feat: add 'project' workflow category

* dont set socketURL until socket is initialized

* cleanup

* feat(ui): simplify `socketUrl` memo

no need to mutate the string; just return early if using baseUrl

---------

Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>

Makefile

@@ -0,0 +1,21 @@
+# simple Makefile with scripts that are otherwise hard to remember
+# to use, run from the repo root `make <command>`
+
+# Runs ruff, fixing any safely-fixable errors and formatting
+ruff:
+		ruff check . --fix
+		ruff format .
+
+# Runs ruff, fixing all errors it can fix and formatting
+ruff-unsafe:
+		ruff check . --fix --unsafe-fixes
+		ruff format .
+
+# Runs mypy, using the config in pyproject.toml
+mypy:
+		mypy scripts/invokeai-web.py
+
+# Runs mypy, ignoring the config in pyproject.toml but still ignoring missing (untyped) imports
+# (many files are ignored by the config, so this is useful for checking all files)
+mypy-all:
+		mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports

README.md

@@ -395,7 +395,7 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
 
 ### Troubleshooting
 
-Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
+Please check out our **[Troubleshooting Guide](https://invoke-ai.github.io/InvokeAI/installation/010_INSTALL_AUTOMATED/#troubleshooting)** to get solutions for common installation
 problems and other issues. For more help, please join our [Discord][discord link]
 
 ## Contributing

docs/contributing/INVOCATIONS.md

@@ -65,7 +65,7 @@ The first set of things we need to do when creating a new Invocation are -
 So let us do that.
 
 ```python
-from .baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -99,8 +99,8 @@ create your own custom field types later in this guide. For now, let's go ahead
 and use it.
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -124,8 +124,8 @@ image: ImageField = InputField(description="The input image")
 Great. Now let us create our other inputs for `width` and `height`
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -160,8 +160,8 @@ that are provided by it by InvokeAI.
 Let us create this function first.
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
+from invokeai.app.invocations.primitives import ImageField
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -189,9 +189,9 @@ all the necessary info related to image outputs. So let us use that.
 We will cover how to create your own output types later in this guide.
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
-from .image import ImageOutput
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.app.invocations.image import ImageOutput
 
 @invocation('resize')
 class ResizeInvocation(BaseInvocation):
@@ -216,9 +216,9 @@ Perfect. Now that we have our Invocation setup, let us do what we want to do.
 So let's do that.
 
 ```python
-from .baseinvocation import BaseInvocation, InputField, invocation
-from .primitives import ImageField
-from .image import ImageOutput
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.app.invocations.image import ImageOutput, ResourceOrigin, ImageCategory
 
 @invocation("resize")
 class ResizeInvocation(BaseInvocation):

docs/features/PROMPTS.md

@@ -120,7 +120,7 @@ Generate an image with a given prompt, record the seed of the image, and then
 use the `prompt2prompt` syntax to substitute words in the original prompt for
 words in a new prompt. This works for `img2img` as well.
 
-For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because of the word words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
+For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because the words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
   - `a cat playing with a ball in the forest`
   - `a dog playing with a ball in the forest`
 

docs/nodes/communityNodes.md

@@ -8,7 +8,7 @@ To use a node, add the node to the `nodes` folder found in your InvokeAI install
 
 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. 
+If you'd prefer, you can also just download the whole node folder 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. 
 
@@ -26,8 +26,10 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Image Picker](#image-picker)
     + [Load Video Frame](#load-video-frame)
     + [Make 3D](#make-3d)
+    + [Match Histogram](#match-histogram)
     + [Oobabooga](#oobabooga)
     + [Prompt Tools](#prompt-tools)
+    + [Remote Image](#remote-image)
     + [Retroize](#retroize)
     + [Size Stepper Nodes](#size-stepper-nodes)
     + [Text font to Image](#text-font-to-image)
@@ -207,6 +209,23 @@ This includes 15 Nodes:
 <img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png" width="300" />
 <img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png" width="300" />
 
+--------------------------------
+### Match Histogram
+
+**Description:** An InvokeAI node to match a histogram from one image to another.  This is a bit like the `color correct` node in the main InvokeAI but this works in the YCbCr colourspace and can handle images of different sizes. Also does not require a mask input.
+- Option to only transfer luminance channel.
+- Option to save output as grayscale
+
+A good use case for this node is to normalize the colors of an image that has been through the tiled scaling workflow of my XYGrid Nodes. 
+
+See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
+
+**Node Link:** https://github.com/skunkworxdark/match_histogram
+
+**Output Examples** 
+
+<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
+
 --------------------------------
 ### Oobabooga
 
@@ -236,22 +255,41 @@ This node works best with SDXL models, especially as the style can be described
 --------------------------------
 ### Prompt Tools 
 
-**Description:** A set of InvokeAI nodes that add general prompt manipulation tools.  These were written to accompany the PromptsFromFile node and other prompt generation nodes.
+**Description:** A set of InvokeAI nodes that add general prompt (string) manipulation tools.  Designed to accompany the `Prompts From File` node and other prompt generation nodes.
+
+1. `Prompt To File` - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
+2. `PTFields Collect` - Converts image generation fields into a Json format string that can be passed to Prompt to file. 
+3. `PTFields Expand` - Takes Json string and converts it to individual generation parameters. This can be fed from the Prompts to file node.
+4. `Prompt Strength` - Formats prompt with strength like the weighted format of compel 
+5. `Prompt Strength Combine` - Combines weighted prompts for .and()/.blend()
+6. `CSV To Index String` - Gets a string from a CSV by index. Includes a Random index option
+
+The following Nodes are now included in v3.2 of Invoke and are nolonger in this set of tools.<br>
+- `Prompt Join` -> `String Join`
+- `Prompt Join Three` -> `String Join Three`
+- `Prompt Replace` -> `String Replace`
+- `Prompt Split Neg` -> `String Split Neg`
 
-1. PromptJoin - Joins to prompts into one.
-2. PromptReplace - performs a search and replace on a prompt. With the option of using regex.
-3. PromptSplitNeg - splits a prompt into positive and negative using the old V2 method of [] for negative.
-4. PromptToFile - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
-5. PTFieldsCollect - Converts image generation fields into a Json format string that can be passed to Prompt to file. 
-6. PTFieldsExpand - Takes Json string and converts it to individual generation parameters This can be fed from the Prompts to file node.
-7. PromptJoinThree -  Joins 3 prompt together.
-8. PromptStrength - This take a string and float and outputs another string in the format of (string)strength like the weighted format of compel. 
-9. PromptStrengthCombine - This takes a collection of prompt strength strings and outputs a string in the .and() or .blend() format that can be fed into a proper prompt node.
 
 See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
 
 **Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
 
+**Workflow Examples** 
+
+<img src="https://github.com/skunkworxdark/prompt-tools/blob/main/images/CSVToIndexStringNode.png" width="300" />
+
+--------------------------------
+### Remote Image
+
+**Description:** This is a pack of nodes to interoperate with other services, be they public websites or bespoke local servers. The pack consists of these nodes:
+
+- *Load Remote Image* - Lets you load remote images such as a realtime webcam image, an image of the day, or dynamically created images.
+- *Post Image to Remote Server* - Lets you upload an image to a remote server using an HTTP POST request, eg for storage, display or further processing.
+
+**Node Link:** https://github.com/fieldOfView/InvokeAI-remote_image
+
+
 --------------------------------
 ### Retroize
 
@@ -327,15 +365,27 @@ Highlights/Midtones/Shadows (with LUT blur enabled):
 --------------------------------
 ### XY Image to Grid and Images to Grids nodes
 
-**Description:** Image to grid nodes and supporting tools.
+**Description:** These nodes add the following to InvokeAI:
+- Generate grids of images from multiple input images
+- Create XY grid images with labels from parameters
+- Split images into overlapping tiles for processing (for super-resolution workflows)
+- Recombine image tiles into a single output image blending the seams 
 
-1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then multiple grids will be created until it runs out of images.
-2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporting nodes. See example node setups for more details.
+The nodes include:
+1. `Images To Grids` - Combine multiple images into a grid of images
+2. `XYImage To Grid` - Take X & Y params and creates a labeled image grid.
+3. `XYImage Tiles` - Super-resolution (embiggen) style tiled resizing
+4. `Image Tot XYImages` - Takes an image and cuts it up into a number of columns and rows.
+5. Multiple supporting nodes - Helper nodes for data wrangling and building `XYImage` collections
 
 See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
 
 **Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
 
+**Output Examples** 
+
+<img src="https://github.com/skunkworxdark/XYGrid_nodes/blob/main/images/collage.png" width="300" />
+
 --------------------------------
 ### Example Node Template
 

docs/nodes/defaultNodes.md

@@ -1,104 +1,106 @@
 # List of Default Nodes
 
-The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
+The table below contains a list of the default nodes shipped with InvokeAI and
+their descriptions.
 
-| Node <img width=160 align="right"> | Function                                                                              |
-|: ---------------------------------- | :--------------------------------------------------------------------------------------|
-|Add Integers 			| Adds two numbers|
-|Boolean Primitive Collection 			| A collection of boolean primitive values|
-|Boolean Primitive 			| A boolean primitive value|
-|Canny Processor 			| Canny edge detection for ControlNet|
-|CLIP Skip 			| Skip layers in clip text_encoder model.|
-|Collect 			| Collects values into a collection|
-|Color Correct 			| Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image.|
-|Color Primitive 			| A color primitive value|
-|Compel Prompt 			| Parse prompt using compel package to conditioning.|
-|Conditioning Primitive Collection 			| A collection of conditioning tensor primitive values|
-|Conditioning Primitive 			| A conditioning tensor primitive value|
-|Content Shuffle Processor 			| Applies content shuffle processing to image|
-|ControlNet 			| Collects ControlNet info to pass to other nodes|
-|Denoise Latents 			| Denoises noisy latents to decodable images|
-|Divide Integers 			| Divides two numbers|
-|Dynamic Prompt 			| Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
-|[FaceMask](./detailedNodes/faceTools.md#facemask) | Generates masks for faces in an image to use with Inpainting|
-|[FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier)             | Identifies and labels faces in an image|
-|[FaceOff](./detailedNodes/faceTools.md#faceoff)             | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting|
-|Float Math             | Perform basic math operations on two floats|
-|Float Primitive Collection 			| A collection of float primitive values|
-|Float Primitive 			| A float primitive value|
-|Float Range 			| Creates a range|
-|HED (softedge) Processor 			| Applies HED edge detection to image|
-|Blur Image 			| Blurs an image|
-|Extract Image Channel 			| Gets a channel from an image.|
-|Image Primitive Collection 			| A collection of image primitive values|
-|Integer Math           | Perform basic math operations on two integers|
-|Convert Image Mode 			| Converts an image to a different mode.|
-|Crop Image 			| Crops an image to a specified box. The box can be outside of the image.|
-|Image Hue Adjustment 			| Adjusts the Hue of an image.|
-|Inverse Lerp Image 			| Inverse linear interpolation of all pixels of an image|
-|Image Primitive 			| An image primitive value|
-|Lerp Image 			| Linear interpolation of all pixels of an image|
-|Offset Image Channel 			| Add to or subtract from an image color channel by a uniform value.|
-|Multiply Image Channel 			| Multiply or Invert an image color channel by a scalar value.|
-|Multiply Images 			| Multiplies two images together using `PIL.ImageChops.multiply()`.|
-|Blur NSFW Image 			| Add blur to NSFW-flagged images|
-|Paste Image 			| Pastes an image into another image.|
-|ImageProcessor 			| Base class for invocations that preprocess images for ControlNet|
-|Resize Image 			| Resizes an image to specific dimensions|
-|Round Float            | Rounds a float to a specified number of decimal places|
-|Float to Integer       | Converts a float to an integer. Optionally rounds to an even multiple of a input number.|
-|Scale Image 			| Scales an image by a factor|
-|Image to Latents 			| Encodes an image into latents.|
-|Add Invisible Watermark 			| Add an invisible watermark to an image|
-|Solid Color Infill 			| Infills transparent areas of an image with a solid color|
-|PatchMatch Infill 			| Infills transparent areas of an image using the PatchMatch algorithm|
-|Tile Infill 			| Infills transparent areas of an image with tiles of the image|
-|Integer Primitive Collection 			| A collection of integer primitive values|
-|Integer Primitive 			| An integer primitive value|
-|Iterate 			| Iterates over a list of items|
-|Latents Primitive Collection 			| A collection of latents tensor primitive values|
-|Latents Primitive 			| A latents tensor primitive value|
-|Latents to Image 			| Generates an image from latents.|
-|Leres (Depth) Processor 			| Applies leres processing to image|
-|Lineart Anime Processor 			| Applies line art anime processing to image|
-|Lineart Processor 			| Applies line art processing to image|
-|LoRA Loader 			| Apply selected lora to unet and text_encoder.|
-|Main Model Loader 			| Loads a main model, outputting its submodels.|
-|Combine Mask 			| Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.|
-|Mask Edge 			| Applies an edge mask to an image|
-|Mask from Alpha 			| Extracts the alpha channel of an image as a mask.|
-|Mediapipe Face Processor 			| Applies mediapipe face processing to image|
-|Midas (Depth) Processor 			| Applies Midas depth processing to image|
-|MLSD Processor 			| Applies MLSD processing to image|
-|Multiply Integers 			| Multiplies two numbers|
-|Noise 			| Generates latent noise.|
-|Normal BAE Processor 			| Applies NormalBae processing to image|
-|ONNX Latents to Image 			| Generates an image from latents.|
-|ONNX Prompt (Raw) 			| A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
-|ONNX Text to Latents 			| Generates latents from conditionings.|
-|ONNX Model Loader 			| Loads a main model, outputting its submodels.|
-|OpenCV Inpaint 			| Simple inpaint using opencv.|
-|Openpose Processor 			| Applies Openpose processing to image|
-|PIDI Processor 			| Applies PIDI processing to image|
-|Prompts from File 			| Loads prompts from a text file|
-|Random Integer 			| Outputs a single random integer.|
-|Random Range 			| Creates a collection of random numbers|
-|Integer Range 			| Creates a range of numbers from start to stop with step|
-|Integer Range of Size 			| Creates a range from start to start + size with step|
-|Resize Latents 			| Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.|
-|SDXL Compel Prompt 			| Parse prompt using compel package to conditioning.|
-|SDXL LoRA Loader 			| Apply selected lora to unet and text_encoder.|
-|SDXL Main Model Loader 			| Loads an sdxl base model, outputting its submodels.|
-|SDXL Refiner Compel Prompt 			| Parse prompt using compel package to conditioning.|
-|SDXL Refiner Model Loader 			| Loads an sdxl refiner model, outputting its submodels.|
-|Scale Latents 			| Scales latents by a given factor.|
-|Segment Anything Processor 			| Applies segment anything processing to image|
-|Show Image 			| Displays a provided image, and passes it forward in the pipeline.|
-|Step Param Easing 			| Experimental per-step parameter easing for denoising steps|
-|String Primitive Collection 			| A collection of string primitive values|
-|String Primitive 			| A string primitive value|
-|Subtract Integers 			| Subtracts two numbers|
-|Tile Resample Processor 			| Tile resampler processor|
-|Upscale (RealESRGAN) 			| Upscales an image using RealESRGAN.|
-|VAE Loader 			| Loads a VAE model, outputting a VaeLoaderOutput|
-|Zoe (Depth) Processor 			| Applies Zoe depth processing to image|
+| Node <img width=160 align="right">                            | Function                                                                                                                                             |
+| :------------------------------------------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Add Integers                                                  | Adds two numbers                                                                                                                                     |
+| Boolean Primitive Collection                                  | A collection of boolean primitive values                                                                                                             |
+| Boolean Primitive                                             | A boolean primitive value                                                                                                                            |
+| Canny Processor                                               | Canny edge detection for ControlNet                                                                                                                  |
+| CenterPadCrop                                                 | Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image.                                          |
+| CLIP Skip                                                     | Skip layers in clip text_encoder model.                                                                                                              |
+| Collect                                                       | Collects values into a collection                                                                                                                    |
+| Color Correct                                                 | Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image. |
+| Color Primitive                                               | A color primitive value                                                                                                                              |
+| Compel Prompt                                                 | Parse prompt using compel package to conditioning.                                                                                                   |
+| Conditioning Primitive Collection                             | A collection of conditioning tensor primitive values                                                                                                 |
+| Conditioning Primitive                                        | A conditioning tensor primitive value                                                                                                                |
+| Content Shuffle Processor                                     | Applies content shuffle processing to image                                                                                                          |
+| ControlNet                                                    | Collects ControlNet info to pass to other nodes                                                                                                      |
+| Denoise Latents                                               | Denoises noisy latents to decodable images                                                                                                           |
+| Divide Integers                                               | Divides two numbers                                                                                                                                  |
+| Dynamic Prompt                                                | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator                                                                      |
+| [FaceMask](./detailedNodes/faceTools.md#facemask)             | Generates masks for faces in an image to use with Inpainting                                                                                         |
+| [FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier) | Identifies and labels faces in an image                                                                                                              |
+| [FaceOff](./detailedNodes/faceTools.md#faceoff)               | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting                                                             |
+| Float Math                                                    | Perform basic math operations on two floats                                                                                                          |
+| Float Primitive Collection                                    | A collection of float primitive values                                                                                                               |
+| Float Primitive                                               | A float primitive value                                                                                                                              |
+| Float Range                                                   | Creates a range                                                                                                                                      |
+| HED (softedge) Processor                                      | Applies HED edge detection to image                                                                                                                  |
+| Blur Image                                                    | Blurs an image                                                                                                                                       |
+| Extract Image Channel                                         | Gets a channel from an image.                                                                                                                        |
+| Image Primitive Collection                                    | A collection of image primitive values                                                                                                               |
+| Integer Math                                                  | Perform basic math operations on two integers                                                                                                        |
+| Convert Image Mode                                            | Converts an image to a different mode.                                                                                                               |
+| Crop Image                                                    | Crops an image to a specified box. The box can be outside of the image.                                                                              |
+| Image Hue Adjustment                                          | Adjusts the Hue of an image.                                                                                                                         |
+| Inverse Lerp Image                                            | Inverse linear interpolation of all pixels of an image                                                                                               |
+| Image Primitive                                               | An image primitive value                                                                                                                             |
+| Lerp Image                                                    | Linear interpolation of all pixels of an image                                                                                                       |
+| Offset Image Channel                                          | Add to or subtract from an image color channel by a uniform value.                                                                                   |
+| Multiply Image Channel                                        | Multiply or Invert an image color channel by a scalar value.                                                                                         |
+| Multiply Images                                               | Multiplies two images together using `PIL.ImageChops.multiply()`.                                                                                    |
+| Blur NSFW Image                                               | Add blur to NSFW-flagged images                                                                                                                      |
+| Paste Image                                                   | Pastes an image into another image.                                                                                                                  |
+| ImageProcessor                                                | Base class for invocations that preprocess images for ControlNet                                                                                     |
+| Resize Image                                                  | Resizes an image to specific dimensions                                                                                                              |
+| Round Float                                                   | Rounds a float to a specified number of decimal places                                                                                               |
+| Float to Integer                                              | Converts a float to an integer. Optionally rounds to an even multiple of a input number.                                                             |
+| Scale Image                                                   | Scales an image by a factor                                                                                                                          |
+| Image to Latents                                              | Encodes an image into latents.                                                                                                                       |
+| Add Invisible Watermark                                       | Add an invisible watermark to an image                                                                                                               |
+| Solid Color Infill                                            | Infills transparent areas of an image with a solid color                                                                                             |
+| PatchMatch Infill                                             | Infills transparent areas of an image using the PatchMatch algorithm                                                                                 |
+| Tile Infill                                                   | Infills transparent areas of an image with tiles of the image                                                                                        |
+| Integer Primitive Collection                                  | A collection of integer primitive values                                                                                                             |
+| Integer Primitive                                             | An integer primitive value                                                                                                                           |
+| Iterate                                                       | Iterates over a list of items                                                                                                                        |
+| Latents Primitive Collection                                  | A collection of latents tensor primitive values                                                                                                      |
+| Latents Primitive                                             | A latents tensor primitive value                                                                                                                     |
+| Latents to Image                                              | Generates an image from latents.                                                                                                                     |
+| Leres (Depth) Processor                                       | Applies leres processing to image                                                                                                                    |
+| Lineart Anime Processor                                       | Applies line art anime processing to image                                                                                                           |
+| Lineart Processor                                             | Applies line art processing to image                                                                                                                 |
+| LoRA Loader                                                   | Apply selected lora to unet and text_encoder.                                                                                                        |
+| Main Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
+| Combine Mask                                                  | Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.                                                                    |
+| Mask Edge                                                     | Applies an edge mask to an image                                                                                                                     |
+| Mask from Alpha                                               | Extracts the alpha channel of an image as a mask.                                                                                                    |
+| Mediapipe Face Processor                                      | Applies mediapipe face processing to image                                                                                                           |
+| Midas (Depth) Processor                                       | Applies Midas depth processing to image                                                                                                              |
+| MLSD Processor                                                | Applies MLSD processing to image                                                                                                                     |
+| Multiply Integers                                             | Multiplies two numbers                                                                                                                               |
+| Noise                                                         | Generates latent noise.                                                                                                                              |
+| Normal BAE Processor                                          | Applies NormalBae processing to image                                                                                                                |
+| ONNX Latents to Image                                         | Generates an image from latents.                                                                                                                     |
+| ONNX Prompt (Raw)                                             | A node to process inputs and produce outputs. May use dependency injection in **init** to receive providers.                                         |
+| ONNX Text to Latents                                          | Generates latents from conditionings.                                                                                                                |
+| ONNX Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
+| OpenCV Inpaint                                                | Simple inpaint using opencv.                                                                                                                         |
+| Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
+| PIDI Processor                                                | Applies PIDI processing to image                                                                                                                     |
+| Prompts from File                                             | Loads prompts from a text file                                                                                                                       |
+| Random Integer                                                | Outputs a single random integer.                                                                                                                     |
+| Random Range                                                  | Creates a collection of random numbers                                                                                                               |
+| Integer Range                                                 | Creates a range of numbers from start to stop with step                                                                                              |
+| Integer Range of Size                                         | Creates a range from start to start + size with step                                                                                                 |
+| Resize Latents                                                | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.                                                    |
+| SDXL Compel Prompt                                            | Parse prompt using compel package to conditioning.                                                                                                   |
+| SDXL LoRA Loader                                              | Apply selected lora to unet and text_encoder.                                                                                                        |
+| SDXL Main Model Loader                                        | Loads an sdxl base model, outputting its submodels.                                                                                                  |
+| SDXL Refiner Compel Prompt                                    | Parse prompt using compel package to conditioning.                                                                                                   |
+| SDXL Refiner Model Loader                                     | Loads an sdxl refiner model, outputting its submodels.                                                                                               |
+| Scale Latents                                                 | Scales latents by a given factor.                                                                                                                    |
+| Segment Anything Processor                                    | Applies segment anything processing to image                                                                                                         |
+| Show Image                                                    | Displays a provided image, and passes it forward in the pipeline.                                                                                    |
+| Step Param Easing                                             | Experimental per-step parameter easing for denoising steps                                                                                           |
+| String Primitive Collection                                   | A collection of string primitive values                                                                                                              |
+| String Primitive                                              | A string primitive value                                                                                                                             |
+| Subtract Integers                                             | Subtracts two numbers                                                                                                                                |
+| Tile Resample Processor                                       | Tile resampler processor                                                                                                                             |
+| Upscale (RealESRGAN)                                          | Upscales an image using RealESRGAN.                                                                                                                  |
+| VAE Loader                                                    | Loads a VAE model, outputting a VaeLoaderOutput                                                                                                      |
+| Zoe (Depth) Processor                                         | Applies Zoe depth processing to image                                                                                                                |

invokeai/app/api/dependencies.py

@@ -2,7 +2,6 @@
 
 from logging import Logger
 
-from invokeai.app.services.workflow_image_records.workflow_image_records_sqlite import SqliteWorkflowImageRecordsStorage
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -30,7 +29,7 @@ from ..services.session_processor.session_processor_default import DefaultSessio
 from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
 from ..services.shared.default_graphs import create_system_graphs
 from ..services.shared.graph import GraphExecutionState, LibraryGraph
-from ..services.shared.sqlite import SqliteDatabase
+from ..services.shared.sqlite.sqlite_database import SqliteDatabase
 from ..services.urls.urls_default import LocalUrlService
 from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from .events import FastAPIEventService
@@ -94,7 +93,6 @@ class ApiDependencies:
         session_processor = DefaultSessionProcessor()
         session_queue = SqliteSessionQueue(db=db)
         urls = LocalUrlService()
-        workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
         workflow_records = SqliteWorkflowRecordsStorage(db=db)
 
         services = InvocationServices(
@@ -121,14 +119,12 @@ class ApiDependencies:
             session_processor=session_processor,
             session_queue=session_queue,
             urls=urls,
-            workflow_image_records=workflow_image_records,
             workflow_records=workflow_records,
         )
 
         create_system_graphs(services.graph_library)
 
         ApiDependencies.invoker = Invoker(services)
-
         db.clean()
 
     @staticmethod

invokeai/app/api/routers/app_info.py

@@ -1,7 +1,11 @@
 import typing
 from enum import Enum
+from importlib.metadata import PackageNotFoundError, version
 from pathlib import Path
+from platform import python_version
+from typing import Optional
 
+import torch
 from fastapi import Body
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
@@ -40,6 +44,24 @@ class AppVersion(BaseModel):
     version: str = Field(description="App version")
 
 
+class AppDependencyVersions(BaseModel):
+    """App depencency Versions Response"""
+
+    accelerate: str = Field(description="accelerate version")
+    compel: str = Field(description="compel version")
+    cuda: Optional[str] = Field(description="CUDA version")
+    diffusers: str = Field(description="diffusers version")
+    numpy: str = Field(description="Numpy version")
+    opencv: str = Field(description="OpenCV version")
+    onnx: str = Field(description="ONNX version")
+    pillow: str = Field(description="Pillow (PIL) version")
+    python: str = Field(description="Python version")
+    torch: str = Field(description="PyTorch version")
+    torchvision: str = Field(description="PyTorch Vision version")
+    transformers: str = Field(description="transformers version")
+    xformers: Optional[str] = Field(description="xformers version")
+
+
 class AppConfig(BaseModel):
     """App Config Response"""
 
@@ -54,6 +76,29 @@ async def get_version() -> AppVersion:
     return AppVersion(version=__version__)
 
 
+@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=AppDependencyVersions)
+async def get_app_deps() -> AppDependencyVersions:
+    try:
+        xformers = version("xformers")
+    except PackageNotFoundError:
+        xformers = None
+    return AppDependencyVersions(
+        accelerate=version("accelerate"),
+        compel=version("compel"),
+        cuda=torch.version.cuda,
+        diffusers=version("diffusers"),
+        numpy=version("numpy"),
+        opencv=version("opencv-python"),
+        onnx=version("onnx"),
+        pillow=version("pillow"),
+        python=python_version(),
+        torch=torch.version.__version__,
+        torchvision=version("torchvision"),
+        transformers=version("transformers"),
+        xformers=xformers,
+    )
+
+
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
 async def get_config() -> AppConfig:
     infill_methods = ["tile", "lama", "cv2"]

invokeai/app/api/routers/images.py

@@ -8,10 +8,11 @@ from fastapi.routing import APIRouter
 from PIL import Image
 from pydantic import BaseModel, Field, ValidationError
 
-from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator, WorkflowFieldValidator
+from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
 from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID, WorkflowWithoutIDValidator
 
 from ..dependencies import ApiDependencies
 
@@ -73,7 +74,7 @@ async def upload_image(
     workflow_raw = pil_image.info.get("invokeai_workflow", None)
     if workflow_raw is not None:
         try:
-            workflow = WorkflowFieldValidator.validate_json(workflow_raw)
+            workflow = WorkflowWithoutIDValidator.validate_json(workflow_raw)
         except ValidationError:
             ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
             pass
@@ -184,6 +185,18 @@ async def get_image_metadata(
         raise HTTPException(status_code=404)
 
 
+@images_router.get(
+    "/i/{image_name}/workflow", operation_id="get_image_workflow", response_model=Optional[WorkflowWithoutID]
+)
+async def get_image_workflow(
+    image_name: str = Path(description="The name of image whose workflow to get"),
+) -> Optional[WorkflowWithoutID]:
+    try:
+        return ApiDependencies.invoker.services.images.get_workflow(image_name)
+    except Exception:
+        raise HTTPException(status_code=404)
+
+
 @images_router.api_route(
     "/i/{image_name}/full",
     methods=["GET", "HEAD"],

invokeai/app/api/routers/model_records.py

@@ -141,7 +141,7 @@ async def del_model_record(
     status_code=201,
 )
 async def add_model_record(
-    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")]
+    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
 ) -> AnyModelConfig:
     """
     Add a model using the configuration information appropriate for its type.

invokeai/app/api/routers/workflows.py

@@ -1,7 +1,19 @@
-from fastapi import APIRouter, Path
+from typing import Optional
+
+from fastapi import APIRouter, Body, HTTPException, Path, Query
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.invocations.baseinvocation import WorkflowField
+from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.workflow_records.workflow_records_common import (
+    Workflow,
+    WorkflowCategory,
+    WorkflowNotFoundError,
+    WorkflowRecordDTO,
+    WorkflowRecordListItemDTO,
+    WorkflowRecordOrderBy,
+    WorkflowWithoutID,
+)
 
 workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
 
@@ -10,11 +22,76 @@ workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
     "/i/{workflow_id}",
     operation_id="get_workflow",
     responses={
-        200: {"model": WorkflowField},
+        200: {"model": WorkflowRecordDTO},
     },
 )
 async def get_workflow(
     workflow_id: str = Path(description="The workflow to get"),
-) -> WorkflowField:
+) -> WorkflowRecordDTO:
     """Gets a workflow"""
-    return ApiDependencies.invoker.services.workflow_records.get(workflow_id)
+    try:
+        return ApiDependencies.invoker.services.workflow_records.get(workflow_id)
+    except WorkflowNotFoundError:
+        raise HTTPException(status_code=404, detail="Workflow not found")
+
+
+@workflows_router.patch(
+    "/i/{workflow_id}",
+    operation_id="update_workflow",
+    responses={
+        200: {"model": WorkflowRecordDTO},
+    },
+)
+async def update_workflow(
+    workflow: Workflow = Body(description="The updated workflow", embed=True),
+) -> WorkflowRecordDTO:
+    """Updates a workflow"""
+    return ApiDependencies.invoker.services.workflow_records.update(workflow=workflow)
+
+
+@workflows_router.delete(
+    "/i/{workflow_id}",
+    operation_id="delete_workflow",
+)
+async def delete_workflow(
+    workflow_id: str = Path(description="The workflow to delete"),
+) -> None:
+    """Deletes a workflow"""
+    ApiDependencies.invoker.services.workflow_records.delete(workflow_id)
+
+
+@workflows_router.post(
+    "/",
+    operation_id="create_workflow",
+    responses={
+        200: {"model": WorkflowRecordDTO},
+    },
+)
+async def create_workflow(
+    workflow: WorkflowWithoutID = Body(description="The workflow to create", embed=True),
+) -> WorkflowRecordDTO:
+    """Creates a workflow"""
+    return ApiDependencies.invoker.services.workflow_records.create(workflow=workflow)
+
+
+@workflows_router.get(
+    "/",
+    operation_id="list_workflows",
+    responses={
+        200: {"model": PaginatedResults[WorkflowRecordListItemDTO]},
+    },
+)
+async def list_workflows(
+    page: int = Query(default=0, description="The page to get"),
+    per_page: int = Query(default=10, description="The number of workflows per page"),
+    order_by: WorkflowRecordOrderBy = Query(
+        default=WorkflowRecordOrderBy.Name, description="The attribute to order by"
+    ),
+    direction: SQLiteDirection = Query(default=SQLiteDirection.Ascending, description="The direction to order by"),
+    category: WorkflowCategory = Query(default=WorkflowCategory.User, description="The category of workflow to get"),
+    query: Optional[str] = Query(default=None, description="The text to query by (matches name and description)"),
+) -> PaginatedResults[WorkflowRecordListItemDTO]:
+    """Gets a page of workflows"""
+    return ApiDependencies.invoker.services.workflow_records.get_many(
+        page=page, per_page=per_page, order_by=order_by, direction=direction, query=query, category=category
+    )

invokeai/app/api_app.py

@@ -1,14 +1,17 @@
-from typing import Any
-
-from fastapi.responses import HTMLResponse
-
-from .services.config import InvokeAIAppConfig
-
 # parse_args() must be called before any other imports. if it is not called first, consumers of the config
 # which are imported/used before parse_args() is called will get the default config values instead of the
 # values from the command line or config file.
+import sys
+
+from invokeai.version.invokeai_version import __version__
+
+from .services.config import InvokeAIAppConfig
+
 app_config = InvokeAIAppConfig.get_config()
 app_config.parse_args()
+if app_config.version:
+    print(f"InvokeAI version {__version__}")
+    sys.exit(0)
 
 if True:  # hack to make flake8 happy with imports coming after setting up the config
     import asyncio
@@ -16,6 +19,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     import socket
     from inspect import signature
     from pathlib import Path
+    from typing import Any
 
     import uvicorn
     from fastapi import FastAPI
@@ -23,7 +27,7 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     from fastapi.middleware.gzip import GZipMiddleware
     from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
     from fastapi.openapi.utils import get_openapi
-    from fastapi.responses import FileResponse
+    from fastapi.responses import FileResponse, HTMLResponse
     from fastapi.staticfiles import StaticFiles
     from fastapi_events.handlers.local import local_handler
     from fastapi_events.middleware import EventHandlerASGIMiddleware
@@ -34,7 +38,6 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     # noinspection PyUnresolvedReferences
     import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
     import invokeai.frontend.web as web_dir
-    from invokeai.version.invokeai_version import __version__
 
     from ..backend.util.logging import InvokeAILogger
     from .api.dependencies import ApiDependencies
@@ -51,7 +54,12 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         workflows,
     )
     from .api.sockets import SocketIO
-    from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
+    from .invocations.baseinvocation import (
+        BaseInvocation,
+        InputFieldJSONSchemaExtra,
+        OutputFieldJSONSchemaExtra,
+        UIConfigBase,
+    )
 
     if is_mps_available():
         import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
@@ -147,7 +155,11 @@ def custom_openapi() -> dict[str, Any]:
 
     # Add Node Editor UI helper schemas
     ui_config_schemas = models_json_schema(
-        [(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
+        [
+            (UIConfigBase, "serialization"),
+            (InputFieldJSONSchemaExtra, "serialization"),
+            (OutputFieldJSONSchemaExtra, "serialization"),
+        ],
         ref_template="#/components/schemas/{model}",
     )
     for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
@@ -155,7 +167,7 @@ def custom_openapi() -> dict[str, Any]:
 
     # Add a reference to the output type to additionalProperties of the invoker schema
     for invoker in all_invocations:
-        invoker_name = invoker.__name__
+        invoker_name = invoker.__name__  # type: ignore [attr-defined] # this is a valid attribute
         output_type = signature(obj=invoker.invoke).return_annotation
         output_type_title = output_type_titles[output_type.__name__]
         invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
@@ -273,7 +285,4 @@ def invoke_api() -> None:
 
 
 if __name__ == "__main__":
-    if app_config.version:
-        print(f"InvokeAI version {__version__}")
-    else:
-        invoke_api()
+    invoke_api()

invokeai/app/invocations/__init__.py

@@ -5,7 +5,7 @@ from pathlib import Path
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 
-custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute())
+custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.resolve())
 custom_nodes_path.mkdir(parents=True, exist_ok=True)
 
 custom_nodes_init_path = str(custom_nodes_path / "__init__.py")

invokeai/app/invocations/baseinvocation.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI team
 
 from __future__ import annotations
 
@@ -8,7 +8,7 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from inspect import signature
 from types import UnionType
-from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
 
 import semver
 from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
@@ -16,12 +16,19 @@ from pydantic.fields import FieldInfo, _Unset
 from pydantic_core import PydanticUndefined
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
+from invokeai.backend.util.logging import InvokeAILogger
 
 if TYPE_CHECKING:
     from ..services.invocation_services import InvocationServices
 
+logger = InvokeAILogger.get_logger()
+
+CUSTOM_NODE_PACK_SUFFIX = "__invokeai-custom-node"
+
 
 class InvalidVersionError(ValueError):
     pass
@@ -31,7 +38,7 @@ class InvalidFieldError(TypeError):
     pass
 
