      "data": {
-        "version": "1.0.0",
        "id": "55705012-79b9-4aac-9f26-c0b10309785b",
        "type": "noise",
        "inputs": {
@ -138,92 +134,21 @@
        "isOpen": true,
        "notes": "",
        "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
      },
      "width": 320,
-      "height": 364,
+      "height": 389,
      "position": {
-        "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
+        "x": 993.4442117555518,
+        "y": 605.6757415334787
      }
    },
    {
      "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
      "type": "invocation",
      "data": {
-        "version": "1.0.0",
        "id": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
        "type": "main_model_loader",
        "inputs": {
@ -261,23 +186,24 @@
          }
        },
        "label": "",
-        "isOpen": false,
+        "isOpen": true,
        "notes": "",
        "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
      },
      "width": 320,
-      "height": 32,
+      "height": 226,
      "position": {
-        "x": 1000,
-        "y": 200
+        "x": 163.04436745878343,
+        "y": 254.63156870373479
      }
    },
    {
      "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
      "type": "invocation",
      "data": {
-        "version": "1.0.0",
        "id": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
        "type": "compel",
        "inputs": {
@ -309,20 +235,21 @@
        "isOpen": true,
        "notes": "",
        "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.0.0"
      },
      "width": 320,
-      "height": 235,
+      "height": 261,
      "position": {
-        "x": 1000,
-        "y": -75
+        "x": 595.7263915923627,
+        "y": 239.67783573351227
      }
    },
    {
      "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
      "type": "invocation",
      "data": {
-        "version": "1.0.0",
        "id": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
        "type": "rand_int",
        "inputs": {
@ -352,51 +279,66 @@
          }
        },
        "label": "Random Seed",
-        "isOpen": false,
+        "isOpen": true,
        "notes": "",
        "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": false,
+        "version": "1.0.0"
      },
      "width": 320,
-      "height": 32,
+      "height": 218,
      "position": {
-        "x": 1000,
-        "y": 275
+        "x": 541.094822888628,
+        "y": 694.5704476446829
      }
    },
    {
-      "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
      "type": "invocation",
      "data": {
-        "version": "1.0.0",
-        "id": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+        "id": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
        "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": "8b18f3eb-40d2-45c1-9a9d-28d6af0dce2b",
+            "id": "8e17f1e5-4f98-40b1-b7f4-86aeeb4554c1",
            "name": "noise",
            "type": "LatentsField",
            "fieldKind": "input",
            "label": ""
          },
          "steps": {
-            "id": "0be4373c-46f3-441c-80a7-a4bb6ceb498c",
+            "id": "9b63302d-6bd2-42c9-ac13-9b1afb51af88",
            "name": "steps",
            "type": "integer",
            "fieldKind": "input",
            "label": "",
-            "value": 36
+            "value": 10
          },
          "cfg_scale": {
-            "id": "107267ce-4666-4cd7-94b3-7476b7973ae9",
+            "id": "87dd04d3-870e-49e1-98bf-af003a810109",
            "name": "cfg_scale",
-            "type": "float",
+            "type": "FloatPolymorphic",
            "fieldKind": "input",
            "label": "",
            "value": 7.5
          },
          "denoising_start": {
-            "id": "d2ce9f0f-5fc2-48b2-b917-53442941e9a1",
+            "id": "f369d80f-4931-4740-9bcd-9f0620719fab",
            "name": "denoising_start",
            "type": "float",
            "fieldKind": "input",
@ -404,7 +346,7 @@
            "value": 0
          },
          "denoising_end": {
-            "id": "8ad51505-b8d0-422a-beb8-96fc6fc6b65f",
+            "id": "747d10e5-6f02-445c-994c-0604d814de8c",
            "name": "denoising_end",
            "type": "float",
            "fieldKind": "input",
@ -412,71 +354,71 @@
            "value": 1
          },
          "scheduler": {
-            "id": "53092874-a43b-4623-91a2-76e62fdb1f2e",
+            "id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b",
            "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": "7abe57cc-469d-437e-ad72-a18efa28215f",
+            "id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
            "name": "control",
-            "type": "ControlField",
+            "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",
            "fieldKind": "input",
            "label": ""
          },
          "latents": {
-            "id": "add8bbe5-14d0-42d4-a867-9c65ab8dd129",
+            "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
            "name": "latents",
            "type": "LatentsField",
            "fieldKind": "input",
            "label": ""
          },
          "denoise_mask": {
-            "id": "f373a190-0fc8-45b7-ae62-c4aa8e9687e1",
+            "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
            "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": "8508d04d-f999-4a44-94d0-388ab1401d27",
+            "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
            "name": "latents",
            "type": "LatentsField",
            "fieldKind": "output"
          },
          "width": {
-            "id": "93dc8287-0a2a-4320-83a4-5e994b7ba23e",
+            "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
            "name": "width",
            "type": "integer",
            "fieldKind": "output"
          },
          "height": {
-            "id": "d9862f5c-0ab5-46fa-8c29-5059bb581d96",
+            "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
            "name": "height",
            "type": "integer",
            "fieldKind": "output"
@ -486,13 +428,95 @@
        "isOpen": true,
        "notes": "",
        "embedWorkflow": false,
-        "isIntermediate": true
+        "isIntermediate": true,
+        "useCache": true,
+        "version": "1.4.0"
      },
      "width": 320,
-      "height": 558,
+      "height": 646,
      "position": {
-        "x": 1400,
-        "y": 200
+        "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
      }
    }
  ],
@ -522,52 +546,52 @@
      "type": "default"
    },
    {
-      "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",
+      "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",
      "type": "default"
    },
    {
      "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
      "sourceHandle": "conditioning",
-      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
      "targetHandle": "positive_conditioning",
-      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7positive_conditioning",
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
      "type": "default"
    },
    {
      "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
      "sourceHandle": "conditioning",
-      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
      "targetHandle": "negative_conditioning",
-      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-75899702-fa44-46d2-b2d5-3e17f234c3e7negative_conditioning",
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
      "type": "default"
    },
    {
      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
      "sourceHandle": "unet",
-      "target": "75899702-fa44-46d2-b2d5-3e17f234c3e7",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
      "targetHandle": "unet",
-      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-75899702-fa44-46d2-b2d5-3e17f234c3e7unet",
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
      "type": "default"
    },
    {
-      "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",
+      "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",
      "type": "default"
    }
  ]
-}
+}
--- a/invokeai/app/invocations/compel.py
+++ b/invokeai/app/invocations/compel.py
@ -108,13 +108,14 @@ 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,
@ -229,13 +230,14 @@ 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,
--- a/invokeai/app/invocations/ip_adapter.py
+++ b/invokeai/app/invocations/ip_adapter.py
@ -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=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
+        default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
    )

    begin_step_percent: float = InputField(
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@ -710,9 +710,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
            )
            with (
                ExitStack() as exit_stack,
-                ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
                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:
--- a/invokeai/app/invocations/primitives.py
+++ b/invokeai/app/invocations/primitives.py
@ -293,7 +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(description="The name of the masked image latents")
+    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")