 
-class Input(str, Enum):
+class Input(str, Enum, metaclass=MetaEnum):
     """
     The type of input a field accepts.
     - `Input.Direct`: The field must have its value provided directly, when the invocation and field \
@@ -45,86 +52,124 @@ class Input(str, Enum):
     Any = "any"
 
 
-class UIType(str, Enum):
+class FieldKind(str, Enum, metaclass=MetaEnum):
     """
-    Type hints for the UI.
-    If a field should be provided a data type that does not exactly match the python type of the field, \
-    use this to provide the type that should be used instead. See the node development docs for detail \
-    on adding a new field type, which involves client-side changes.
+    The kind of field.
+    - `Input`: An input field on a node.
+    - `Output`: An output field on a node.
+    - `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
+    one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
+    "metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
+    allowing "metadata" for that field.
+    - `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
+    but which are used to store information about the node. For example, the `id` and `type` fields are node
+    attributes.
+
+    The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
+    startup, and when generating the OpenAPI schema for the workflow editor.
     """
 
-    # region Primitives
-    Boolean = "boolean"
-    Color = "ColorField"
-    Conditioning = "ConditioningField"
-    Control = "ControlField"
-    Float = "float"
-    Image = "ImageField"
-    Integer = "integer"
-    Latents = "LatentsField"
-    String = "string"
-    # endregion
+    Input = "input"
+    Output = "output"
+    Internal = "internal"
+    NodeAttribute = "node_attribute"
 
-    # region Collection Primitives
-    BooleanCollection = "BooleanCollection"
-    ColorCollection = "ColorCollection"
-    ConditioningCollection = "ConditioningCollection"
-    ControlCollection = "ControlCollection"
-    FloatCollection = "FloatCollection"
-    ImageCollection = "ImageCollection"
-    IntegerCollection = "IntegerCollection"
-    LatentsCollection = "LatentsCollection"
-    StringCollection = "StringCollection"
-    # endregion
 
-    # region Polymorphic Primitives
-    BooleanPolymorphic = "BooleanPolymorphic"
-    ColorPolymorphic = "ColorPolymorphic"
-    ConditioningPolymorphic = "ConditioningPolymorphic"
-    ControlPolymorphic = "ControlPolymorphic"
-    FloatPolymorphic = "FloatPolymorphic"
-    ImagePolymorphic = "ImagePolymorphic"
-    IntegerPolymorphic = "IntegerPolymorphic"
-    LatentsPolymorphic = "LatentsPolymorphic"
-    StringPolymorphic = "StringPolymorphic"
-    # endregion
+class UIType(str, Enum, metaclass=MetaEnum):
+    """
+    Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
 
-    # region Models
-    MainModel = "MainModelField"
+    - Model Fields
+    The most common node-author-facing use will be for model fields. Internally, there is no difference
+    between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
+    base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
+    the field is an SDXL main model field.
+
+    - Any Field
+    We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
+    indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
+
+    - Scheduler Field
+    Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
+
+    - Internal Fields
+    Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
+    handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
+    should not be used by node authors.
+
+    - DEPRECATED Fields
+    These types are deprecated and should not be used by node authors. A warning will be logged if one is
+    used, and the type will be ignored. They are included here for backwards compatibility.
+    """
+
+    # region Model Field Types
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
-    VaeModel = "VaeModelField"
+    VaeModel = "VAEModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
-    UNet = "UNetField"
-    Vae = "VaeField"
-    CLIP = "ClipField"
     # endregion
 
-    # region Iterate/Collect
-    Collection = "Collection"
-    CollectionItem = "CollectionItem"
+    # region Misc Field Types
+    Scheduler = "SchedulerField"
+    Any = "AnyField"
     # endregion
 
-    # region Misc
-    Enum = "enum"
-    Scheduler = "Scheduler"
-    WorkflowField = "WorkflowField"
-    IsIntermediate = "IsIntermediate"
-    BoardField = "BoardField"
-    Any = "Any"
-    MetadataItem = "MetadataItem"
-    MetadataItemCollection = "MetadataItemCollection"
-    MetadataItemPolymorphic = "MetadataItemPolymorphic"
-    MetadataDict = "MetadataDict"
+    # region Internal Field Types
+    _Collection = "CollectionField"
+    _CollectionItem = "CollectionItemField"
+    # endregion
+
+    # region DEPRECATED
+    Boolean = "DEPRECATED_Boolean"
+    Color = "DEPRECATED_Color"
+    Conditioning = "DEPRECATED_Conditioning"
+    Control = "DEPRECATED_Control"
+    Float = "DEPRECATED_Float"
+    Image = "DEPRECATED_Image"
+    Integer = "DEPRECATED_Integer"
+    Latents = "DEPRECATED_Latents"
+    String = "DEPRECATED_String"
+    BooleanCollection = "DEPRECATED_BooleanCollection"
+    ColorCollection = "DEPRECATED_ColorCollection"
+    ConditioningCollection = "DEPRECATED_ConditioningCollection"
+    ControlCollection = "DEPRECATED_ControlCollection"
+    FloatCollection = "DEPRECATED_FloatCollection"
+    ImageCollection = "DEPRECATED_ImageCollection"
+    IntegerCollection = "DEPRECATED_IntegerCollection"
+    LatentsCollection = "DEPRECATED_LatentsCollection"
+    StringCollection = "DEPRECATED_StringCollection"
+    BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
+    ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
+    ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
+    ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
+    FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
+    ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
+    IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
+    LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
+    StringPolymorphic = "DEPRECATED_StringPolymorphic"
+    MainModel = "DEPRECATED_MainModel"
+    UNet = "DEPRECATED_UNet"
+    Vae = "DEPRECATED_Vae"
+    CLIP = "DEPRECATED_CLIP"
+    Collection = "DEPRECATED_Collection"
+    CollectionItem = "DEPRECATED_CollectionItem"
+    Enum = "DEPRECATED_Enum"
+    WorkflowField = "DEPRECATED_WorkflowField"
+    IsIntermediate = "DEPRECATED_IsIntermediate"
+    BoardField = "DEPRECATED_BoardField"
+    MetadataItem = "DEPRECATED_MetadataItem"
+    MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
+    MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
+    MetadataDict = "DEPRECATED_MetadataDict"
     # endregion
 
 
-class UIComponent(str, Enum):
+class UIComponent(str, Enum, metaclass=MetaEnum):
     """
-    The type of UI component to use for a field, used to override the default components, which are \
+    The type of UI component to use for a field, used to override the default components, which are
     inferred from the field type.
     """
 
@@ -133,21 +178,22 @@ class UIComponent(str, Enum):
     Slider = "slider"
 
 
-class _InputField(BaseModel):
+class InputFieldJSONSchemaExtra(BaseModel):
     """
-    *DO NOT USE*
-    This helper class is used to tell the client about our custom field attributes via OpenAPI
-    schema generation, and Typescript type generation from that schema. It serves no functional
-    purpose in the backend.
+    Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
+    and by the workflow editor during schema parsing and UI rendering.
     """
 
     input: Input
-    ui_hidden: bool
-    ui_type: Optional[UIType]
-    ui_component: Optional[UIComponent]
-    ui_order: Optional[int]
-    ui_choice_labels: Optional[dict[str, str]]
-    item_default: Optional[Any]
+    orig_required: bool
+    field_kind: FieldKind
+    default: Optional[Any] = None
+    orig_default: Optional[Any] = None
+    ui_hidden: bool = False
+    ui_type: Optional[UIType] = None
+    ui_component: Optional[UIComponent] = None
+    ui_order: Optional[int] = None
+    ui_choice_labels: Optional[dict[str, str]] = None
 
     model_config = ConfigDict(
         validate_assignment=True,
@@ -155,14 +201,13 @@ class _InputField(BaseModel):
     )
 
 
-class _OutputField(BaseModel):
+class OutputFieldJSONSchemaExtra(BaseModel):
     """
-    *DO NOT USE*
-    This helper class is used to tell the client about our custom field attributes via OpenAPI
-    schema generation, and Typescript type generation from that schema. It serves no functional
-    purpose in the backend.
+    Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
+    during schema parsing and UI rendering.
     """
 
+    field_kind: FieldKind
     ui_hidden: bool
     ui_type: Optional[UIType]
     ui_order: Optional[int]
@@ -173,13 +218,9 @@ class _OutputField(BaseModel):
     )
 
 
-def get_type(klass: BaseModel) -> str:
-    """Helper function to get an invocation or invocation output's type. This is the default value of the `type` field."""
-    return klass.model_fields["type"].default
-
-
 def InputField(
     # copied from pydantic's Field
+    # TODO: Can we support default_factory?
     default: Any = _Unset,
     default_factory: Callable[[], Any] | None = _Unset,
     title: str | None = _Unset,
@@ -203,12 +244,11 @@ def InputField(
     ui_hidden: bool = False,
     ui_order: Optional[int] = None,
     ui_choice_labels: Optional[dict[str, str]] = None,
-    item_default: Optional[Any] = None,
 ) -> Any:
     """
     Creates an input field for an invocation.
 
-    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
+    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
     that adds a few extra parameters to support graph execution and the node editor UI.
 
     :param Input input: [Input.Any] The kind of input this field requires. \
@@ -228,28 +268,58 @@ def InputField(
       For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
       For this case, you could provide `UIComponent.Textarea`.
 
-    : param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
+    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
 
-    : param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
+    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
 
-    : param bool item_default: [None] Specifies the default item value, if this is a collection input. \
-      Ignored for non-collection fields.
+    :param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
     """
 
-    json_schema_extra_: dict[str, Any] = {
-        "input": input,
-        "ui_type": ui_type,
-        "ui_component": ui_component,
-        "ui_hidden": ui_hidden,
-        "ui_order": ui_order,
-        "item_default": item_default,
-        "ui_choice_labels": ui_choice_labels,
-        "_field_kind": "input",
-    }
+    json_schema_extra_ = InputFieldJSONSchemaExtra(
+        input=input,
+        ui_type=ui_type,
+        ui_component=ui_component,
+        ui_hidden=ui_hidden,
+        ui_order=ui_order,
+        ui_choice_labels=ui_choice_labels,
+        field_kind=FieldKind.Input,
+        orig_required=True,
+    )
 
+    """
+    There is a conflict between the typing of invocation definitions and the typing of an invocation's
+    `invoke()` function.
+
+    On instantiation of a node, the invocation definition is used to create the python class. At this time,
+    any number of fields may be optional, because they may be provided by connections.
+
+    On calling of `invoke()`, however, those fields may be required.
+
+    For example, consider an ResizeImageInvocation with an `image: ImageField` field.
+
+    `image` is required during the call to `invoke()`, but when the python class is instantiated,
+    the field may not be present. This is fine, because that image field will be provided by a
+    connection from an ancestor node, which outputs an image.
+
+    This means we want to type the `image` field as optional for the node class definition, but required
+    for the `invoke()` function.
+
+    If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
+    `invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
+    any static type analysis tools will complain.
+
+    To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
+    but secretly make them optional in `InputField()`. We also store the original required bool and/or default
+    value. When we call `invoke()`, we use this stored information to do an additional check on the class.
+    """
+
+    if default_factory is not _Unset and default_factory is not None:
+        default = default_factory()
+        logger.warn('"default_factory" is not supported, calling it now to set "default"')
+
+    # These are the args we may wish pass to the pydantic `Field()` function
     field_args = {
         "default": default,
-        "default_factory": default_factory,
         "title": title,
         "description": description,
         "pattern": pattern,
@@ -266,70 +336,34 @@ def InputField(
         "max_length": max_length,
     }
 
-    """
-    Invocation definitions have their fields typed correctly for their `invoke()` functions.
-    This typing is often more specific than the actual invocation definition requires, because
-    fields may have values provided only by connections.
-
-    For example, consider an ResizeImageInvocation with an `image: ImageField` field.
-
-    `image` is required during the call to `invoke()`, but when the python class is instantiated,
-    the field may not be present. This is fine, because that image field will be provided by a
-    an ancestor node that outputs the image.
-
-    So we'd like to type that `image` field as `Optional[ImageField]`. If we do that, however, then
-    we need to handle a lot of extra logic in the `invoke()` function to check if the field has a
-    value or not. This is very tedious.
-
-    Ideally, the invocation definition would be able to specify that the field is required during
-    invocation, but optional during instantiation. So the field would be typed as `image: ImageField`,
-    but when calling the `invoke()` function, we raise an error if the field is not present.
-
-    To do this, we need to do a bit of fanagling to make the pydantic field optional, and then do
-    extra validation when calling `invoke()`.
-
-    There is some additional logic here to cleaning create the pydantic field via the wrapper.
-    """
-
-    # Filter out field args not provided
+    # We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
     provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
 
-    if (default is not PydanticUndefined) and (default_factory is not PydanticUndefined):
-        raise ValueError("Cannot specify both default and default_factory")
+    # Because we are manually making fields optional, we need to store the original required bool for reference later
+    json_schema_extra_.orig_required = default is PydanticUndefined
 
-    # because we are manually making fields optional, we need to store the original required bool for reference later
-    if default is PydanticUndefined and default_factory is PydanticUndefined:
-        json_schema_extra_.update({"orig_required": True})
-    else:
-        json_schema_extra_.update({"orig_required": False})
-
-    # make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
-    if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined:
+    # Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
+    if input is Input.Any or input is Input.Connection:
         default_ = None if default is PydanticUndefined else default
         provided_args.update({"default": default_})
         if default is not PydanticUndefined:
-            # before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value
-            json_schema_extra_.update({"default": default})
-            json_schema_extra_.update({"orig_default": default})
-    elif default is not PydanticUndefined and default_factory is PydanticUndefined:
+            # Before invoking, we'll check for the original default value and set it on the field if the field has no value
+            json_schema_extra_.default = default
+            json_schema_extra_.orig_default = default
+    elif default is not PydanticUndefined:
         default_ = default
         provided_args.update({"default": default_})
-        json_schema_extra_.update({"orig_default": default_})
-    elif default_factory is not PydanticUndefined:
-        provided_args.update({"default_factory": default_factory})
-        # TODO: cannot serialize default_factory...
-        # json_schema_extra_.update(dict(orig_default_factory=default_factory))
+        json_schema_extra_.orig_default = default_
 
     return Field(
         **provided_args,
-        json_schema_extra=json_schema_extra_,
+        json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
     )
 
 
 def OutputField(
     # copied from pydantic's Field
     default: Any = _Unset,
-    default_factory: Callable[[], Any] | None = _Unset,
     title: str | None = _Unset,
     description: str | None = _Unset,
     pattern: str | None = _Unset,
@@ -362,13 +396,12 @@ def OutputField(
       `MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
       `UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
 
-    : param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
+    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
 
-    : param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
+    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
     """
     return Field(
         default=default,
-        default_factory=default_factory,
         title=title,
         description=description,
         pattern=pattern,
@@ -383,12 +416,12 @@ def OutputField(
         decimal_places=decimal_places,
         min_length=min_length,
         max_length=max_length,
-        json_schema_extra={
-            "ui_type": ui_type,
-            "ui_hidden": ui_hidden,
-            "ui_order": ui_order,
-            "_field_kind": "output",
-        },
+        json_schema_extra=OutputFieldJSONSchemaExtra(
+            ui_type=ui_type,
+            ui_hidden=ui_hidden,
+            ui_order=ui_order,
+            field_kind=FieldKind.Output,
+        ).model_dump(exclude_none=True),
     )
 
 
@@ -401,10 +434,10 @@ class UIConfigBase(BaseModel):
     tags: Optional[list[str]] = Field(default_factory=None, description="The node's tags")
     title: Optional[str] = Field(default=None, description="The node's display name")
     category: Optional[str] = Field(default=None, description="The node's category")
-    version: Optional[str] = Field(
-        default=None,
+    version: str = Field(
         description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".',
     )
+    node_pack: Optional[str] = Field(default=None, description="Whether or not this is a custom node")
 
     model_config = ConfigDict(
         validate_assignment=True,
@@ -420,6 +453,7 @@ class InvocationContext:
     queue_id: str
     queue_item_id: int
     queue_batch_id: str
+    workflow: Optional[WorkflowWithoutID]
 
     def __init__(
         self,
@@ -428,12 +462,14 @@ class InvocationContext:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
+        workflow: Optional[WorkflowWithoutID],
     ):
         self.services = services
         self.graph_execution_state_id = graph_execution_state_id
         self.queue_id = queue_id
         self.queue_item_id = queue_item_id
         self.queue_batch_id = queue_batch_id
+        self.workflow = workflow
 
 
 class BaseInvocationOutput(BaseModel):
@@ -447,29 +483,39 @@ class BaseInvocationOutput(BaseModel):
 
     @classmethod
     def register_output(cls, output: BaseInvocationOutput) -> None:
+        """Registers an invocation output."""
         cls._output_classes.add(output)
 
     @classmethod
     def get_outputs(cls) -> Iterable[BaseInvocationOutput]:
+        """Gets all invocation outputs."""
         return cls._output_classes
 
     @classmethod
     def get_outputs_union(cls) -> UnionType:
+        """Gets a union of all invocation outputs."""
         outputs_union = Union[tuple(cls._output_classes)]  # type: ignore [valid-type]
         return outputs_union  # type: ignore [return-value]
 
     @classmethod
     def get_output_types(cls) -> Iterable[str]:
-        return (get_type(i) for i in BaseInvocationOutput.get_outputs())
+        """Gets all invocation output types."""
+        return (i.get_type() for i in BaseInvocationOutput.get_outputs())
 
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
+        """Adds various UI-facing attributes to the invocation output's OpenAPI schema."""
         # Because we use a pydantic Literal field with default value for the invocation type,
         # it will be typed as optional in the OpenAPI schema. Make it required manually.
         if "required" not in schema or not isinstance(schema["required"], list):
             schema["required"] = []
         schema["required"].extend(["type"])
 
+    @classmethod
+    def get_type(cls) -> str:
+        """Gets the invocation output's type, as provided by the `@invocation_output` decorator."""
+        return cls.model_fields["type"].default
+
     model_config = ConfigDict(
         protected_namespaces=(),
         validate_assignment=True,
@@ -499,21 +545,29 @@ class BaseInvocation(ABC, BaseModel):
 
     _invocation_classes: ClassVar[set[BaseInvocation]] = set()
 
+    @classmethod
+    def get_type(cls) -> str:
+        """Gets the invocation's type, as provided by the `@invocation` decorator."""
+        return cls.model_fields["type"].default
+
     @classmethod
     def register_invocation(cls, invocation: BaseInvocation) -> None:
+        """Registers an invocation."""
         cls._invocation_classes.add(invocation)
 
     @classmethod
     def get_invocations_union(cls) -> UnionType:
+        """Gets a union of all invocation types."""
         invocations_union = Union[tuple(cls._invocation_classes)]  # type: ignore [valid-type]
         return invocations_union  # type: ignore [return-value]
 
     @classmethod
     def get_invocations(cls) -> Iterable[BaseInvocation]:
+        """Gets all invocations, respecting the allowlist and denylist."""
         app_config = InvokeAIAppConfig.get_config()
         allowed_invocations: set[BaseInvocation] = set()
         for sc in cls._invocation_classes:
-            invocation_type = get_type(sc)
+            invocation_type = sc.get_type()
             is_in_allowlist = (
                 invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True
             )
@@ -526,28 +580,32 @@ class BaseInvocation(ABC, BaseModel):
 
     @classmethod
     def get_invocations_map(cls) -> dict[str, BaseInvocation]:
-        # Get the type strings out of the literals and into a dictionary
-        return {get_type(i): i for i in BaseInvocation.get_invocations()}
+        """Gets a map of all invocation types to their invocation classes."""
+        return {i.get_type(): i for i in BaseInvocation.get_invocations()}
 
     @classmethod
     def get_invocation_types(cls) -> Iterable[str]:
-        return (get_type(i) for i in BaseInvocation.get_invocations())
+        """Gets all invocation types."""
+        return (i.get_type() for i in BaseInvocation.get_invocations())
 
     @classmethod
-    def get_output_type(cls) -> BaseInvocationOutput:
+    def get_output_annotation(cls) -> BaseInvocationOutput:
+        """Gets the invocation's output annotation (i.e. the return annotation of its `invoke()` method)."""
         return signature(cls.invoke).return_annotation
 
     @staticmethod
-    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
-        # Add the various UI-facing attributes to the schema. These are used to build the invocation templates.
-        uiconfig = getattr(model_class, "UIConfig", None)
-        if uiconfig and hasattr(uiconfig, "title"):
-            schema["title"] = uiconfig.title
-        if uiconfig and hasattr(uiconfig, "tags"):
-            schema["tags"] = uiconfig.tags
-        if uiconfig and hasattr(uiconfig, "category"):
-            schema["category"] = uiconfig.category
-        if uiconfig and hasattr(uiconfig, "version"):
+    def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel], *args, **kwargs) -> None:
+        """Adds various UI-facing attributes to the invocation's OpenAPI schema."""
+        uiconfig = cast(UIConfigBase | None, getattr(model_class, "UIConfig", None))
+        if uiconfig is not None:
+            if uiconfig.title is not None:
+                schema["title"] = uiconfig.title
+            if uiconfig.tags is not None:
+                schema["tags"] = uiconfig.tags
+            if uiconfig.category is not None:
+                schema["category"] = uiconfig.category
+            if uiconfig.node_pack is not None:
+                schema["node_pack"] = uiconfig.node_pack
             schema["version"] = uiconfig.version
         if "required" not in schema or not isinstance(schema["required"], list):
             schema["required"] = []
@@ -559,6 +617,10 @@ class BaseInvocation(ABC, BaseModel):
         pass
 
     def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
+        """
+        Internal invoke method, calls `invoke()` after some prep.
+        Handles optional fields that are required to call `invoke()` and invocation cache.
+        """
         for field_name, field in self.model_fields.items():
             if not field.json_schema_extra or callable(field.json_schema_extra):
                 # something has gone terribly awry, we should always have this and it should be a dict
@@ -598,21 +660,20 @@ class BaseInvocation(ABC, BaseModel):
             context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
             return self.invoke(context)
 
-    def get_type(self) -> str:
-        return self.model_fields["type"].default
-
     id: str = Field(
         default_factory=uuid_string,
         description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
-        json_schema_extra={"_field_kind": "internal"},
+        json_schema_extra={"field_kind": FieldKind.NodeAttribute},
     )
     is_intermediate: bool = Field(
         default=False,
         description="Whether or not this is an intermediate invocation.",
-        json_schema_extra={"ui_type": UIType.IsIntermediate, "_field_kind": "internal"},
+        json_schema_extra={"ui_type": "IsIntermediate", "field_kind": FieldKind.NodeAttribute},
     )
     use_cache: bool = Field(
-        default=True, description="Whether or not to use the cache", json_schema_extra={"_field_kind": "internal"}
+        default=True,
+        description="Whether or not to use the cache",
+        json_schema_extra={"field_kind": FieldKind.NodeAttribute},
     )
 
     UIConfig: ClassVar[Type[UIConfigBase]]
@@ -629,12 +690,15 @@ class BaseInvocation(ABC, BaseModel):
 TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
 
 
-RESERVED_INPUT_FIELD_NAMES = {
+RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
     "id",
     "is_intermediate",
     "use_cache",
     "type",
     "workflow",
+}
+
+RESERVED_INPUT_FIELD_NAMES = {
     "metadata",
 }
 
@@ -652,40 +716,59 @@ RESERVED_PYDANTIC_FIELD_NAMES = {m[0] for m in inspect.getmembers(_Model())}
 def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None:
     """
     Validates the fields of an invocation or invocation output:
-    - must not override any pydantic reserved fields
-    - must be created via `InputField`, `OutputField`, or be an internal field defined in this file
+    - Must not override any pydantic reserved fields
+    - Must have a type annotation
+    - Must have a json_schema_extra dict
+    - Must have field_kind in json_schema_extra
+    - Field name must not be reserved, according to its field_kind
     """
     for name, field in model_fields.items():
         if name in RESERVED_PYDANTIC_FIELD_NAMES:
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved by pydantic)')
 
-        field_kind = (
-            # _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
-            field.json_schema_extra.get("_field_kind", None) if field.json_schema_extra else None
-        )
+        if not field.annotation:
+            raise InvalidFieldError(f'Invalid field type "{name}" on "{model_type}" (missing annotation)')
+
+        if not isinstance(field.json_schema_extra, dict):
+            raise InvalidFieldError(
+                f'Invalid field definition for "{name}" on "{model_type}" (missing json_schema_extra dict)'
+            )
+
+        field_kind = field.json_schema_extra.get("field_kind", None)
 
         # must have a field_kind
-        if field_kind is None or field_kind not in {"input", "output", "internal"}:
+        if not isinstance(field_kind, FieldKind):
             raise InvalidFieldError(
                 f'Invalid field definition for "{name}" on "{model_type}" (maybe it\'s not an InputField or OutputField?)'
             )
 
-        if field_kind == "input" and name in RESERVED_INPUT_FIELD_NAMES:
+        if field_kind is FieldKind.Input and (
+            name in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES or name in RESERVED_INPUT_FIELD_NAMES
+        ):
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved input field name)')
 
-        if field_kind == "output" and name in RESERVED_OUTPUT_FIELD_NAMES:
+        if field_kind is FieldKind.Output and name in RESERVED_OUTPUT_FIELD_NAMES:
             raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved output field name)')
 
-        # internal fields *must* be in the reserved list
-        if (
-            field_kind == "internal"
-            and name not in RESERVED_INPUT_FIELD_NAMES
-            and name not in RESERVED_OUTPUT_FIELD_NAMES
-        ):
+        if (field_kind is FieldKind.Internal) and name not in RESERVED_INPUT_FIELD_NAMES:
             raise InvalidFieldError(
                 f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)'
             )
 
+        # node attribute fields *must* be in the reserved list
+        if (
+            field_kind is FieldKind.NodeAttribute
+            and name not in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES
+            and name not in RESERVED_OUTPUT_FIELD_NAMES
+        ):
+            raise InvalidFieldError(
+                f'Invalid field name "{name}" on "{model_type}" (node attribute field without reserved name)'
+            )
+
+        ui_type = field.json_schema_extra.get("ui_type", None)
+        if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
+            logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
+            field.json_schema_extra.pop("ui_type")
     return None
 
 
@@ -720,21 +803,30 @@ def invocation(
         validate_fields(cls.model_fields, invocation_type)
 
         # Add OpenAPI schema extras
-        uiconf_name = cls.__qualname__ + ".UIConfig"
-        if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
-            cls.UIConfig = type(uiconf_name, (UIConfigBase,), {})
-        if title is not None:
-            cls.UIConfig.title = title
-        if tags is not None:
-            cls.UIConfig.tags = tags
-        if category is not None:
-            cls.UIConfig.category = category
+        uiconfig_name = cls.__qualname__ + ".UIConfig"
+        if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconfig_name:
+            cls.UIConfig = type(uiconfig_name, (UIConfigBase,), {})
+        cls.UIConfig.title = title
+        cls.UIConfig.tags = tags
+        cls.UIConfig.category = category
+
+        # Grab the node pack's name from the module name, if it's a custom node
+        is_custom_node = cls.__module__.rsplit(".", 1)[0] == "invokeai.app.invocations"
+        if is_custom_node:
+            cls.UIConfig.node_pack = cls.__module__.split(".")[0]
+        else:
+            cls.UIConfig.node_pack = None
+
         if version is not None:
             try:
                 semver.Version.parse(version)
             except ValueError as e:
                 raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
             cls.UIConfig.version = version
+        else:
+            logger.warn(f'No version specified for node "{invocation_type}", using "1.0.0"')
+            cls.UIConfig.version = "1.0.0"
+
         if use_cache is not None:
             cls.model_fields["use_cache"].default = use_cache
 
@@ -749,7 +841,7 @@ def invocation(
 
         invocation_type_annotation = Literal[invocation_type]  # type: ignore
         invocation_type_field = Field(
-            title="type", default=invocation_type, json_schema_extra={"_field_kind": "internal"}
+            title="type", default=invocation_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
         )
 
         docstring = cls.__doc__
@@ -795,7 +887,9 @@ def invocation_output(
         # Add the output type to the model.
 
         output_type_annotation = Literal[output_type]  # type: ignore
-        output_type_field = Field(title="type", default=output_type, json_schema_extra={"_field_kind": "internal"})
+        output_type_field = Field(
+            title="type", default=output_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
+        )
 
         docstring = cls.__doc__
         cls = create_model(
@@ -813,24 +907,6 @@ def invocation_output(
     return wrapper
 
 
-class WorkflowField(RootModel):
-    """
-    Pydantic model for workflows with custom root of type dict[str, Any].
-    Workflows are stored without a strict schema.
-    """
-
-    root: dict[str, Any] = Field(description="The workflow")
-
-
-WorkflowFieldValidator = TypeAdapter(WorkflowField)
-
-
-class WithWorkflow(BaseModel):
-    workflow: Optional[WorkflowField] = Field(
-        default=None, description=FieldDescriptions.workflow, json_schema_extra={"_field_kind": "internal"}
-    )
-
-
 class MetadataField(RootModel):
     """
     Pydantic model for metadata with custom root of type dict[str, Any].
@@ -845,5 +921,21 @@ MetadataFieldValidator = TypeAdapter(MetadataField)
 
 class WithMetadata(BaseModel):
     metadata: Optional[MetadataField] = Field(
-        default=None, description=FieldDescriptions.metadata, json_schema_extra={"_field_kind": "internal"}
+        default=None,
+        description=FieldDescriptions.metadata,
+        json_schema_extra=InputFieldJSONSchemaExtra(
+            field_kind=FieldKind.Internal,
+            input=Input.Connection,
+            orig_required=False,
+        ).model_dump(exclude_none=True),
     )
+
+
+class WithWorkflow:
+    workflow = None
+
+    def __init_subclass__(cls) -> None:
+        logger.warn(
+            f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
+        )
+        super().__init_subclass__()

invokeai/app/invocations/collections.py

@@ -5,7 +5,7 @@ import numpy as np
 from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import IntegerCollectionOutput
-from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from invokeai.app.util.misc import SEED_MAX
 
 from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
@@ -55,7 +55,7 @@ class RangeOfSizeInvocation(BaseInvocation):
     title="Random Range",
     tags=["range", "integer", "random", "collection"],
     category="collections",
-    version="1.0.0",
+    version="1.0.1",
     use_cache=False,
 )
 class RandomRangeInvocation(BaseInvocation):
@@ -65,10 +65,10 @@ class RandomRangeInvocation(BaseInvocation):
     high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
     size: int = InputField(default=1, description="The number of values to generate")
     seed: int = InputField(
+        default=0,
         ge=0,
         le=SEED_MAX,
         description="The seed for the RNG (omit for random)",
-        default_factory=get_random_seed,
     )
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:

invokeai/app/invocations/controlnet_image_processors.py

@@ -39,7 +39,6 @@ from .baseinvocation import (
     InvocationContext,
     OutputField,
     WithMetadata,
-    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -129,7 +128,7 @@ class ControlNetInvocation(BaseInvocation):
 
 
 # This invocation exists for other invocations to subclass it - do not register with @invocation!
-class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+class ImageProcessorInvocation(BaseInvocation, WithMetadata):
     """Base class for invocations that preprocess images for ControlNet"""
 
     image: ImageField = InputField(description="The image to process")
@@ -153,7 +152,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         """Builds an ImageOutput and its ImageField"""
@@ -173,7 +172,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -196,7 +195,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -225,7 +224,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -247,7 +246,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -270,7 +269,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Openpose Processor",
     tags=["controlnet", "openpose", "pose"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Openpose processing to image"""
@@ -295,7 +294,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -322,7 +321,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -339,7 +338,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.1.0"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
@@ -362,7 +361,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.1.0"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
@@ -389,7 +388,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
@@ -419,7 +418,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -435,7 +434,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -458,7 +457,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -487,7 +486,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -527,7 +526,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -569,7 +568,7 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.1.0",
+    version="1.2.0",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""

invokeai/app/invocations/custom_nodes/init.py

@@ -32,13 +32,15 @@ for d in Path(__file__).parent.iterdir():
     if module_name in globals():
         continue
 
-    # we have a legit module to import
+    # load the module, appending adding a suffix to identify it as a custom node pack
     spec = spec_from_file_location(module_name, init.absolute())
 
     if spec is None or spec.loader is None:
         logger.warn(f"Could not load {init}")
         continue
 
+    logger.info(f"Loading node pack {module_name}")
+
     module = module_from_spec(spec)
     sys.modules[spec.name] = module
     spec.loader.exec_module(module)
@@ -47,5 +49,5 @@ for d in Path(__file__).parent.iterdir():
 
     del init, module_name
 
-
-logger.info(f"Loaded {loaded_count} modules from {Path(__file__).parent}")
+if loaded_count > 0:
+    logger.info(f"Loaded {loaded_count} node packs from {Path(__file__).parent}")

invokeai/app/invocations/cv.py

@@ -8,11 +8,11 @@ from PIL import Image, ImageOps
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
 
 
-@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.1.0")
-class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
+class CvInpaintInvocation(BaseInvocation, WithMetadata):
     """Simple inpaint using opencv."""
 
     image: ImageField = InputField(description="The image to inpaint")
@@ -41,7 +41,7 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/facetools.py

@@ -17,7 +17,6 @@ from invokeai.app.invocations.baseinvocation import (
     InvocationContext,
     OutputField,
     WithMetadata,
-    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -438,8 +437,8 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.1.0")
-class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
+class FaceOffInvocation(BaseInvocation, WithMetadata):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
     image: ImageField = InputField(description="Image for face detection")
@@ -508,7 +507,7 @@ class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         mask_dto = context.services.images.create(
@@ -532,8 +531,8 @@ class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.1.0")
-class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
+class FaceMaskInvocation(BaseInvocation, WithMetadata):
     """Face mask creation using mediapipe face detection"""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -627,7 +626,7 @@ class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         mask_dto = context.services.images.create(
@@ -650,9 +649,9 @@ class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.1.0"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
 )
-class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -716,7 +715,7 @@ class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             node_id=self.id,
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/image.py

@@ -13,7 +13,7 @@ from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
-from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
+from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, invocation
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@@ -36,8 +36,14 @@ class ShowImageInvocation(BaseInvocation):
         )
 
 
-@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.1.0")
-class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+@invocation(
+    "blank_image",
+    title="Blank Image",
+    tags=["image"],
+    category="image",
+    version="1.2.0",
+)
+class BlankImageInvocation(BaseInvocation, WithMetadata):
     """Creates a blank image and forwards it to the pipeline"""
 
     width: int = InputField(default=512, description="The width of the image")
@@ -56,7 +62,7 @@ class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -66,8 +72,14 @@ class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
         )
 
 
-@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.1.0")
-class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_crop",
+    title="Crop Image",
+    tags=["image", "crop"],
+    category="image",
+    version="1.2.0",
+)
+class ImageCropInvocation(BaseInvocation, WithMetadata):
     """Crops an image to a specified box. The box can be outside of the image."""
 
     image: ImageField = InputField(description="The image to crop")
@@ -90,7 +102,7 @@ class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -100,8 +112,69 @@ class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.1.0")
-class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_paste",
+    title="Paste Image",
+    tags=["image", "paste"],
+    category="image",
+    version="1.2.0",
+)
+class CenterPadCropInvocation(BaseInvocation):
+    """Pad or crop an image's sides from the center by specified pixels. Positive values are outside of the image."""
+
+    image: ImageField = InputField(description="The image to crop")
+    left: int = InputField(
+        default=0,
+        description="Number of pixels to pad/crop from the left (negative values crop inwards, positive values pad outwards)",
+    )
+    right: int = InputField(
+        default=0,
+        description="Number of pixels to pad/crop from the right (negative values crop inwards, positive values pad outwards)",
+    )
+    top: int = InputField(
+        default=0,
+        description="Number of pixels to pad/crop from the top (negative values crop inwards, positive values pad outwards)",
+    )
+    bottom: int = InputField(
+        default=0,
+        description="Number of pixels to pad/crop from the bottom (negative values crop inwards, positive values pad outwards)",
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+
+        # Calculate and create new image dimensions
+        new_width = image.width + self.right + self.left
+        new_height = image.height + self.top + self.bottom
+        image_crop = Image.new(mode="RGBA", size=(new_width, new_height), color=(0, 0, 0, 0))
+
+        # Paste new image onto input
+        image_crop.paste(image, (self.left, self.top))
+
+        image_dto = context.services.images.create(
+            image=image_crop,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+
+
+@invocation(
+    invocation_type="img_pad_crop",
+    title="Center Pad or Crop Image",
+    category="image",
+    tags=["image", "pad", "crop"],
+    version="1.0.0",
+)
+class ImagePasteInvocation(BaseInvocation, WithMetadata):
     """Pastes an image into another image."""
 