@invocation_output("denoise_mask_output")
--- a/invokeai/app/services/config/config_default.py
+++ b/invokeai/app/services/config/config_default.py
@ -45,6 +45,7 @@ InvokeAI:
    ram: 13.5
    vram: 0.25
    lazy_offload: true
+    log_memory_usage: false
  Device:
    device: auto
    precision: auto
@ -261,6 +262,7 @@ 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)
--- a/invokeai/app/services/workflow_records/workflow_records_sqlite.py
+++ b/invokeai/app/services/workflow_records/workflow_records_sqlite.py
@ -57,7 +57,7 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                INSERT INTO workflows(workflow)
                VALUES (?);
                """,
-                (workflow.json(),),
+                (workflow.model_dump_json(),),
            )
            self._conn.commit()
        except Exception:
--- a/invokeai/backend/model_management/lora.py
+++ b/invokeai/backend/model_management/lora.py
@ -1,6 +1,6 @@
 from __future__ import annotations

-import copy
+import pickle
 from contextlib import contextmanager
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
@ -54,24 +54,6 @@ 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(
@ -129,21 +111,40 @@ 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)

-                        # enable autocast to calc fp16 loras on cpu
-                        # with torch.autocast(device_type="cpu"):
-                        layer.to(dtype=torch.float32)
                        layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
-                        layer_weight = layer.get_weight(original_weights[module_key]) * lora_weight * layer_scale
+
+                        # 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)
+                        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")

                        if module.weight.shape != layer_weight.shape:
                            # TODO: debug on lycoris
                            layer_weight = layer_weight.reshape(module.weight.shape)

-                        module.weight += layer_weight.to(device=module.weight.device, dtype=module.weight.dtype)
+                        module.weight += layer_weight.to(dtype=dtype)

            yield  # wait for context manager exit

@ -164,7 +165,13 @@ class ModelPatcher:
        new_tokens_added = None

        try:
-            ti_tokenizer = copy.deepcopy(tokenizer)
+            # 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_manager = TextualInversionManager(ti_tokenizer)
            init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings

@ -196,7 +203,9 @@ 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 {embedding.shape[0]}, but the current model has token dimension {model_embeddings.weight.data[token_id].shape[0]}."
+                            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]}."
                        )

                    model_embeddings.weight.data[token_id] = embedding.to(
@ -257,7 +266,8 @@ 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()))
@ -435,7 +445,13 @@ class ONNXModelPatcher:
        orig_embeddings = None

        try:
-            ti_tokenizer = copy.deepcopy(tokenizer)
+            # 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_manager = TextualInversionManager(ti_tokenizer)

            def _get_trigger(ti_name, index):
@ -470,7 +486,9 @@ 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 {embedding.shape[0]}, but the current model has token dimension {embeddings[token_id].shape[0]}."
+                            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]}."
                        )

                    embeddings[token_id] = embedding
--- a/invokeai/backend/model_management/memory_snapshot.py
+++ b/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: MemorySnapshot, snapshot_2: MemorySnapshot) -> str:
+def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: Optional[MemorySnapshot]) -> str:
    """Get a pretty string describing the difference between two `MemorySnapshot`s."""

    def get_msg_line(prefix: str, val1: int, val2: int):
@ -73,6 +73,9 @@ def get_pretty_snapshot_diff(snapshot_1: MemorySnapshot, snapshot_2: MemorySnaps

    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:
--- a/invokeai/backend/model_management/model_cache.py
+++ b/invokeai/backend/model_management/model_cache.py
@ -117,6 +117,7 @@ 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]
@ -126,6 +127,10 @@ 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] = dict()
        # allow lazy offloading only when vram cache enabled
@ -137,6 +142,7 @@ 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
@ -144,6 +150,11 @@ class ModelCache(object):
        self._cached_models = dict()
        self._cache_stack = list()

+    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
+        if self._log_memory_usage:
+            return MemorySnapshot.capture()
+        return None
+
    def get_key(
        self,
        model_path: str,
@ -223,10 +234,10 @@ class ModelCache(object):

            # Load the model from disk and capture a memory snapshot before/after.
            start_load_time = time.time()
-            snapshot_before = MemorySnapshot.capture()
+            snapshot_before = self._capture_memory_snapshot()
            with skip_torch_weight_init():
                model = model_info.get_model(child_type=submodel, torch_dtype=self.precision)
-            snapshot_after = MemorySnapshot.capture()
+            snapshot_after = self._capture_memory_snapshot()
            end_load_time = time.time()

            self_reported_model_size_after_load = model_info.get_size(submodel)
@ -275,9 +286,9 @@ class ModelCache(object):
            return

        start_model_to_time = time.time()
-        snapshot_before = MemorySnapshot.capture()
+        snapshot_before = self._capture_memory_snapshot()
        cache_entry.model.to(target_device)
-        snapshot_after = MemorySnapshot.capture()
+        snapshot_after = self._capture_memory_snapshot()
        end_model_to_time = time.time()
        self.logger.debug(
            f"Moved model '{key}' from {source_device} to"
@ -286,7 +297,12 @@ class ModelCache(object):
            f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
        )

-        if snapshot_before.vram is not None and snapshot_after.vram is not None:
+        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
+        ):
            vram_change = abs(snapshot_before.vram - snapshot_after.vram)

            # If the estimated model size does not match the change in VRAM, log a warning.
@ -422,12 +438,17 @@ 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:
@ -453,15 +474,16 @@ class ModelCache(object):
                f" refs: {refs}"
            )

-            # 2 refs:
+            # Expected 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]
@ -471,7 +493,20 @@ class ModelCache(object):
            else:
                pos += 1

-        gc.collect()
+        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()
+
        torch.cuda.empty_cache()
        if choose_torch_device() == torch.device("mps"):
            mps.empty_cache()
@ -491,7 +526,6 @@ 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()
--- a/invokeai/backend/model_management/model_load_optimizations.py
+++ b/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_modules = [torch.nn.Linear, torch.nn.modules.conv._ConvNd, torch.nn.Embedding]
    saved_functions = [m.reset_parameters for m in torch_modules]

    try:
--- a/invokeai/backend/model_management/model_manager.py
+++ b/invokeai/backend/model_management/model_manager.py
@ -351,6 +351,7 @@ class ModelManager(object):
            precision=precision,
            sequential_offload=sequential_offload,
            logger=logger,
+            log_memory_usage=self.app_config.log_memory_usage,
        )

        self._read_models(config)
--- a/invokeai/backend/model_management/models/lora.py
+++ b/invokeai/backend/model_management/models/lora.py
@ -440,33 +440,19 @@ 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,
@ -475,8 +461,6 @@ class LoRAModelRaw:  # (torch.nn.Module):
        # TODO: try revert if exception?
        for key, layer in self.layers.items():
            layer.to(device=device, dtype=dtype)
-        self._device = device
-        self._dtype = dtype

    def calc_size(self) -> int:
        model_size = 0
@ -557,8 +541,6 @@ class LoRAModelRaw:  # (torch.nn.Module):
            file_path = Path(file_path)

        model = cls(
-            device=device,
-            dtype=dtype,
            name=file_path.stem,  # TODO:
            layers=dict(),
        )
--- a/invokeai/frontend/install/invokeai_update.py
+++ b/invokeai/frontend/install/invokeai_update.py
@ -50,7 +50,7 @@ def invokeai_is_running() -> bool:
    return False


-def welcome(versions: dict):
+def welcome(latest_release: str, latest_prerelease: str):
    @group()
    def text():
        yield f"InvokeAI Version: [bold yellow]{__version__}"
@ -61,7 +61,8 @@ def welcome(versions: dict):
        yield "making the web frontend unusable. Please downgrade to the latest release if this happens."
        yield ""
        yield "[bold yellow]Options:"
-        yield f"""[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
+        yield f"""[1] Update to the latest [bold]official release[/bold] ([italic]{latest_release}[/italic])
+[2] Update to the latest [bold]pre-release[/bold] (may be buggy; caveat emptor!) ([italic]{latest_prerelease}[/italic])
 [2] Manually enter the [bold]tag name[/bold] for the version you wish to update to
 [3] Manually enter the [bold]branch name[/bold] for the version you wish to update to"""

@ -91,13 +92,18 @@ def get_extras():


 def main():
-    versions = [x for x in get_versions() if not (x['draft'] or x['prerelease'])]
+    versions = get_versions()
+    released_versions = [x for x in versions if not (x["draft"] or x["prerelease"])]
+    prerelease_versions = [x for x in versions if not x["draft"] and x["prerelease"]]
+    latest_release = released_versions[0]["tag_name"] if len(released_versions) else None
+    latest_prerelease = prerelease_versions[0]["tag_name"] if len(prerelease_versions) else None
+
    if invokeai_is_running():
        print(":exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]")
        input("Press any key to continue...")
        return

-    welcome(versions)
+    welcome(latest_release, latest_prerelease)

    tag = None
    branch = None
@ -105,11 +111,13 @@ def main():
    choice = Prompt.ask("Choice:", choices=["1", "2", "3", "4"], default="1")

    if choice == "1":
-        release = versions[0]["tag_name"]
+        release = latest_release
    elif choice == "2":
+        release = latest_prerelease
+    elif choice == "3":
        while not tag:
            tag = Prompt.ask("Enter an InvokeAI tag name")
-    elif choice == "3":
+    elif choice == "4":
        while not branch:
            branch = Prompt.ask("Enter an InvokeAI branch name")

--- a/invokeai/frontend/merge/merge_diffusers.py
+++ b/invokeai/frontend/merge/merge_diffusers.py
@ -90,6 +90,7 @@ def _parse_args() -> Namespace:
 # ------------------------- GUI HERE -------------------------
 class mergeModelsForm(npyscreen.FormMultiPageAction):
    interpolations = ["weighted_sum", "sigmoid", "inv_sigmoid"]
+    bases = ["sd-1", "sd-2", "sdxl"]

    def __init__(self, parentApp, name):
        self.parentApp = parentApp
@ -276,7 +277,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):

        args = dict(
            model_names=models,
-            base_model=tuple(BaseModelType)[self.base_select.value[0]],
+            base_model=BaseModelType(self.bases[self.base_select.value[0]]),
            alpha=self.alpha.value,
            interp=interp,
            force=self.force.value,
@ -319,8 +320,7 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
        return sorted(model_names)

    def _populate_models(self, value=None):
-        bases = ["sd-1", "sd-2", "sdxl"]
-        base_model = BaseModelType(bases[value[0]])
+        base_model = BaseModelType(self.bases[value[0]])
        self.model_names = self.get_model_names(base_model)