     base_image: ImageField = InputField(description="The base image")
@@ -144,7 +217,7 @@ class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -154,8 +227,14 @@ class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.1.0")
-class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "tomask",
+    title="Mask from Alpha",
+    tags=["image", "mask"],
+    category="image",
+    version="1.2.0",
+)
+class MaskFromAlphaInvocation(BaseInvocation, WithMetadata):
     """Extracts the alpha channel of an image as a mask."""
 
     image: ImageField = InputField(description="The image to create the mask from")
@@ -176,7 +255,7 @@ class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -186,8 +265,14 @@ class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.1.0")
-class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_mul",
+    title="Multiply Images",
+    tags=["image", "multiply"],
+    category="image",
+    version="1.2.0",
+)
+class ImageMultiplyInvocation(BaseInvocation, WithMetadata):
     """Multiplies two images together using `PIL.ImageChops.multiply()`."""
 
     image1: ImageField = InputField(description="The first image to multiply")
@@ -207,7 +292,7 @@ class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -220,8 +305,14 @@ class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
 IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
 
 
-@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.1.0")
-class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_chan",
+    title="Extract Image Channel",
+    tags=["image", "channel"],
+    category="image",
+    version="1.2.0",
+)
+class ImageChannelInvocation(BaseInvocation, WithMetadata):
     """Gets a channel from an image."""
 
     image: ImageField = InputField(description="The image to get the channel from")
@@ -240,7 +331,7 @@ class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -253,8 +344,14 @@ class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 
 
-@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.1.0")
-class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_conv",
+    title="Convert Image Mode",
+    tags=["image", "convert"],
+    category="image",
+    version="1.2.0",
+)
+class ImageConvertInvocation(BaseInvocation, WithMetadata):
     """Converts an image to a different mode."""
 
     image: ImageField = InputField(description="The image to convert")
@@ -273,7 +370,7 @@ class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -283,8 +380,14 @@ class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.1.0")
-class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_blur",
+    title="Blur Image",
+    tags=["image", "blur"],
+    category="image",
+    version="1.2.0",
+)
+class ImageBlurInvocation(BaseInvocation, WithMetadata):
     """Blurs an image"""
 
     image: ImageField = InputField(description="The image to blur")
@@ -308,7 +411,7 @@ class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -338,8 +441,14 @@ PIL_RESAMPLING_MAP = {
 }
 
 
-@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.1.0")
-class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+@invocation(
+    "img_resize",
+    title="Resize Image",
+    tags=["image", "resize"],
+    category="image",
+    version="1.2.0",
+)
+class ImageResizeInvocation(BaseInvocation, WithMetadata):
     """Resizes an image to specific dimensions"""
 
     image: ImageField = InputField(description="The image to resize")
@@ -365,7 +474,7 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -375,8 +484,14 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
         )
 
 
-@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.1.0")
-class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+@invocation(
+    "img_scale",
+    title="Scale Image",
+    tags=["image", "scale"],
+    category="image",
+    version="1.2.0",
+)
+class ImageScaleInvocation(BaseInvocation, WithMetadata):
     """Scales an image by a factor"""
 
     image: ImageField = InputField(description="The image to scale")
@@ -407,7 +522,7 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -417,8 +532,14 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
         )
 
 
-@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.1.0")
-class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_lerp",
+    title="Lerp Image",
+    tags=["image", "lerp"],
+    category="image",
+    version="1.2.0",
+)
+class ImageLerpInvocation(BaseInvocation, WithMetadata):
     """Linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -441,7 +562,7 @@ class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -451,8 +572,14 @@ class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.1.0")
-class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_ilerp",
+    title="Inverse Lerp Image",
+    tags=["image", "ilerp"],
+    category="image",
+    version="1.2.0",
+)
+class ImageInverseLerpInvocation(BaseInvocation, WithMetadata):
     """Inverse linear interpolation of all pixels of an image"""
 
     image: ImageField = InputField(description="The image to lerp")
@@ -475,7 +602,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -485,8 +612,14 @@ class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.1.0")
-class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+@invocation(
+    "img_nsfw",
+    title="Blur NSFW Image",
+    tags=["image", "nsfw"],
+    category="image",
+    version="1.2.0",
+)
+class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata):
     """Add blur to NSFW-flagged images"""
 
     image: ImageField = InputField(description="The image to check")
@@ -511,7 +644,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -532,9 +665,9 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
     title="Add Invisible Watermark",
     tags=["image", "watermark"],
     category="image",
-    version="1.1.0",
+    version="1.2.0",
 )
-class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+class ImageWatermarkInvocation(BaseInvocation, WithMetadata):
     """Add an invisible watermark to an image"""
 
     image: ImageField = InputField(description="The image to check")
@@ -551,7 +684,7 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -561,8 +694,14 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
         )
 
 
-@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.1.0")
-class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "mask_edge",
+    title="Mask Edge",
+    tags=["image", "mask", "inpaint"],
+    category="image",
+    version="1.2.0",
+)
+class MaskEdgeInvocation(BaseInvocation, WithMetadata):
     """Applies an edge mask to an image"""
 
     image: ImageField = InputField(description="The image to apply the mask to")
@@ -597,7 +736,7 @@ class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -612,9 +751,9 @@ class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     title="Combine Masks",
     tags=["image", "mask", "multiply"],
     category="image",
-    version="1.1.0",
+    version="1.2.0",
 )
-class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+class MaskCombineInvocation(BaseInvocation, WithMetadata):
     """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
 
     mask1: ImageField = InputField(description="The first mask to combine")
@@ -634,7 +773,7 @@ class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -644,8 +783,14 @@ class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.1.0")
-class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "color_correct",
+    title="Color Correct",
+    tags=["image", "color"],
+    category="image",
+    version="1.2.0",
+)
+class ColorCorrectInvocation(BaseInvocation, WithMetadata):
     """
     Shifts the colors of a target image to match the reference image, optionally
     using a mask to only color-correct certain regions of the target image.
@@ -745,7 +890,7 @@ class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -755,8 +900,14 @@ class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.1.0")
-class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation(
+    "img_hue_adjust",
+    title="Adjust Image Hue",
+    tags=["image", "hue"],
+    category="image",
+    version="1.2.0",
+)
+class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata):
     """Adjusts the Hue of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -785,7 +936,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -858,9 +1009,9 @@ CHANNEL_FORMATS = {
         "value",
     ],
     category="image",
-    version="1.1.0",
+    version="1.2.0",
 )
-class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata):
     """Add or subtract a value from a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -895,7 +1046,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -929,9 +1080,9 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         "value",
     ],
     category="image",
-    version="1.1.0",
+    version="1.2.0",
 )
-class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata):
     """Scale a specific color channel of an image."""
 
     image: ImageField = InputField(description="The image to adjust")
@@ -970,7 +1121,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata)
             node_id=self.id,
             is_intermediate=self.is_intermediate,
             session_id=context.graph_execution_state_id,
-            workflow=self.workflow,
+            workflow=context.workflow,
             metadata=self.metadata,
         )
 
@@ -988,10 +1139,10 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata)
     title="Save Image",
     tags=["primitives", "image"],
     category="primitives",
-    version="1.1.0",
+    version="1.2.0",
     use_cache=False,
 )
-class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+class SaveImageInvocation(BaseInvocation, WithMetadata):
     """Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
 
     image: ImageField = InputField(description=FieldDescriptions.image)
@@ -1009,7 +1160,7 @@ class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -1027,7 +1178,7 @@ class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
     version="1.0.1",
     use_cache=False,
 )
-class LinearUIOutputInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+class LinearUIOutputInvocation(BaseInvocation, WithMetadata):
     """Handles Linear UI Image Outputting tasks."""
 
     image: ImageField = InputField(description=FieldDescriptions.image)

invokeai/app/invocations/infill.py

@@ -8,12 +8,12 @@ from PIL import Image, ImageOps
 
 from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from invokeai.app.util.misc import SEED_MAX
 from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
 from invokeai.backend.image_util.lama import LaMA
 from invokeai.backend.image_util.patchmatch import PatchMatch
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
 
@@ -118,8 +118,8 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
     return si
 
 
-@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
-class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+class InfillColorInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image with a solid color"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -144,7 +144,7 @@ class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -154,17 +154,17 @@ class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
-class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
+class InfillTileInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image with tiles of the image"""
 
     image: ImageField = InputField(description="The image to infill")
     tile_size: int = InputField(default=32, ge=1, description="The tile size (px)")
     seed: int = InputField(
+        default=0,
         ge=0,
         le=SEED_MAX,
         description="The seed to use for tile generation (omit for random)",
-        default_factory=get_random_seed,
     )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
@@ -181,7 +181,7 @@ class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -192,9 +192,9 @@ class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
 
 
 @invocation(
-    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0"
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
 )
-class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -235,7 +235,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -245,8 +245,8 @@ class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
-class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+class LaMaInfillInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -264,7 +264,7 @@ class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -274,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
         )
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
-class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+class CV2InfillInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -293,7 +293,7 @@ class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/ip_adapter.py

@@ -11,7 +11,6 @@ from invokeai.app.invocations.baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
-    UIType,
     invocation,
     invocation_output,
 )
@@ -67,7 +66,7 @@ class IPAdapterInvocation(BaseInvocation):
 
     # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
     weight: Union[float, List[float]] = InputField(
-        default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
+        default=1, ge=-1, description="The weight given to the IP-Adapter", title="Weight"
     )
 
     begin_step_percent: float = InputField(

invokeai/app/invocations/latent.py

@@ -64,7 +64,6 @@ from .baseinvocation import (
     OutputField,
     UIType,
     WithMetadata,
-    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -79,6 +78,12 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
 SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
 
+# HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
+# be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
+# factor is hard-coded to a literal '8' rather than using this constant.
+# The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
+LATENT_SCALE_FACTOR = 8
+
 
 @invocation_output("scheduler_output")
 class SchedulerOutput(BaseInvocationOutput):
@@ -215,7 +220,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.4.0",
+    version="1.5.0",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -273,8 +278,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
         input=Input.Connection,
         ui_order=7,
     )
+    cfg_rescale_multiplier: float = InputField(
+        default=0, ge=0, lt=1, description=FieldDescriptions.cfg_rescale_multiplier
+    )
     latents: Optional[LatentsField] = InputField(
-        default=None, description=FieldDescriptions.latents, input=Input.Connection
+        default=None,
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+        ui_order=4,
     )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None,
@@ -329,6 +340,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             unconditioned_embeddings=uc,
             text_embeddings=c,
             guidance_scale=self.cfg_scale,
+            guidance_rescale_multiplier=self.cfg_rescale_multiplier,
             extra=extra_conditioning_info,
             postprocessing_settings=PostprocessingSettings(
                 threshold=0.0,  # threshold,
@@ -387,9 +399,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         exit_stack: ExitStack,
         do_classifier_free_guidance: bool = True,
     ) -> List[ControlNetData]:
-        # assuming fixed dimensional scaling of 8:1 for image:latents
-        control_height_resize = latents_shape[2] * 8
-        control_width_resize = latents_shape[3] * 8
+        # Assuming fixed dimensional scaling of LATENT_SCALE_FACTOR.
+        control_height_resize = latents_shape[2] * LATENT_SCALE_FACTOR
+        control_width_resize = latents_shape[3] * LATENT_SCALE_FACTOR
         if control_input is None:
             control_list = None
         elif isinstance(control_input, list) and len(control_input) == 0:
@@ -789,9 +801,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.1.0",
+    version="1.2.0",
 )
-class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+class LatentsToImageInvocation(BaseInvocation, WithMetadata):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -873,7 +885,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(
@@ -902,12 +914,12 @@ class ResizeLatentsInvocation(BaseInvocation):
     )
     width: int = InputField(
         ge=64,
-        multiple_of=8,
+        multiple_of=LATENT_SCALE_FACTOR,
         description=FieldDescriptions.width,
     )
     height: int = InputField(
         ge=64,
-        multiple_of=8,
+        multiple_of=LATENT_SCALE_FACTOR,
         description=FieldDescriptions.width,
     )
     mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
@@ -921,7 +933,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         resized_latents = torch.nn.functional.interpolate(
             latents.to(device),
-            size=(self.height // 8, self.width // 8),
+            size=(self.height // LATENT_SCALE_FACTOR, self.width // LATENT_SCALE_FACTOR),
             mode=self.mode,
             antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
         )
@@ -1159,3 +1171,60 @@ class BlendLatentsInvocation(BaseInvocation):
         # context.services.latents.set(name, resized_latents)
         context.services.latents.save(name, blended_latents)
         return build_latents_output(latents_name=name, latents=blended_latents)
+
+
+# The Crop Latents node was copied from @skunkworxdark's implementation here:
+# https://github.com/skunkworxdark/XYGrid_nodes/blob/74647fa9c1fa57d317a94bd43ca689af7f0aae5e/images_to_grids.py#L1117C1-L1167C80
+@invocation(
+    "crop_latents",
+    title="Crop Latents",
+    tags=["latents", "crop"],
+    category="latents",
+    version="1.0.0",
+)
+# TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
+# Currently, if the class names conflict then 'GET /openapi.json' fails.
+class CropLatentsCoreInvocation(BaseInvocation):
+    """Crops a latent-space tensor to a box specified in image-space. The box dimensions and coordinates must be
+    divisible by the latent scale factor of 8.
+    """
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    x: int = InputField(
+        ge=0,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The left x coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    y: int = InputField(
+        ge=0,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The top y coordinate (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    width: int = InputField(
+        ge=1,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The width (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+    height: int = InputField(
+        ge=1,
+        multiple_of=LATENT_SCALE_FACTOR,
+        description="The height (in px) of the crop rectangle in image space. This value will be converted to a dimension in latent space.",
+    )
+
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = context.services.latents.get(self.latents.latents_name)
+
+        x1 = self.x // LATENT_SCALE_FACTOR
+        y1 = self.y // LATENT_SCALE_FACTOR
+        x2 = x1 + (self.width // LATENT_SCALE_FACTOR)
+        y2 = y1 + (self.height // LATENT_SCALE_FACTOR)
+
+        cropped_latents = latents[..., y1:y2, x1:x2]
+
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.save(name, cropped_latents)
+
+        return build_latents_output(latents_name=name, latents=cropped_latents)

invokeai/app/invocations/metadata.py

@@ -127,6 +127,9 @@ class CoreMetadataInvocation(BaseInvocation):
     seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
     rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
     cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
+    cfg_rescale_multiplier: Optional[float] = InputField(
+        default=None, description=FieldDescriptions.cfg_rescale_multiplier
+    )
     steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
     scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
     seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")

invokeai/app/invocations/model.py

@@ -14,7 +14,6 @@ from .baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
-    UIType,
     invocation,
     invocation_output,
 )
@@ -395,7 +394,6 @@ class VaeLoaderInvocation(BaseInvocation):
     vae_model: VAEModelField = InputField(
         description=FieldDescriptions.vae_model,
         input=Input.Direct,
-        ui_type=UIType.VaeModel,
         title="VAE",
     )
 

invokeai/app/invocations/noise.py

@@ -6,7 +6,7 @@ from pydantic import field_validator
 
 from invokeai.app.invocations.latent import LatentsField
 from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.app.util.misc import SEED_MAX, get_random_seed
+from invokeai.app.util.misc import SEED_MAX
 
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from .baseinvocation import (
@@ -83,16 +83,16 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
     title="Noise",
     tags=["latents", "noise"],
     category="latents",
-    version="1.0.0",
+    version="1.0.1",
 )
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""
 
     seed: int = InputField(
+        default=0,
         ge=0,
         le=SEED_MAX,
         description=FieldDescriptions.seed,
-        default_factory=get_random_seed,
     )
     width: int = InputField(
         default=512,

invokeai/app/invocations/onnx.py

@@ -31,7 +31,6 @@ from .baseinvocation import (
     UIComponent,
     UIType,
     WithMetadata,
-    WithWorkflow,
     invocation,
     invocation_output,
 )
@@ -326,9 +325,9 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
     title="ONNX Latents to Image",
     tags=["latents", "image", "vae", "onnx"],
     category="image",
-    version="1.1.0",
+    version="1.2.0",
 )
-class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
+class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -378,7 +377,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(

invokeai/app/invocations/primitives.py

@@ -62,12 +62,12 @@ class BooleanInvocation(BaseInvocation):
     title="Boolean Collection Primitive",
     tags=["primitives", "boolean", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class BooleanCollectionInvocation(BaseInvocation):
     """A collection of boolean primitive values"""
 
-    collection: list[bool] = InputField(default_factory=list, description="The collection of boolean values")
+    collection: list[bool] = InputField(default=[], description="The collection of boolean values")
 
     def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
         return BooleanCollectionOutput(collection=self.collection)
@@ -111,12 +111,12 @@ class IntegerInvocation(BaseInvocation):
     title="Integer Collection Primitive",
     tags=["primitives", "integer", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class IntegerCollectionInvocation(BaseInvocation):
     """A collection of integer primitive values"""
 
-    collection: list[int] = InputField(default_factory=list, description="The collection of integer values")
+    collection: list[int] = InputField(default=[], description="The collection of integer values")
 
     def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
         return IntegerCollectionOutput(collection=self.collection)
@@ -158,12 +158,12 @@ class FloatInvocation(BaseInvocation):
     title="Float Collection Primitive",
     tags=["primitives", "float", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class FloatCollectionInvocation(BaseInvocation):
     """A collection of float primitive values"""
 
-    collection: list[float] = InputField(default_factory=list, description="The collection of float values")
+    collection: list[float] = InputField(default=[], description="The collection of float values")
 
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         return FloatCollectionOutput(collection=self.collection)
@@ -205,12 +205,12 @@ class StringInvocation(BaseInvocation):
     title="String Collection Primitive",
     tags=["primitives", "string", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class StringCollectionInvocation(BaseInvocation):
     """A collection of string primitive values"""
 
-    collection: list[str] = InputField(default_factory=list, description="The collection of string values")
+    collection: list[str] = InputField(default=[], description="The collection of string values")
 
     def invoke(self, context: InvocationContext) -> StringCollectionOutput:
         return StringCollectionOutput(collection=self.collection)
@@ -467,13 +467,13 @@ class ConditioningInvocation(BaseInvocation):
     title="Conditioning Collection Primitive",
     tags=["primitives", "conditioning", "collection"],
     category="primitives",
-    version="1.0.0",
+    version="1.0.1",
 )
 class ConditioningCollectionInvocation(BaseInvocation):
     """A collection of conditioning tensor primitive values"""
 
     collection: list[ConditioningField] = InputField(
-        default_factory=list,
+        default=[],
         description="The collection of conditioning tensors",
     )
 

invokeai/app/invocations/prompt.py

@@ -44,7 +44,7 @@ class DynamicPromptInvocation(BaseInvocation):
     title="Prompts from File",
     tags=["prompt", "file"],
     category="prompt",
-    version="1.0.0",
+    version="1.0.1",
 )
 class PromptsFromFileInvocation(BaseInvocation):
     """Loads prompts from a text file"""
@@ -82,7 +82,7 @@ class PromptsFromFileInvocation(BaseInvocation):
         end_line = start_line + max_prompts
         if max_prompts <= 0:
             end_line = np.iinfo(np.int32).max
-        with open(file_path) as f:
+        with open(file_path, encoding="utf-8") as f:
             for i, line in enumerate(f):
                 if i >= start_line and i < end_line:
                     prompts.append((pre_prompt or "") + line.strip() + (post_prompt or ""))

invokeai/app/invocations/t2i_adapter.py

@@ -9,7 +9,6 @@ from invokeai.app.invocations.baseinvocation import (
     InputField,
     InvocationContext,
     OutputField,
-    UIType,
     invocation,
     invocation_output,
 )
@@ -59,7 +58,7 @@ class T2IAdapterInvocation(BaseInvocation):
         ui_order=-1,
     )
     weight: Union[float, list[float]] = InputField(
-        default=1, ge=0, description="The weight given to the T2I-Adapter", ui_type=UIType.Float, title="Weight"
+        default=1, ge=0, description="The weight given to the T2I-Adapter", title="Weight"
     )
     begin_step_percent: float = InputField(
         default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"

invokeai/app/invocations/tiles.py

@@ -0,0 +1,180 @@
+import numpy as np
+from PIL import Image
+from pydantic import BaseModel
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    InputField,
+    InvocationContext,
+    OutputField,
+    WithMetadata,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.backend.tiles.tiles import calc_tiles_with_overlap, merge_tiles_with_linear_blending
+from invokeai.backend.tiles.utils import Tile
+
+
+class TileWithImage(BaseModel):
+    tile: Tile
+    image: ImageField
+
+
+@invocation_output("calculate_image_tiles_output")
+class CalculateImageTilesOutput(BaseInvocationOutput):
+    tiles: list[Tile] = OutputField(description="The tiles coordinates that cover a particular image shape.")
+
+
+@invocation("calculate_image_tiles", title="Calculate Image Tiles", tags=["tiles"], category="tiles", version="1.0.0")
+class CalculateImageTilesInvocation(BaseInvocation):
+    """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
+
+    image_width: int = InputField(ge=1, default=1024, description="The image width, in pixels, to calculate tiles for.")
+    image_height: int = InputField(
+        ge=1, default=1024, description="The image height, in pixels, to calculate tiles for."
+    )
+    tile_width: int = InputField(ge=1, default=576, description="The tile width, in pixels.")
+    tile_height: int = InputField(ge=1, default=576, description="The tile height, in pixels.")
+    overlap: int = InputField(
+        ge=0,
+        default=128,
+        description="The target overlap, in pixels, between adjacent tiles. Adjacent tiles will overlap by at least this amount",
+    )
+
+    def invoke(self, context: InvocationContext) -> CalculateImageTilesOutput:
+        tiles = calc_tiles_with_overlap(
+            image_height=self.image_height,
+            image_width=self.image_width,
+            tile_height=self.tile_height,
+            tile_width=self.tile_width,
+            overlap=self.overlap,
+        )
+        return CalculateImageTilesOutput(tiles=tiles)
+
+
+@invocation_output("tile_to_properties_output")
+class TileToPropertiesOutput(BaseInvocationOutput):
+    coords_left: int = OutputField(description="Left coordinate of the tile relative to its parent image.")
+    coords_right: int = OutputField(description="Right coordinate of the tile relative to its parent image.")
+    coords_top: int = OutputField(description="Top coordinate of the tile relative to its parent image.")
+    coords_bottom: int = OutputField(description="Bottom coordinate of the tile relative to its parent image.")
+
+    # HACK: The width and height fields are 'meta' fields that can easily be calculated from the other fields on this
+    # object. Including redundant fields that can cheaply/easily be re-calculated goes against conventional API design
+    # principles. These fields are included, because 1) they are often useful in tiled workflows, and 2) they are
+    # difficult to calculate in a workflow (even though it's just a couple of subtraction nodes the graph gets
+    # surprisingly complicated).
+    width: int = OutputField(description="The width of the tile. Equal to coords_right - coords_left.")
+    height: int = OutputField(description="The height of the tile. Equal to coords_bottom - coords_top.")
+
+    overlap_top: int = OutputField(description="Overlap between this tile and its top neighbor.")
+    overlap_bottom: int = OutputField(description="Overlap between this tile and its bottom neighbor.")
+    overlap_left: int = OutputField(description="Overlap between this tile and its left neighbor.")
+    overlap_right: int = OutputField(description="Overlap between this tile and its right neighbor.")
+
+
+@invocation("tile_to_properties", title="Tile to Properties", tags=["tiles"], category="tiles", version="1.0.0")
+class TileToPropertiesInvocation(BaseInvocation):
+    """Split a Tile into its individual properties."""
+
+    tile: Tile = InputField(description="The tile to split into properties.")
+
+    def invoke(self, context: InvocationContext) -> TileToPropertiesOutput:
+        return TileToPropertiesOutput(
+            coords_left=self.tile.coords.left,
+            coords_right=self.tile.coords.right,
+            coords_top=self.tile.coords.top,
+            coords_bottom=self.tile.coords.bottom,
+            width=self.tile.coords.right - self.tile.coords.left,
+            height=self.tile.coords.bottom - self.tile.coords.top,
+            overlap_top=self.tile.overlap.top,
+            overlap_bottom=self.tile.overlap.bottom,
+            overlap_left=self.tile.overlap.left,
+            overlap_right=self.tile.overlap.right,
+        )
+
+
+@invocation_output("pair_tile_image_output")
+class PairTileImageOutput(BaseInvocationOutput):
+    tile_with_image: TileWithImage = OutputField(description="A tile description with its corresponding image.")
+
+
+@invocation("pair_tile_image", title="Pair Tile with Image", tags=["tiles"], category="tiles", version="1.0.0")
+class PairTileImageInvocation(BaseInvocation):
+    """Pair an image with its tile properties."""
+
+    # TODO(ryand): The only reason that PairTileImage is needed is because the iterate/collect nodes don't preserve
+    # order. Can this be fixed?
+
+    image: ImageField = InputField(description="The tile image.")
+    tile: Tile = InputField(description="The tile properties.")
+
+    def invoke(self, context: InvocationContext) -> PairTileImageOutput:
+        return PairTileImageOutput(
+            tile_with_image=TileWithImage(
+                tile=self.tile,
+                image=self.image,
+            )
+        )
+
+
+@invocation("merge_tiles_to_image", title="Merge Tiles to Image", tags=["tiles"], category="tiles", version="1.1.0")
+class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
+    """Merge multiple tile images into a single image."""
+
+    # Inputs
+    tiles_with_images: list[TileWithImage] = InputField(description="A list of tile images with tile properties.")
+    blend_amount: int = InputField(
+        ge=0,
+        description="The amount to blend adjacent tiles in pixels. Must be <= the amount of overlap between adjacent tiles.",
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        images = [twi.image for twi in self.tiles_with_images]
+        tiles = [twi.tile for twi in self.tiles_with_images]
+
+        # Infer the output image dimensions from the max/min tile limits.
+        height = 0
+        width = 0
+        for tile in tiles:
+            height = max(height, tile.coords.bottom)
+            width = max(width, tile.coords.right)
+
+        # Get all tile images for processing.
+        # TODO(ryand): It pains me that we spend time PNG decoding each tile from disk when they almost certainly
+        # existed in memory at an earlier point in the graph.
+        tile_np_images: list[np.ndarray] = []
+        for image in images:
+            pil_image = context.services.images.get_pil_image(image.image_name)
+            pil_image = pil_image.convert("RGB")
+            tile_np_images.append(np.array(pil_image))
+
+        # Prepare the output image buffer.
+        # Check the first tile to determine how many image channels are expected in the output.
+        channels = tile_np_images[0].shape[-1]
+        dtype = tile_np_images[0].dtype
+        np_image = np.zeros(shape=(height, width, channels), dtype=dtype)
+
+        merge_tiles_with_linear_blending(
+            dst_image=np_image, tiles=tiles, tile_images=tile_np_images, blend_amount=self.blend_amount
+        )
+        pil_image = Image.fromarray(np_image)
+
+        image_dto = context.services.images.create(
+            image=pil_image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
+            workflow=context.workflow,
+        )
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/upscale.py

@@ -2,19 +2,19 @@
 from pathlib import Path
 from typing import Literal
 
-import cv2 as cv
+import cv2
 import numpy as np
 import torch
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from pydantic import ConfigDict
-from realesrgan import RealESRGANer
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
 from invokeai.backend.util.devices import choose_torch_device
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
@@ -29,8 +29,8 @@ if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.2.0")
-class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.0")
+class ESRGANInvocation(BaseInvocation, WithMetadata):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
@@ -92,9 +92,9 @@ class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
 
         esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
 
-        upsampler = RealESRGANer(
+        upscaler = RealESRGAN(
             scale=netscale,
-            model_path=str(models_path / esrgan_model_path),
+            model_path=models_path / esrgan_model_path,
             model=rrdbnet_model,
             half=False,
             tile=self.tile_size,
@@ -102,15 +102,9 @@ class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
 
         # prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
         # TODO: This strips the alpha... is that okay?
-        cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
-
-        # We can pass an `outscale` value here, but it just resizes the image by that factor after
-        # upscaling, so it's kinda pointless for our purposes. If you want something other than 4x
-        # upscaling, you'll need to add a resize node after this one.
-        upscaled_image, img_mode = upsampler.enhance(cv_image)
-
-        # back to PIL
-        pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
+        cv2_image = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
+        upscaled_image = upscaler.upscale(cv2_image)
+        pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
 
         torch.cuda.empty_cache()
         if choose_torch_device() == torch.device("mps"):
@@ -124,7 +118,7 @@ class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
             session_id=context.graph_execution_state_id,
             is_intermediate=self.is_intermediate,
             metadata=self.metadata,
-            workflow=self.workflow,
+            workflow=context.workflow,
         )
 
         return ImageOutput(

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -4,7 +4,7 @@ from typing import Optional, cast
 
 from invokeai.app.services.image_records.image_records_common import ImageRecord, deserialize_image_record
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 
 from .board_image_records_base import BoardImageRecordStorageBase
 

invokeai/app/services/board_records/board_records_sqlite.py

@@ -3,7 +3,7 @@ import threading
 from typing import Union, cast
 
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.app.util.misc import uuid_string
 
 from .board_records_base import BoardRecordStorageBase

invokeai/app/services/config/config_base.py

@@ -15,7 +15,7 @@ import os
 import sys
 from argparse import ArgumentParser
 from pathlib import Path
-from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
+from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
 
 from omegaconf import DictConfig, ListConfig, OmegaConf
 from pydantic_settings import BaseSettings, SettingsConfigDict
@@ -24,10 +24,7 @@ from invokeai.app.services.config.config_common import PagingArgumentParser, int
 
 
 class InvokeAISettings(BaseSettings):
-    """
-    Runtime configuration settings in which default values are
-    read from an omegaconf .yaml file.
-    """
+    """Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
 
     initconf: ClassVar[Optional[DictConfig]] = None
     argparse_groups: ClassVar[Dict] = {}
@@ -35,6 +32,7 @@ class InvokeAISettings(BaseSettings):
     model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
 
     def parse_args(self, argv: Optional[list] = sys.argv[1:]):
+        """Call to parse command-line arguments."""
         parser = self.get_parser()
         opt, unknown_opts = parser.parse_known_args(argv)
         if len(unknown_opts) > 0:
@@ -49,20 +47,19 @@ class InvokeAISettings(BaseSettings):
                 setattr(self, name, value)
 
     def to_yaml(self) -> str:
-        """
-        Return a YAML string representing our settings. This can be used
-        as the contents of `invokeai.yaml` to restore settings later.
-        """
+        """Return a YAML string representing our settings. This can be used as the contents of `invokeai.yaml` to restore settings later."""
         cls = self.__class__
         type = get_args(get_type_hints(cls)["type"])[0]
-        field_dict = {type: {}}
+        field_dict: Dict[str, Dict[str, Any]] = {type: {}}
         for name, field in self.model_fields.items():
             if name in cls._excluded_from_yaml():
                 continue
+            assert isinstance(field.json_schema_extra, dict)
             category = (
                 field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
             )
             value = getattr(self, name)
+            assert isinstance(category, str)
             if category not in field_dict[type]:
                 field_dict[type][category] = {}
             # keep paths as strings to make it easier to read
@@ -72,6 +69,7 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def add_parser_arguments(cls, parser):
+        """Dynamically create arguments for a settings parser."""
         if "type" in get_type_hints(cls):
             settings_stanza = get_args(get_type_hints(cls)["type"])[0]
         else:
@@ -116,6 +114,7 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def cmd_name(cls, command_field: str = "type") -> str:
+        """Return the category of a setting."""
         hints = get_type_hints(cls)
         if command_field in hints:
             return get_args(hints[command_field])[0]
@@ -124,6 +123,7 @@ class InvokeAISettings(BaseSettings):
 
     @classmethod
     def get_parser(cls) -> ArgumentParser:
+        """Get the command-line parser for a setting."""
         parser = PagingArgumentParser(
             prog=cls.cmd_name(),
             description=cls.__doc__,
@@ -152,10 +152,14 @@ class InvokeAISettings(BaseSettings):
             "free_gpu_mem",
             "xformers_enabled",
             "tiled_decode",
+            "lora_dir",
+            "embedding_dir",
+            "controlnet_dir",
         ]
 
     @classmethod
     def add_field_argument(cls, command_parser, name: str, field, default_override=None):
+        """Add the argparse arguments for a setting parser."""
         field_type = get_type_hints(cls).get(name)
         default = (
             default_override

invokeai/app/services/config/config_default.py

@@ -177,6 +177,7 @@ from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hint
 
 from omegaconf import DictConfig, OmegaConf
 from pydantic import Field, TypeAdapter
+from pydantic.config import JsonDict
 from pydantic_settings import SettingsConfigDict
 
 from .config_base import InvokeAISettings
@@ -188,28 +189,24 @@ DEFAULT_MAX_VRAM = 0.5
 
 
 class Categories(object):
-    WebServer = {"category": "Web Server"}
-    Features = {"category": "Features"}
-    Paths = {"category": "Paths"}
-    Logging = {"category": "Logging"}
-    Development = {"category": "Development"}
-    Other = {"category": "Other"}
-    ModelCache = {"category": "Model Cache"}
-    Device = {"category": "Device"}
-    Generation = {"category": "Generation"}
-    Queue = {"category": "Queue"}
-    Nodes = {"category": "Nodes"}
-    MemoryPerformance = {"category": "Memory/Performance"}
+    """Category headers for configuration variable groups."""
+
+    WebServer: JsonDict = {"category": "Web Server"}
+    Features: JsonDict = {"category": "Features"}
+    Paths: JsonDict = {"category": "Paths"}
+    Logging: JsonDict = {"category": "Logging"}
+    Development: JsonDict = {"category": "Development"}
+    Other: JsonDict = {"category": "Other"}
+    ModelCache: JsonDict = {"category": "Model Cache"}
+    Device: JsonDict = {"category": "Device"}
+    Generation: JsonDict = {"category": "Generation"}
+    Queue: JsonDict = {"category": "Queue"}
+    Nodes: JsonDict = {"category": "Nodes"}
+    MemoryPerformance: JsonDict = {"category": "Memory/Performance"}
 
 
 class InvokeAIAppConfig(InvokeAISettings):
-    """
-    Generate images using Stable Diffusion. Use "invokeai" to launch
-    the command-line client (recommended for experts only), or
-    "invokeai-web" to launch the web server. Global options
-    can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
-    setting environment variables INVOKEAI_<setting>.
-    """
+    """Configuration object for InvokeAI App."""
 
     singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
     singleton_init: ClassVar[Optional[Dict]] = None
@@ -234,15 +231,12 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     # PATHS
     root                : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
-    autoimport_dir      : Optional[Path] = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
-    lora_dir            : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
-    embedding_dir       : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
-    controlnet_dir      : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
-    conf_path           : Optional[Path] = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
-    models_dir          : Optional[Path] = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
-    legacy_conf_dir     : Optional[Path] = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
-    db_dir              : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
-    outdir              : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
+    autoimport_dir      : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
+    conf_path           : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
+    models_dir          : Path = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
+    legacy_conf_dir     : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
+    db_dir              : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
+    outdir              : Path = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
     use_memory_db       : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
     custom_nodes_dir    : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes', json_schema_extra=Categories.Paths)
     from_file           : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths)
@@ -285,11 +279,15 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     # DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
     always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)
-    free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
     max_cache_size      : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.MemoryPerformance)
     max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.MemoryPerformance)
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.MemoryPerformance)
     tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.MemoryPerformance)
+    lora_dir            : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
+    embedding_dir       : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
+    controlnet_dir      : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
+    # this is not referred to in the source code and can be removed entirely
+    #free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
 
     # See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
     # fmt: on
@@ -303,8 +301,8 @@ class InvokeAIAppConfig(InvokeAISettings):
         clobber=False,
     ):
         """
-        Update settings with contents of init file, environment, and
-        command-line settings.
+        Update settings with contents of init file, environment, and command-line settings.
+
         :param conf: alternate Omegaconf dictionary object
         :param argv: aternate sys.argv list
         :param clobber: ovewrite any initialization parameters passed during initialization
@@ -337,9 +335,7 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @classmethod
     def get_config(cls, **kwargs) -> InvokeAIAppConfig:
-        """
-        This returns a singleton InvokeAIAppConfig configuration object.
-        """
+        """Return a singleton InvokeAIAppConfig configuration object."""
         if (
             cls.singleton_config is None
             or type(cls.singleton_config) is not cls
@@ -351,9 +347,7 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def root_path(self) -> Path:
-        """
-        Path to the runtime root directory
-        """
+        """Path to the runtime root directory."""
         if self.root:
             root = Path(self.root).expanduser().absolute()
         else:
@@ -363,9 +357,7 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def root_dir(self) -> Path:
-        """
-        Alias for above.
-        """
+        """Alias for above."""
         return self.root_path
 
     def _resolve(self, partial_path: Path) -> Path:
@@ -373,108 +365,95 @@ class InvokeAIAppConfig(InvokeAISettings):
 