        models_plus_none = self.model_names.copy()
--- a/invokeai/frontend/web/public/locales/de.json
+++ b/invokeai/frontend/web/public/locales/de.json
@ -86,7 +86,10 @@
        "batch": "Batch-Manager",
        "advanced": "Erweitert",
        "langBrPortuguese": "Portugiesisch (Brasilien)",
-        "unifiedCanvas": "Einheitliche Leinwand"
+        "unifiedCanvas": "Einheitliche Leinwand",
+        "openInNewTab": "In einem neuem Tab öffnen",
+        "statusProcessing": "wird bearbeitet",
+        "linear": "Linear"
    },
    "gallery": {
        "generations": "Erzeugungen",
@ -101,7 +104,15 @@
        "singleColumnLayout": "Einspaltiges Layout",
        "allImagesLoaded": "Alle Bilder geladen",
        "loadMore": "Mehr laden",
-        "noImagesInGallery": "Keine Bilder in der Galerie"
+        "noImagesInGallery": "Keine Bilder in der Galerie",
+        "loading": "Lade",
+        "preparingDownload": "bereite Download vor",
+        "preparingDownloadFailed": "Problem beim Download vorbereiten",
+        "deleteImage": "Lösche Bild",
+        "images": "Bilder",
+        "copy": "Kopieren",
+        "download": "Runterladen",
+        "setCurrentImage": "Setze aktuelle Bild"
    },
    "hotkeys": {
        "keyboardShortcuts": "Tastenkürzel",
@ -110,7 +121,8 @@
        "galleryHotkeys": "Galerie Tastenkürzel",
        "unifiedCanvasHotkeys": "Unified Canvas Tastenkürzel",
        "invoke": {
-            "desc": "Ein Bild erzeugen"
+            "desc": "Ein Bild erzeugen",
+            "title": "Invoke"
        },
        "cancel": {
            "title": "Abbrechen",
@ -307,6 +319,10 @@
        "acceptStagingImage": {
            "title": "Staging-Bild akzeptieren",
            "desc": "Akzeptieren Sie das aktuelle Bild des Staging-Bereichs"
+        },
+        "nodesHotkeys": "Knoten Tastenkürzel",
+        "addNodes": {
+            "title": "Knotenpunkt hinzufügen"
        }
    },
    "modelManager": {
@ -356,7 +372,7 @@
        "deleteModel": "Model löschen",
        "deleteConfig": "Konfiguration löschen",
        "deleteMsg1": "Möchten Sie diesen Model-Eintrag wirklich aus InvokeAI löschen?",
-        "deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen.",
+        "deleteMsg2": "Dadurch WIRD das Modell von der Festplatte gelöscht WENN es im InvokeAI Root Ordner liegt. Wenn es in einem anderem Ordner liegt wird das Modell NICHT von der Festplatte gelöscht.",
        "customConfig": "Benutzerdefinierte Konfiguration",
        "invokeRoot": "InvokeAI Ordner",
        "formMessageDiffusersVAELocationDesc": "Falls nicht angegeben, sucht InvokeAI nach der VAE-Datei innerhalb des oben angegebenen Modell Speicherortes.",
@ -402,7 +418,17 @@
        "v2_768": "v2 (768px)",
        "none": "Nix",
        "repoIDValidationMsg": "Online Repo Ihres Modells",
-        "vaeRepoIDValidationMsg": "Online Repo Ihrer VAE"
+        "vaeRepoIDValidationMsg": "Online Repo Ihrer VAE",
+        "importModels": "Importiere Modelle",
+        "merge": "Zusammenführen",
+        "addDiffuserModel": "Diffusers hinzufügen",
+        "advanced": "Erweitert",
+        "closeAdvanced": "Schließe Erweitert",
+        "convertingModelBegin": "Konvertiere Modell. Bitte warten.",
+        "customConfigFileLocation": "Benutzerdefinierte Konfiguration Datei Speicherort",
+        "baseModel": "Basis Modell",
+        "convertToDiffusers": "Konvertiere zu Diffusers",
+        "diffusersModels": "Diffusers"
    },
    "parameters": {
        "images": "Bilder",
@ -422,7 +448,7 @@
        "type": "Art",
        "strength": "Stärke",
        "upscaling": "Hochskalierung",
-        "upscale": "Hochskalieren",
+        "upscale": "Hochskalieren (Shift + U)",
        "upscaleImage": "Bild hochskalieren",
        "scale": "Maßstab",
        "otherOptions": "Andere Optionen",
@ -478,7 +504,7 @@
        "resetWebUI": "Web-Oberfläche zurücksetzen",
        "resetWebUIDesc1": "Das Zurücksetzen der Web-Oberfläche setzt nur den lokalen Cache des Browsers mit Ihren Bildern und gespeicherten Einstellungen zurück. Es werden keine Bilder von der Festplatte gelöscht.",
        "resetWebUIDesc2": "Wenn die Bilder nicht in der Galerie angezeigt werden oder etwas anderes nicht funktioniert, versuchen Sie bitte, die Einstellungen zurückzusetzen, bevor Sie einen Fehler auf GitHub melden.",
-        "resetComplete": "Die Web-Oberfläche wurde zurückgesetzt. Aktualisieren Sie die Seite, um sie neu zu laden.",
+        "resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.",
        "models": "Modelle",
        "useSlidersForAll": "Schieberegler für alle Optionen verwenden"
    },
@ -490,7 +516,7 @@
        "imageCopied": "Bild kopiert",
        "imageLinkCopied": "Bildlink kopiert",
        "imageNotLoaded": "Kein Bild geladen",
-        "imageNotLoadedDesc": "Kein Bild gefunden, das an das Bild zu Bild-Modul gesendet werden kann",
+        "imageNotLoadedDesc": "Konnte kein Bild finden",
        "imageSavedToGallery": "Bild in die Galerie gespeichert",
        "canvasMerged": "Leinwand zusammengeführt",
        "sentToImageToImage": "Gesendet an Bild zu Bild",
@ -585,7 +611,10 @@
        "betaClear": "Löschen",
        "betaDarkenOutside": "Außen abdunkeln",
        "betaLimitToBox": "Begrenzung auf das Feld",
-        "betaPreserveMasked": "Maskiertes bewahren"
+        "betaPreserveMasked": "Maskiertes bewahren",
+        "antialiasing": "Kantenglättung",
+        "showResultsOn": "Zeige Ergebnisse (An)",
+        "showResultsOff": "Zeige Ergebnisse (Aus)"
    },
    "accessibility": {
        "modelSelect": "Model Auswahl",
@ -627,5 +656,137 @@
        "changeBoard": "Ordner wechseln",
        "loading": "Laden...",
        "clearSearch": "Suche leeren"
+    },
+    "controlnet": {
+        "showAdvanced": "Zeige Erweitert",
+        "contentShuffleDescription": "Mischt den Inhalt von einem Bild",
+        "addT2IAdapter": "$t(common.t2iAdapter) hinzufügen",
+        "importImageFromCanvas": "Importieren Bild von Zeichenfläche",
+        "lineartDescription": "Konvertiere Bild zu Lineart",
+        "importMaskFromCanvas": "Importiere Maske von Zeichenfläche",
+        "hed": "HED",
+        "hideAdvanced": "Verstecke Erweitert",
+        "contentShuffle": "Inhalt mischen",
+        "controlNetEnabledT2IDisabled": "$t(common.controlNet) ist aktiv, $t(common.t2iAdapter) ist deaktiviert",
+        "ipAdapterModel": "Adapter Modell",
+        "beginEndStepPercent": "Start / Ende Step Prozent",
+        "duplicate": "Kopieren",
+        "f": "F",
+        "h": "H",
+        "depthMidasDescription": "Tiefenmap erstellen mit Midas",
+        "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
+        "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ist aktiv, $t(common.controlNet) ist deaktiviert",
+        "weight": "Breite",
+        "selectModel": "Wähle ein Modell",
+        "depthMidas": "Tiefe (Midas)",
+        "w": "W",
+        "addControlNet": "$t(common.controlNet) hinzufügen",
+        "none": "Kein",
+        "incompatibleBaseModel": "Inkompatibles Basismodell:",
+        "enableControlnet": "Aktiviere ControlNet",
+        "detectResolution": "Auflösung erkennen",
+        "controlNetT2IMutexDesc": "$t(common.controlNet) und $t(common.t2iAdapter) zur gleichen Zeit wird nicht unterstützt.",
+        "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
+        "fill": "Füllen",
+        "addIPAdapter": "$t(common.ipAdapter) hinzufügen",
+        "colorMapDescription": "Erstelle eine Farbkarte von diesem Bild",
+        "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
+        "imageResolution": "Bild Auflösung",
+        "depthZoe": "Tiefe (Zoe)",
+        "colorMap": "Farbe",
+        "lowThreshold": "Niedrige Schwelle",
+        "highThreshold": "Hohe Schwelle",
+        "toggleControlNet": "Schalten ControlNet um",
+        "delete": "Löschen",
+        "controlAdapter_one": "Control Adapter",
+        "controlAdapter_other": "Control Adapters",
+        "colorMapTileSize": "Tile Größe",
+        "depthZoeDescription": "Tiefenmap erstellen mit Zoe",
+        "setControlImageDimensions": "Setze Control Bild Auflösung auf Breite/Höhe",
+        "handAndFace": "Hand und Gesicht",
+        "enableIPAdapter": "Aktiviere IP Adapter"
+    },
+    "queue": {
+        "status": "Status",
+        "cancelTooltip": "Aktuellen Aufgabe abbrechen",
+        "queueEmpty": "Warteschlange leer",
+        "in_progress": "In Arbeit",
+        "queueFront": "An den Anfang der Warteschlange tun",
+        "completed": "Fertig",
+        "queueBack": "In die Warteschlange",
+        "clearFailed": "Probleme beim leeren der Warteschlange",
+        "clearSucceeded": "Warteschlange geleert",
+        "pause": "Pause",
+        "cancelSucceeded": "Auftrag abgebrochen",
+        "queue": "Warteschlange",
+        "batch": "Stapel",
+        "pending": "Ausstehend",
+        "clear": "Leeren",
+        "prune": "Leeren",
+        "total": "Gesamt",
+        "canceled": "Abgebrochen",
+        "clearTooltip": "Abbrechen und alle Aufträge leeren",
+        "current": "Aktuell",
+        "failed": "Fehler",
+        "cancelItem": "Abbruch Auftrag",
+        "next": "Nächste",
+        "cancel": "Abbruch",
+        "session": "Sitzung",
+        "queueTotal": "{{total}} Gesamt",
+        "resume": "Wieder aufnehmen",
+        "item": "Auftrag"
+    },
+    "metadata": {
+        "negativePrompt": "Negativ Beschreibung",
+        "metadata": "Meta-Data",
+        "strength": "Bild zu Bild stärke",
+        "imageDetails": "Bild Details",
+        "model": "Modell",
+        "noImageDetails": "Keine Bild Details gefunden",
+        "cfgScale": "CFG-Skala",
+        "fit": "Bild zu Bild passen",
+        "height": "Höhe",
+        "noMetaData": "Keine Meta-Data gefunden",
+        "width": "Breite",
+        "createdBy": "Erstellt von",
+        "steps": "Schritte"
+    },
+    "popovers": {
+        "noiseUseCPU": {
+            "heading": "Nutze Prozessor rauschen"
+        },
+        "paramModel": {
+            "heading": "Modell"
+        },
+        "paramIterations": {
+            "heading": "Iterationen"
+        },
+        "paramCFGScale": {
+            "heading": "CFG-Skala"
+        },
+        "paramSteps": {
+            "heading": "Schritte"
+        },
+        "lora": {
+            "heading": "LoRA Gewichte"
+        },
+        "infillMethod": {
+            "heading": "Füllmethode"
+        },
+        "paramVAE": {
+            "heading": "VAE"
+        }
+    },
+    "ui": {
+        "lockRatio": "Verhältnis sperren",
+        "hideProgressImages": "Verstecke Prozess Bild",
+        "showProgressImages": "Zeige Prozess Bild"
+    },
+    "invocationCache": {
+        "disable": "Deaktivieren",
+        "misses": "Cache Nötig",
+        "hits": "Cache Treffer",
+        "enable": "Aktivieren",
+        "clear": "Leeren"
    }
 }
--- a/invokeai/frontend/web/public/locales/en.json
+++ b/invokeai/frontend/web/public/locales/en.json
@ -722,7 +722,9 @@
        "noMatchingModels": "No matching Models",
        "noModelsAvailable": "No models available",
        "selectLoRA": "Select a LoRA",
-        "selectModel": "Select a Model"
+        "selectModel": "Select a Model",
+        "noLoRAsInstalled": "No LoRAs installed",
+        "noRefinerModelsInstalled": "No SDXL Refiner models installed"
    },
    "nodes": {
        "addNode": "Add Node",
--- a/invokeai/frontend/web/public/locales/zh_CN.json
+++ b/invokeai/frontend/web/public/locales/zh_CN.json
@ -866,7 +866,7 @@
        "version": "版本",
        "validateConnections": "验证连接和节点图",
        "inputMayOnlyHaveOneConnection": "输入仅能有一个连接",
-        "notes": "节点",
+        "notes": "注释",
        "nodeOutputs": "节点输出",
        "currentImageDescription": "在节点编辑器中显示当前图像",
        "validateConnectionsHelp": "防止建立无效连接和调用无效节点图",
@ -892,11 +892,11 @@
        "currentImage": "当前图像",
        "workflowName": "名称",
        "cannotConnectInputToInput": "无法将输入连接到输入",
-        "workflowNotes": "节点",
+        "workflowNotes": "注释",
        "cannotConnectOutputToOutput": "无法将输出连接到输出",
        "connectionWouldCreateCycle": "连接将创建一个循环",
        "cannotConnectToSelf": "无法连接自己",
-        "notesDescription": "添加有关您的工作流的节点",
+        "notesDescription": "添加有关您的工作流的注释",
        "unknownField": "未知",
        "colorCodeEdges": "边缘颜色编码",
        "unknownNode": "未知节点",
--- a/invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx
+++ b/invokeai/frontend/web/src/features/dynamicPrompts/components/ParamDynamicPromptsCollapse.tsx
@ -16,15 +16,13 @@ const ParamDynamicPromptsCollapse = () => {
    () =>
      createSelector(stateSelector, ({ dynamicPrompts }) => {
        const count = dynamicPrompts.prompts.length;
-        if (count === 1) {
-          return t('dynamicPrompts.promptsWithCount_one', {
-            count,
-          });
-        } else {
+        if (count > 1) {
          return t('dynamicPrompts.promptsWithCount_other', {
            count,
          });
        }
+
+        return;
      }),
    [t]
  );
--- a/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx
+++ b/invokeai/frontend/web/src/features/lora/components/ParamLoraSelect.tsx
@ -10,6 +10,7 @@ import { loraAdded } from 'features/lora/store/loraSlice';
 import { MODEL_TYPE_MAP } from 'features/parameters/types/constants';
 import { forEach } from 'lodash-es';
 import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
 import { useGetLoRAModelsQuery } from 'services/api/endpoints/models';