     @property
     def init_file_path(self) -> Path:
-        """
-        Path to invokeai.yaml
-        """
-        return self._resolve(INIT_FILE)
+        """Path to invokeai.yaml."""
+        resolved_path = self._resolve(INIT_FILE)
+        assert resolved_path is not None
+        return resolved_path
 
     @property
-    def output_path(self) -> Path:
-        """
-        Path to defaults outputs directory.
-        """
+    def output_path(self) -> Optional[Path]:
+        """Path to defaults outputs directory."""
         return self._resolve(self.outdir)
 
     @property
     def db_path(self) -> Path:
-        """
-        Path to the invokeai.db file.
-        """
-        return self._resolve(self.db_dir) / DB_FILE
+        """Path to the invokeai.db file."""
+        db_dir = self._resolve(self.db_dir)
+        assert db_dir is not None
+        return db_dir / DB_FILE
 
     @property
-    def model_conf_path(self) -> Path:
-        """
-        Path to models configuration file.
-        """
+    def model_conf_path(self) -> Optional[Path]:
+        """Path to models configuration file."""
         return self._resolve(self.conf_path)
 
     @property
-    def legacy_conf_path(self) -> Path:
-        """
-        Path to directory of legacy configuration files (e.g. v1-inference.yaml)
-        """
+    def legacy_conf_path(self) -> Optional[Path]:
+        """Path to directory of legacy configuration files (e.g. v1-inference.yaml)."""
         return self._resolve(self.legacy_conf_dir)
 
     @property
-    def models_path(self) -> Path:
-        """
-        Path to the models directory
-        """
+    def models_path(self) -> Optional[Path]:
+        """Path to the models directory."""
         return self._resolve(self.models_dir)
 
     @property
     def custom_nodes_path(self) -> Path:
-        """
-        Path to the custom nodes directory
-        """
-        return self._resolve(self.custom_nodes_dir)
+        """Path to the custom nodes directory."""
+        custom_nodes_path = self._resolve(self.custom_nodes_dir)
+        assert custom_nodes_path is not None
+        return custom_nodes_path
 
     # the following methods support legacy calls leftover from the Globals era
     @property
     def full_precision(self) -> bool:
-        """Return true if precision set to float32"""
+        """Return true if precision set to float32."""
         return self.precision == "float32"
 
     @property
     def try_patchmatch(self) -> bool:
-        """Return true if patchmatch true"""
+        """Return true if patchmatch true."""
         return self.patchmatch
 
     @property
     def nsfw_checker(self) -> bool:
-        """NSFW node is always active and disabled from Web UIe"""
+        """Return value for NSFW checker. The NSFW node is always active and disabled from Web UI."""
         return True
 
     @property
     def invisible_watermark(self) -> bool:
-        """invisible watermark node is always active and disabled from Web UIe"""
+        """Return value of invisible watermark. It is always active and disabled from Web UI."""
         return True
 
     @property
     def ram_cache_size(self) -> Union[Literal["auto"], float]:
+        """Return the ram cache size using the legacy or modern setting."""
         return self.max_cache_size or self.ram
 
     @property
     def vram_cache_size(self) -> Union[Literal["auto"], float]:
+        """Return the vram cache size using the legacy or modern setting."""
         return self.max_vram_cache_size or self.vram
 
     @property
     def use_cpu(self) -> bool:
+        """Return true if the device is set to CPU or the always_use_cpu flag is set."""
         return self.always_use_cpu or self.device == "cpu"
 
     @property
     def disable_xformers(self) -> bool:
-        """
-        Return true if enable_xformers is false (reversed logic)
-        and attention type is not set to xformers.
-        """
+        """Return true if enable_xformers is false (reversed logic) and attention type is not set to xformers."""
         disabled_in_config = not self.xformers_enabled
         return disabled_in_config and self.attention_type != "xformers"
 
     @staticmethod
     def find_root() -> Path:
-        """
-        Choose the runtime root directory when not specified on command line or
-        init file.
-        """
+        """Choose the runtime root directory when not specified on command line or init file."""
         return _find_root()
 
 
 def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
-    """
-    Legacy function which returns InvokeAIAppConfig.get_config()
-    """
+    """Legacy function which returns InvokeAIAppConfig.get_config()."""
     return InvokeAIAppConfig.get_config(**kwargs)
 
 

invokeai/app/services/image_files/image_files_base.py

@@ -4,7 +4,8 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
+from invokeai.app.invocations.baseinvocation import MetadataField
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 
 class ImageFileStorageBase(ABC):
@@ -33,7 +34,7 @@ class ImageFileStorageBase(ABC):
         image: PILImageType,
         image_name: str,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
         thumbnail_size: int = 256,
     ) -> None:
         """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
@@ -43,3 +44,8 @@ class ImageFileStorageBase(ABC):
     def delete(self, image_name: str) -> None:
         """Deletes an image and its thumbnail (if one exists)."""
         pass
+
+    @abstractmethod
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
+        """Gets the workflow of an image."""
+        pass

invokeai/app/services/image_files/image_files_disk.py

@@ -7,8 +7,9 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
-from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
+from invokeai.app.invocations.baseinvocation import MetadataField
 from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
 
 from .image_files_base import ImageFileStorageBase
@@ -56,7 +57,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
         image: PILImageType,
         image_name: str,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
         thumbnail_size: int = 256,
     ) -> None:
         try:
@@ -64,12 +65,19 @@ class DiskImageFileStorage(ImageFileStorageBase):
             image_path = self.get_path(image_name)
 
             pnginfo = PngImagePlugin.PngInfo()
+            info_dict = {}
 
             if metadata is not None:
-                pnginfo.add_text("invokeai_metadata", metadata.model_dump_json())
+                metadata_json = metadata.model_dump_json()
+                info_dict["invokeai_metadata"] = metadata_json
+                pnginfo.add_text("invokeai_metadata", metadata_json)
             if workflow is not None:
-                pnginfo.add_text("invokeai_workflow", workflow.model_dump_json())
+                workflow_json = workflow.model_dump_json()
+                info_dict["invokeai_workflow"] = workflow_json
+                pnginfo.add_text("invokeai_workflow", workflow_json)
 
+            # When saving the image, the image object's info field is not populated. We need to set it
+            image.info = info_dict
             image.save(
                 image_path,
                 "PNG",
@@ -121,6 +129,13 @@ class DiskImageFileStorage(ImageFileStorageBase):
         path = path if isinstance(path, Path) else Path(path)
         return path.exists()
 
+    def get_workflow(self, image_name: str) -> WorkflowWithoutID | None:
+        image = self.get(image_name)
+        workflow = image.info.get("invokeai_workflow", None)
+        if workflow is not None:
+            return WorkflowWithoutID.model_validate_json(workflow)
+        return None
+
     def __validate_storage_folders(self) -> None:
         """Checks if the required output folders exist and create them if they don't"""
         folders: list[Path] = [self.__output_folder, self.__thumbnails_folder]

invokeai/app/services/image_records/image_records_base.py

@@ -75,6 +75,7 @@ class ImageRecordStorageBase(ABC):
         image_category: ImageCategory,
         width: int,
         height: int,
+        has_workflow: bool,
         is_intermediate: Optional[bool] = False,
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,

invokeai/app/services/image_records/image_records_common.py

@@ -100,6 +100,7 @@ IMAGE_DTO_COLS = ", ".join(
             "height",
             "session_id",
             "node_id",
+            "has_workflow",
             "is_intermediate",
             "created_at",
             "updated_at",
@@ -145,6 +146,7 @@ class ImageRecord(BaseModelExcludeNull):
     """The node ID that generated this image, if it is a generated image."""
     starred: bool = Field(description="Whether this image is starred.")
     """Whether this image is starred."""
+    has_workflow: bool = Field(description="Whether this image has a workflow.")
 
 
 class ImageRecordChanges(BaseModelExcludeNull, extra="allow"):
@@ -188,6 +190,7 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
     deleted_at = image_dict.get("deleted_at", get_iso_timestamp())
     is_intermediate = image_dict.get("is_intermediate", False)
     starred = image_dict.get("starred", False)
+    has_workflow = image_dict.get("has_workflow", False)
 
     return ImageRecord(
         image_name=image_name,
@@ -202,4 +205,5 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
         deleted_at=deleted_at,
         is_intermediate=is_intermediate,
         starred=starred,
+        has_workflow=has_workflow,
     )

invokeai/app/services/image_records/image_records_sqlite.py

@@ -5,7 +5,7 @@ from typing import Optional, Union, cast
 
 from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 
 from .image_records_base import ImageRecordStorageBase
 from .image_records_common import (
@@ -117,6 +117,16 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             """
         )
 
+        self._cursor.execute("PRAGMA table_info(images)")
+        columns = [column[1] for column in self._cursor.fetchall()]
+        if "has_workflow" not in columns:
+            self._cursor.execute(
+                """--sql
+                ALTER TABLE images
+                ADD COLUMN has_workflow BOOLEAN DEFAULT FALSE;
+                """
+            )
+
     def get(self, image_name: str) -> ImageRecord:
         try:
             self._lock.acquire()
@@ -408,6 +418,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         image_category: ImageCategory,
         width: int,
         height: int,
+        has_workflow: bool,
         is_intermediate: Optional[bool] = False,
         starred: Optional[bool] = False,
         session_id: Optional[str] = None,
@@ -429,9 +440,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     session_id,
                     metadata,
                     is_intermediate,
-                    starred
+                    starred,
+                    has_workflow
                     )
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
                 """,
                 (
                     image_name,
@@ -444,6 +456,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     metadata_json,
                     is_intermediate,
                     starred,
+                    has_workflow,
                 ),
             )
             self._conn.commit()

invokeai/app/services/images/images_base.py

@@ -3,7 +3,7 @@ from typing import Callable, Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
+from invokeai.app.invocations.baseinvocation import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
     ImageRecord,
@@ -12,6 +12,7 @@ from invokeai.app.services.image_records.image_records_common import (
 )
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 
 class ImageServiceABC(ABC):
@@ -51,7 +52,7 @@ class ImageServiceABC(ABC):
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
         pass
@@ -85,6 +86,11 @@ class ImageServiceABC(ABC):
         """Gets an image's metadata."""
         pass
 
+    @abstractmethod
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
+        """Gets an image's workflow."""
+        pass
+
     @abstractmethod
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         """Gets an image's path."""

invokeai/app/services/images/images_common.py

@@ -24,11 +24,6 @@ class ImageDTO(ImageRecord, ImageUrlsDTO):
         default=None, description="The id of the board the image belongs to, if one exists."
     )
     """The id of the board the image belongs to, if one exists."""
-    workflow_id: Optional[str] = Field(
-        default=None,
-        description="The workflow that generated this image.",
-    )
-    """The workflow that generated this image."""
 
 
 def image_record_to_dto(
@@ -36,7 +31,6 @@ def image_record_to_dto(
     image_url: str,
     thumbnail_url: str,
     board_id: Optional[str],
-    workflow_id: Optional[str],
 ) -> ImageDTO:
     """Converts an image record to an image DTO."""
     return ImageDTO(
@@ -44,5 +38,4 @@ def image_record_to_dto(
         image_url=image_url,
         thumbnail_url=thumbnail_url,
         board_id=board_id,
-        workflow_id=workflow_id,
     )

invokeai/app/services/images/images_default.py

@@ -2,9 +2,10 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
+from invokeai.app.invocations.baseinvocation import MetadataField
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 from ..image_files.image_files_common import (
     ImageFileDeleteException,
@@ -42,7 +43,7 @@ class ImageService(ImageServiceABC):
         board_id: Optional[str] = None,
         is_intermediate: Optional[bool] = False,
         metadata: Optional[MetadataField] = None,
-        workflow: Optional[WorkflowField] = None,
+        workflow: Optional[WorkflowWithoutID] = None,
     ) -> ImageDTO:
         if image_origin not in ResourceOrigin:
             raise InvalidOriginException
@@ -55,12 +56,6 @@ class ImageService(ImageServiceABC):
         (width, height) = image.size
 
         try:
-            if workflow is not None:
-                created_workflow = self.__invoker.services.workflow_records.create(workflow)
-                workflow_id = created_workflow.model_dump()["id"]
-            else:
-                workflow_id = None
-
             # TODO: Consider using a transaction here to ensure consistency between storage and database
             self.__invoker.services.image_records.save(
                 # Non-nullable fields
@@ -69,6 +64,7 @@ class ImageService(ImageServiceABC):
                 image_category=image_category,
                 width=width,
                 height=height,
+                has_workflow=workflow is not None,
                 # Meta fields
                 is_intermediate=is_intermediate,
                 # Nullable fields
@@ -78,8 +74,6 @@ class ImageService(ImageServiceABC):
             )
             if board_id is not None:
                 self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
-            if workflow_id is not None:
-                self.__invoker.services.workflow_image_records.create(workflow_id=workflow_id, image_name=image_name)
             self.__invoker.services.image_files.save(
                 image_name=image_name, image=image, metadata=metadata, workflow=workflow
             )
@@ -143,7 +137,6 @@ class ImageService(ImageServiceABC):
                 image_url=self.__invoker.services.urls.get_image_url(image_name),
                 thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
                 board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name),
-                workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name),
             )
 
             return image_dto
@@ -164,18 +157,15 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Problem getting image DTO")
             raise e
 
-    def get_workflow(self, image_name: str) -> Optional[WorkflowField]:
+    def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:
         try:
-            workflow_id = self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name)
-            if workflow_id is None:
-                return None
-            return self.__invoker.services.workflow_records.get(workflow_id)
-        except ImageRecordNotFoundException:
-            self.__invoker.services.logger.error("Image record not found")
+            return self.__invoker.services.image_files.get_workflow(image_name)
+        except ImageFileNotFoundException:
+            self.__invoker.services.logger.error("Image file not found")
+            raise
+        except Exception:
+            self.__invoker.services.logger.error("Problem getting image workflow")
             raise
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image DTO")
-            raise e
 
     def get_path(self, image_name: str, thumbnail: bool = False) -> str:
         try:
@@ -223,7 +213,6 @@ class ImageService(ImageServiceABC):
                     image_url=self.__invoker.services.urls.get_image_url(r.image_name),
                     thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
                     board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
-                    workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
                 )
                 for r in results.items
             ]

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -108,6 +108,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                                 queue_item_id=queue_item.session_queue_item_id,
                                 queue_id=queue_item.session_queue_id,
                                 queue_batch_id=queue_item.session_queue_batch_id,
+                                workflow=queue_item.workflow,
                             )
                         )
 
@@ -178,6 +179,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                             session_queue_item_id=queue_item.session_queue_item_id,
                             session_queue_id=queue_item.session_queue_id,
                             graph_execution_state=graph_execution_state,
+                            workflow=queue_item.workflow,
                             invoke_all=True,
                         )
                     except Exception as e:

invokeai/app/services/invocation_queue/invocation_queue_common.py

@@ -1,9 +1,12 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import time
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+
 
 class InvocationQueueItem(BaseModel):
     graph_execution_state_id: str = Field(description="The ID of the graph execution state")
@@ -15,5 +18,6 @@ class InvocationQueueItem(BaseModel):
     session_queue_batch_id: str = Field(
         description="The ID of the session batch from which this invocation queue item came"
     )
+    workflow: Optional[WorkflowWithoutID] = Field(description="The workflow associated with this queue item")
     invoke_all: bool = Field(default=False)
     timestamp: float = Field(default_factory=time.time)

invokeai/app/services/invocation_services.py

@@ -28,7 +28,6 @@ if TYPE_CHECKING:
     from .session_queue.session_queue_base import SessionQueueBase
     from .shared.graph import GraphExecutionState, LibraryGraph
     from .urls.urls_base import UrlServiceBase
-    from .workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
     from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
 
 
@@ -59,7 +58,6 @@ class InvocationServices:
     invocation_cache: "InvocationCacheBase"
     names: "NameServiceBase"
     urls: "UrlServiceBase"
-    workflow_image_records: "WorkflowImageRecordsStorageBase"
     workflow_records: "WorkflowRecordsStorageBase"
 
     def __init__(
@@ -87,7 +85,6 @@ class InvocationServices:
         invocation_cache: "InvocationCacheBase",
         names: "NameServiceBase",
         urls: "UrlServiceBase",
-        workflow_image_records: "WorkflowImageRecordsStorageBase",
         workflow_records: "WorkflowRecordsStorageBase",
     ):
         self.board_images = board_images
@@ -113,5 +110,4 @@ class InvocationServices:
         self.invocation_cache = invocation_cache
         self.names = names
         self.urls = urls
-        self.workflow_image_records = workflow_image_records
         self.workflow_records = workflow_records

invokeai/app/services/invoker.py

@@ -2,6 +2,8 @@
 
 from typing import Optional
 
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+
 from .invocation_queue.invocation_queue_common import InvocationQueueItem
 from .invocation_services import InvocationServices
 from .shared.graph import Graph, GraphExecutionState
@@ -22,6 +24,7 @@ class Invoker:
         session_queue_item_id: int,
         session_queue_batch_id: str,
         graph_execution_state: GraphExecutionState,
+        workflow: Optional[WorkflowWithoutID] = None,
         invoke_all: bool = False,
     ) -> Optional[str]:
         """Determines the next node to invoke and enqueues it, preparing if needed.
@@ -43,6 +46,7 @@ class Invoker:
                 session_queue_batch_id=session_queue_batch_id,
                 graph_execution_state_id=graph_execution_state.id,
                 invocation_id=invocation.id,
+                workflow=workflow,
                 invoke_all=invoke_all,
             )
         )

invokeai/app/services/item_storage/item_storage_sqlite.py

@@ -5,7 +5,7 @@ from typing import Generic, Optional, TypeVar, get_args
 from pydantic import BaseModel, TypeAdapter
 
 from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 
 from .item_storage_base import ItemStorageABC
 

invokeai/app/services/latents_storage/latents_storage_disk.py

@@ -5,6 +5,8 @@ from typing import Union
 
 import torch
 
+from invokeai.app.services.invoker import Invoker
+
 from .latents_storage_base import LatentsStorageBase
 
 
@@ -17,6 +19,10 @@ class DiskLatentsStorage(LatentsStorageBase):
         self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
         self.__output_folder.mkdir(parents=True, exist_ok=True)
 
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        self._delete_all_latents()
+
     def get(self, name: str) -> torch.Tensor:
         latent_path = self.get_path(name)
         return torch.load(latent_path)
@@ -32,3 +38,21 @@ class DiskLatentsStorage(LatentsStorageBase):
 
     def get_path(self, name: str) -> Path:
         return self.__output_folder / name
+
+    def _delete_all_latents(self) -> None:
+        """
+        Deletes all latents from disk.
+        Must be called after we have access to `self._invoker` (e.g. in `start()`).
+        """
+        deleted_latents_count = 0
+        freed_space = 0
+        for latents_file in Path(self.__output_folder).glob("*"):
+            if latents_file.is_file():
+                freed_space += latents_file.stat().st_size
+                deleted_latents_count += 1
+                latents_file.unlink()
+        if deleted_latents_count > 0:
+            freed_space_in_mb = round(freed_space / 1024 / 1024, 2)
+            self._invoker.services.logger.info(
+                f"Deleted {deleted_latents_count} latents files (freed {freed_space_in_mb}MB)"
+            )

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

@@ -5,6 +5,8 @@ from typing import Dict, Optional
 
 import torch
 
+from invokeai.app.services.invoker import Invoker
+
 from .latents_storage_base import LatentsStorageBase
 
 
@@ -23,6 +25,18 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
         self.__cache_ids = Queue()
         self.__max_cache_size = max_cache_size
 
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        start_op = getattr(self.__underlying_storage, "start", None)
+        if callable(start_op):
+            start_op(invoker)
+
+    def stop(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        stop_op = getattr(self.__underlying_storage, "stop", None)
+        if callable(stop_op):
+            stop_op(invoker)
+
     def get(self, name: str) -> torch.Tensor:
         cache_item = self.__get_cache(name)
         if cache_item is not None:

invokeai/app/services/model_records/model_records_sql.py

@@ -48,12 +48,11 @@ from typing import List, Optional, Union
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
-    ModelConfigBase,
     ModelConfigFactory,
     ModelType,
 )
 
-from ..shared.sqlite import SqliteDatabase
+from ..shared.sqlite.sqlite_database import SqliteDatabase
 from .model_records_base import (
     CONFIG_FILE_VERSION,
     DuplicateModelException,
@@ -158,7 +157,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             ("version", CONFIG_FILE_VERSION),
         )
 
-    def add_model(self, key: str, config: Union[dict, ModelConfigBase]) -> AnyModelConfig:
+    def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
         """
         Add a model to the database.
 
@@ -255,7 +254,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                 self._db.conn.rollback()
                 raise e
 
-    def update_model(self, key: str, config: ModelConfigBase) -> AnyModelConfig:
+    def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
         """
         Update the model, returning the updated version.
 
@@ -368,7 +367,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
 
-    def search_by_path(self, path: Union[str, Path]) -> List[ModelConfigBase]:
+    def search_by_path(self, path: Union[str, Path]) -> List[AnyModelConfig]:
         """Return models with the indicated path."""
         results = []
         with self._db.lock:
@@ -382,7 +381,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
 
-    def search_by_hash(self, hash: str) -> List[ModelConfigBase]:
+    def search_by_hash(self, hash: str) -> List[AnyModelConfig]:
         """Return models with the indicated original_hash."""
         results = []
         with self._db.lock:

invokeai/app/services/session_processor/session_processor_default.py

@@ -1,7 +1,6 @@
 import traceback
-from threading import BoundedSemaphore
+from threading import BoundedSemaphore, Thread
 from threading import Event as ThreadEvent
-from threading import Thread
 from typing import Optional
 
 from fastapi_events.handlers.local import local_handler
@@ -115,6 +114,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
                                 session_queue_id=queue_item.queue_id,
                                 session_queue_item_id=queue_item.item_id,
                                 graph_execution_state=queue_item.session,
+                                workflow=queue_item.workflow,
                                 invoke_all=True,
                             )
                             queue_item = None

invokeai/app/services/session_queue/session_queue_common.py

@@ -8,6 +8,10 @@ from pydantic_core import to_jsonable_python
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.shared.graph import Graph, GraphExecutionState, NodeNotFoundError
+from invokeai.app.services.workflow_records.workflow_records_common import (
+    WorkflowWithoutID,
+    WorkflowWithoutIDValidator,
+)
 from invokeai.app.util.misc import uuid_string
 
 # region Errors
@@ -66,6 +70,9 @@ class Batch(BaseModel):
     batch_id: str = Field(default_factory=uuid_string, description="The ID of the batch")
     data: Optional[BatchDataCollection] = Field(default=None, description="The batch data collection.")
     graph: Graph = Field(description="The graph to initialize the session with")
+    workflow: Optional[WorkflowWithoutID] = Field(
+        default=None, description="The workflow to initialize the session with"
+    )
     runs: int = Field(
         default=1, ge=1, description="Int stating how many times to iterate through all possible batch indices"
     )
@@ -164,6 +171,14 @@ def get_session(queue_item_dict: dict) -> GraphExecutionState:
     return session
 
 
+def get_workflow(queue_item_dict: dict) -> Optional[WorkflowWithoutID]:
+    workflow_raw = queue_item_dict.get("workflow", None)
+    if workflow_raw is not None:
+        workflow = WorkflowWithoutIDValidator.validate_json(workflow_raw, strict=False)
+        return workflow
+    return None
+
+
 class SessionQueueItemWithoutGraph(BaseModel):
     """Session queue item without the full graph. Used for serialization."""
 
@@ -213,12 +228,16 @@ class SessionQueueItemDTO(SessionQueueItemWithoutGraph):
 
 class SessionQueueItem(SessionQueueItemWithoutGraph):
     session: GraphExecutionState = Field(description="The fully-populated session to be executed")
+    workflow: Optional[WorkflowWithoutID] = Field(
+        default=None, description="The workflow associated with this queue item"
+    )
 
     @classmethod
     def queue_item_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItem":
         # must parse these manually
         queue_item_dict["field_values"] = get_field_values(queue_item_dict)
         queue_item_dict["session"] = get_session(queue_item_dict)
+        queue_item_dict["workflow"] = get_workflow(queue_item_dict)
         return SessionQueueItem(**queue_item_dict)
 
     model_config = ConfigDict(
@@ -334,7 +353,7 @@ def populate_graph(graph: Graph, node_field_values: Iterable[NodeFieldValue]) ->
 
 def create_session_nfv_tuples(
     batch: Batch, maximum: int
-) -> Generator[tuple[GraphExecutionState, list[NodeFieldValue]], None, None]:
+) -> Generator[tuple[GraphExecutionState, list[NodeFieldValue], Optional[WorkflowWithoutID]], None, None]:
     """
     Create all graph permutations from the given batch data and graph. Yields tuples
     of the form (graph, batch_data_items) where batch_data_items is the list of BatchDataItems
@@ -365,7 +384,7 @@ def create_session_nfv_tuples(
                 return
             flat_node_field_values = list(chain.from_iterable(d))
             graph = populate_graph(batch.graph, flat_node_field_values)
-            yield (GraphExecutionState(graph=graph), flat_node_field_values)
+            yield (GraphExecutionState(graph=graph), flat_node_field_values, batch.workflow)
             count += 1
 
 
@@ -391,12 +410,14 @@ def calc_session_count(batch: Batch) -> int:
 class SessionQueueValueToInsert(NamedTuple):
     """A tuple of values to insert into the session_queue table"""
 
+    # Careful with the ordering of this - it must match the insert statement
     queue_id: str  # queue_id
     session: str  # session json
     session_id: str  # session_id
     batch_id: str  # batch_id
     field_values: Optional[str]  # field_values json
     priority: int  # priority
+    workflow: Optional[str]  # workflow json
 
 
 ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
@@ -404,7 +425,7 @@ ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
 
 def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new_queue_items: int) -> ValuesToInsert:
     values_to_insert: ValuesToInsert = []
-    for session, field_values in create_session_nfv_tuples(batch, max_new_queue_items):
+    for session, field_values, workflow in create_session_nfv_tuples(batch, max_new_queue_items):
         # sessions must have unique id
         session.id = uuid_string()
         values_to_insert.append(
@@ -416,6 +437,7 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new
                 # must use pydantic_encoder bc field_values is a list of models
                 json.dumps(field_values, default=to_jsonable_python) if field_values else None,  # field_values (json)
                 priority,  # priority
+                json.dumps(workflow, default=to_jsonable_python) if workflow else None,  # workflow (json)
             )
         )
     return values_to_insert

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -28,7 +28,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     prepare_values_to_insert,
 )
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 
 
 class SqliteSessionQueue(SessionQueueBase):
@@ -42,7 +42,8 @@ class SqliteSessionQueue(SessionQueueBase):
         self._set_in_progress_to_canceled()
         prune_result = self.prune(DEFAULT_QUEUE_ID)
         local_handler.register(event_name=EventServiceBase.queue_event, _func=self._on_session_event)
-        self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
+        if prune_result.deleted > 0:
+            self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
@@ -198,6 +199,15 @@ class SqliteSessionQueue(SessionQueueBase):
                 """
             )
 
+            self.__cursor.execute("PRAGMA table_info(session_queue)")
+            columns = [column[1] for column in self.__cursor.fetchall()]
+            if "workflow" not in columns:
+                self.__cursor.execute(
+                    """--sql
+                    ALTER TABLE session_queue ADD COLUMN workflow TEXT;
+                    """
+                )
+
             self.__conn.commit()
         except Exception:
             self.__conn.rollback()
@@ -280,8 +290,8 @@ class SqliteSessionQueue(SessionQueueBase):
 
             self.__cursor.executemany(
                 """--sql
-                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority)
-                VALUES (?, ?, ?, ?, ?, ?)
+                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
                 """,
                 values_to_insert,
             )

invokeai/app/services/shared/graph.py

@@ -49,7 +49,7 @@ class Edge(BaseModel):
 
 def get_output_field(node: BaseInvocation, field: str) -> Any:
     node_type = type(node)
-    node_outputs = get_type_hints(node_type.get_output_type())
+    node_outputs = get_type_hints(node_type.get_output_annotation())
     node_output_field = node_outputs.get(field) or None
     return node_output_field
 
@@ -188,7 +188,7 @@ class GraphInvocationOutput(BaseInvocationOutput):
 
 
 # TODO: Fill this out and move to invocations
-@invocation("graph")
+@invocation("graph", version="1.0.0")
 class GraphInvocation(BaseInvocation):
     """Execute a graph"""
 
@@ -205,29 +205,31 @@ class IterateInvocationOutput(BaseInvocationOutput):
     """Used to connect iteration outputs. Will be expanded to a specific output."""
 
     item: Any = OutputField(
-        description="The item being iterated over", title="Collection Item", ui_type=UIType.CollectionItem
+        description="The item being iterated over", title="Collection Item", ui_type=UIType._CollectionItem
     )
+    index: int = OutputField(description="The index of the item", title="Index")
+    total: int = OutputField(description="The total number of items", title="Total")
 
 
 # TODO: Fill this out and move to invocations
-@invocation("iterate", version="1.0.0")
+@invocation("iterate", version="1.1.0")
 class IterateInvocation(BaseInvocation):
     """Iterates over a list of items"""
 
     collection: list[Any] = InputField(
-        description="The list of items to iterate over", default_factory=list, ui_type=UIType.Collection
+        description="The list of items to iterate over", default=[], ui_type=UIType._Collection
     )
     index: int = InputField(description="The index, will be provided on executed iterators", default=0, ui_hidden=True)
 
     def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
         """Produces the outputs as values"""
-        return IterateInvocationOutput(item=self.collection[self.index])
+        return IterateInvocationOutput(item=self.collection[self.index], index=self.index, total=len(self.collection))
 
 
 @invocation_output("collect_output")
 class CollectInvocationOutput(BaseInvocationOutput):
     collection: list[Any] = OutputField(
-        description="The collection of input items", title="Collection", ui_type=UIType.Collection
+        description="The collection of input items", title="Collection", ui_type=UIType._Collection
     )
 
 
@@ -238,12 +240,12 @@ class CollectInvocation(BaseInvocation):
     item: Optional[Any] = InputField(
         default=None,
         description="The item to collect (all inputs must be of the same type)",
-        ui_type=UIType.CollectionItem,
+        ui_type=UIType._CollectionItem,
         title="Collection Item",
         input=Input.Connection,
     )
     collection: list[Any] = InputField(
-        description="The collection, will be provided on execution", default_factory=list, ui_hidden=True
+        description="The collection, will be provided on execution", default=[], ui_hidden=True
     )
 
     def invoke(self, context: InvocationContext) -> CollectInvocationOutput:
@@ -379,7 +381,7 @@ class Graph(BaseModel):
                 raise NodeNotFoundError(f"Edge destination node {edge.destination.node_id} does not exist in the graph")
 