 const selector = createSelector(
@ -24,7 +25,7 @@ const ParamLoRASelect = () => {
  const dispatch = useAppDispatch();
  const { loras } = useAppSelector(selector);
  const { data: loraModels } = useGetLoRAModelsQuery();
-
+  const { t } = useTranslation();
  const currentMainModel = useAppSelector(
    (state: RootState) => state.generation.model
  );
@ -79,7 +80,7 @@ const ParamLoRASelect = () => {
    return (
      <Flex sx={{ justifyContent: 'center', p: 2 }}>
        <Text sx={{ fontSize: 'sm', color: 'base.500', _dark: 'base.700' }}>
-          No LoRAs Loaded
+          {t('models.noLoRAsInstalled')}
        </Text>
      </Flex>
    );
--- a/invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse.tsx
+++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/Advanced/ParamAdvancedCollapse.tsx
@ -28,9 +28,7 @@ export default function ParamAdvancedCollapse() {
  const activeLabel = useMemo(() => {
    const activeLabel: string[] = [];

-    if (shouldUseCpuNoise) {
-      activeLabel.push(t('parameters.cpuNoise'));
-    } else {
+    if (!shouldUseCpuNoise) {
      activeLabel.push(t('parameters.gpuNoise'));
    }

--- a/invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfCollapse.tsx
+++ b/invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfCollapse.tsx
@ -4,12 +4,13 @@ import { RootState, stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
 import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import IAICollapse from 'common/components/IAICollapse';
+import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import { useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import ParamHrfHeight from './ParamHrfHeight';
 import ParamHrfStrength from './ParamHrfStrength';
 import ParamHrfToggle from './ParamHrfToggle';
 import ParamHrfWidth from './ParamHrfWidth';
-import ParamHrfHeight from './ParamHrfHeight';
-import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';

 const selector = createSelector(
  stateSelector,
@ -22,15 +23,14 @@ const selector = createSelector(
 );

 export default function ParamHrfCollapse() {
+  const { t } = useTranslation();
  const isHRFFeatureEnabled = useFeatureStatus('hrf').isFeatureEnabled;
  const { hrfEnabled } = useAppSelector(selector);
  const activeLabel = useMemo(() => {
    if (hrfEnabled) {
-      return 'On';
-    } else {
-      return 'Off';
+      return t('common.on');
    }
-  }, [hrfEnabled]);
+  }, [t, hrfEnabled]);

  if (!isHRFFeatureEnabled) {
    return null;
--- a/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx
+++ b/invokeai/frontend/web/src/features/sdxl/components/ParamSDXLRefinerCollapse.tsx
@ -1,4 +1,4 @@
-import { Flex } from '@chakra-ui/react';
+import { Flex, Text } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { stateSelector } from 'app/store/store';
 import { useAppSelector } from 'app/store/storeHooks';
@ -14,6 +14,7 @@ import ParamSDXLRefinerStart from './SDXLRefiner/ParamSDXLRefinerStart';
 import ParamSDXLRefinerSteps from './SDXLRefiner/ParamSDXLRefinerSteps';
 import ParamUseSDXLRefiner from './SDXLRefiner/ParamUseSDXLRefiner';
 import { useTranslation } from 'react-i18next';
+import { useIsRefinerAvailable } from 'services/api/hooks/useIsRefinerAvailable';

 const selector = createSelector(
  stateSelector,
@ -31,6 +32,19 @@ const selector = createSelector(
 const ParamSDXLRefinerCollapse = () => {
  const { activeLabel, shouldUseSliders } = useAppSelector(selector);
  const { t } = useTranslation();
+  const isRefinerAvailable = useIsRefinerAvailable();
+
+  if (!isRefinerAvailable) {
+    return (
+      <IAICollapse label={t('sdxl.refiner')} activeLabel={activeLabel}>
+        <Flex sx={{ justifyContent: 'center', p: 2 }}>
+          <Text sx={{ fontSize: 'sm', color: 'base.500', _dark: 'base.700' }}>
+            {t('models.noRefinerModelsInstalled')}
+          </Text>
+        </Flex>
+      </IAICollapse>
+    );
+  }

  return (
    <IAICollapse label={t('sdxl.refiner')} activeLabel={activeLabel}>
--- a/pyproject.toml
+++ b/pyproject.toml
@ -206,6 +206,7 @@ exclude = [
  "build",
  "dist",
  "invokeai/frontend/web/node_modules/",
+  ".venv*",
 ]