             # output fields are not on the node object directly, they are on the output type
-            if edge.source.field not in source_node.get_output_type().model_fields:
+            if edge.source.field not in source_node.get_output_annotation().model_fields:
                 raise NodeFieldNotFoundError(
                     f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
                 )

invokeai/app/services/shared/sqlite.py

@@ -1,48 +0,0 @@
-import sqlite3
-import threading
-from logging import Logger
-
-from invokeai.app.services.config import InvokeAIAppConfig
-
-sqlite_memory = ":memory:"
-
-
-class SqliteDatabase:
-    conn: sqlite3.Connection
-    lock: threading.RLock
-    _logger: Logger
-    _config: InvokeAIAppConfig
-
-    def __init__(self, config: InvokeAIAppConfig, logger: Logger):
-        self._logger = logger
-        self._config = config
-
-        if self._config.use_memory_db:
-            location = sqlite_memory
-            logger.info("Using in-memory database")
-        else:
-            db_path = self._config.db_path
-            db_path.parent.mkdir(parents=True, exist_ok=True)
-            location = str(db_path)
-            self._logger.info(f"Using database at {location}")
-
-        self.conn = sqlite3.connect(location, check_same_thread=False)
-        self.lock = threading.RLock()
-        self.conn.row_factory = sqlite3.Row
-
-        if self._config.log_sql:
-            self.conn.set_trace_callback(self._logger.debug)
-
-        self.conn.execute("PRAGMA foreign_keys = ON;")
-
-    def clean(self) -> None:
-        try:
-            self.lock.acquire()
-            self.conn.execute("VACUUM;")
-            self.conn.commit()
-            self._logger.info("Cleaned database")
-        except Exception as e:
-            self._logger.error(f"Error cleaning database: {e}")
-            raise e
-        finally:
-            self.lock.release()

invokeai/app/services/shared/sqlite/sqlite_common.py

@@ -0,0 +1,10 @@
+from enum import Enum
+
+from invokeai.app.util.metaenum import MetaEnum
+
+sqlite_memory = ":memory:"
+
+
+class SQLiteDirection(str, Enum, metaclass=MetaEnum):
+    Ascending = "ASC"
+    Descending = "DESC"

invokeai/app/services/shared/sqlite/sqlite_database.py

@@ -0,0 +1,47 @@
+import sqlite3
+import threading
+from logging import Logger
+from pathlib import Path
+
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.shared.sqlite.sqlite_common import sqlite_memory
+
+
+class SqliteDatabase:
+    def __init__(self, config: InvokeAIAppConfig, logger: Logger):
+        self._logger = logger
+        self._config = config
+
+        if self._config.use_memory_db:
+            self.db_path = sqlite_memory
+            logger.info("Using in-memory database")
+        else:
+            db_path = self._config.db_path
+            db_path.parent.mkdir(parents=True, exist_ok=True)
+            self.db_path = str(db_path)
+            self._logger.info(f"Using database at {self.db_path}")
+
+        self.conn = sqlite3.connect(self.db_path, check_same_thread=False)
+        self.lock = threading.RLock()
+        self.conn.row_factory = sqlite3.Row
+
+        if self._config.log_sql:
+            self.conn.set_trace_callback(self._logger.debug)
+
+        self.conn.execute("PRAGMA foreign_keys = ON;")
+
+    def clean(self) -> None:
+        with self.lock:
+            try:
+                if self.db_path == sqlite_memory:
+                    return
+                initial_db_size = Path(self.db_path).stat().st_size
+                self.conn.execute("VACUUM;")
+                self.conn.commit()
+                final_db_size = Path(self.db_path).stat().st_size
+                freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
+                if freed_space_in_mb > 0:
+                    self._logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
+            except Exception as e:
+                self._logger.error(f"Error cleaning database: {e}")
+                raise

invokeai/app/services/workflow_image_records/workflow_image_records_base.py

@@ -1,23 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional
-
-
-class WorkflowImageRecordsStorageBase(ABC):
-    """Abstract base class for the one-to-many workflow-image relationship record storage."""
-
-    @abstractmethod
-    def create(
-        self,
-        workflow_id: str,
-        image_name: str,
-    ) -> None:
-        """Creates a workflow-image record."""
-        pass
-
-    @abstractmethod
-    def get_workflow_for_image(
-        self,
-        image_name: str,
-    ) -> Optional[str]:
-        """Gets an image's workflow id, if it has one."""
-        pass

invokeai/app/services/workflow_image_records/workflow_image_records_sqlite.py

@@ -1,122 +0,0 @@
-import sqlite3
-import threading
-from typing import Optional, cast
-
-from invokeai.app.services.shared.sqlite import SqliteDatabase
-from invokeai.app.services.workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
-
-
-class SqliteWorkflowImageRecordsStorage(WorkflowImageRecordsStorageBase):
-    """SQLite implementation of WorkflowImageRecordsStorageBase."""
-
-    _conn: sqlite3.Connection
-    _cursor: sqlite3.Cursor
-    _lock: threading.RLock
-
-    def __init__(self, db: SqliteDatabase) -> None:
-        super().__init__()
-        self._lock = db.lock
-        self._conn = db.conn
-        self._cursor = self._conn.cursor()
-
-        try:
-            self._lock.acquire()
-            self._create_tables()
-            self._conn.commit()
-        finally:
-            self._lock.release()
-
-    def _create_tables(self) -> None:
-        # Create the `workflow_images` junction table.
-        self._cursor.execute(
-            """--sql
-            CREATE TABLE IF NOT EXISTS workflow_images (
-                workflow_id TEXT NOT NULL,
-                image_name TEXT NOT NULL,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Soft delete, currently unused
-                deleted_at DATETIME,
-                -- enforce one-to-many relationship between workflows and images using PK
-                -- (we can extend this to many-to-many later)
-                PRIMARY KEY (image_name),
-                FOREIGN KEY (workflow_id) REFERENCES workflows (workflow_id) ON DELETE CASCADE,
-                FOREIGN KEY (image_name) REFERENCES images (image_name) ON DELETE CASCADE
-            );
-            """
-        )
-
-        # Add index for workflow id
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id ON workflow_images (workflow_id);
-            """
-        )
-
-        # Add index for workflow id, sorted by created_at
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id_created_at ON workflow_images (workflow_id, created_at);
-            """
-        )
-
-        # Add trigger for `updated_at`.
-        self._cursor.execute(
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_workflow_images_updated_at
-            AFTER UPDATE
-            ON workflow_images FOR EACH ROW
-            BEGIN
-                UPDATE workflow_images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE workflow_id = old.workflow_id AND image_name = old.image_name;
-            END;
-            """
-        )
-
-    def create(
-        self,
-        workflow_id: str,
-        image_name: str,
-    ) -> None:
-        """Creates a workflow-image record."""
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                INSERT INTO workflow_images (workflow_id, image_name)
-                VALUES (?, ?);
-                """,
-                (workflow_id, image_name),
-            )
-            self._conn.commit()
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise e
-        finally:
-            self._lock.release()
-
-    def get_workflow_for_image(
-        self,
-        image_name: str,
-    ) -> Optional[str]:
-        """Gets an image's workflow id, if it has one."""
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                SELECT workflow_id
-                FROM workflow_images
-                WHERE image_name = ?;
-                """,
-                (image_name,),
-            )
-            result = self._cursor.fetchone()
-            if result is None:
-                return None
-            return cast(str, result[0])
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise e
-        finally:
-            self._lock.release()

invokeai/app/services/workflow_records/default_workflows/README.md

@@ -0,0 +1,17 @@
+# Default Workflows
+
+Workflows placed in this directory will be synced to the `workflow_library` as
+_default workflows_ on app startup.
+
+- Default workflows are not editable by users. If they are loaded and saved,
+  they will save as a copy of the default workflow.
+- Default workflows must have the `meta.category` property set to `"default"`.
+  An exception will be raised during sync if this is not set correctly.
+- Default workflows appear on the "Default Workflows" tab of the Workflow
+  Library.
+
+After adding or updating default workflows, you **must** start the app up and
+load them to ensure:
+
+- The workflow loads without warning or errors
+- The workflow runs successfully

invokeai/app/services/workflow_records/default_workflows/TextToImage_SD15.json

@@ -0,0 +1,798 @@
+{
+  "name": "Text to Image - SD1.5",
+  "author": "InvokeAI",
+  "description": "Sample text to image workflow for Stable Diffusion 1.5/2",
+  "version": "1.1.0",
+  "contact": "invoke@invoke.ai",
+  "tags": "text2image, SD1.5, SD2, default",
+  "notes": "",
+  "exposedFields": [
+    {
+      "nodeId": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "fieldName": "model"
+    },
+    {
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+      "fieldName": "prompt"
+    },
+    {
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+      "fieldName": "prompt"
+    },
+    {
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+      "fieldName": "width"
+    },
+    {
+      "nodeId": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "fieldName": "height"
+    }
+  ],
+  "meta": {
+    "category": "default",
+    "version": "2.0.0"
+  },
+  "nodes": [
+    {
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+            },
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+              "isCollectionOrScalar": false,
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+            "name": "conditioning",
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+            }
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+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "BooleanField"
+            },
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+        "outputs": {
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+            "name": "noise",
+            "fieldKind": "output",
+            "type": {
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+            }
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+            },
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+              "base_model": "sd-1",
+              "model_type": "main"
+            }
+          }
+        },
+        "outputs": {
+          "unet": {
+            "id": "5c18c9db-328d-46d0-8cb9-143391c410be",
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+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "FloatField"
+            },
+            "value": 1
+          },
+          "scheduler": {
+            "id": "1de84a4e-3a24-4ec8-862b-16ce49633b9b",
+            "name": "scheduler",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "SchedulerField"
+            },
+            "value": "unipc"
+          },
+          "unet": {
+            "id": "ffa6fef4-3ce2-4bdb-9296-9a834849489b",
+            "name": "unet",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "UNetField"
+            }
+          },
+          "control": {
+            "id": "077b64cb-34be-4fcc-83f2-e399807a02bd",
+            "name": "control",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": true,
+              "name": "ControlField"
+            }
+          },
+          "ip_adapter": {
+            "id": "1d6948f7-3a65-4a65-a20c-768b287251aa",
+            "name": "ip_adapter",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": true,
+              "name": "IPAdapterField"
+            }
+          },
+          "t2i_adapter": {
+            "id": "75e67b09-952f-4083-aaf4-6b804d690412",
+            "name": "t2i_adapter",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": true,
+              "name": "T2IAdapterField"
+            }
+          },
+          "cfg_rescale_multiplier": {
+            "id": "9101f0a6-5fe0-4826-b7b3-47e5d506826c",
+            "name": "cfg_rescale_multiplier",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "FloatField"
+            },
+            "value": 0
+          },
+          "latents": {
+            "id": "334d4ba3-5a99-4195-82c5-86fb3f4f7d43",
+            "name": "latents",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "LatentsField"
+            }
+          },
+          "denoise_mask": {
+            "id": "0d3dbdbf-b014-4e95-8b18-ff2ff9cb0bfa",
+            "name": "denoise_mask",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "DenoiseMaskField"
+            }
+          }
+        },
+        "outputs": {
+          "latents": {
+            "id": "70fa5bbc-0c38-41bb-861a-74d6d78d2f38",
+            "name": "latents",
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "LatentsField"
+            }
+          },
+          "width": {
+            "id": "98ee0e6c-82aa-4e8f-8be5-dc5f00ee47f0",
+            "name": "width",
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
+          },
+          "height": {
+            "id": "e8cb184a-5e1a-47c8-9695-4b8979564f5d",
+            "name": "height",
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
+          }
+        }
+      },
+      "width": 320,
+      "height": 703,
+      "position": {
+        "x": 1400,
+        "y": 25
+      }
+    },
+    {
+      "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
+      "type": "invocation",
+      "data": {
+        "id": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
+        "type": "l2i",
+        "label": "",
+        "isOpen": true,
+        "notes": "",
+        "isIntermediate": false,
+        "useCache": true,
+        "version": "1.2.0",
+        "nodePack": "invokeai",
+        "inputs": {
+          "metadata": {
+            "id": "ab375f12-0042-4410-9182-29e30db82c85",
+            "name": "metadata",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "MetadataField"
+            }
+          },
+          "latents": {
+            "id": "3a7e7efd-bff5-47d7-9d48-615127afee78",
+            "name": "latents",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "LatentsField"
+            }
+          },
+          "vae": {
+            "id": "a1f5f7a1-0795-4d58-b036-7820c0b0ef2b",
+            "name": "vae",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "VaeField"
+            }
+          },
+          "tiled": {
+            "id": "da52059a-0cee-4668-942f-519aa794d739",
+            "name": "tiled",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "BooleanField"
+            },
+            "value": false
+          },
+          "fp32": {
+            "id": "c4841df3-b24e-4140-be3b-ccd454c2522c",
+            "name": "fp32",
+            "fieldKind": "input",
+            "label": "",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "BooleanField"
+            },
+            "value": true
+          }
+        },
+        "outputs": {
+          "image": {
+            "id": "72d667d0-cf85-459d-abf2-28bd8b823fe7",
+            "name": "image",
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "ImageField"
+            }
+          },
+          "width": {
+            "id": "c8c907d8-1066-49d1-b9a6-83bdcd53addc",
+            "name": "width",
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
+          },
+          "height": {
+            "id": "230f359c-b4ea-436c-b372-332d7dcdca85",
+            "name": "height",
+            "fieldKind": "output",
+            "type": {
+              "isCollection": false,
+              "isCollectionOrScalar": false,
+              "name": "IntegerField"
+            }
+          }
+        }
+      },
+      "width": 320,
+      "height": 266,
+      "position": {
+        "x": 1800,
+        "y": 25
+      }
+    }
+  ],
+  "edges": [
+    {
+      "id": "reactflow__edge-ea94bc37-d995-4a83-aa99-4af42479f2f2value-55705012-79b9-4aac-9f26-c0b10309785bseed",
+      "source": "ea94bc37-d995-4a83-aa99-4af42479f2f2",
+      "target": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "type": "default",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
+    },
+    {
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-7d8bf987-284f-413a-b2fd-d825445a5d6cclip",
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "target": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "type": "default",
+      "sourceHandle": "clip",
+      "targetHandle": "clip"
+    },
+    {
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8clip-93dc02a4-d05b-48ed-b99c-c9b616af3402clip",
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "target": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
+      "type": "default",
+      "sourceHandle": "clip",
+      "targetHandle": "clip"
+    },
+    {
+      "id": "reactflow__edge-55705012-79b9-4aac-9f26-c0b10309785bnoise-eea2702a-19fb-45b5-9d75-56b4211ec03cnoise",
+      "source": "55705012-79b9-4aac-9f26-c0b10309785b",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "type": "default",
+      "sourceHandle": "noise",
+      "targetHandle": "noise"
+    },
+    {
+      "id": "reactflow__edge-7d8bf987-284f-413a-b2fd-d825445a5d6cconditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cpositive_conditioning",
+      "source": "7d8bf987-284f-413a-b2fd-d825445a5d6c",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "type": "default",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_conditioning"
+    },
+    {
+      "id": "reactflow__edge-93dc02a4-d05b-48ed-b99c-c9b616af3402conditioning-eea2702a-19fb-45b5-9d75-56b4211ec03cnegative_conditioning",
+      "source": "93dc02a4-d05b-48ed-b99c-c9b616af3402",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "type": "default",
+      "sourceHandle": "conditioning",
+      "targetHandle": "negative_conditioning"
+    },
+    {
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8unet-eea2702a-19fb-45b5-9d75-56b4211ec03cunet",
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "target": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "type": "default",
+      "sourceHandle": "unet",
+      "targetHandle": "unet"
+    },
+    {
+      "id": "reactflow__edge-eea2702a-19fb-45b5-9d75-56b4211ec03clatents-58c957f5-0d01-41fc-a803-b2bbf0413d4flatents",
+      "source": "eea2702a-19fb-45b5-9d75-56b4211ec03c",
+      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
+      "type": "default",
+      "sourceHandle": "latents",
+      "targetHandle": "latents"
+    },
+    {
+      "id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
+      "source": "c8d55139-f380-4695-b7f2-8b3d1e1e3db8",
+      "target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
+      "type": "default",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    }
+  ]
+}

invokeai/app/services/workflow_records/workflow_records_base.py

@@ -1,17 +1,50 @@
 from abc import ABC, abstractmethod
+from typing import Optional
 
-from invokeai.app.invocations.baseinvocation import WorkflowField
+from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.workflow_records.workflow_records_common import (
+    Workflow,
+    WorkflowCategory,
+    WorkflowRecordDTO,
+    WorkflowRecordListItemDTO,
+    WorkflowRecordOrderBy,
+    WorkflowWithoutID,
+)
 
 
 class WorkflowRecordsStorageBase(ABC):
     """Base class for workflow storage services."""
 
     @abstractmethod
-    def get(self, workflow_id: str) -> WorkflowField:
+    def get(self, workflow_id: str) -> WorkflowRecordDTO:
         """Get workflow by id."""
         pass
 
     @abstractmethod
-    def create(self, workflow: WorkflowField) -> WorkflowField:
+    def create(self, workflow: WorkflowWithoutID) -> WorkflowRecordDTO:
         """Creates a workflow."""
         pass
+
+    @abstractmethod
+    def update(self, workflow: Workflow) -> WorkflowRecordDTO:
+        """Updates a workflow."""
+        pass
+
+    @abstractmethod
+    def delete(self, workflow_id: str) -> None:
+        """Deletes a workflow."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        page: int,
+        per_page: int,
+        order_by: WorkflowRecordOrderBy,
+        direction: SQLiteDirection,
+        category: WorkflowCategory,
+        query: Optional[str],
+    ) -> PaginatedResults[WorkflowRecordListItemDTO]:
+        """Gets many workflows."""
+        pass

invokeai/app/services/workflow_records/workflow_records_common.py

@@ -1,2 +1,104 @@
+import datetime
+from enum import Enum
+from typing import Any, Union
+
+import semver
+from pydantic import BaseModel, Field, JsonValue, TypeAdapter, field_validator
+
+from invokeai.app.util.metaenum import MetaEnum
+from invokeai.app.util.misc import uuid_string
+
+__workflow_meta_version__ = semver.Version.parse("1.0.0")
+
+
+class ExposedField(BaseModel):
+    nodeId: str
+    fieldName: str
+
+
 class WorkflowNotFoundError(Exception):
     """Raised when a workflow is not found"""
+
+
+class WorkflowRecordOrderBy(str, Enum, metaclass=MetaEnum):
+    """The order by options for workflow records"""
+
+    CreatedAt = "created_at"
+    UpdatedAt = "updated_at"
+    OpenedAt = "opened_at"
+    Name = "name"
+
+
+class WorkflowCategory(str, Enum, metaclass=MetaEnum):
+    User = "user"
+    Default = "default"
+    Project = "project"
+
+
+class WorkflowMeta(BaseModel):
+    version: str = Field(description="The version of the workflow schema.")
+    category: WorkflowCategory = Field(description="The category of the workflow (user or default).")
+
+    @field_validator("version")
+    def validate_version(cls, version: str):
+        try:
+            semver.Version.parse(version)
+            return version
+        except Exception:
+            raise ValueError(f"Invalid workflow meta version: {version}")
+
+    def to_semver(self) -> semver.Version:
+        return semver.Version.parse(self.version)
+
+
+class WorkflowWithoutID(BaseModel):
+    name: str = Field(description="The name of the workflow.")
+    author: str = Field(description="The author of the workflow.")
+    description: str = Field(description="The description of the workflow.")
+    version: str = Field(description="The version of the workflow.")
+    contact: str = Field(description="The contact of the workflow.")
+    tags: str = Field(description="The tags of the workflow.")
+    notes: str = Field(description="The notes of the workflow.")
+    exposedFields: list[ExposedField] = Field(description="The exposed fields of the workflow.")
+    meta: WorkflowMeta = Field(description="The meta of the workflow.")
+    # TODO: nodes and edges are very loosely typed
+    nodes: list[dict[str, JsonValue]] = Field(description="The nodes of the workflow.")
+    edges: list[dict[str, JsonValue]] = Field(description="The edges of the workflow.")
+
+
+WorkflowWithoutIDValidator = TypeAdapter(WorkflowWithoutID)
+
+
+class Workflow(WorkflowWithoutID):
+    id: str = Field(default_factory=uuid_string, description="The id of the workflow.")
+
+
+WorkflowValidator = TypeAdapter(Workflow)
+
+
+class WorkflowRecordDTOBase(BaseModel):
+    workflow_id: str = Field(description="The id of the workflow.")
+    name: str = Field(description="The name of the workflow.")
+    created_at: Union[datetime.datetime, str] = Field(description="The created timestamp of the workflow.")
+    updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the workflow.")
+    opened_at: Union[datetime.datetime, str] = Field(description="The opened timestamp of the workflow.")
+
+
+class WorkflowRecordDTO(WorkflowRecordDTOBase):
+    workflow: Workflow = Field(description="The workflow.")
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "WorkflowRecordDTO":
+        data["workflow"] = WorkflowValidator.validate_json(data.get("workflow", ""))
+        return WorkflowRecordDTOValidator.validate_python(data)
+
+
+WorkflowRecordDTOValidator = TypeAdapter(WorkflowRecordDTO)
+
+
+class WorkflowRecordListItemDTO(WorkflowRecordDTOBase):
+    description: str = Field(description="The description of the workflow.")
+    category: WorkflowCategory = Field(description="The description of the workflow.")
+
+
+WorkflowRecordListItemDTOValidator = TypeAdapter(WorkflowRecordListItemDTO)

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -1,20 +1,25 @@
-import sqlite3
-import threading
+from pathlib import Path
+from typing import Optional
 
-from invokeai.app.invocations.baseinvocation import WorkflowField, WorkflowFieldValidator
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowNotFoundError
-from invokeai.app.util.misc import uuid_string
+from invokeai.app.services.workflow_records.workflow_records_common import (
+    Workflow,
+    WorkflowCategory,
+    WorkflowNotFoundError,
+    WorkflowRecordDTO,
+    WorkflowRecordListItemDTO,
+    WorkflowRecordListItemDTOValidator,
+    WorkflowRecordOrderBy,
+    WorkflowValidator,
+    WorkflowWithoutID,
+)
 
 
 class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
-    _invoker: Invoker
-    _conn: sqlite3.Connection
-    _cursor: sqlite3.Cursor
-    _lock: threading.RLock
-
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
         self._lock = db.lock
@@ -24,14 +29,25 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
 
     def start(self, invoker: Invoker) -> None:
         self._invoker = invoker
+        self._sync_default_workflows()
 
-    def get(self, workflow_id: str) -> WorkflowField:
+    def get(self, workflow_id: str) -> WorkflowRecordDTO:
+        """Gets a workflow by ID. Updates the opened_at column."""
         try:
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
-                SELECT workflow
-                FROM workflows
+                UPDATE workflow_library
+                SET opened_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
+                WHERE workflow_id = ?;
+                """,
+                (workflow_id,),
+            )
+            self._conn.commit()
+            self._cursor.execute(
+                """--sql
+                SELECT workflow_id, workflow, name, created_at, updated_at, opened_at
+                FROM workflow_library
                 WHERE workflow_id = ?;
                 """,
                 (workflow_id,),
@@ -39,25 +55,28 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
             row = self._cursor.fetchone()
             if row is None:
                 raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found")
-            return WorkflowFieldValidator.validate_json(row[0])
+            return WorkflowRecordDTO.from_dict(dict(row))
         except Exception:
             self._conn.rollback()
             raise
         finally:
             self._lock.release()
 
-    def create(self, workflow: WorkflowField) -> WorkflowField:
+    def create(self, workflow: WorkflowWithoutID) -> WorkflowRecordDTO:
         try:
-            # workflows do not have ids until they are saved
-            workflow_id = uuid_string()
-            workflow.root["id"] = workflow_id
+            # Only user workflows may be created by this method
+            assert workflow.meta.category is WorkflowCategory.User
+            workflow_with_id = WorkflowValidator.validate_python(workflow.model_dump())
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
-                INSERT INTO workflows(workflow)
-                VALUES (?);
+                INSERT OR IGNORE INTO workflow_library (
+                    workflow_id,
+                    workflow
+                )
+                VALUES (?, ?);
                 """,
-                (workflow.model_dump_json(),),
+                (workflow_with_id.id, workflow_with_id.model_dump_json()),
             )
             self._conn.commit()
         except Exception:
@@ -65,35 +84,231 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
             raise
         finally:
             self._lock.release()
-        return self.get(workflow_id)
+        return self.get(workflow_with_id.id)
+
+    def update(self, workflow: Workflow) -> WorkflowRecordDTO:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """--sql
+                UPDATE workflow_library
+                SET workflow = ?
+                WHERE workflow_id = ? AND category = 'user';
+                """,
+                (workflow.model_dump_json(), workflow.id),
+            )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
+        finally:
+            self._lock.release()
+        return self.get(workflow.id)
+
+    def delete(self, workflow_id: str) -> None:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(
+                """--sql
+                DELETE from workflow_library
+                WHERE workflow_id = ? AND category = 'user';
+                """,
+                (workflow_id,),
+            )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
+        finally:
+            self._lock.release()
+        return None
+
+    def get_many(
+        self,
+        page: int,
+        per_page: int,
+        order_by: WorkflowRecordOrderBy,
+        direction: SQLiteDirection,
+        category: WorkflowCategory,
+        query: Optional[str] = None,
+    ) -> PaginatedResults[WorkflowRecordListItemDTO]:
+        try:
+            self._lock.acquire()
+            # sanitize!
+            assert order_by in WorkflowRecordOrderBy
+            assert direction in SQLiteDirection
+            assert category in WorkflowCategory
+            count_query = "SELECT COUNT(*) FROM workflow_library WHERE category = ?"
+            main_query = """
+                SELECT
+                    workflow_id,
+                    category,
+                    name,
+                    description,
+                    created_at,
+                    updated_at,
+                    opened_at
+                FROM workflow_library
+                WHERE category = ?
+                """
+            main_params: list[int | str] = [category.value]
+            count_params: list[int | str] = [category.value]
+            stripped_query = query.strip() if query else None
+            if stripped_query:
+                wildcard_query = "%" + stripped_query + "%"
+                main_query += " AND name LIKE ? OR description LIKE ? "
+                count_query += " AND name LIKE ? OR description LIKE ?;"
+                main_params.extend([wildcard_query, wildcard_query])
+                count_params.extend([wildcard_query, wildcard_query])
+
+            main_query += f" ORDER BY {order_by.value} {direction.value} LIMIT ? OFFSET ?;"
+            main_params.extend([per_page, page * per_page])
+            self._cursor.execute(main_query, main_params)
+            rows = self._cursor.fetchall()
+            workflows = [WorkflowRecordListItemDTOValidator.validate_python(dict(row)) for row in rows]
+
+            self._cursor.execute(count_query, count_params)
+            total = self._cursor.fetchone()[0]
+            pages = int(total / per_page) + 1
+
+            return PaginatedResults(
+                items=workflows,
+                page=page,
+                per_page=per_page,
+                pages=pages,
+                total=total,
+            )
+        except Exception:
+            self._conn.rollback()
+            raise
+        finally:
+            self._lock.release()
+
+    def _sync_default_workflows(self) -> None:
+        """Syncs default workflows to the database. Internal use only."""
+
+        """
+        An enhancement might be to only update workflows that have changed. This would require stable
+        default workflow IDs, and properly incrementing the workflow version.
+
+        It's much simpler to just replace them all with whichever workflows are in the directory.
+
+        The downside is that the `updated_at` and `opened_at` timestamps for default workflows are
+        meaningless, as they are overwritten every time the server starts.
+        """
+
+        try:
+            self._lock.acquire()
+            workflows: list[Workflow] = []
+            workflows_dir = Path(__file__).parent / Path("default_workflows")
+            workflow_paths = workflows_dir.glob("*.json")
+            for path in workflow_paths:
+                bytes_ = path.read_bytes()
+                workflow = WorkflowValidator.validate_json(bytes_)
+                workflows.append(workflow)
+            # Only default workflows may be managed by this method
+            assert all(w.meta.category is WorkflowCategory.Default for w in workflows)
+            self._cursor.execute(
+                """--sql
+                DELETE FROM workflow_library
+                WHERE category = 'default';
+                """
+            )
+            for w in workflows:
+                self._cursor.execute(
+                    """--sql
+                    INSERT OR REPLACE INTO workflow_library (
+                        workflow_id,
+                        workflow
+                    )
+                    VALUES (?, ?);
+                    """,
+                    (w.id, w.model_dump_json()),
+                )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
+        finally:
+            self._lock.release()
 
     def _create_tables(self) -> None:
         try:
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
-                CREATE TABLE IF NOT EXISTS workflows (
+                CREATE TABLE IF NOT EXISTS workflow_library (
+                    workflow_id TEXT NOT NULL PRIMARY KEY,
                     workflow TEXT NOT NULL,
-                    workflow_id TEXT GENERATED ALWAYS AS (json_extract(workflow, '$.id')) VIRTUAL NOT NULL UNIQUE, -- gets implicit index
                     created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                    updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')) -- updated via trigger
+                    -- updated via trigger
+                    updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                    -- updated manually when retrieving workflow
+                    opened_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
+                    -- Generated columns, needed for indexing and searching
+                    category TEXT GENERATED ALWAYS as (json_extract(workflow, '$.meta.category')) VIRTUAL NOT NULL,
+                    name TEXT GENERATED ALWAYS as (json_extract(workflow, '$.name')) VIRTUAL NOT NULL,
+                    description TEXT GENERATED ALWAYS as (json_extract(workflow, '$.description')) VIRTUAL NOT NULL
                 );
                 """
             )
 
             self._cursor.execute(
                 """--sql
-                CREATE TRIGGER IF NOT EXISTS tg_workflows_updated_at
+                CREATE TRIGGER IF NOT EXISTS tg_workflow_library_updated_at
                 AFTER UPDATE
-                ON workflows FOR EACH ROW
+                ON workflow_library FOR EACH ROW
                 BEGIN
-                    UPDATE workflows
+                    UPDATE workflow_library
                     SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
                     WHERE workflow_id = old.workflow_id;
                 END;
                 """
             )
 
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_created_at ON workflow_library(created_at);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_updated_at ON workflow_library(updated_at);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_opened_at ON workflow_library(opened_at);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_category ON workflow_library(category);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_name ON workflow_library(name);
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                CREATE INDEX IF NOT EXISTS idx_workflow_library_description ON workflow_library(description);
+                """
+            )
+
+            # We do not need the original `workflows` table or `workflow_images` junction table.
+            self._cursor.execute(
+                """--sql
+                DROP TABLE IF EXISTS workflow_images;
+                """
+            )
+            self._cursor.execute(
+                """--sql
+                DROP TABLE IF EXISTS workflows;
+                """
+            )
+
             self._conn.commit()
         except Exception:
             self._conn.rollback()

invokeai/app/shared/fields.py

@@ -2,6 +2,7 @@ class FieldDescriptions:
     denoising_start = "When to start denoising, expressed a percentage of total steps"
     denoising_end = "When to stop denoising, expressed a percentage of total steps"
     cfg_scale = "Classifier-Free Guidance scale"
+    cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
     scheduler = "Scheduler to use during inference"
     positive_cond = "Positive conditioning tensor"
     negative_cond = "Negative conditioning tensor"

invokeai/backend/image_util/realesrgan/LICENSE

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2021, Xintao Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -0,0 +1,274 @@
+import math
+from enum import Enum
+from pathlib import Path
+from typing import Any, Optional
+
+import cv2
+import numpy as np
+import numpy.typing as npt
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from cv2.typing import MatLike
+from tqdm import tqdm
+
+from invokeai.backend.util.devices import choose_torch_device
+
+"""
+Adapted from https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
+License is BSD3, copied to `LICENSE` in this directory.
+
+The adaptation here has a few changes:
+- Remove print statements, use `tqdm` to show progress
+- Remove unused "outscale" logic, which simply scales the final image to a given factor
+- Remove `dni_weight` logic, which was only used when multiple models were used
+- Remove logic to fetch models from network
+- Add types, rename a few things
+"""
+
+
+class ImageMode(str, Enum):
+    L = "L"
+    RGB = "RGB"
+    RGBA = "RGBA"
+
+
+class RealESRGAN:
+    """A helper class for upsampling images with RealESRGAN.
+
+    Args:
+        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
+        model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
+        model (nn.Module): The defined network. Default: None.
+        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
+            input images into tiles, and then process each of them. Finally, they will be merged into one image.
+            0 denotes for do not use tile. Default: 0.
+        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
+        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
+        half (float): Whether to use half precision during inference. Default: False.
+    """
+
+    output: torch.Tensor
+
+    def __init__(
+        self,
+        scale: int,
+        model_path: Path,
+        model: RRDBNet,
+        tile: int = 0,
+        tile_pad: int = 10,
+        pre_pad: int = 10,
+        half: bool = False,
+    ) -> None:
+        self.scale = scale
+        self.tile_size = tile
+        self.tile_pad = tile_pad
+        self.pre_pad = pre_pad
+        self.mod_scale: Optional[int] = None
+        self.half = half
+        self.device = choose_torch_device()
+
+        loadnet = torch.load(model_path, map_location=torch.device("cpu"))
+
+        # prefer to use params_ema
+        if "params_ema" in loadnet:
+            keyname = "params_ema"
+        else:
+            keyname = "params"
+
+        model.load_state_dict(loadnet[keyname], strict=True)
+        model.eval()
+        self.model = model.to(self.device)
+
+        if self.half:
+            self.model = self.model.half()
+
+    def pre_process(self, img: MatLike) -> None:
+        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible"""
+        img_tensor: torch.Tensor = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
+        self.img = img_tensor.unsqueeze(0).to(self.device)
+        if self.half:
+            self.img = self.img.half()
+
+        # pre_pad
+        if self.pre_pad != 0:
+            self.img = torch.nn.functional.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), "reflect")
+        # mod pad for divisible borders
+        if self.scale == 2:
+            self.mod_scale = 2
+        elif self.scale == 1:
+            self.mod_scale = 4
+        if self.mod_scale is not None:
+            self.mod_pad_h, self.mod_pad_w = 0, 0
+            _, _, h, w = self.img.size()
+            if h % self.mod_scale != 0:
+                self.mod_pad_h = self.mod_scale - h % self.mod_scale
+            if w % self.mod_scale != 0:
+                self.mod_pad_w = self.mod_scale - w % self.mod_scale
+            self.img = torch.nn.functional.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), "reflect")
+
+    def process(self) -> None:
+        # model inference
+        self.output = self.model(self.img)
+
+    def tile_process(self) -> None:
+        """It will first crop input images to tiles, and then process each tile.
+        Finally, all the processed tiles are merged into one images.
+
+        Modified from: https://github.com/ata4/esrgan-launcher
+        """
+        batch, channel, height, width = self.img.shape
+        output_height = height * self.scale
+        output_width = width * self.scale
+        output_shape = (batch, channel, output_height, output_width)
+
+        # start with black image
+        self.output = self.img.new_zeros(output_shape)
+        tiles_x = math.ceil(width / self.tile_size)
+        tiles_y = math.ceil(height / self.tile_size)
+
+        # loop over all tiles
+        total_steps = tiles_y * tiles_x
+        for i in tqdm(range(total_steps), desc="Upscaling"):
+            y = i // tiles_x
+            x = i % tiles_x
+            # extract tile from input image
+            ofs_x = x * self.tile_size
+            ofs_y = y * self.tile_size
+            # input tile area on total image
+            input_start_x = ofs_x
+            input_end_x = min(ofs_x + self.tile_size, width)
+            input_start_y = ofs_y
+            input_end_y = min(ofs_y + self.tile_size, height)
+
+            # input tile area on total image with padding
+            input_start_x_pad = max(input_start_x - self.tile_pad, 0)
+            input_end_x_pad = min(input_end_x + self.tile_pad, width)
+            input_start_y_pad = max(input_start_y - self.tile_pad, 0)
+            input_end_y_pad = min(input_end_y + self.tile_pad, height)
+
+            # input tile dimensions
+            input_tile_width = input_end_x - input_start_x
+            input_tile_height = input_end_y - input_start_y
+            input_tile = self.img[
+                :,
+                :,
+                input_start_y_pad:input_end_y_pad,
+                input_start_x_pad:input_end_x_pad,
+            ]
+
+            # upscale tile
+            with torch.no_grad():
+                output_tile = self.model(input_tile)
+
+            # output tile area on total image
+            output_start_x = input_start_x * self.scale
+            output_end_x = input_end_x * self.scale
+            output_start_y = input_start_y * self.scale
+            output_end_y = input_end_y * self.scale
+
+            # output tile area without padding
+            output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
+            output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
+            output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
+            output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
+
+            # put tile into output image
+            self.output[:, :, output_start_y:output_end_y, output_start_x:output_end_x] = output_tile[
+                :,
+                :,
+                output_start_y_tile:output_end_y_tile,
+                output_start_x_tile:output_end_x_tile,
+            ]
+
+    def post_process(self) -> torch.Tensor:
+        # remove extra pad
+        if self.mod_scale is not None:
+            _, _, h, w = self.output.size()
+            self.output = self.output[
+                :,
+                :,
+                0 : h - self.mod_pad_h * self.scale,
+                0 : w - self.mod_pad_w * self.scale,
+            ]
+        # remove prepad
+        if self.pre_pad != 0:
+            _, _, h, w = self.output.size()
+            self.output = self.output[
+                :,
+                :,
+                0 : h - self.pre_pad * self.scale,
+                0 : w - self.pre_pad * self.scale,
+            ]
+        return self.output
+
+    @torch.no_grad()
+    def upscale(self, img: MatLike, esrgan_alpha_upscale: bool = True) -> npt.NDArray[Any]:
+        np_img = img.astype(np.float32)
+        alpha: Optional[np.ndarray] = None
+        if np.max(np_img) > 256:
+            # 16-bit image
+            max_range = 65535
+        else:
+            max_range = 255
+        np_img = np_img / max_range
+        if len(np_img.shape) == 2:
+            # grayscale image
+            img_mode = ImageMode.L
+            np_img = cv2.cvtColor(np_img, cv2.COLOR_GRAY2RGB)
+        elif np_img.shape[2] == 4:
+            # RGBA image with alpha channel
+            img_mode = ImageMode.RGBA
+            alpha = np_img[:, :, 3]
+            np_img = np_img[:, :, 0:3]
+            np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)
+            if esrgan_alpha_upscale:
+                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
+        else:
+            img_mode = ImageMode.RGB
+            np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)
+
+        # ------------------- process image (without the alpha channel) ------------------- #
+        self.pre_process(np_img)
+        if self.tile_size > 0:
+            self.tile_process()
+        else:
+            self.process()
+        output_tensor = self.post_process()
+        output_img: npt.NDArray[Any] = output_tensor.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+        output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
+        if img_mode is ImageMode.L:
+            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
+
+        # ------------------- process the alpha channel if necessary ------------------- #
+        if img_mode is ImageMode.RGBA:
+            if esrgan_alpha_upscale:
+                assert alpha is not None
+                self.pre_process(alpha)
+                if self.tile_size > 0:
+                    self.tile_process()
+                else:
+                    self.process()
+                output_alpha_tensor = self.post_process()
+                output_alpha: npt.NDArray[Any] = output_alpha_tensor.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
+                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
+            else:  # use the cv2 resize for alpha channel
+                assert alpha is not None
+                h, w = alpha.shape[0:2]
+                output_alpha = cv2.resize(
+                    alpha,
+                    (w * self.scale, h * self.scale),
+                    interpolation=cv2.INTER_LINEAR,
+                )
+
+            # merge the alpha channel
+            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
+            output_img[:, :, 3] = output_alpha
+
+        # ------------------------------ return ------------------------------ #
+        if max_range == 65535:  # 16-bit image
+            output = (output_img * 65535.0).round().astype(np.uint16)
+        else:
+            output = (output_img * 255.0).round().astype(np.uint8)
+
+        return output

invokeai/backend/ip_adapter/ip_adapter.py

@@ -54,6 +54,44 @@ class ImageProjModel(torch.nn.Module):
         return clip_extra_context_tokens
 
 
+class MLPProjModel(torch.nn.Module):
+    """SD model with image prompt"""
+
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
+        super().__init__()
+
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
+            torch.nn.GELU(),
+            torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
+            torch.nn.LayerNorm(cross_attention_dim),
+        )
+
+    @classmethod
+    def from_state_dict(cls, state_dict: dict[torch.Tensor]):
+        """Initialize an MLPProjModel from a state_dict.
+
+        The cross_attention_dim and clip_embeddings_dim are inferred from the shape of the tensors in the state_dict.
+
+        Args:
+            state_dict (dict[torch.Tensor]): The state_dict of model weights.
+
+        Returns:
+            MLPProjModel
+        """
+        cross_attention_dim = state_dict["proj.3.weight"].shape[0]
+        clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
+
+        model = cls(cross_attention_dim, clip_embeddings_dim)
+
+        model.load_state_dict(state_dict)
+        return model
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+
 class IPAdapter:
     """IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
 
@@ -130,6 +168,13 @@ class IPAdapterPlus(IPAdapter):
         return image_prompt_embeds, uncond_image_prompt_embeds
 
 
+class IPAdapterFull(IPAdapterPlus):
+    """IP-Adapter Plus with full features."""
+
+    def _init_image_proj_model(self, state_dict: dict[torch.Tensor]):
+        return MLPProjModel.from_state_dict(state_dict).to(self.device, dtype=self.dtype)
+
+
 class IPAdapterPlusXL(IPAdapterPlus):
     """IP-Adapter Plus for SDXL."""
 