 [tool.black]
--- a/tests/backend/model_management/test_lora.py
+++ b/tests/backend/model_management/test_lora.py
@ -0,0 +1,102 @@
+# test that if the model's device changes while the lora is applied, the weights can still be restored
+
+# test that LoRA patching works on both CPU and CUDA
+
+import pytest
+import torch
+
+from invokeai.backend.model_management.lora import ModelPatcher
+from invokeai.backend.model_management.models.lora import LoRALayer, LoRAModelRaw
+
+
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device")),
+    ],
+)
+@torch.no_grad()
+def test_apply_lora(device):
+    """Test the basic behavior of ModelPatcher.apply_lora(...). Check that patching and unpatching produce the correct
+    result, and that model/LoRA tensors are moved between devices as expected.
+    """
+
+    linear_in_features = 4
+    linear_out_features = 8
+    lora_dim = 2
+    model = torch.nn.ModuleDict(
+        {"linear_layer_1": torch.nn.Linear(linear_in_features, linear_out_features, device=device, dtype=torch.float16)}
+    )
+
+    lora_layers = {
+        "linear_layer_1": LoRALayer(
+            layer_key="linear_layer_1",
+            values={
+                "lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16),
+                "lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16),
+            },
+        )
+    }
+    lora = LoRAModelRaw("lora_name", lora_layers)
+
+    lora_weight = 0.5
+    orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone()
+    expected_patched_linear_weight = orig_linear_weight + (lora_dim * lora_weight)
+
+    with ModelPatcher.apply_lora(model, [(lora, lora_weight)], prefix=""):
+        # After patching, all LoRA layer weights should have been moved back to the cpu.
+        assert lora_layers["linear_layer_1"].up.device.type == "cpu"
+        assert lora_layers["linear_layer_1"].down.device.type == "cpu"
+
+        # After patching, the patched model should still be on its original device.
+        assert model["linear_layer_1"].weight.data.device.type == device
+
+        torch.testing.assert_close(model["linear_layer_1"].weight.data, expected_patched_linear_weight)
+
+    # After unpatching, the original model weights should have been restored on the original device.
+    assert model["linear_layer_1"].weight.data.device.type == device
+    torch.testing.assert_close(model["linear_layer_1"].weight.data, orig_linear_weight)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires CUDA device")
+@torch.no_grad()
+def test_apply_lora_change_device():
+    """Test that if LoRA patching is applied on the CPU, and then the patched model is moved to the GPU, unpatching
+    still behaves correctly.
+    """
+    linear_in_features = 4
+    linear_out_features = 8
+    lora_dim = 2
+    # Initialize the model on the CPU.
+    model = torch.nn.ModuleDict(
+        {"linear_layer_1": torch.nn.Linear(linear_in_features, linear_out_features, device="cpu", dtype=torch.float16)}
+    )
+
+    lora_layers = {
+        "linear_layer_1": LoRALayer(
+            layer_key="linear_layer_1",
+            values={
+                "lora_down.weight": torch.ones((lora_dim, linear_in_features), device="cpu", dtype=torch.float16),
+                "lora_up.weight": torch.ones((linear_out_features, lora_dim), device="cpu", dtype=torch.float16),
+            },
+        )
+    }
+    lora = LoRAModelRaw("lora_name", lora_layers)
+
+    orig_linear_weight = model["linear_layer_1"].weight.data.detach().clone()
+
+    with ModelPatcher.apply_lora(model, [(lora, 0.5)], prefix=""):
+        # After patching, all LoRA layer weights should have been moved back to the cpu.
+        assert lora_layers["linear_layer_1"].up.device.type == "cpu"
+        assert lora_layers["linear_layer_1"].down.device.type == "cpu"
+
+        # After patching, the patched model should still be on the CPU.
+        assert model["linear_layer_1"].weight.data.device.type == "cpu"
+
+        # Move the model to the GPU.
+        assert model.to("cuda")
+
+    # After unpatching, the original model weights should have been restored on the GPU.
+    assert model["linear_layer_1"].weight.data.device.type == "cuda"
+    torch.testing.assert_close(model["linear_layer_1"].weight.data, orig_linear_weight, check_device=False)
--- a/tests/backend/model_management/test_memory_snapshot.py
+++ b/tests/backend/model_management/test_memory_snapshot.py
@ -13,10 +13,11 @@ def test_memory_snapshot_capture():


 snapshots = [
-    MemorySnapshot(process_ram=1.0, vram=2.0, malloc_info=Struct_mallinfo2()),
-    MemorySnapshot(process_ram=1.0, vram=2.0, malloc_info=None),
-    MemorySnapshot(process_ram=1.0, vram=None, malloc_info=Struct_mallinfo2()),
-    MemorySnapshot(process_ram=1.0, vram=None, malloc_info=None),
+    MemorySnapshot(process_ram=1, vram=2, malloc_info=Struct_mallinfo2()),
+    MemorySnapshot(process_ram=1, vram=2, malloc_info=None),
+    MemorySnapshot(process_ram=1, vram=None, malloc_info=Struct_mallinfo2()),
+    MemorySnapshot(process_ram=1, vram=None, malloc_info=None),
+    None,
 ]


@ -26,10 +27,12 @@ def test_get_pretty_snapshot_diff(snapshot_1, snapshot_2):
    """Test that get_pretty_snapshot_diff() works with various combinations of missing MemorySnapshot fields."""
    msg = get_pretty_snapshot_diff(snapshot_1, snapshot_2)

-    expected_lines = 1
-    if snapshot_1.vram is not None and snapshot_2.vram is not None:
+    expected_lines = 0
+    if snapshot_1 is not None and snapshot_2 is not None:
        expected_lines += 1
-    if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None:
-        expected_lines += 5
+        if snapshot_1.vram is not None and snapshot_2.vram is not None:
+            expected_lines += 1
+        if snapshot_1.malloc_info is not None and snapshot_2.malloc_info is not None:
+            expected_lines += 5

    assert len(msg.splitlines()) == expected_lines
--- a/tests/backend/model_management/test_model_load_optimization.py
+++ b/tests/backend/model_management/test_model_load_optimization.py
@ -11,6 +11,7 @@ from invokeai.backend.model_management.model_load_optimizations import _no_op, s
        (torch.nn.Conv1d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}),
        (torch.nn.Conv2d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}),
        (torch.nn.Conv3d, {"in_channels": 10, "out_channels": 20, "kernel_size": 3}),
+        (torch.nn.Embedding, {"num_embeddings": 10, "embedding_dim": 10}),
    ],
 )
 def test_skip_torch_weight_init_linear(torch_module, layer_args):
@ -36,12 +37,14 @@ def test_skip_torch_weight_init_linear(torch_module, layer_args):
    # Check that reset_parameters is skipped while `skip_torch_weight_init()` is active.
    assert reset_params_fn_during == _no_op
    assert not torch.allclose(layer_before.weight, layer_during.weight)
-    assert not torch.allclose(layer_before.bias, layer_during.bias)
+    if hasattr(layer_before, "bias"):
+        assert not torch.allclose(layer_before.bias, layer_during.bias)

    # Check that the original behavior is restored after `skip_torch_weight_init()` ends.
    assert reset_params_fn_before is reset_params_fn_after
    assert torch.allclose(layer_before.weight, layer_after.weight)
-    assert torch.allclose(layer_before.bias, layer_after.bias)
+    if hasattr(layer_before, "bias"):
+        assert torch.allclose(layer_before.bias, layer_after.bias)