@@ -149,11 +194,9 @@ def build_ip_adapter(
 ) -> Union[IPAdapter, IPAdapterPlus]:
     state_dict = torch.load(ip_adapter_ckpt_path, map_location="cpu")
 
-    # Determine if the state_dict is from an IPAdapter or IPAdapterPlus based on the image_proj weights that it
-    # contains.
-    is_plus = "proj.weight" not in state_dict["image_proj"]
-
-    if is_plus:
+    if "proj.weight" in state_dict["image_proj"]:  # IPAdapter (with ImageProjModel).
+        return IPAdapter(state_dict, device=device, dtype=dtype)
+    elif "proj_in.weight" in state_dict["image_proj"]:  # IPAdaterPlus or IPAdapterPlusXL (with Resampler).
         cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
         if cross_attention_dim == 768:
             # SD1 IP-Adapter Plus
@@ -163,5 +206,7 @@ def build_ip_adapter(
             return IPAdapterPlusXL(state_dict, device=device, dtype=dtype)
         else:
             raise Exception(f"Unsupported IP-Adapter Plus cross-attention dimension: {cross_attention_dim}.")
+    elif "proj.0.weight" in state_dict["image_proj"]:  # IPAdapterFull (with MLPProjModel).
+        return IPAdapterFull(state_dict, device=device, dtype=dtype)
     else:
-        return IPAdapter(state_dict, device=device, dtype=dtype)
+        raise ValueError(f"'{ip_adapter_ckpt_path}' has an unrecognized IP-Adapter model architecture.")

invokeai/backend/model_management/__init__.py

@@ -1,12 +1,13 @@
+# ruff: noqa: I001, F401
 """
 Initialization file for invokeai.backend.model_management
 """
 # This import must be first
-from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType  # noqa: F401 isort: split
+from .model_manager import AddModelResult, ModelInfo, ModelManager, SchedulerPredictionType
+from .lora import ModelPatcher, ONNXModelPatcher
+from .model_cache import ModelCache
 
-from .lora import ModelPatcher, ONNXModelPatcher  # noqa: F401
-from .model_cache import ModelCache  # noqa: F401
-from .models import (  # noqa: F401
+from .models import (
     BaseModelType,
     DuplicateModelException,
     ModelNotFoundException,
@@ -16,4 +17,4 @@ from .models import (  # noqa: F401
 )
 
 # This import must be last
-from .model_merge import ModelMerger, MergeInterpolationMethod  # noqa: F401 isort: split
+from .model_merge import MergeInterpolationMethod, ModelMerger

invokeai/backend/model_management/lora.py

@@ -192,20 +192,33 @@ class ModelPatcher:
                     trigger += f"-!pad-{i}"
                 return f"<{trigger}>"
 
+            def _get_ti_embedding(model_embeddings, ti):
+                # for SDXL models, select the embedding that matches the text encoder's dimensions
+                if ti.embedding_2 is not None:
+                    return (
+                        ti.embedding_2
+                        if ti.embedding_2.shape[1] == model_embeddings.weight.data[0].shape[0]
+                        else ti.embedding
+                    )
+                else:
+                    return ti.embedding
+
             # modify tokenizer
             new_tokens_added = 0
             for ti_name, ti in ti_list:
-                for i in range(ti.embedding.shape[0]):
+                ti_embedding = _get_ti_embedding(text_encoder.get_input_embeddings(), ti)
+
+                for i in range(ti_embedding.shape[0]):
                     new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
             # modify text_encoder
             text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
             model_embeddings = text_encoder.get_input_embeddings()
 
-            for ti_name, ti in ti_list:
+            for ti_name, _ in ti_list:
                 ti_tokens = []
-                for i in range(ti.embedding.shape[0]):
-                    embedding = ti.embedding[i]
+                for i in range(ti_embedding.shape[0]):
+                    embedding = ti_embedding[i]
                     trigger = _get_trigger(ti_name, i)
 
                     token_id = ti_tokenizer.convert_tokens_to_ids(trigger)
@@ -273,6 +286,7 @@ class ModelPatcher:
 
 class TextualInversionModel:
     embedding: torch.Tensor  # [n, 768]|[n, 1280]
+    embedding_2: Optional[torch.Tensor] = None  # [n, 768]|[n, 1280]   - for SDXL models
 
     @classmethod
     def from_checkpoint(
@@ -296,8 +310,8 @@ 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()))
@@ -306,6 +320,11 @@ class TextualInversionModel:
         elif "emb_params" in state_dict:
             result.embedding = state_dict["emb_params"]
 
+        # v5(sdxl safetensors file)
+        elif "clip_g" in state_dict and "clip_l" in state_dict:
+            result.embedding = state_dict["clip_g"]
+            result.embedding_2 = state_dict["clip_l"]
+
         # v4(diffusers bin files)
         else:
             result.embedding = next(iter(state_dict.values()))
@@ -342,6 +361,13 @@ class TextualInversionManager(BaseTextualInversionManager):
             if token_id in self.pad_tokens:
                 new_token_ids.extend(self.pad_tokens[token_id])
 
+        # Do not exceed the max model input size
+        # The -2 here is compensating for compensate compel.embeddings_provider.get_token_ids(),
+        # which first removes and then adds back the start and end tokens.
+        max_length = list(self.tokenizer.max_model_input_sizes.values())[0] - 2
+        if len(new_token_ids) > max_length:
+            new_token_ids = new_token_ids[0:max_length]
+
         return new_token_ids
 
 
@@ -490,24 +516,31 @@ class ONNXModelPatcher:
                     trigger += f"-!pad-{i}"
                 return f"<{trigger}>"
 
+            # modify text_encoder
+            orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
+
             # modify tokenizer
             new_tokens_added = 0
             for ti_name, ti in ti_list:
-                for i in range(ti.embedding.shape[0]):
-                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
+                if ti.embedding_2 is not None:
+                    ti_embedding = (
+                        ti.embedding_2 if ti.embedding_2.shape[1] == orig_embeddings.shape[0] else ti.embedding
+                    )
+                else:
+                    ti_embedding = ti.embedding
 
-            # modify text_encoder
-            orig_embeddings = text_encoder.tensors["text_model.embeddings.token_embedding.weight"]
+                for i in range(ti_embedding.shape[0]):
+                    new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
             embeddings = np.concatenate(
                 (np.copy(orig_embeddings), np.zeros((new_tokens_added, orig_embeddings.shape[1]))),
                 axis=0,
             )
 
-            for ti_name, ti in ti_list:
+            for ti_name, _ in ti_list:
                 ti_tokens = []
-                for i in range(ti.embedding.shape[0]):
-                    embedding = ti.embedding[i].detach().numpy()
+                for i in range(ti_embedding.shape[0]):
+                    embedding = ti_embedding[i].detach().numpy()
                     trigger = _get_trigger(ti_name, i)
 
                     token_id = ti_tokenizer.convert_tokens_to_ids(trigger)

invokeai/backend/model_management/model_probe.py

@@ -53,6 +53,7 @@ class ModelProbe(object):
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
+        "LatentConsistencyModelPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.Vae,
         "AutoencoderTiny": ModelType.Vae,
         "ControlNetModel": ModelType.ControlNet,
@@ -224,7 +225,7 @@ class ModelProbe(object):
         with SilenceWarnings():
             if model_path.suffix.endswith((".ckpt", ".pt", ".bin")):
                 cls._scan_model(model_path, model_path)
-                return torch.load(model_path)
+                return torch.load(model_path, map_location="cpu")
             else:
                 return safetensors.torch.load_file(model_path)
 
@@ -372,12 +373,16 @@ class TextualInversionCheckpointProbe(CheckpointProbeBase):
             token_dim = list(checkpoint["string_to_param"].values())[0].shape[-1]
         elif "emb_params" in checkpoint:
             token_dim = checkpoint["emb_params"].shape[-1]
+        elif "clip_g" in checkpoint:
+            token_dim = checkpoint["clip_g"].shape[-1]
         else:
             token_dim = list(checkpoint.values())[0].shape[0]
         if token_dim == 768:
             return BaseModelType.StableDiffusion1
         elif token_dim == 1024:
             return BaseModelType.StableDiffusion2
+        elif token_dim == 1280:
+            return BaseModelType.StableDiffusionXL
         else:
             return None
 

invokeai/backend/model_manager/migrate_to_db.py

@@ -11,7 +11,7 @@ from invokeai.app.services.model_records import (
     DuplicateModelException,
     ModelRecordServiceSQL,
 )
-from invokeai.app.services.shared.sqlite import SqliteDatabase
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -607,11 +607,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if isinstance(guidance_scale, list):
             guidance_scale = guidance_scale[step_index]
 
-        noise_pred = self.invokeai_diffuser._combine(
-            uc_noise_pred,
-            c_noise_pred,
-            guidance_scale,
-        )
+        noise_pred = self.invokeai_diffuser._combine(uc_noise_pred, c_noise_pred, guidance_scale)
+        guidance_rescale_multiplier = conditioning_data.guidance_rescale_multiplier
+        if guidance_rescale_multiplier > 0:
+            noise_pred = self._rescale_cfg(
+                noise_pred,
+                c_noise_pred,
+                guidance_rescale_multiplier,
+            )
 
         # compute the previous noisy sample x_t -> x_t-1
         step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)
@@ -634,6 +637,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         return step_output
 
+    @staticmethod
+    def _rescale_cfg(total_noise_pred, pos_noise_pred, multiplier=0.7):
+        """Implementation of Algorithm 2 from https://arxiv.org/pdf/2305.08891.pdf."""
+        ro_pos = torch.std(pos_noise_pred, dim=(1, 2, 3), keepdim=True)
+        ro_cfg = torch.std(total_noise_pred, dim=(1, 2, 3), keepdim=True)
+
+        x_rescaled = total_noise_pred * (ro_pos / ro_cfg)
+        x_final = multiplier * x_rescaled + (1.0 - multiplier) * total_noise_pred
+        return x_final
+
     def _unet_forward(
         self,
         latents,

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -67,13 +67,17 @@ class IPAdapterConditioningInfo:
 class ConditioningData:
     unconditioned_embeddings: BasicConditioningInfo
     text_embeddings: BasicConditioningInfo
-    guidance_scale: Union[float, List[float]]
     """
     Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
     `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
     Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
     images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
     """
+    guidance_scale: Union[float, List[float]]
+    """ for models trained using zero-terminal SNR ("ztsnr"), it's suggested to use guidance_rescale_multiplier of 0.7 .
+     ref [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf)
+    """
+    guidance_rescale_multiplier: float = 0
     extra: Optional[ExtraConditioningInfo] = None
     scheduler_args: dict[str, Any] = field(default_factory=dict)
     """

invokeai/backend/tiles/tiles.py

@@ -0,0 +1,201 @@
+import math
+from typing import Union
+
+import numpy as np
+
+from invokeai.backend.tiles.utils import TBLR, Tile, paste
+
+
+def calc_tiles_with_overlap(
+    image_height: int, image_width: int, tile_height: int, tile_width: int, overlap: int = 0
+) -> list[Tile]:
+    """Calculate the tile coordinates for a given image shape under a simple tiling scheme with overlaps.
+
+    Args:
+        image_height (int): The image height in px.
+        image_width (int): The image width in px.
+        tile_height (int): The tile height in px. All tiles will have this height.
+        tile_width (int): The tile width in px. All tiles will have this width.
+        overlap (int, optional): The target overlap between adjacent tiles. If the tiles do not evenly cover the image
+            shape, then the last row/column of tiles will overlap more than this. Defaults to 0.
+
+    Returns:
+        list[Tile]: A list of tiles that cover the image shape. Ordered from left-to-right, top-to-bottom.
+    """
+    assert image_height >= tile_height
+    assert image_width >= tile_width
+    assert overlap < tile_height
+    assert overlap < tile_width
+
+    non_overlap_per_tile_height = tile_height - overlap
+    non_overlap_per_tile_width = tile_width - overlap
+
+    num_tiles_y = math.ceil((image_height - overlap) / non_overlap_per_tile_height)
+    num_tiles_x = math.ceil((image_width - overlap) / non_overlap_per_tile_width)
+
+    # tiles[y * num_tiles_x + x] is the tile for the y'th row, x'th column.
+    tiles: list[Tile] = []
+
+    # Calculate tile coordinates. (Ignore overlap values for now.)
+    for tile_idx_y in range(num_tiles_y):
+        for tile_idx_x in range(num_tiles_x):
+            tile = Tile(
+                coords=TBLR(
+                    top=tile_idx_y * non_overlap_per_tile_height,
+                    bottom=tile_idx_y * non_overlap_per_tile_height + tile_height,
+                    left=tile_idx_x * non_overlap_per_tile_width,
+                    right=tile_idx_x * non_overlap_per_tile_width + tile_width,
+                ),
+                overlap=TBLR(top=0, bottom=0, left=0, right=0),
+            )
+
+            if tile.coords.bottom > image_height:
+                # If this tile would go off the bottom of the image, shift it so that it is aligned with the bottom
+                # of the image.
+                tile.coords.bottom = image_height
+                tile.coords.top = image_height - tile_height
+
+            if tile.coords.right > image_width:
+                # If this tile would go off the right edge of the image, shift it so that it is aligned with the
+                # right edge of the image.
+                tile.coords.right = image_width
+                tile.coords.left = image_width - tile_width
+
+            tiles.append(tile)
+
+    def get_tile_or_none(idx_y: int, idx_x: int) -> Union[Tile, None]:
+        if idx_y < 0 or idx_y > num_tiles_y or idx_x < 0 or idx_x > num_tiles_x:
+            return None
+        return tiles[idx_y * num_tiles_x + idx_x]
+
+    # Iterate over tiles again and calculate overlaps.
+    for tile_idx_y in range(num_tiles_y):
+        for tile_idx_x in range(num_tiles_x):
+            cur_tile = get_tile_or_none(tile_idx_y, tile_idx_x)
+            top_neighbor_tile = get_tile_or_none(tile_idx_y - 1, tile_idx_x)
+            left_neighbor_tile = get_tile_or_none(tile_idx_y, tile_idx_x - 1)
+
+            assert cur_tile is not None
+
+            # Update cur_tile top-overlap and corresponding top-neighbor bottom-overlap.
+            if top_neighbor_tile is not None:
+                cur_tile.overlap.top = max(0, top_neighbor_tile.coords.bottom - cur_tile.coords.top)
+                top_neighbor_tile.overlap.bottom = cur_tile.overlap.top
+
+            # Update cur_tile left-overlap and corresponding left-neighbor right-overlap.
+            if left_neighbor_tile is not None:
+                cur_tile.overlap.left = max(0, left_neighbor_tile.coords.right - cur_tile.coords.left)
+                left_neighbor_tile.overlap.right = cur_tile.overlap.left
+
+    return tiles
+
+
+def merge_tiles_with_linear_blending(
+    dst_image: np.ndarray, tiles: list[Tile], tile_images: list[np.ndarray], blend_amount: int
+):
+    """Merge a set of image tiles into `dst_image` with linear blending between the tiles.
+
+    We expect every tile edge to either:
+    1) have an overlap of 0, because it is aligned with the image edge, or
+    2) have an overlap >= blend_amount.
+    If neither of these conditions are satisfied, we raise an exception.
+
+    The linear blending is centered at the halfway point of the overlap between adjacent tiles.
+
+    Args:
+        dst_image (np.ndarray): The destination image. Shape: (H, W, C).
+        tiles (list[Tile]): The list of tiles describing the locations of the respective `tile_images`.
+        tile_images (list[np.ndarray]): The tile images to merge into `dst_image`.
+        blend_amount (int): The amount of blending (in px) between adjacent overlapping tiles.
+    """
+    # Sort tiles and images first by left x coordinate, then by top y coordinate. During tile processing, we want to
+    # iterate over tiles left-to-right, top-to-bottom.
+    tiles_and_images = list(zip(tiles, tile_images, strict=True))
+    tiles_and_images = sorted(tiles_and_images, key=lambda x: x[0].coords.left)
+    tiles_and_images = sorted(tiles_and_images, key=lambda x: x[0].coords.top)
+
+    # Organize tiles into rows.
+    tile_and_image_rows: list[list[tuple[Tile, np.ndarray]]] = []
+    cur_tile_and_image_row: list[tuple[Tile, np.ndarray]] = []
+    first_tile_in_cur_row, _ = tiles_and_images[0]
+    for tile_and_image in tiles_and_images:
+        tile, _ = tile_and_image
+        if not (
+            tile.coords.top == first_tile_in_cur_row.coords.top
+            and tile.coords.bottom == first_tile_in_cur_row.coords.bottom
+        ):
+            # Store the previous row, and start a new one.
+            tile_and_image_rows.append(cur_tile_and_image_row)
+            cur_tile_and_image_row = []
+            first_tile_in_cur_row, _ = tile_and_image
+
+        cur_tile_and_image_row.append(tile_and_image)
+    tile_and_image_rows.append(cur_tile_and_image_row)
+
+    # Prepare 1D linear gradients for blending.
+    gradient_left_x = np.linspace(start=0.0, stop=1.0, num=blend_amount)
+    gradient_top_y = np.linspace(start=0.0, stop=1.0, num=blend_amount)
+    # Convert shape: (blend_amount, ) -> (blend_amount, 1). The extra dimension enables the gradient to be applied
+    # to a 2D image via broadcasting. Note that no additional dimension is needed on gradient_left_x for
+    # broadcasting to work correctly.
+    gradient_top_y = np.expand_dims(gradient_top_y, axis=1)
+
+    for tile_and_image_row in tile_and_image_rows:
+        first_tile_in_row, _ = tile_and_image_row[0]
+        row_height = first_tile_in_row.coords.bottom - first_tile_in_row.coords.top
+        row_image = np.zeros((row_height, dst_image.shape[1], dst_image.shape[2]), dtype=dst_image.dtype)
+
+        # Blend the tiles in the row horizontally.
+        for tile, tile_image in tile_and_image_row:
+            # We expect the tiles to be ordered left-to-right. For each tile, we construct a mask that applies linear
+            # blending to the left of the current tile. The inverse linear blending is automatically applied to the
+            # right of the tiles that have already been pasted by the paste(...) operation.
+            tile_height, tile_width, _ = tile_image.shape
+            mask = np.ones(shape=(tile_height, tile_width), dtype=np.float64)
+
+            # Left blending:
+            if tile.overlap.left > 0:
+                assert tile.overlap.left >= blend_amount
+                # Center the blending gradient in the middle of the overlap.
+                blend_start_left = tile.overlap.left // 2 - blend_amount // 2
+                # The region left of the blending region is masked completely.
+                mask[:, :blend_start_left] = 0.0
+                # Apply the blend gradient to the mask.
+                mask[:, blend_start_left : blend_start_left + blend_amount] = gradient_left_x
+                # For visual debugging:
+                # tile_image[:, blend_start_left : blend_start_left + blend_amount] = 0
+
+            paste(
+                dst_image=row_image,
+                src_image=tile_image,
+                box=TBLR(
+                    top=0, bottom=tile.coords.bottom - tile.coords.top, left=tile.coords.left, right=tile.coords.right
+                ),
+                mask=mask,
+            )
+
+        # Blend the row into the dst_image vertically.
+        # We construct a mask that applies linear blending to the top of the current row. The inverse linear blending is
+        # automatically applied to the bottom of the tiles that have already been pasted by the paste(...) operation.
+        mask = np.ones(shape=(row_image.shape[0], row_image.shape[1]), dtype=np.float64)
+        # Top blending:
+        # (See comments under 'Left blending' for an explanation of the logic.)
+        # We assume that the entire row has the same vertical overlaps as the first_tile_in_row.
+        if first_tile_in_row.overlap.top > 0:
+            assert first_tile_in_row.overlap.top >= blend_amount
+            blend_start_top = first_tile_in_row.overlap.top // 2 - blend_amount // 2
+            mask[:blend_start_top, :] = 0.0
+            mask[blend_start_top : blend_start_top + blend_amount, :] = gradient_top_y
+            # For visual debugging:
+            # row_image[blend_start_top : blend_start_top + blend_amount, :] = 0
+        paste(
+            dst_image=dst_image,
+            src_image=row_image,
+            box=TBLR(
+                top=first_tile_in_row.coords.top,
+                bottom=first_tile_in_row.coords.bottom,
+                left=0,
+                right=row_image.shape[1],
+            ),
+            mask=mask,
+        )

invokeai/backend/tiles/utils.py

@@ -0,0 +1,47 @@
+from typing import Optional
+
+import numpy as np
+from pydantic import BaseModel, Field
+
+
+class TBLR(BaseModel):
+    top: int
+    bottom: int
+    left: int
+    right: int
+
+    def __eq__(self, other):
+        return (
+            self.top == other.top
+            and self.bottom == other.bottom
+            and self.left == other.left
+            and self.right == other.right
+        )
+
+
+class Tile(BaseModel):
+    coords: TBLR = Field(description="The coordinates of this tile relative to its parent image.")
+    overlap: TBLR = Field(description="The amount of overlap with adjacent tiles on each side of this tile.")
+
+    def __eq__(self, other):
+        return self.coords == other.coords and self.overlap == other.overlap
+
+
+def paste(dst_image: np.ndarray, src_image: np.ndarray, box: TBLR, mask: Optional[np.ndarray] = None):
+    """Paste a source image into a destination image.
+
+    Args:
+        dst_image (torch.Tensor): The destination image to paste into. Shape: (H, W, C).
+        src_image (torch.Tensor): The source image to paste. Shape: (H, W, C). H and W must be compatible with 'box'.
+        box (TBLR): Box defining the region in the 'dst_image' where 'src_image' will be pasted.
+        mask (Optional[torch.Tensor]): A mask that defines the blending between 'src_image' and 'dst_image'.
+            Range: [0.0, 1.0], Shape: (H, W). The output is calculate per-pixel according to
+            `src * mask + dst * (1 - mask)`.
+    """
+
+    if mask is None:
+        dst_image[box.top : box.bottom, box.left : box.right] = src_image
+    else:
+        mask = np.expand_dims(mask, -1)
+        dst_image_box = dst_image[box.top : box.bottom, box.left : box.right]
+        dst_image[box.top : box.bottom, box.left : box.right] = src_image * mask + dst_image_box * (1.0 - mask)

invokeai/backend/util/logging.py

@@ -1,8 +1,7 @@
 # Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
 
-"""invokeai.backend.util.logging
-
-Logging class for InvokeAI that produces console messages
+"""
+Logging class for InvokeAI that produces console messages.
 
 Usage:
 
@@ -178,8 +177,8 @@ InvokeAI:
 import logging.handlers
 import socket
 import urllib.parse
-from abc import abstractmethod
 from pathlib import Path
+from typing import Any, Dict, Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
 
@@ -192,36 +191,36 @@ except ImportError:
 
 
 # module level functions
-def debug(msg, *args, **kwargs):
+def debug(msg: str, *args: str, **kwargs: Any) -> None:  # noqa D103
     InvokeAILogger.get_logger().debug(msg, *args, **kwargs)
 
 
-def info(msg, *args, **kwargs):
+def info(msg: str, *args: str, **kwargs: Any) -> None:  # noqa D103
     InvokeAILogger.get_logger().info(msg, *args, **kwargs)
 
 
-def warning(msg, *args, **kwargs):
+def warning(msg: str, *args: str, **kwargs: Any) -> None:  # noqa D103
     InvokeAILogger.get_logger().warning(msg, *args, **kwargs)
 
 
-def error(msg, *args, **kwargs):
+def error(msg: str, *args: str, **kwargs: Any) -> None:  # noqa D103
     InvokeAILogger.get_logger().error(msg, *args, **kwargs)
 
 
-def critical(msg, *args, **kwargs):
+def critical(msg: str, *args: str, **kwargs: Any) -> None:  # noqa D103
     InvokeAILogger.get_logger().critical(msg, *args, **kwargs)
 
 
-def log(level, msg, *args, **kwargs):
+def log(level: int, msg: str, *args: str, **kwargs: Any) -> None:  # noqa D103
     InvokeAILogger.get_logger().log(level, msg, *args, **kwargs)
 
 
-def disable(level=logging.CRITICAL):
-    InvokeAILogger.get_logger().disable(level)
+def disable(level: int = logging.CRITICAL) -> None:  # noqa D103
+    logging.disable(level)
 
 
-def basicConfig(**kwargs):
-    InvokeAILogger.get_logger().basicConfig(**kwargs)
+def basicConfig(**kwargs: Any) -> None:  # noqa D103
+    logging.basicConfig(**kwargs)
 
 
 _FACILITY_MAP = (
@@ -256,33 +255,25 @@ _SOCK_MAP = {
 
 
 class InvokeAIFormatter(logging.Formatter):
-    """
-    Base class for logging formatter
+    """Base class for logging formatter."""
 
-    """
-
-    def format(self, record):
+    def format(self, record: logging.LogRecord) -> str:  # noqa D102
         formatter = logging.Formatter(self.log_fmt(record.levelno))
         return formatter.format(record)
 
-    @abstractmethod
-    def log_fmt(self, levelno: int) -> str:
-        pass
+    def log_fmt(self, levelno: int) -> str:  # noqa D102
+        return "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
 
 
 class InvokeAISyslogFormatter(InvokeAIFormatter):
-    """
-    Formatting for syslog
-    """
+    """Formatting for syslog."""
 
-    def log_fmt(self, levelno: int) -> str:
+    def log_fmt(self, levelno: int) -> str:  # noqa D102
         return "%(name)s [%(process)d] <%(levelname)s> %(message)s"
 
 
-class InvokeAILegacyLogFormatter(InvokeAIFormatter):
-    """
-    Formatting for the InvokeAI Logger (legacy version)
-    """
+class InvokeAILegacyLogFormatter(InvokeAIFormatter):  # noqa D102
+    """Formatting for the InvokeAI Logger (legacy version)."""
 
     FORMATS = {
         logging.DEBUG: "   | %(message)s",
@@ -292,23 +283,21 @@ class InvokeAILegacyLogFormatter(InvokeAIFormatter):
         logging.CRITICAL: "### %(message)s",
     }
 
-    def log_fmt(self, levelno: int) -> str:
-        return self.FORMATS.get(levelno)
+    def log_fmt(self, levelno: int) -> str:  # noqa D102
+        format = self.FORMATS.get(levelno)
+        assert format is not None
+        return format
 
 
 class InvokeAIPlainLogFormatter(InvokeAIFormatter):
-    """
-    Custom Formatting for the InvokeAI Logger (plain version)
-    """
+    """Custom Formatting for the InvokeAI Logger (plain version)."""
 
-    def log_fmt(self, levelno: int) -> str:
+    def log_fmt(self, levelno: int) -> str:  # noqa D102
         return "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
 
 
 class InvokeAIColorLogFormatter(InvokeAIFormatter):
-    """
-    Custom Formatting for the InvokeAI Logger
-    """
+    """Custom Formatting for the InvokeAI Logger."""
 
     # Color Codes
     grey = "\x1b[38;20m"
@@ -331,8 +320,10 @@ class InvokeAIColorLogFormatter(InvokeAIFormatter):
         logging.CRITICAL: bold_red + log_format + reset,
     }
 
-    def log_fmt(self, levelno: int) -> str:
-        return self.FORMATS.get(levelno)
+    def log_fmt(self, levelno: int) -> str:  # noqa D102
+        format = self.FORMATS.get(levelno)
+        assert format is not None
+        return format
 
 
 LOG_FORMATTERS = {
@@ -343,13 +334,13 @@ LOG_FORMATTERS = {
 }
 
 
-class InvokeAILogger(object):
-    loggers = {}
+class InvokeAILogger(object):  # noqa D102
+    loggers: Dict[str, logging.Logger] = {}
 
     @classmethod
     def get_logger(
         cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
-    ) -> logging.Logger:
+    ) -> logging.Logger:  # noqa D102
         if name in cls.loggers:
             logger = cls.loggers[name]
             logger.handlers.clear()
@@ -362,7 +353,7 @@ class InvokeAILogger(object):
         return cls.loggers[name]
 
     @classmethod
-    def get_loggers(cls, config: InvokeAIAppConfig) -> list[logging.Handler]:
+    def get_loggers(cls, config: InvokeAIAppConfig) -> list[logging.Handler]:  # noqa D102
         handler_strs = config.log_handlers
         handlers = []
         for handler in handler_strs:
@@ -374,7 +365,7 @@ class InvokeAILogger(object):
             # http gets no custom formatter
             formatter = LOG_FORMATTERS[config.log_format]
             if handler_name == "console":
-                ch = logging.StreamHandler()
+                ch: logging.Handler = logging.StreamHandler()
                 ch.setFormatter(formatter())
                 handlers.append(ch)
 
@@ -393,18 +384,18 @@ class InvokeAILogger(object):
         return handlers
 
     @staticmethod
-    def _parse_syslog_args(args: str = None) -> logging.Handler:
+    def _parse_syslog_args(args: Optional[str] = None) -> logging.Handler:
         if not SYSLOG_AVAILABLE:
             raise ValueError("syslog is not available on this system")
         if not args:
             args = "/dev/log" if Path("/dev/log").exists() else "address:localhost:514"
-        syslog_args = {}
+        syslog_args: Dict[str, Any] = {}
         try:
             for a in args.split(","):
                 arg_name, *arg_value = a.split(":", 2)
                 if arg_name == "address":
-                    host, *port = arg_value
-                    port = 514 if len(port) == 0 else int(port[0])
+                    host, *port_list = arg_value
+                    port = 514 if not port_list else int(port_list[0])
                     syslog_args["address"] = (host, port)
                 elif arg_name == "facility":
                     syslog_args["facility"] = _FACILITY_MAP[arg_value[0]]
@@ -417,13 +408,13 @@ class InvokeAILogger(object):
         return logging.handlers.SysLogHandler(**syslog_args)
 
     @staticmethod
-    def _parse_file_args(args: str = None) -> logging.Handler:
+    def _parse_file_args(args: Optional[str] = None) -> logging.Handler:  # noqa D102
         if not args:
             raise ValueError("please provide filename for file logging using format 'file=/path/to/logfile.txt'")
         return logging.FileHandler(args)
 
     @staticmethod
-    def _parse_http_args(args: str = None) -> logging.Handler:
+    def _parse_http_args(args: Optional[str] = None) -> logging.Handler:  # noqa D102
         if not args:
             raise ValueError("please provide destination for http logging using format 'http=url'")
         arg_list = args.split(",")
@@ -434,12 +425,12 @@ class InvokeAILogger(object):
         path = url.path
         port = url.port or 80
 
-        syslog_args = {}
+        syslog_args: Dict[str, Any] = {}
         for a in arg_list:
             arg_name, *arg_value = a.split(":", 2)
             if arg_name == "method":
-                arg_value = arg_value[0] if len(arg_value) > 0 else "GET"
-                syslog_args[arg_name] = arg_value
+                method = arg_value[0] if len(arg_value) > 0 else "GET"
+                syslog_args[arg_name] = method
             else:  # TODO: Provide support for SSL context and credentials
                 pass
         return logging.handlers.HTTPHandler(f"{host}:{port}", path, **syslog_args)

invokeai/frontend/web/.eslintrc.js

@@ -20,10 +20,18 @@ module.exports = {
     ecmaVersion: 2018,
     sourceType: 'module',
   },
-  plugins: ['react', '@typescript-eslint', 'eslint-plugin-react-hooks'],
+  plugins: [
+    'react',
+    '@typescript-eslint',
+    'eslint-plugin-react-hooks',
+    'i18next',
+    'path',
+  ],
   root: true,
   rules: {
+    'path/no-relative-imports': ['error', { maxDepth: 0 }],
     curly: 'error',
+    'i18next/no-literal-string': 2,
     'react/jsx-no-bind': ['error', { allowBind: true }],
     'react/jsx-curly-brace-presence': [
       'error',

invokeai/frontend/web/.prettierignore

@@ -1,5 +1,6 @@
 dist/
 public/locales/*.json
+!public/locales/en.json
 .husky/
 node_modules/
 patches/
@@ -9,6 +10,5 @@ index.html
 .yalc/
 *.scss
 src/services/api/schema.d.ts
-docs/
 static/
 src/theme/css/overlayscrollbars.css

invokeai/frontend/web/docs/API_CLIENT.md

@@ -1,87 +0,0 @@
-# Generated axios API client
-
-- [Generated axios API client](#generated-axios-api-client)
-  - [Generation](#generation)
-    - [Generate the API client from the nodes web server](#generate-the-api-client-from-the-nodes-web-server)
-    - [Generate the API client from JSON](#generate-the-api-client-from-json)
-      - [Getting the JSON from the nodes web server](#getting-the-json-from-the-nodes-web-server)
-      - [Getting the JSON with a python script](#getting-the-json-with-a-python-script)
-      - [Generate the API client](#generate-the-api-client)
-  - [The generated client](#the-generated-client)
-  - [API client customisation](#api-client-customisation)
-
-This API client is generated by an [openapi code generator](https://github.com/ferdikoomen/openapi-typescript-codegen).
-
-All files in `invokeai/frontend/web/src/services/api/` are made by the generator.
-
-## Generation
-
-The axios client may be generated by from the OpenAPI schema from the nodes web server, or from JSON.
-
-### Generate the API client from the nodes web server
-
-We need to start the nodes web server, which serves the OpenAPI schema to the generator.
-
-1. Start the nodes web server.
-
-```bash
-# from the repo root
-python scripts/invokeai-web.py
-```
-
-2. Generate the API client. 
-
-```bash
-# from invokeai/frontend/web/
-yarn api:web
-```
-
-### Generate the API client from JSON
-
-The JSON can be acquired from the nodes web server, or with a python script.
-
-#### Getting the JSON from the nodes web server
-
-Start the nodes web server as described above, then download the file.
-
-```bash
-# from invokeai/frontend/web/
-curl http://localhost:9090/openapi.json -o openapi.json
-```
-
-#### Getting the JSON with a python script
-
-Run this python script from the repo root, so it can access the nodes server modules.
-
-The script will output `openapi.json` in the repo root. Then we need to move it to `invokeai/frontend/web/`.
-
-```bash
-# from the repo root
-python invokeai/app/util/generate_openapi_json.py
-mv invokeai/app/util/openapi.json invokeai/frontend/web/services/fixtures/
-```
-
-#### Generate the API client
-
-Now we can generate the API client from the JSON.
-
-```bash
-# from invokeai/frontend/web/
-yarn api:file
-```
-
-## The generated client
-
-The client will be written to `invokeai/frontend/web/services/api/`:
-
-- `axios` client
-- TS types
-- An easily parseable schema, which we can use to generate UI
-
-## API client customisation
-
-The generator has a default `request.ts` file that implements a base `axios` client. The generated client uses this base client.
-
-One shortcoming of this is base client is it does not provide response headers unless the response body is empty. To fix this, we provide our own lightly-patched `request.ts`.
-
-To access the headers, call `getHeaders(response)` on any response from the generated api client. This function is exported from `invokeai/frontend/web/src/services/util/getHeaders.ts`.

invokeai/frontend/web/docs/EVENTS.md

@@ -1,21 +0,0 @@
-# Events
-
-Events via `socket.io`
-
-## `actions.ts`
-
-Redux actions for all socket events. Payloads all include a timestamp, and optionally some other data.
-
-Any reducer (or middleware) can respond to the actions.
-
-## `middleware.ts`
-
-Redux middleware for events.
-
-Handles dispatching the event actions. Only put logic here if it can't really go anywhere else.
-
-For example, on connect we want to load images to the gallery if it's not populated. This requires dispatching a thunk, so we need to directly dispatch this in the middleware.
-
-## `types.ts`
-
-Hand-written types for the socket events. Cannot generate these from the server, but fortunately they are few and simple.

invokeai/frontend/web/docs/NODE_EDITOR.md

@@ -1,17 +0,0 @@
-# Node Editor Design
-
-WIP
-
-nodes
-
-everything in `src/features/nodes/`
-
-have a look at `state.nodes.invocation`
-
-- on socket connect, if no schema saved, fetch `localhost:9090/openapi.json`, save JSON to `state.nodes.schema`
-- on fulfilled schema fetch, `parseSchema()` the schema. this outputs a `Record<string, Invocation>` which is saved to `state.nodes.invocations` - `Invocation` is like a template for the node
-- when you add a node, the the `Invocation` template is passed to `InvocationComponent.tsx` to build the UI component for that node
-- inputs/outputs have field types - and each field type gets an `FieldComponent` which includes a dispatcher to write state changes to redux `nodesSlice`
-- `reactflow` sends changes to nodes/edges to redux
-- to invoke, `buildNodesGraph()` state, then send this
-- changed onClick Invoke button actions to build the schema, then when schema builds it dispatches the actual network request to create the session - see `session.ts`

invokeai/frontend/web/docs/PACKAGE_SCRIPTS.md

@@ -1,17 +0,0 @@
-# Package Scripts
-
-WIP walkthrough of `package.json` scripts.
-
-## `theme` & `theme:watch`
-
-These run the Chakra CLI to generate types for the theme, or watch for code change and re-generate the types.
-
-The CLI essentially monkeypatches Chakra's files in `node_modules`.
-
-## `postinstall`
-
-The `postinstall` script patches a few packages and runs the Chakra CLI to generate types for the theme.
-
-### Patch `@chakra-ui/cli`
-
-See: <https://github.com/chakra-ui/chakra-ui/issues/7394>

invokeai/frontend/web/docs/README.md

@@ -1,43 +1,118 @@
 # InvokeAI Web UI
 
+<!-- @import "[TOC]" {cmd="toc" depthFrom=1 depthTo=6 orderedList=false} -->
+
+<!-- code_chunk_output -->
+
 - [InvokeAI Web UI](#invokeai-web-ui)
-  - [Stack](#stack)
+  - [Core Libraries](#core-libraries)
+    - [Redux Toolkit](#redux-toolkit)
+    - [Socket\.IO](#socketio)
+    - [Chakra UI](#chakra-ui)
+    - [KonvaJS](#konvajs)
+    - [Vite](#vite)
+    - [i18next & Weblate](#i18next--weblate)
+    - [openapi-typescript](#openapi-typescript)
+    - [reactflow](#reactflow)
+    - [zod](#zod)
+  - [Client Types Generation](#client-types-generation)
+  - [Package Scripts](#package-scripts)
   - [Contributing](#contributing)
     - [Dev Environment](#dev-environment)
+      - [VSCode Remote Dev](#vscode-remote-dev)
     - [Production builds](#production-builds)
 
-The UI is a fairly straightforward Typescript React app. The only really fancy stuff is the Unified Canvas.
+<!-- /code_chunk_output -->
 
-Code in `invokeai/frontend/web/` if you want to have a look.
+The UI is a fairly straightforward Typescript React app.
 
-## Stack
+## Core Libraries
 
-State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). We lean heavily on RTK:
-- `createAsyncThunk` for HTTP requests
-- `createEntityAdapter` for fetching images and models
-- `createListenerMiddleware` for workflows
+InvokeAI's UI is made possible by a number of excellent open-source libraries. The most heavily-used are listed below, but there are many others.
 
-The API client and associated types are generated from the OpenAPI schema. See API_CLIENT.md.
+### Redux Toolkit
 
-Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a simple socket.io redux middleware to help).
+[Redux Toolkit] is used for state management and fetching/caching:
 
-[Chakra-UI](https://github.com/chakra-ui/chakra-ui) for components and styling.
+- `RTK-Query` for data fetching and caching
+- `createAsyncThunk` for a couple other HTTP requests
+- `createEntityAdapter` to normalize things like images and models
+- `createListenerMiddleware` for async workflows
 
-[Konva](https://github.com/konvajs/react-konva) for the canvas, but we are pushing the limits of what is feasible with it (and HTML canvas in general). We plan to rebuild it with [PixiJS](https://github.com/pixijs/pixijs) to take advantage of WebGL's improved raster handling.
+We use [redux-remember] for persistence.
 
-[Vite](https://vitejs.dev/) for bundling.
+### Socket\.IO
 
-Localisation is via [i18next](https://github.com/i18next/react-i18next), but translation happens on our [Weblate](https://hosted.weblate.org/engage/invokeai/) project. Only the English source strings should be changed on this repo.
+[Socket.IO] is used for server-to-client events, like generation process and queue state changes.
+
+### Chakra UI
+
+[Chakra UI] is our primary UI library, but we also use a few components from [Mantine v6].
+
+### KonvaJS
+
+[KonvaJS] powers the canvas. In the future, we'd like to explore [PixiJS] or WebGPU.
+
+### Vite
+
+[Vite] is our bundler.
+
+### i18next & Weblate
+
+We use [i18next] for localization, but translation to languages other than English happens on our [Weblate] project. **Only the English source strings should be changed on this repo.**
+
+### openapi-typescript
+
+[openapi-typescript] is used to generate types from the server's OpenAPI schema. See TYPES_CODEGEN.md.
+
+### reactflow
+
+[reactflow] powers the Workflow Editor.
+
+### zod
+
+[zod] schemas are used to model data structures and provide runtime validation.
+
+## Client Types Generation
+
+We use [openapi-typescript] to generate types from the app's OpenAPI schema.
+
+The generated types are written to `invokeai/frontend/web/src/services/api/schema.d.ts`. This file is committed to the repo.
+
+The server must be started and available at <http://127.0.0.1:9090>.
+
+```sh
+# from the repo root, start the server
+python scripts/invokeai-web.py
+# from invokeai/frontend/web/, run the script
+yarn typegen
+```
+
+## Package Scripts
+
+See `package.json` for all scripts.
+
+Run with `yarn <script name>`.
+
+- `dev`: run the frontend in dev mode, enabling hot reloading
+- `build`: run all checks (madge, eslint, prettier, tsc) and then build the frontend
+- `typegen`: generate types from the OpenAPI schema (see [Client Types Generation](#client-types-generation))
+- `lint:madge`: check frontend for circular dependencies
+- `lint:eslint`: check frontend for code quality
+- `lint:prettier`: check frontend for code formatting
+- `lint:tsc`: check frontend for type issues
+- `lint`: run all checks concurrently
+- `fix`: run `eslint` and `prettier`, fixing fixable issues
 
 ## Contributing
 
 Thanks for your interest in contributing to the InvokeAI Web UI!
 
-We encourage you to ping @psychedelicious and @blessedcoolant on [Discord](https://discord.gg/ZmtBAhwWhy) if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
+We encourage you to ping @psychedelicious and @blessedcoolant on [discord] if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
 
 ### Dev Environment
 
-Install [node](https://nodejs.org/en/download/) and [yarn classic](https://classic.yarnpkg.com/lang/en/).
+Install [node] and [yarn classic].
 
 From `invokeai/frontend/web/` run `yarn install` to get everything set up.
 
@@ -60,3 +135,20 @@ For a number of technical and logistical reasons, we need to commit UI build art
 If you submit a PR, there is a good chance we will ask you to include a separate commit with a build of the app.
 
 To build for production, run `yarn build`.
+
+[node]: https://nodejs.org/en/download/
+[yarn classic]: https://classic.yarnpkg.com/lang/en/
+[discord]: https://discord.gg/ZmtBAhwWhy
+[Redux Toolkit]: https://github.com/reduxjs/redux-toolkit
+[redux-remember]: https://github.com/zewish/redux-remember
+[Socket.IO]: https://github.com/socketio/socket.io
+[Chakra UI]: https://github.com/chakra-ui/chakra-ui
+[Mantine v6]: https://v6.mantine.dev/
+[KonvaJS]: https://github.com/konvajs/react-konva
+[PixiJS]: https://github.com/pixijs/pixijs
+[Vite]: https://github.com/vitejs/vite
+[i18next]: https://github.com/i18next/react-i18next
+[Weblate]: https://hosted.weblate.org/engage/invokeai/
+[openapi-typescript]: https://github.com/drwpow/openapi-typescript
+[reactflow]: https://github.com/xyflow/xyflow
+[zod]: https://github.com/colinhacks/zod

invokeai/frontend/web/docs/WORKFLOWS_DESIGN_IMPLEMENTATION.md

@@ -0,0 +1,350 @@
+# Workflows - Design and Implementation
+
+<!-- @import "[TOC]" {cmd="toc" depthFrom=1 depthTo=6 orderedList=false} -->
+
+<!-- code_chunk_output -->
+
+- [Workflows - Design and Implementation](#workflows---design-and-implementation)
+  - [Design](#design)
+    - [Linear UI](#linear-ui)
+    - [Workflow Editor](#workflow-editor)
+      - [Workflows](#workflows)
+        - [Workflow -> reactflow state -> InvokeAI graph](#workflow---reactflow-state---invokeai-graph)
+        - [Nodes vs Invocations](#nodes-vs-invocations)
+        - [Workflow Linear View](#workflow-linear-view)
+      - [OpenAPI Schema](#openapi-schema)
+        - [Field Instances and Templates](#field-instances-and-templates)
+        - [Stateful vs Stateless Fields](#stateful-vs-stateless-fields)
+        - [Collection and Polymorphic Fields](#collection-and-polymorphic-fields)
+  - [Implementation](#implementation)
+    - [zod Schemas and Types](#zod-schemas-and-types)
+    - [OpenAPI Schema Parsing](#openapi-schema-parsing)
+      - [Parsing Field Types](#parsing-field-types)
+        - [Primitive Types](#primitive-types)
+        - [Complex Types](#complex-types)
+        - [Collection Types](#collection-types)
+        - [Polymorphic Types](#polymorphic-types)
+        - [Optional Fields](#optional-fields)
+      - [Building Field Input Templates](#building-field-input-templates)
+      - [Building Field Output Templates](#building-field-output-templates)
+    - [Managing reactflow State](#managing-reactflow-state)
+      - [Building Nodes and Edges](#building-nodes-and-edges)
+      - [Building a Workflow](#building-a-workflow)
+      - [Loading a Workflow](#loading-a-workflow)
+    - [Workflow Migrations](#workflow-migrations)
+
+<!-- /code_chunk_output -->
+
+> This document describes, at a high level, the design and implementation of workflows in the InvokeAI frontend. There are a substantial number of implementation details not included, but which are hopefully clear from the code.
+
+InvokeAI's backend uses graphs, composed of **nodes** and **edges**, to process data and generate images.
+
+Nodes have any number of **input fields** and **output fields**. Edges connect nodes together via their inputs and outputs. Fields have data types which dictate how they may be connected.
+
+During execution, a nodes' outputs may be passed along to any number of other nodes' inputs.
+
+Workflows are an enriched abstraction over a graph.
+
+## Design
+
+InvokeAI provide two ways to build graphs in the frontend: the [Linear UI](#linear-ui) and [Workflow Editor](#workflow-editor).
+
+To better understand the use case and challenges related to workflows, we will review both of these modes.
+
+### Linear UI
+
+This includes the **Text to Image**, **Image to Image** and **Unified Canvas** tabs.
+
+The user-managed parameters on these tabs are stored as simple objects in the application state. When the user invokes, adding a generation to the queue, we internally build a graph from these parameters.
+
+This logic can be fairly complex due to the range of features available and their interactions. Depending on the parameters selected, the graph may be very different. Building graphs in code can be challenging - you are trying to construct a non-linear structure in a linear context.
+
+The simplest graph building logic is for **Text to Image** with a SD1.5 model: [buildLinearTextToImageGraph.ts]
+
+There are many other graph builders in the same directory for different tabs or base models (e.g. SDXL). Some are pretty hairy.
+
+In the Linear UI, we go straight from **simple application state** to **graph** via these builders.
+
+### Workflow Editor
+
+The Workflow Editor is a visual graph editor, allowing users to draw edges from node to node to construct a graph. This _far_ more approachable way to create complex graphs.
+
+InvokeAI uses the [reactflow] library to power the Workflow Editor. It provides both a graph editor UI and manages its own internal graph state.
+
+#### Workflows
+
+A workflow is a representation of a graph plus additional metadata:
+
+- Name
+- Description
+- Version
+- Notes
+- [Exposed fields](#workflow-linear-view)
+- Author, tags, category, etc.
+
+Workflows should have other qualities:
+
+- Portable: you should be able to load a workflow created by another person.
+- Resilient: you should be able to "upgrade" a workflow as the application changes.
+- Abstract: as much as is possible, workflows should not be married to the specific implementation details of the application.
+
+To support these qualities, workflows are serializable, have a versioned schemas, and represent graphs as minimally as possible. Fortunately, the reactflow state for nodes and edges works perfectly for this.
+
+##### Workflow -> reactflow state -> InvokeAI graph
+
+Given a workflow, we need to be able to derive reactflow state and/or an InvokeAI graph from it.
+
+The first step - workflow to reactflow state - is very simple. The logic is in [nodesSlice.ts], in the `workflowLoaded` reducer.
+
+The reactflow state is, however, structurally incompatible with our backend's graph structure. When a user invokes on a Workflow, we need to convert the reactflow state into an InvokeAI graph. This is far simpler than the graph building logic from the Linear UI:
+[buildNodesGraph.ts]
+
+##### Nodes vs Invocations
+
+We often use the terms "node" and "invocation" interchangeably, but they may refer to different things in the frontend.
+
+reactflow [has its own definitions][reactflow-concepts] of "node", "edge" and "handle" which are closely related to InvokeAI graph concepts.
+
+- A reactflow node is related to an InvokeAI invocation. It has a "data" property, which holds the InvokeAI-specific invocation data.
+- A reactflow edge is roughly equivalent to an InvokeAI edge.
+- A reactflow handle is roughly equivalent to an InvokeAI input or output field.
+
+##### Workflow Linear View
+
+Graphs are very capable data structures, but not everyone wants to work with them all the time.
+
+To allow less technical users - or anyone who wants a less visually noisy workspace - to benefit from the power of nodes, InvokeAI has a workflow feature called the Linear View.
+
+A workflow input field can be added to this Linear View, and its input component can be presented similarly to the Linear UI tabs. Internally, we add the field to the workflow's list of exposed fields.
+
+#### OpenAPI Schema
+
+OpenAPI is a schema specification that can represent complex data structures and relationships. The backend is capable of generating an OpenAPI schema for all invocations.
+
+When the UI connects, it requests this schema and parses each invocation into an **invocation template**. Invocation templates have a number of properties, like title, description and type, but the most important ones are their input and output **field templates**.
+
+Invocation and field templates are the "source of truth" for graphs, because they indicate what the backend is able to process.
+
+When a user adds a new node to their workflow, these templates are used to instantiate a node with fields instantiated from the input and output field templates.
+
+##### Field Instances and Templates
+
+Field templates consist of:
+
+- Name: the identifier of the field, its variable name in python
+- Type: derived from the field's type annotation in python (e.g. IntegerField, ImageField, MainModelField)
+- Constraints: derived from the field's creation args in python (e.g. minimum value for an integer)
+- Default value: optionally provided in the field's creation args (e.g. 42 for an integer)
+
+Field instances are created from the templates and have name, type and optionally a value.
+
+The type of the field determines the UI components that are rendered for it.
+
+A field instance's name associates it with its template.
+
+##### Stateful vs Stateless Fields
+
+**Stateful** fields store their value in the frontend graph. Think primitives, model identifiers, images, etc. Fields are only stateful if the frontend allows the user to directly input a value for them.
+
+Many field types, however, are **stateless**. An example is a `UNetField`, which contains some data describing a UNet. Users cannot directly provide this data - it is created and consumed in the backend.
+
+Stateless fields do not store their value in the node, so their field instances do not have values.
+
+"Custom" fields will always be treated as stateless fields.
+
+##### Collection and Polymorphic Fields
+
+Field types have a name and two flags which may identify it as a **collection** or **polymorphic** field.
+
+If a field is annotated in python as a list, its field type is parsed and flagged as a collection type (e.g. `list[int]`).
+
+If it is annotated as a union of a type and list, the type will be flagged as a polymorphic type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
+
+## Implementation
+
+The majority of data structures in the backend are [pydantic] models. Pydantic provides OpenAPI schemas for all models and we then generate TypeScript types from those.
+
+The OpenAPI schema is parsed at runtime into our invocation templates.
+
+Workflows and all related data are modeled in the frontend using [zod]. Related types are inferred from the zod schemas.
+
+> In python, invocations are pydantic models with fields. These fields become node inputs. The invocation's `invoke()` function returns a pydantic model - its output. Like the invocation itself, the output model has any number of fields, which become node outputs.
+
+### zod Schemas and Types
+
+The zod schemas, inferred types, and type guards are in [types/].
+
+Roughly order from lowest-level to highest:
+
+- `common.ts`: stateful field data, and couple other misc types
+- `field.ts`: fields - types, values, instances, templates
+- `invocation.ts`: invocations and other node types
+- `workflow.ts`: workflows and constituents
+
+We customize the OpenAPI schema to include additional properties on invocation and field schemas. To facilitate parsing this schema into templates, we modify/wrap the types from [openapi-types] in `openapi.ts`.
+
+### OpenAPI Schema Parsing
+
+The entrypoint for OpenAPI schema parsing is [parseSchema.ts].
+
+General logic flow:
+
+- Iterate over all invocation schema objects
+  - Extract relevant invocation-level attributes (e.g. title, type, version, etc)
+  - Iterate over the invocation's input fields
+    - [Parse each field's type](#parsing-field-types)
+    - [Build a field input template](#building-field-input-templates) from the type - either a stateful template or "generic" stateless template
+  - Iterate over the invocation's output fields
+    - Parse the field's type (same as inputs)
+    - [Build a field output template](#building-field-output-templates)
+  - Assemble the attributes and fields into an invocation template
+
+Most of these involve very straightforward `reduce`s, but the less intuitive steps are detailed below.
+
+#### Parsing Field Types
+
+Field types are represented as structured objects:
+
+```ts
+type FieldType = {
+  name: string;
+  isCollection: boolean;
+  isCollectionOrScalar: boolean;
+};
+```
+
+The parsing logic is in `parseFieldType.ts`.
+
+There are 4 general cases for field type parsing.
+
+##### Primitive Types
+
+When a field is annotated as a primitive values (e.g. `int`, `str`, `float`), the field type parsing is fairly straightforward. The field is represented by a simple OpenAPI **schema object**, which has a `type` property.
+
+We create a field type name from this `type` string (e.g. `string` -> `StringField`).
+
+##### Complex Types
+
+When a field is annotated as a pydantic model (e.g. `ImageField`, `MainModelField`, `ControlField`), it is represented as a **reference object**. Reference objects are pointers to another schema or reference object within the schema.
+
+We need to **dereference** the schema to pull these out. Dereferencing may require recursion. We use the reference object's name directly for the field type name.
+
+> Unfortunately, at this time, we've had limited success using external libraries to deference at runtime, so we do this ourselves.
+
+##### Collection Types
+
+When a field is annotated as a list of a single type, the schema object has an `items` property. They may be a schema object or reference object and must be parsed to determine the item type.
+
+We use the item type for field type name, adding `isCollection: true` to the field type.
+
+##### Collection or Scalar Types
+
+When a field is annotated as a union of a type and list of that type, the schema object has an `anyOf` property, which holds a list of valid types for the union.
+
+After verifying that the union has two members (a type and list of the same type), we use the type for field type name, adding `isCollectionOrScalar: true` to the field type.
+
+##### Optional Fields
+
+In OpenAPI v3.1, when an object is optional, it is put into an `anyOf` along with a primitive schema object with `type: 'null'`.
+
+Handling this adds a fair bit of complexity, as we now must filter out the `'null'` types and work with the remaining types as described above.
+
+If there is a single remaining schema object, we must recursively call to `parseFieldType()` to get parse it.
+
+#### Building Field Input Templates
+
+Now that we have a field type, we can build an input template for the field.
+
+Stateful fields all get a function to build their template, while stateless fields are constructed directly. This is possible because stateless fields have no default value or constraints.
+
+See [buildFieldInputTemplate.ts].
+
+#### Building Field Output Templates
+
+Field outputs are similar to stateless fields - they do not have any value in the frontend. When building their templates, we don't need a special function for each field type.
+
+See [buildFieldOutputTemplate.ts].
+
+### Managing reactflow State
+
+As described above, the workflow editor state is the essentially the reactflow state, plus some extra metadata.
+
+We provide reactflow with an array of nodes and edges via redux, and a number of [event handlers][reactflow-events]. These handlers dispatch redux actions, managing nodes and edges.
+
+The pieces of redux state relevant to workflows are:
+
+- `state.nodes.nodes`: the reactflow nodes state
+- `state.nodes.edges`: the reactflow edges state
+- `state.nodes.workflow`: the workflow metadata
+
+#### Building Nodes and Edges
+
+A reactflow node has a few important top-level properties:
+
+- `id`: unique identifier
+- `type`: a string that maps to a react component to render the node
+- `position`: XY coordinates
+- `data`: arbitrary data
+
+When the user adds a node, we build **invocation node data**, storing it in `data`. Invocation properties (e.g. type, version, label, etc.) are copied from the invocation template. Inputs and outputs are built from the invocation template's field templates.
+
+See [buildInvocationNode.ts].
+
+Edges are managed by reactflow, but briefly, they consist of:
+
+- `source`: id of the source node
+- `sourceHandle`: id of the source node handle (output field)
+- `target`: id of the target node
+- `targetHandle`: id of the target node handle (input field)
+
+> Edge creation is gated behind validation logic. This validation compares the input and output field types and overall graph state.
+
+#### Building a Workflow
+
+Building a workflow entity is as simple as dropping the nodes, edges and metadata into an object.
+
+Each node and edge is parsed with a zod schema, which serves to strip out any unneeded data.
+
+See [buildWorkflow.ts].
+
+#### Loading a Workflow
+
+Workflows may be loaded from external sources or the user's local instance. In all cases, the workflow needs to be handled with care, as an untrusted object.
+
+Loading has a few stages which may throw or warn if there are problems:
+
+- Parsing the workflow data structure itself, [migrating](#workflow-migrations) it if necessary (throws)
+- Check for a template for each node (warns)
+- Check each node's version against its template (warns)
+- Validate the source and target of each edge (warns)
+
+This validation occurs in [validateWorkflow.ts].
+
+If there are no fatal errors, the workflow is then stored in redux state.
+
+### Workflow Migrations
+
+When the workflow schema changes, we may need to perform some data migrations. This occurs as workflows are loaded. zod schemas for each workflow schema version is retained to facilitate migrations.
+
+Previous schemas are in folders in `invokeai/frontend/web/src/features/nodes/types/`, eg `v1/`.
+
+Migration logic is in [migrations.ts].
+
+<!-- links -->
+
+[pydantic]: https://github.com/pydantic/pydantic 'pydantic'
+[zod]: https://github.com/colinhacks/zod 'zod'
+[openapi-types]: https://github.com/kogosoftwarellc/open-api/tree/main/packages/openapi-types 'openapi-types'
+[reactflow]: https://github.com/xyflow/xyflow 'reactflow'
+[reactflow-concepts]: https://reactflow.dev/learn/concepts/terms-and-definitions
+[reactflow-events]: https://reactflow.dev/api-reference/react-flow#event-handlers
+[buildWorkflow.ts]: ../src/features/nodes/util/workflow/buildWorkflow.ts
+[nodesSlice.ts]: ../src/features/nodes/store/nodesSlice.ts
+[buildLinearTextToImageGraph.ts]: ../src/features/nodes/util/graph/buildLinearTextToImageGraph.ts
+[buildNodesGraph.ts]: ../src/features/nodes/util/graph/buildNodesGraph.ts
+[buildInvocationNode.ts]: ../src/features/nodes/util/node/buildInvocationNode.ts
+[validateWorkflow.ts]: ../src/features/nodes/util/workflow/validateWorkflow.ts
+[migrations.ts]: ../src/features/nodes/util/workflow/migrations.ts
+[parseSchema.ts]: ../src/features/nodes/util/schema/parseSchema.ts
+[buildFieldInputTemplate.ts]: ../src/features/nodes/util/schema/buildFieldInputTemplate.ts
+[buildFieldOutputTemplate.ts]: ../src/features/nodes/util/schema/buildFieldOutputTemplate.ts

invokeai/frontend/web/package.json

@@ -19,7 +19,6 @@
     "dist"
   ],
   "scripts": {
-    "prepare": "cd ../../../ && husky install invokeai/frontend/web/.husky",
     "dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
     "dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"",
     "build": "yarn run lint && vite build",
@@ -30,7 +29,7 @@
     "lint:prettier": "prettier --check .",
     "lint:tsc": "tsc --noEmit",
     "lint": "concurrently -g -n eslint,prettier,tsc,madge -c cyan,green,magenta,yellow \"yarn run lint:eslint\" \"yarn run lint:prettier\" \"yarn run lint:tsc\" \"yarn run lint:madge\"",
-    "fix": "eslint --fix . && prettier --loglevel warn --write . && tsc --noEmit",
+    "fix": "eslint --fix . && prettier --log-level warn --write .",
     "lint-staged": "lint-staged",
     "postinstall": "patch-package && yarn run theme",
     "theme": "chakra-cli tokens src/theme/theme.ts",
@@ -76,11 +75,11 @@
     "framer-motion": "^10.16.4",
     "i18next": "^23.6.0",
     "i18next-http-backend": "^2.3.1",
+    "idb-keyval": "^6.2.1",
     "konva": "^9.2.3",
     "lodash-es": "^4.17.21",
     "nanostores": "^0.9.4",
     "new-github-issue-url": "^1.0.0",
-    "openapi-fetch": "^0.8.1",
     "overlayscrollbars": "^2.4.4",
     "overlayscrollbars-react": "^0.5.3",
     "patch-package": "^8.0.0",
@@ -133,6 +132,8 @@
     "concurrently": "^8.2.2",
     "eslint": "^8.53.0",
     "eslint-config-prettier": "^9.0.0",
+    "eslint-plugin-i18next": "^6.0.3",
+    "eslint-plugin-path": "^1.2.2",
     "eslint-plugin-react": "^7.33.2",
     "eslint-plugin-react-hooks": "^4.6.0",
     "husky": "^8.0.3",

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

@@ -90,7 +90,16 @@
         "openInNewTab": "In einem neuem Tab öffnen",
         "statusProcessing": "wird bearbeitet",
         "linear": "Linear",
-        "imagePrompt": "Bild Prompt"
+        "imagePrompt": "Bild Prompt",
+        "checkpoint": "Checkpoint",
+        "inpaint": "inpaint",
+        "simple": "Einfach",
+        "template": "Vorlage",
+        "outputs": "Ausgabe",
+        "data": "Daten",
+        "safetensors": "Safetensors",
+        "outpaint": "outpaint",
+        "details": "Details"
     },
     "gallery": {
         "generations": "Erzeugungen",
@@ -110,7 +119,6 @@
         "preparingDownload": "bereite Download vor",
         "preparingDownloadFailed": "Problem beim Download vorbereiten",
         "deleteImage": "Lösche Bild",
-        "images": "Bilder",
         "copy": "Kopieren",
         "download": "Runterladen",
         "setCurrentImage": "Setze aktuelle Bild",
@@ -120,7 +128,8 @@
         "downloadSelection": "Auswahl herunterladen",
         "currentlyInUse": "Dieses Bild wird derzeit in den folgenden Funktionen verwendet:",
         "deleteImagePermanent": "Gelöschte Bilder können nicht wiederhergestellt werden.",
-        "autoAssignBoardOnClick": "Board per Klick automatisch zuweisen"
+        "autoAssignBoardOnClick": "Board per Klick automatisch zuweisen",
+        "noImageSelected": "Kein Bild ausgewählt"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tastenkürzel",
@@ -454,7 +463,7 @@
         "quickAdd": "Schnell hinzufügen",
         "simpleModelDesc": "Geben Sie einen Pfad zu einem lokalen Diffusers-Modell, einem lokalen Checkpoint-/Safetensors-Modell, einer HuggingFace-Repo-ID oder einer Checkpoint-/Diffusers-Modell-URL an.",
         "modelDeleted": "Modell gelöscht",
-        "inpainting": "v1 Ausmalen",
+        "inpainting": "v1 Inpainting",
         "modelUpdateFailed": "Modellaktualisierung fehlgeschlagen",
         "useCustomConfig": "Benutzerdefinierte Konfiguration verwenden",
         "settings": "Einstellungen",
@@ -473,7 +482,10 @@
         "variant": "Variante",
         "loraModels": "LoRAs",
         "modelDeleteFailed": "Modell konnte nicht gelöscht werden",
-        "mergedModelName": "Zusammengeführter Modellname"
+        "mergedModelName": "Zusammengeführter Modellname",
+        "checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
+        "formMessageDiffusersModelLocation": "Diffusers Modell Speicherort",
+        "noModelSelected": "Kein Modell ausgewählt"
     },
     "parameters": {
         "images": "Bilder",
@@ -683,7 +695,8 @@
         "exitViewer": "Betrachten beenden",
         "menu": "Menü",
         "loadMore": "Mehr laden",
-        "invokeProgressBar": "Invoke Fortschrittsanzeige"
+        "invokeProgressBar": "Invoke Fortschrittsanzeige",
+        "mode": "Modus"
     },
     "boards": {
         "autoAddBoard": "Automatisches Hinzufügen zum Ordner",
@@ -701,7 +714,11 @@
         "changeBoard": "Ordner wechseln",
         "loading": "Laden...",
         "clearSearch": "Suche leeren",
-        "bottomMessage": "Durch das Löschen dieses Ordners und seiner Bilder werden alle Funktionen zurückgesetzt, die sie derzeit verwenden."
+        "bottomMessage": "Durch das Löschen dieses Ordners und seiner Bilder werden alle Funktionen zurückgesetzt, die sie derzeit verwenden.",
+        "deleteBoardOnly": "Nur Ordner löschen",
+        "deleteBoard": "Löschen Ordner",
+        "deleteBoardAndImages": "Löschen Ordner und Bilder",
+        "deletedBoardsCannotbeRestored": "Gelöschte Ordner könnte nicht wiederhergestellt werden"
     },
     "controlnet": {
         "showAdvanced": "Zeige Erweitert",
@@ -840,7 +857,8 @@
         "pauseTooltip": "Pause von Prozessor",
         "back": "Hinten",
         "resumeSucceeded": "Prozessor wieder aufgenommen",
-        "resumeTooltip": "Prozessor wieder aufnehmen"
+        "resumeTooltip": "Prozessor wieder aufnehmen",
+        "time": "Zeit"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
@@ -868,7 +886,8 @@
         "vae": "VAE",
         "workflow": "Arbeitsablauf",
         "scheduler": "Scheduler",
-        "noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden"
+        "noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden",
+        "recallParameters": "Recall Parameters"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -944,7 +963,9 @@
         "booleanCollection": "Boolesche Werte Sammlung",
         "cannotConnectToSelf": "Es kann keine Verbindung zu sich selbst hergestellt werden",
         "colorCodeEdges": "Farbkodierte Kanten",
-        "addNodeToolTip": "Knoten hinzufügen (Umschalt+A, Leertaste)"
+        "addNodeToolTip": "Knoten hinzufügen (Umschalt+A, Leertaste)",
+        "boardField": "Ordner",
+        "boardFieldDescription": "Ein Galerie Ordner"
     },
     "hrf": {
         "enableHrf": "Aktivieren Sie die Korrektur für hohe Auflösungen",
@@ -968,6 +989,8 @@
         "selectModel": "Wählen ein Modell aus",
         "noRefinerModelsInstalled": "Keine SDXL Refiner-Modelle installiert",
         "noLoRAsInstalled": "Keine LoRAs installiert",
-        "selectLoRA": "Wählen ein LoRA aus"
+        "selectLoRA": "Wählen ein LoRA aus",
+        "esrganModel": "ESRGAN Modell",
+        "addLora": "LoRA hinzufügen"
     }
 }