 def test_skip_torch_weight_init_restores_base_class_behavior():
Author	SHA1	Message	Date
Lincoln Stein	ec1e66dcd3	refactor the model bases variable	2023-11-04 11:25:13 -04:00
Lincoln Stein	69543c23d0	fix model-not-found error	2023-11-04 11:12:29 -04:00
Ryan Dick	2bbba323c6	Fix model cache gc.collect() condition. (#5036 ) See https://github.com/invoke-ai/InvokeAI/pull/5034#discussion_r1382381733	2023-11-04 09:09:46 -04:00
Ryan Dick	aa02ebf8f5	Fix model cache gc.collect() condition.	2023-11-04 08:52:10 -04:00
Ryan Dick	fb3d0c4b12	Fix bug in model cache reference count checking.	2023-11-03 13:50:40 -07:00
Ryan Dick	8488ab0134	Reduce frequency that we call gc.collect() in the model cache.	2023-11-03 13:50:40 -07:00
Ryan Dick	875231ed3d	Add reminder to clean up our model cache clearing logic.	2023-11-03 13:50:40 -07:00
Ryan Dick	43b300498f	Remove explicit gc.collect() after transferring models from device to CPU. I'm not sure why this was there in the first place, but it was taking a significant amount of time (up to ~1sec in my tests).	2023-11-03 13:50:40 -07:00
psychedelicious	5b420653f9	feat(ui): show placeholder in refiner collapse instead of hiding it, if no refiner models installed	2023-11-03 14:15:24 +11:00
psychedelicious	3d32ce2b58	fix(ui): hide refiner collapse if refiner not installed	2023-11-03 14:15:24 +11:00
Ryan Dick	e391f3c9a8	Skip torch.nn.Embedding.reset_parameters(...) when loading a text encoder model.	2023-11-02 19:41:33 -07:00
Ryan Dick	6e7a3f0546	(minor) Fix static checks and typo.	2023-11-02 19:20:37 -07:00
Ryan Dick	4a683cc669	Add a app config parameter to control the ModelCache logging behavior.	2023-11-02 19:20:37 -07:00
Ryan Dick	3781e56e57	Add log_memory_usage param to ModelCache.	2023-11-02 19:20:37 -07:00
Ryan Dick	267e709ba2	(minor) Fix int literal typing error.	2023-11-02 19:20:37 -07:00
Ryan Dick	8ff49109a8	Update get_pretty_snapshot_diff(...) to handle None-snapshots.	2023-11-02 19:20:37 -07:00
Ryan Dick	bac2a757e8	Replace deepcopy with a pickle roundtrip in apply_ti(...) to improve speed.	2023-11-02 19:05:24 -07:00
Ryan Dick	a4a7b601a1	Improve LoRA patching speed (#5017 ) ## What type of PR is this? (check all applicable) - [ ] Refactor - [ ] Feature - [ ] Bug Fix - [x] Optimization - [ ] Documentation Update - [ ] Community Node Submission ## Have you discussed this change with the InvokeAI team? - [x] Yes - [ ] No, because: ## Have you updated all relevant documentation? - [x] Yes - [ ] No ## Description Improve LoRA patching speed with the following changes: - Calculate LoRA layer weights on the same device as the target model. Prior to this change, weights were always calculated on the CPU. If the target model is on the GPU, this significantly improves performance. - Move models to their target devices _before_ applying LoRA patches. - Improve the ordering of Tensor copy / cast operations. ## QA Instructions, Screenshots, Recordings Tests: - [x] Tested with a CUDA GPU, saw savings of ~10secs with 1 LoRA applied to an SDXL model. - [x] No regression in CPU-only environment - [ ] No regression (and possible improvement?) on Mac with MPS. - [x] Weights get restored correctly after using a LoRA - [x] Stacking multiple LoRAs Please hammer away with a variety of LoRAs in case there is some edge case that I've missed. ## Added/updated tests? - [x] Yes (Added some minimal unit tests. Definitely would benefit from more, but it's a step in the right direction.) - [ ] No	2023-11-02 13:34:10 -04:00
Ryan Dick	fa7f6a6a10	Further tidying of LoRA patching. Revert some changes that didn't end up being important under the constraint that calculations are done on the same device as the model.	2023-11-02 10:03:17 -07:00
Ryan Dick	e92b84955c	Add minimal unit tests for ModelPatcher.apply_lora(...)	2023-11-02 10:03:17 -07:00
Ryan Dick	61b17c475a	Add TODO note about improving _resolve_lora_key(...).	2023-11-02 10:03:17 -07:00
Ryan Dick	379d68f595	Patch LoRA on device when model is already on device.	2023-11-02 10:03:17 -07:00
Ryan Dick	545c811bf1	Remove device and dtype members from LoRAModelRaw, they can too easily get out-of-sync with the underlying layer states.	2023-11-02 10:03:17 -07:00
Ryan Dick	2ba5b44ec4	Remove unused _lora_forward_hook(...).	2023-11-02 10:03:17 -07:00
Ryan Dick	7f4ce518b7	auto-format lora.py	2023-11-02 10:03:17 -07:00
psychedelicious	6c66adcd90	fix(ui): show collapse labels only if not default value	2023-11-01 14:41:13 +11:00
nemuruibai	94055ae54a	translationBot(ui): update translation (Chinese (Simplified)) Currently translated at 99.8% (1215 of 1217 strings) Co-authored-by: nemuruibai <nemuruibai@gmail.com> Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/ Translation: InvokeAI/Web UI	2023-11-01 05:31:16 +11:00
Alexander Eichhorn	a79c86b901	translationBot(ui): update translation (German) Currently translated at 51.7% (630 of 1217 strings) Co-authored-by: Alexander Eichhorn <pfannkuchensack@einfach-doof.de> Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/ Translation: InvokeAI/Web UI	2023-11-01 05:31:16 +11:00
Millun Atluri	ed81d6d533	Update contributingNodes.md	2023-10-31 07:17:14 -07:00
Millun Atluri	63548c5ea7	Update community node installation instructions	2023-10-31 07:17:14 -07:00
Millun Atluri	8481db96ed	Updated workflows	2023-10-31 07:17:14 -07:00
Kent Keirsey	bb68175fd0	Add negative IP Adapter support	2023-10-31 14:30:24 +11:00
Millun Atluri	316131f69b	Add option to invokeai update script to install latest prerelease (#5008 ) ## What type of PR is this? (check all applicable) - [ ] Refactor - [X] Feature - [ ] Bug Fix - [ ] Optimization - [ ] Documentation Update - [ ] Community Node Submission ## Have you discussed this change with the InvokeAI team? - [X] Yes - [ ] No, because: ## Have you updated all relevant documentation? - [X] Yes - [ ] No ## Description This PR gives the user the option of upgrading to the latest PRE-RELEASE in addition to the default of updating to the latest release. This will allow users to conveniently try out the latest pre-release for a while and then back out to the official release if it doesn't work for them.	2023-10-31 08:45:25 +11:00
Lincoln Stein	9721e1382d	add option to install latest prerelease	2023-10-30 15:49:27 -04:00
psychedelicious	03a64275c6	fix(db): fix deprecated pydantic `.json()` method	2023-10-31 04:34:51 +11:00
psychedelicious	55bfadfd0b	fix(nodes): fix DenoiseMaskField.masked_latents_name This optional field needs to have a default of `None`.	2023-10-31 04:18:09 +11:00
Eugene Brodsky	224b09f8fd	Enforce Unix line endings in container (#4990 ) * (fix) enforce Unix (LF) line endings in docker/ directory * (fix) update docker docs wrt line endings on Windows * (fix) static check fixes	2023-10-30 12:34:30 -04:00