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

@@ -1,6 +1,7 @@
 {
     "accessibility": {
         "copyMetadataJson": "Copy metadata JSON",
+        "createIssue": "Create Issue",
         "exitViewer": "Exit Viewer",
         "flipHorizontally": "Flip Horizontally",
         "flipVertically": "Flip Vertically",
@@ -12,6 +13,7 @@
         "nextImage": "Next Image",
         "previousImage": "Previous Image",
         "reset": "Reset",
+        "resetUI": "$t(accessibility.reset) UI",
         "rotateClockwise": "Rotate Clockwise",
         "rotateCounterClockwise": "Rotate Counter-Clockwise",
         "showGalleryPanel": "Show Gallery Panel",
@@ -38,6 +40,8 @@
         "loading": "Loading...",
         "menuItemAutoAdd": "Auto-add to this Board",
         "move": "Move",
+        "movingImagesToBoard_one": "Moving {{count}} image to board:",
+        "movingImagesToBoard_other": "Moving {{count}} images to board:",
         "myBoard": "My Board",
         "noMatching": "No matching Boards",
         "searchBoard": "Search Boards...",
@@ -49,11 +53,13 @@
     "common": {
         "accept": "Accept",
         "advanced": "Advanced",
+        "ai": "ai",
         "areYouSure": "Are you sure?",
         "auto": "Auto",
         "back": "Back",
         "batch": "Batch Manager",
         "cancel": "Cancel",
+        "copyError": "$t(gallery.copy) Error",
         "close": "Close",
         "on": "On",
         "checkpoint": "Checkpoint",
@@ -61,12 +67,18 @@
         "controlNet": "ControlNet",
         "controlAdapter": "Control Adapter",
         "data": "Data",
+        "delete": "Delete",
         "details": "Details",
+        "direction": "Direction",
         "ipAdapter": "IP Adapter",
         "t2iAdapter": "T2I Adapter",
         "darkMode": "Dark Mode",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
+        "error": "Error",
+        "file": "File",
+        "folder": "Folder",
+        "format": "format",
         "generate": "Generate",
         "githubLabel": "Github",
         "hotkeysLabel": "Hotkeys",
@@ -74,6 +86,8 @@
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
         "inpaint": "inpaint",
+        "input": "Input",
+        "installed": "Installed",
         "langArabic": "العربية",
         "langBrPortuguese": "Português do Brasil",
         "langDutch": "Nederlands",
@@ -101,7 +115,9 @@
         "nodeEditor": "Node Editor",
         "nodes": "Workflow Editor",
         "nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
+        "notInstalled": "Not $t(common.installed)",
         "openInNewTab": "Open in New Tab",
+        "orderBy": "Order By",
         "outpaint": "outpaint",
         "outputs": "Outputs",
         "postProcessDesc1": "Invoke AI offers a wide variety of post processing features. Image Upscaling and Face Restoration are already available in the WebUI. You can access them from the Advanced Options menu of the Text To Image and Image To Image tabs. You can also process images directly, using the image action buttons above the current image display or in the viewer.",
@@ -112,8 +128,11 @@
         "random": "Random",
         "reportBugLabel": "Report Bug",
         "safetensors": "Safetensors",
+        "save": "Save",
+        "saveAs": "Save As",
         "settingsLabel": "Settings",
         "simple": "Simple",
+        "somethingWentWrong": "Something went wrong",
         "statusConnected": "Connected",
         "statusConvertingModel": "Converting Model",
         "statusDisconnected": "Disconnected",
@@ -146,7 +165,12 @@
         "trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.",
         "txt2img": "Text To Image",
         "unifiedCanvas": "Unified Canvas",
-        "upload": "Upload"
+        "unknown": "Unknown",
+        "upload": "Upload",
+        "updated": "Updated",
+        "created": "Created",
+        "prevPage": "Previous Page",
+        "nextPage": "Next Page"
     },
     "controlnet": {
         "controlAdapter_one": "Control Adapter",
@@ -228,7 +252,6 @@
         "setControlImageDimensions": "Set Control Image Dimensions To W/H",
         "showAdvanced": "Show Advanced",
         "toggleControlNet": "Toggle this ControlNet",
-        "unstarImage": "Unstar Image",
         "w": "W",
         "weight": "Weight",
         "enableIPAdapter": "Enable IP Adapter",
@@ -252,6 +275,7 @@
     "embedding": {
         "addEmbedding": "Add Embedding",
         "incompatibleModel": "Incompatible base model:",
+        "noEmbeddingsLoaded": "No Embeddings Loaded",
         "noMatchingEmbedding": "No matching Embeddings"
     },
     "queue": {
@@ -330,7 +354,8 @@
         "enableFailed": "Problem Enabling Invocation Cache",
         "disable": "Disable",
         "disableSucceeded": "Invocation Cache Disabled",
-        "disableFailed": "Problem Disabling Invocation Cache"
+        "disableFailed": "Problem Disabling Invocation Cache",
+        "useCache": "Use Cache"
     },
     "gallery": {
         "allImagesLoaded": "All Images Loaded",
@@ -339,6 +364,9 @@
         "autoSwitchNewImages": "Auto-Switch to New Images",
         "copy": "Copy",
         "currentlyInUse": "This image is currently in use in the following features:",
+        "drop": "Drop",
+        "dropOrUpload": "$t(gallery.drop) or Upload",
+        "dropToUpload": "$t(gallery.drop) to Upload",
         "deleteImage": "Delete Image",
         "deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
         "deleteImagePermanent": "Deleted images cannot be restored.",
@@ -348,7 +376,7 @@
         "galleryImageSize": "Image Size",
         "gallerySettings": "Gallery Settings",
         "generations": "Generations",
-        "images": "Images",
+        "image": "image",
         "loading": "Loading",
         "loadMore": "Load More",
         "maintainAspectRatio": "Maintain Aspect Ratio",
@@ -358,8 +386,11 @@
         "showGenerations": "Show Generations",
         "showUploads": "Show Uploads",
         "singleColumnLayout": "Single Column Layout",
+        "starImage": "Star Image",
+        "unstarImage": "Unstar Image",
         "unableToLoad": "Unable to load Gallery",
         "uploads": "Uploads",
+        "deleteSelection": "Delete Selection",
         "downloadSelection": "Download Selection",
         "preparingDownload": "Preparing Download",
         "preparingDownloadFailed": "Problem Preparing Download"
@@ -578,6 +609,7 @@
     },
     "metadata": {
         "cfgScale": "CFG scale",
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
         "createdBy": "Created By",
         "fit": "Image to image fit",
         "generationMode": "Generation Mode",
@@ -629,6 +661,7 @@
         "closeAdvanced": "Close Advanced",
         "config": "Config",
         "configValidationMsg": "Path to the config file of your model.",
+        "conversionNotSupported": "Conversion Not Supported",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
         "convertToDiffusers": "Convert To Diffusers",
@@ -754,6 +787,7 @@
         "esrganModel": "ESRGAN Model",
         "loading": "loading",
         "noLoRAsAvailable": "No LoRAs available",
+        "noLoRAsLoaded": "No LoRAs Loaded",
         "noMatchingLoRAs": "No matching LoRAs",
         "noMatchingModels": "No matching Models",
         "noModelsAvailable": "No models available",
@@ -765,6 +799,7 @@
     "nodes": {
         "addNode": "Add Node",
         "addNodeToolTip": "Add Node (Shift+A, Space)",
+        "addLinearView": "Add to Linear View",
         "animatedEdges": "Animated Edges",
         "animatedEdgesHelp": "Animate selected edges and edges connected to selected nodes",
         "boardField": "Board",
@@ -779,9 +814,12 @@
         "cannotConnectOutputToOutput": "Cannot connect output to output",
         "cannotConnectToSelf": "Cannot connect to self",
         "cannotDuplicateConnection": "Cannot create duplicate connections",
+        "nodePack": "Node pack",
         "clipField": "Clip",
         "clipFieldDescription": "Tokenizer and text_encoder submodels.",
         "collection": "Collection",
+        "collectionFieldType": "{{name}} Collection",
+        "collectionOrScalarFieldType": "{{name}} Collection|Scalar",
         "collectionDescription": "TODO",
         "collectionItem": "Collection Item",
         "collectionItemDescription": "TODO",
@@ -868,10 +906,15 @@
         "mainModelField": "Model",
         "mainModelFieldDescription": "TODO",
         "maybeIncompatible": "May be Incompatible With Installed",
-        "mismatchedVersion": "Has Mismatched Version",
+        "mismatchedVersion": "Invalid node: node {{node}} of type {{type}} has mismatched version (try updating?)",
         "missingCanvaInitImage": "Missing canvas init image",
         "missingCanvaInitMaskImages": "Missing canvas init and mask images",
-        "missingTemplate": "Missing Template",
+        "missingTemplate": "Invalid node: node {{node}} of type {{type}} missing template (not installed?)",
+        "sourceNodeDoesNotExist": "Invalid edge: source/output node {{node}} does not exist",
+        "targetNodeDoesNotExist": "Invalid edge: target/input node {{node}} does not exist",
+        "sourceNodeFieldDoesNotExist": "Invalid edge: source/output field {{node}}.{{field}} does not exist",
+        "targetNodeFieldDoesNotExist": "Invalid edge: target/input field {{node}}.{{field}} does not exist",
+        "deletedInvalidEdge": "Deleted invalid edge {{source}} -> {{target}}",
         "noConnectionData": "No connection data",
         "noConnectionInProgress": "No connection in progress",
         "node": "Node",
@@ -885,6 +928,7 @@
         "noMatchingNodes": "No matching nodes",
         "noNodeSelected": "No node selected",
         "nodeOpacity": "Node Opacity",
+        "nodeVersion": "Node Version",
         "noOutputRecorded": "No outputs recorded",
         "noOutputSchemaName": "No output schema name found in ref object",
         "notes": "Notes",
@@ -892,6 +936,7 @@
         "oNNXModelField": "ONNX Model",
         "oNNXModelFieldDescription": "ONNX model field.",
         "outputField": "Output Field",
+        "outputFieldInInput": "Output field in input",
         "outputFields": "Output Fields",
         "outputNode": "Output node",
         "outputSchemaNotFound": "Output schema not found",
@@ -901,9 +946,9 @@
         "problemSettingTitle": "Problem Setting Title",
         "reloadNodeTemplates": "Reload Node Templates",
         "removeLinearView": "Remove from Linear View",
-        "resetWorkflow": "Reset Workflow",
-        "resetWorkflowDesc": "Are you sure you want to reset this workflow?",
-        "resetWorkflowDesc2": "Resetting the workflow will clear all nodes, edges and workflow details.",
+        "resetWorkflow": "Reset Workflow Editor",
+        "resetWorkflowDesc": "Are you sure you want to reset the Workflow Editor?",
+        "resetWorkflowDesc2": "Resetting the Workflow Editor will clear all nodes, edges and workflow details. Saved workflows will not be affected.",
         "scheduler": "Scheduler",
         "schedulerDescription": "TODO",
         "sDXLMainModelField": "SDXL Model",
@@ -929,21 +974,37 @@
         "stringDescription": "Strings are text.",
         "stringPolymorphic": "String Polymorphic",
         "stringPolymorphicDescription": "A collection of strings.",
-        "unableToLoadWorkflow": "Unable to Validate Workflow",
+        "unableToLoadWorkflow": "Unable to Load Workflow",
         "unableToParseEdge": "Unable to parse edge",
         "unableToParseNode": "Unable to parse node",
+        "unableToUpdateNode": "Unable to update node",
         "unableToValidateWorkflow": "Unable to Validate Workflow",
+        "unableToMigrateWorkflow": "Unable to Migrate Workflow",
+        "unknownErrorValidatingWorkflow": "Unknown error validating workflow",
+        "inputFieldTypeParseError": "Unable to parse type of input field {{node}}.{{field}} ({{message}})",
+        "outputFieldTypeParseError": "Unable to parse type of output field {{node}}.{{field}} ({{message}})",
+        "unableToExtractSchemaNameFromRef": "unable to extract schema name from ref",
+        "unsupportedArrayItemType": "unsupported array item type \"{{type}}\"",
+        "unsupportedAnyOfLength": "too many union members ({{count}})",
+        "unsupportedMismatchedUnion": "mismatched CollectionOrScalar type with base types {{firstType}} and {{secondType}}",
+        "unableToParseFieldType": "unable to parse field type",
+        "unableToExtractEnumOptions": "unable to extract enum options",
         "uNetField": "UNet",
         "uNetFieldDescription": "UNet submodel.",
         "unhandledInputProperty": "Unhandled input property",
         "unhandledOutputProperty": "Unhandled output property",
-        "unknownField": "Unknown Field",
+        "unknownField": "Unknown field",
+        "unknownFieldType": "$t(nodes.unknownField) type: {{type}}",
         "unknownNode": "Unknown Node",
+        "unknownNodeType": "Unknown node type",
         "unknownTemplate": "Unknown Template",
+        "unknownInput": "Unknown input: {{name}}",
         "unkownInvocation": "Unknown Invocation type",
+        "unknownOutput": "Unknown output: {{name}}",
         "updateNode": "Update Node",
-        "updateAllNodes": "Update All Nodes",
         "updateApp": "Update App",
+        "updateAllNodes": "Update Nodes",
+        "allNodesUpdated": "All Nodes Updated",
         "unableToUpdateNodes_one": "Unable to update {{count}} node",
         "unableToUpdateNodes_other": "Unable to update {{count}} nodes",
         "vaeField": "Vae",
@@ -952,6 +1013,8 @@
         "vaeModelFieldDescription": "TODO",
         "validateConnections": "Validate Connections and Graph",
         "validateConnectionsHelp": "Prevent invalid connections from being made, and invalid graphs from being invoked",
+        "unableToGetWorkflowVersion": "Unable to get workflow schema version",
+        "unrecognizedWorkflowVersion": "Unrecognized workflow schema version {{version}}",
         "version": "Version",
         "versionUnknown": " Version Unknown",
         "workflow": "Workflow",
@@ -981,6 +1044,8 @@
             "setType": "Set cancel type"
         },
         "cfgScale": "CFG Scale",
+        "cfgRescaleMultiplier": "CFG Rescale Multiplier",
+        "cfgRescale": "CFG Rescale",
         "clipSkip": "CLIP Skip",
         "clipSkipWithLayerCount": "CLIP Skip {{layerCount}}",
         "closeViewer": "Close Viewer",
@@ -1082,6 +1147,7 @@
         "upscaling": "Upscaling",
         "unmasked": "Unmasked",
         "useAll": "Use All",
+        "useSize": "Use Size",
         "useCpuNoise": "Use CPU Noise",
         "cpuNoise": "CPU Noise",
         "gpuNoise": "GPU Noise",
@@ -1171,7 +1237,8 @@
         "clearIntermediatesWithCount_other": "Clear {{count}} Intermediates",
         "intermediatesCleared_one": "Cleared {{count}} Intermediate",
         "intermediatesCleared_other": "Cleared {{count}} Intermediates",
-        "intermediatesClearedFailed": "Problem Clearing Intermediates"
+        "intermediatesClearedFailed": "Problem Clearing Intermediates",
+        "reloadingIn": "Reloading in"
     },
     "toast": {
         "addedToBoard": "Added to board",
@@ -1199,6 +1266,7 @@
         "initialImageNotSet": "Initial Image Not Set",
         "initialImageNotSetDesc": "Could not load initial image",
         "initialImageSet": "Initial Image Set",
+        "invalidUpload": "Invalid Upload",
         "loadedWithWarnings": "Workflow Loaded with Warnings",
         "maskSavedAssets": "Mask Saved to Assets",
         "maskSentControlnetAssets": "Mask Sent to ControlNet & Assets",
@@ -1207,7 +1275,6 @@
         "modelAddedSimple": "Model Added",
         "modelAddFailed": "Model Add Failed",
         "nodesBrokenConnections": "Cannot load. Some connections are broken.",
-        "nodesCleared": "Nodes Cleared",
         "nodesCorruptedGraph": "Cannot load. Graph seems to be corrupted.",
         "nodesLoaded": "Nodes Loaded",
         "nodesLoadedFailed": "Failed To Load Nodes",
@@ -1256,7 +1323,10 @@
         "uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
         "uploadFailedUnableToLoadDesc": "Unable to load file",
         "upscalingFailed": "Upscaling Failed",
-        "workflowLoaded": "Workflow Loaded"
+        "workflowLoaded": "Workflow Loaded",
+        "problemRetrievingWorkflow": "Problem Retrieving Workflow",
+        "workflowDeleted": "Workflow Deleted",
+        "problemDeletingWorkflow": "Problem Deleting Workflow"
     },
     "tooltip": {
         "feature": {
@@ -1304,15 +1374,11 @@
         },
         "compositingBlur": {
             "heading": "Blur",
-            "paragraphs": [
-                "The blur radius of the mask."
-            ]
+            "paragraphs": ["The blur radius of the mask."]
         },
         "compositingBlurMethod": {
             "heading": "Blur Method",
-            "paragraphs": [
-                "The method of blur applied to the masked area."
-            ]
+            "paragraphs": ["The method of blur applied to the masked area."]
         },
         "compositingCoherencePass": {
             "heading": "Coherence Pass",
@@ -1322,9 +1388,7 @@
         },
         "compositingCoherenceMode": {
             "heading": "Mode",
-            "paragraphs": [
-                "The mode of the Coherence Pass."
-            ]
+            "paragraphs": ["The mode of the Coherence Pass."]
         },
         "compositingCoherenceSteps": {
             "heading": "Steps",
@@ -1342,9 +1406,7 @@
         },
         "compositingMaskAdjustments": {
             "heading": "Mask Adjustments",
-            "paragraphs": [
-                "Adjust the mask."
-            ]
+            "paragraphs": ["Adjust the mask."]
         },
         "controlNetBeginEnd": {
             "heading": "Begin / End Step Percentage",
@@ -1402,9 +1464,7 @@
         },
         "infillMethod": {
             "heading": "Infill Method",
-            "paragraphs": [
-                "Method to infill the selected area."
-            ]
+            "paragraphs": ["Method to infill the selected area."]
         },
         "lora": {
             "heading": "LoRA Weight",
@@ -1426,6 +1486,12 @@
                 "Controls how much your prompt influences the generation process."
             ]
         },
+        "paramCFGRescaleMultiplier": {
+            "heading": "CFG Rescale Multiplier",
+            "paragraphs": [
+                "Rescale multiplier for CFG guidance, used for models trained using zero-terminal SNR (ztsnr). Suggested value 0.7."
+            ]
+        },
         "paramDenoisingStrength": {
             "heading": "Denoising Strength",
             "paragraphs": [
@@ -1517,7 +1583,7 @@
         "clearCanvasHistoryConfirm": "Are you sure you want to clear the canvas history?",
         "clearCanvasHistoryMessage": "Clearing the canvas history leaves your current canvas intact, but irreversibly clears the undo and redo history.",
         "clearHistory": "Clear History",
-        "clearMask": "Clear Mask",
+        "clearMask": "Clear Mask (Shift+C)",
         "colorPicker": "Color Picker",
         "copyToClipboard": "Copy to Clipboard",
         "cursorPosition": "Cursor Position",
@@ -1544,6 +1610,7 @@
         "redo": "Redo",
         "resetView": "Reset View",
         "saveBoxRegionOnly": "Save Box Region Only",
+        "saveMask": "Save $t(unifiedCanvas.mask)",
         "saveToGallery": "Save To Gallery",
         "scaledBoundingBox": "Scaled Bounding Box",
         "showCanvasDebugInfo": "Show Additional Canvas Info",
@@ -1554,5 +1621,32 @@
         "showIntermediates": "Show Intermediates",
         "snapToGrid": "Snap to Grid",
         "undo": "Undo"
+    },
+    "workflows": {
+        "workflows": "Workflows",
+        "workflowLibrary": "Workflow Library",
+        "userWorkflows": "My Workflows",
+        "defaultWorkflows": "Defaults",
+        "projectWorkflows": "Project",
+        "openWorkflow": "Open Workflow",
+        "uploadWorkflow": "Upload Workflow",
+        "deleteWorkflow": "Delete Workflow",
+        "unnamedWorkflow": "Unnamed Workflow",
+        "downloadWorkflow": "Download Workflow",
+        "saveWorkflow": "Save Workflow",
+        "saveWorkflowAs": "Save Workflow As",
+        "problemSavingWorkflow": "Problem Saving Workflow",
+        "workflowSaved": "Workflow Saved",
+        "noWorkflows": "No Workflows",
+        "problemLoading": "Problem Loading Workflows",
+        "loading": "Loading Workflows",
+        "noDescription": "No description",
+        "searchWorkflows": "Search Workflows",
+        "clearWorkflowSearchFilter": "Clear Workflow Search Filter",
+        "workflowName": "Workflow Name",
+        "workflowEditorReset": "Workflow Editor Reset"
+    },
+    "app": {
+        "storeNotInitialized": "Store is not initialized"
     }
 }

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

@@ -98,7 +98,6 @@
         "deleteImage": "Eliminar Imagen",
         "deleteImageBin": "Las imágenes eliminadas se enviarán a la papelera de tu sistema operativo.",
         "deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
-        "images": "Imágenes",
         "assets": "Activos",
         "autoAssignBoardOnClick": "Asignación automática de tableros al hacer clic"
     },

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

@@ -89,7 +89,21 @@
         "t2iAdapter": "Adattatore T2I",
         "controlAdapter": "Adattatore di Controllo",
         "controlNet": "ControlNet",
-        "auto": "Automatico"
+        "auto": "Automatico",
+        "simple": "Semplice",
+        "details": "Dettagli",
+        "format": "formato",
+        "unknown": "Sconosciuto",
+        "folder": "Cartella",
+        "error": "Errore",
+        "installed": "Installato",
+        "template": "Schema",
+        "outputs": "Uscite",
+        "data": "Dati",
+        "somethingWentWrong": "Qualcosa è andato storto",
+        "copyError": "$t(gallery.copy) Errore",
+        "input": "Ingresso",
+        "notInstalled": "Non $t(common.installed)"
     },
     "gallery": {
         "generations": "Generazioni",
@@ -108,7 +122,6 @@
         "deleteImage": "Elimina l'immagine",
         "deleteImagePermanent": "Le immagini eliminate non possono essere ripristinate.",
         "deleteImageBin": "Le immagini eliminate verranno spostate nel Cestino del tuo sistema operativo.",
-        "images": "Immagini",
         "assets": "Risorse",
         "autoAssignBoardOnClick": "Assegna automaticamente la bacheca al clic",
         "featuresWillReset": "Se elimini questa immagine, quelle funzionalità verranno immediatamente ripristinate.",
@@ -120,7 +133,15 @@
         "setCurrentImage": "Imposta come immagine corrente",
         "preparingDownload": "Preparazione del download",
         "preparingDownloadFailed": "Problema durante la preparazione del download",
-        "downloadSelection": "Scarica gli elementi selezionati"
+        "downloadSelection": "Scarica gli elementi selezionati",
+        "noImageSelected": "Nessuna immagine selezionata",
+        "deleteSelection": "Elimina la selezione",
+        "image": "immagine",
+        "drop": "Rilascia",
+        "unstarImage": "Rimuovi preferenza immagine",
+        "dropOrUpload": "$t(gallery.drop) o carica",
+        "starImage": "Immagine preferita",
+        "dropToUpload": "$t(gallery.drop) per aggiornare"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tasti rapidi",
@@ -395,7 +416,7 @@
         "deleteModel": "Elimina modello",
         "deleteConfig": "Elimina configurazione",
         "deleteMsg1": "Sei sicuro di voler eliminare questo modello da InvokeAI?",
-        "deleteMsg2": "Questo eliminerà il modello dal disco se si trova nella cartella principale di InvokeAI. Se utilizzi una cartella personalizzata, il modello NON verrà eliminato dal disco.",
+        "deleteMsg2": "Questo eliminerà il modello dal disco se si trova nella cartella principale di InvokeAI. Se invece utilizzi una cartella personalizzata, il modello NON verrà eliminato dal disco.",
         "formMessageDiffusersModelLocation": "Ubicazione modelli diffusori",
         "formMessageDiffusersModelLocationDesc": "Inseriscine almeno uno.",
         "formMessageDiffusersVAELocation": "Ubicazione file VAE",
@@ -429,7 +450,7 @@
         "mergedModelSaveLocation": "Ubicazione salvataggio",
         "convertToDiffusersHelpText1": "Questo modello verrà convertito nel formato 🧨 Diffusore.",
         "custom": "Personalizzata",
-        "convertToDiffusersHelpText3": "Il file checkpoint su disco SARÀ eliminato se si trova nella cartella principale di InvokeAI. Se si trova in una posizione personalizzata, NON verrà eliminato.",
+        "convertToDiffusersHelpText3": "Il file Checkpoint su disco verrà eliminato se si trova nella cartella principale di InvokeAI. Se si trova invece in una posizione personalizzata, NON verrà eliminato.",
         "v1": "v1",
         "pathToCustomConfig": "Percorso alla configurazione personalizzata",
         "modelThree": "Modello 3",
@@ -456,7 +477,7 @@
         "modelDeleteFailed": "Impossibile eliminare il modello",
         "noCustomLocationProvided": "Nessuna posizione personalizzata fornita",
         "convertingModelBegin": "Conversione del modello. Attendere prego.",
-        "importModels": "Importa modelli",
+        "importModels": "Importa Modelli",
         "modelsSynced": "Modelli sincronizzati",
         "modelSyncFailed": "Sincronizzazione modello non riuscita",
         "settings": "Impostazioni",
@@ -474,7 +495,9 @@
         "closeAdvanced": "Chiudi Avanzate",
         "modelType": "Tipo di modello",
         "customConfigFileLocation": "Posizione del file di configurazione personalizzato",
-        "vaePrecision": "Precisione VAE"
+        "vaePrecision": "Precisione VAE",
+        "noModelSelected": "Nessun modello selezionato",
+        "conversionNotSupported": "Conversione non supportata"
     },
     "parameters": {
         "images": "Immagini",
@@ -601,7 +624,9 @@
         "seamlessX": "Senza cuciture X",
         "seamlessY": "Senza cuciture Y",
         "imageActions": "Azioni Immagine",
-        "aspectRatioFree": "Libere"
+        "aspectRatioFree": "Libere",
+        "maskEdge": "Maschera i bordi",
+        "unmasked": "No maschera"
     },
     "settings": {
         "models": "Modelli",
@@ -642,7 +667,10 @@
         "clearIntermediatesWithCount_one": "Cancella {{count}} immagine intermedia",
         "clearIntermediatesWithCount_many": "Cancella {{count}} immagini intermedie",
         "clearIntermediatesWithCount_other": "Cancella {{count}} immagini intermedie",
-        "clearIntermediatesDisabled": "La coda deve essere vuota per cancellare le immagini intermedie"
+        "clearIntermediatesDisabled": "La coda deve essere vuota per cancellare le immagini intermedie",
+        "enableNSFWChecker": "Abilita controllo NSFW",
+        "enableInvisibleWatermark": "Abilita filigrana invisibile",
+        "enableInformationalPopovers": "Abilita testo informativo a comparsa"
     },
     "toast": {
         "tempFoldersEmptied": "Cartella temporanea svuotata",
@@ -727,7 +755,8 @@
         "setCanvasInitialImage": "Imposta come immagine iniziale della tela",
         "workflowLoaded": "Flusso di lavoro caricato",
         "setIPAdapterImage": "Imposta come immagine per l'Adattatore IP",
-        "problemSavingMaskDesc": "Impossibile salvare la maschera"
+        "problemSavingMaskDesc": "Impossibile salvare la maschera",
+        "setAsCanvasInitialImage": "Imposta come immagine iniziale della tela"
     },
     "tooltip": {
         "feature": {
@@ -828,7 +857,9 @@
         "modifyConfig": "Modifica configurazione",
         "menu": "Menu",
         "showGalleryPanel": "Mostra il pannello Galleria",
-        "loadMore": "Carica altro"
+        "loadMore": "Carica altro",
+        "mode": "Modalità",
+        "resetUI": "$t(accessibility.reset) l'Interfaccia Utente"
     },
     "ui": {
         "hideProgressImages": "Nascondi avanzamento immagini",
@@ -1026,7 +1057,19 @@
         "unableToParseEdge": "Impossibile analizzare il bordo",
         "latentsCollectionDescription": "Le immagini latenti possono essere passate tra i nodi.",
         "imageCollection": "Raccolta Immagini",
-        "loRAModelField": "LoRA"
+        "loRAModelField": "LoRA",
+        "updateAllNodes": "Aggiorna tutti i nodi",
+        "unableToUpdateNodes_one": "Impossibile aggiornare {{count}} nodo",
+        "unableToUpdateNodes_many": "Impossibile aggiornare {{count}} nodi",
+        "unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi",
+        "addLinearView": "Aggiungi alla vista Lineare",
+        "outputFieldInInput": "Campo di uscita in ingresso",
+        "unableToMigrateWorkflow": "Impossibile migrare il flusso di lavoro",
+        "unableToUpdateNode": "Impossibile aggiornare nodo",
+        "unknownErrorValidatingWorkflow": "Errore sconosciuto durante la convalida del flusso di lavoro",
+        "collectionFieldType": "{{name}} Raccolta",
+        "collectionOrScalarFieldType": "{{name}} Raccolta|Scalare",
+        "nodeVersion": "Versione Nodo"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1044,7 +1087,14 @@
         "noMatching": "Nessuna bacheca corrispondente",
         "selectBoard": "Seleziona una Bacheca",
         "uncategorized": "Non categorizzato",
-        "downloadBoard": "Scarica la bacheca"
+        "downloadBoard": "Scarica la bacheca",
+        "deleteBoardOnly": "Elimina solo la Bacheca",
+        "deleteBoard": "Elimina Bacheca",
+        "deleteBoardAndImages": "Elimina Bacheca e Immagini",
+        "deletedBoardsCannotbeRestored": "Le bacheche eliminate non possono essere ripristinate",
+        "movingImagesToBoard_one": "Spostare {{count}} immagine nella bacheca:",
+        "movingImagesToBoard_many": "Spostare {{count}} immagini nella bacheca:",
+        "movingImagesToBoard_other": "Spostare {{count}} immagini nella bacheca:"
     },
     "controlnet": {
         "contentShuffleDescription": "Rimescola il contenuto di un'immagine",
@@ -1085,7 +1135,7 @@
         "none": "Nessuno",
         "incompatibleBaseModel": "Modello base incompatibile:",
         "pidiDescription": "Elaborazione immagini PIDI",
-        "fill": "Riempire",
+        "fill": "Riempie",
         "colorMapDescription": "Genera una mappa dei colori dall'immagine",
         "lineartAnimeDescription": "Elaborazione lineart in stile anime",
         "imageResolution": "Risoluzione dell'immagine",
@@ -1118,7 +1168,8 @@
         "megaControl": "Mega ControlNet",
         "minConfidence": "Confidenza minima",
         "scribble": "Scribble",
-        "amult": "Angolo di illuminazione"
+        "amult": "Angolo di illuminazione",
+        "coarse": "Approssimativo"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -1179,12 +1230,15 @@
         "clearQueueAlertDialog2": "Sei sicuro di voler cancellare la coda?",
         "item": "Elemento",
         "graphFailedToQueue": "Impossibile mettere in coda il grafico",
-        "queueMaxExceeded": "È stato superato il limite massimo di {{max_queue_size}} e {{skip}} elementi verrebbero saltati"
+        "queueMaxExceeded": "È stato superato il limite massimo di {{max_queue_size}} e {{skip}} elementi verrebbero saltati",
+        "batchFieldValues": "Valori Campi Lotto",
+        "time": "Tempo"
     },
     "embedding": {
         "noMatchingEmbedding": "Nessun Incorporamento corrispondente",
         "addEmbedding": "Aggiungi Incorporamento",
-        "incompatibleModel": "Modello base incompatibile:"
+        "incompatibleModel": "Modello base incompatibile:",
+        "noEmbeddingsLoaded": "Nessun incorporamento caricato"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1195,7 +1249,10 @@
         "selectModel": "Seleziona un modello",
         "selectLoRA": "Seleziona un LoRA",
         "noRefinerModelsInstalled": "Nessun modello SDXL Refiner installato",
-        "noLoRAsInstalled": "Nessun LoRA installato"
+        "noLoRAsInstalled": "Nessun LoRA installato",
+        "esrganModel": "Modello ESRGAN",
+        "addLora": "Aggiungi LoRA",
+        "noLoRAsLoaded": "Nessuna LoRA caricata"
     },
     "invocationCache": {
         "disable": "Disabilita",
@@ -1211,7 +1268,8 @@
         "enable": "Abilita",
         "clear": "Svuota",
         "maxCacheSize": "Dimensione max cache",
-        "cacheSize": "Dimensione cache"
+        "cacheSize": "Dimensione cache",
+        "useCache": "Usa Cache"
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -1227,7 +1285,8 @@
         "promptsWithCount_one": "{{count}} Prompt",
         "promptsWithCount_many": "{{count}} Prompt",
         "promptsWithCount_other": "{{count}} Prompt",
-        "dynamicPrompts": "Prompt dinamici"
+        "dynamicPrompts": "Prompt dinamici",
+        "promptsPreview": "Anteprima dei prompt"
     },
     "popovers": {
         "paramScheduler": {

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

@@ -438,7 +438,15 @@
         "useSeed": "シード値を使用",
         "useAll": "すべてを使用",
         "info": "情報",
-        "showOptionsPanel": "オプションパネルを表示"
+        "showOptionsPanel": "オプションパネルを表示",
+        "aspectRatioFree": "自由",
+        "invoke": {
+            "noControlImageForControlAdapter": "コントロールアダプター #{{number}} に画像がありません",
+            "noModelForControlAdapter": "コントロールアダプター #{{number}} のモデルが選択されていません。"
+        },
+        "aspectRatio": "縦横比",
+        "iterations": "生成回数",
+        "general": "基本設定"
     },
     "settings": {
         "models": "モデル",
@@ -603,7 +611,7 @@
         "delete": "削除",
         "controlAdapter_other": "コントロールアダプター",
         "colorMapTileSize": "タイルサイズ",
-        "ipAdapterImageFallback": "IP Adapterの画像が選択されていません",
+        "ipAdapterImageFallback": "IPアダプターの画像が選択されていません",
         "mediapipeFaceDescription": "Mediapipeを使用して顔を検出",
         "depthZoeDescription": "Zoeを使用して深度マップを生成",
         "setControlImageDimensions": "コントロール画像のサイズを幅と高さにセット",
@@ -652,7 +660,7 @@
         "queueTotal": "合計 {{total}}",
         "resumeSucceeded": "処理が再開されました",
         "resumeTooltip": "処理を再開",
-        "resume": "再会",
+        "resume": "再開",
         "status": "ステータス",
         "pruneSucceeded": "キューから完了アイテム{{item_count}}件を削除しました",
         "cancelTooltip": "現在のアイテムをキャンセル",
@@ -812,5 +820,13 @@
         "clear": "クリア",
         "maxCacheSize": "最大キャッシュサイズ",
         "cacheSize": "キャッシュサイズ"
+    },
+    "popovers": {
+        "paramRatio": {
+            "heading": "縦横比",
+            "paragraphs": [
+                "生成された画像の縦横比。"
+            ]
+        }
     }
 }

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

@@ -110,7 +110,6 @@
         "deleteImageBin": "Verwijderde afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
         "deleteImagePermanent": "Verwijderde afbeeldingen kunnen niet worden hersteld.",
         "assets": "Eigen onderdelen",
-        "images": "Afbeeldingen",
         "autoAssignBoardOnClick": "Ken automatisch bord toe bij klikken",
         "featuresWillReset": "Als je deze afbeelding verwijdert, dan worden deze functies onmiddellijk teruggezet.",
         "loading": "Bezig met laden",

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

@@ -101,7 +101,6 @@
         "deleteImagePermanent": "Удаленные изображения невозможно восстановить.",
         "deleteImageBin": "Удаленные изображения будут отправлены в корзину вашей операционной системы.",
         "deleteImage": "Удалить изображение",
-        "images": "Изображения",
         "assets": "Ресурсы",
         "autoAssignBoardOnClick": "Авто-назначение доски по клику"
     },

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

@@ -90,7 +90,16 @@
         "controlAdapter": "Control Adapter",
         "controlNet": "ControlNet",
         "on": "开",
-        "auto": "自动"
+        "auto": "自动",
+        "checkpoint": "Checkpoint",
+        "inpaint": "内补重绘",
+        "simple": "简单",
+        "template": "模板",
+        "outputs": "输出",
+        "data": "数据",
+        "safetensors": "Safetensors",
+        "outpaint": "外扩绘制",
+        "details": "详情"
     },
     "gallery": {
         "generations": "生成的图像",
@@ -109,7 +118,6 @@
         "deleteImage": "删除图片",
         "deleteImageBin": "被删除的图片会发送到你操作系统的回收站。",
         "deleteImagePermanent": "删除的图片无法被恢复。",
-        "images": "图片",
         "assets": "素材",
         "autoAssignBoardOnClick": "点击后自动分配面板",
         "featuresWillReset": "如果您删除该图像，这些功能会立即被重置。",
@@ -121,7 +129,8 @@
         "setCurrentImage": "设为当前图像",
         "preparingDownload": "准备下载",
         "preparingDownloadFailed": "准备下载时出现问题",
-        "downloadSelection": "下载所选内容"
+        "downloadSelection": "下载所选内容",
+        "noImageSelected": "无选中的图像"
     },
     "hotkeys": {
         "keyboardShortcuts": "键盘快捷键",
@@ -475,7 +484,9 @@
         "oliveModels": "Olive",
         "loraModels": "LoRA",
         "alpha": "Alpha",
-        "vaePrecision": "VAE 精度"
+        "vaePrecision": "VAE 精度",
+        "checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
+        "noModelSelected": "无选中的模型"
     },
     "parameters": {
         "images": "图像",
@@ -602,7 +613,9 @@
         "seamlessX&Y": "无缝 X & Y",
         "aspectRatioFree": "自由",
         "seamlessX": "无缝 X",
-        "seamlessY": "无缝 Y"
+        "seamlessY": "无缝 Y",
+        "maskEdge": "遮罩边缘",
+        "unmasked": "取消遮罩"
     },
     "settings": {
         "models": "模型",
@@ -639,7 +652,10 @@
         "clearIntermediatesDesc1": "清除中间产物会重置您的画布和 ControlNet 状态。",
         "intermediatesClearedFailed": "清除中间产物时出现问题",
         "clearIntermediatesWithCount_other": "清除 {{count}} 个中间产物",
-        "clearIntermediatesDisabled": "队列为空才能清理中间产物"
+        "clearIntermediatesDisabled": "队列为空才能清理中间产物",
+        "enableNSFWChecker": "启用成人内容检测器",
+        "enableInvisibleWatermark": "启用不可见水印",
+        "enableInformationalPopovers": "启用信息弹窗"
     },
     "toast": {
         "tempFoldersEmptied": "临时文件夹已清空",
@@ -705,7 +721,7 @@
         "modelAddFailed": "模型添加失败",
         "problemDownloadingCanvas": "下载画布时出现问题",
         "problemMergingCanvas": "合并画布时出现问题",
-        "setCanvasInitialImage": "设为画布初始图像",
+        "setCanvasInitialImage": "设定画布初始图像",
         "imageUploaded": "图像已上传",
         "addedToBoard": "已添加到面板",
         "workflowLoaded": "工作流已加载",
@@ -722,7 +738,8 @@
         "canvasSavedGallery": "画布已保存到图库",
         "imageUploadFailed": "图像上传失败",
         "problemImportingMask": "导入遮罩时出现问题",
-        "baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{count}} 个不兼容的子模型"
+        "baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{count}} 个不兼容的子模型",
+        "setAsCanvasInitialImage": "设为画布初始图像"
     },
     "unifiedCanvas": {
         "layer": "图层",
@@ -808,7 +825,8 @@
         "toggleAutoscroll": "切换自动缩放",
         "menu": "菜单",
         "showGalleryPanel": "显示图库浮窗",
-        "loadMore": "加载更多"
+        "loadMore": "加载更多",
+        "mode": "模式"
     },
     "ui": {
         "showProgressImages": "显示处理中的图片",
@@ -1031,7 +1049,9 @@
         "integerPolymorphic": "整数多态",
         "latentsPolymorphic": "Latents 多态",
         "conditioningField": "条件",
-        "latentsField": "Latents"
+        "latentsField": "Latents",
+        "updateAllNodes": "更新所有节点",
+        "unableToUpdateNodes_other": "{{count}} 个节点无法完成更新"
     },
     "controlnet": {
         "resize": "直接缩放",
@@ -1176,7 +1196,9 @@
         "queueTotal": "总计 {{total}}",
         "enqueueing": "队列中的批次",
         "queueMaxExceeded": "超出最大值 {{max_queue_size}}，将跳过 {{skip}}",
-        "graphFailedToQueue": "节点图加入队列失败"
+        "graphFailedToQueue": "节点图加入队列失败",
+        "batchFieldValues": "批处理值",
+        "time": "时间"
     },
     "sdxl": {
         "refinerStart": "Refiner 开始作用时机",
@@ -1234,7 +1256,9 @@
         "selectModel": "选择一个模型",
         "selectLoRA": "选择一个 LoRA",
         "noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
-        "noLoRAsInstalled": "无已安装的 LoRA"
+        "noLoRAsInstalled": "无已安装的 LoRA",
+        "esrganModel": "ESRGAN 模型",
+        "addLora": "添加 LoRA"
     },
     "boards": {
         "autoAddBoard": "自动添加面板",
@@ -1252,7 +1276,11 @@
         "changeBoard": "更改面板",
         "loading": "加载中...",
         "clearSearch": "清除检索",
-        "downloadBoard": "下载面板"
+        "downloadBoard": "下载面板",
+        "deleteBoardOnly": "仅删除面板",
+        "deleteBoard": "删除面板",
+        "deleteBoardAndImages": "删除面板和图像",
+        "deletedBoardsCannotbeRestored": "已删除的面板无法被恢复"
     },
     "embedding": {
         "noMatchingEmbedding": "不匹配的 Embedding",
@@ -1271,7 +1299,8 @@
         "combinatorial": "组合生成",
         "maxPrompts": "最大提示词数",
         "dynamicPrompts": "动态提示词",
-        "promptsWithCount_other": "{{count}} 个提示词"
+        "promptsWithCount_other": "{{count}} 个提示词",
+        "promptsPreview": "提示词预览"
     },
     "popovers": {
         "compositingMaskAdjustments": {