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2
.github/workflows/pypi-release.yml vendored
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@ -28,7 +28,7 @@ jobs:
run: twine check dist/* run: twine check dist/*
- name: check PyPI versions - name: check PyPI versions
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3' if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')
run: | run: |
pip install --upgrade requests pip install --upgrade requests
python -c "\ python -c "\

379
docs/nodes/communityNodes.md
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@ -8,28 +8,42 @@ To download a node, simply download the `.py` node file from the link and add it
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. To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor.
-------------------------------- - Community Nodes
+ [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
+ [Film Grain](#film-grain)
+ [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
+ [GPT2RandomPromptMaker](#gpt2randompromptmaker)
+ [Grid to Gif](#grid-to-gif)
+ [Halftone](#halftone)
+ [Ideal Size](#ideal-size)
+ [Image and Mask Composition Pack](#image-and-mask-composition-pack)
+ [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
+ [Image Picker](#image-picker)
+ [Load Video Frame](#load-video-frame)
+ [Make 3D](#make-3d)
+ [Oobabooga](#oobabooga)
+ [Prompt Tools](#prompt-tools)
+ [Retroize](#retroize)
+ [Size Stepper Nodes](#size-stepper-nodes)
+ [Text font to Image](#text-font-to-image)
+ [Thresholding](#thresholding)
+ [XY Image to Grid and Images to Grids nodes](#xy-image-to-grid-and-images-to-grids-nodes)
- [Example Node Template](#example-node-template)
- [Disclaimer](#disclaimer)
- [Help](#help)
-------------------------------- --------------------------------
### Make 3D ### Depth Map from Wavefront OBJ
**Description:** Create compelling 3D stereo images from 2D originals. **Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation.
**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d) To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations.
**Example Node Graph:** https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false **Node Link:** https://github.com/dwringer/depth-from-obj-node
**Output Examples** **Example Usage:**
</br><img src="https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg" width="500" />
![Painting of a cozy delapidated house](https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png){: style="height:512px;width:512px"}
![Photo of cute puppies](https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png){: style="height:512px;width:512px"}
--------------------------------
### Ideal Size
**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
**Node Link:** https://github.com/JPPhoto/ideal-size-node
-------------------------------- --------------------------------
### Film Grain ### Film Grain
@ -39,68 +53,19 @@ To use a community workflow, download the the `.json` node graph file and load i
**Node Link:** https://github.com/JPPhoto/film-grain-node **Node Link:** https://github.com/JPPhoto/film-grain-node
-------------------------------- --------------------------------
### Image Picker ### Generative Grammar-Based Prompt Nodes
**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose. **Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no nonterminal terms remain in the string.
**Node Link:** https://github.com/JPPhoto/image-picker-node This includes 3 Nodes:
- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output)
- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading
- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts.
-------------------------------- **Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes
### Thresholding
**Description:** This node generates masks for highlights, midtones, and shadows given an input image. You can optionally specify a blur for the lookup table used in making those masks from the source image. **Example Usage:**
</br><img src="https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg" width="500" />
**Node Link:** https://github.com/JPPhoto/thresholding-node
**Examples**
Input:
![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/c88ada13-fb3d-484c-a4fe-947b44712632){: style="height:512px;width:512px"}
Highlights/Midtones/Shadows:
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/727021c1-36ff-4ec8-90c8-105e00de986d" style="width: 30%" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0b721bfc-f051-404e-b905-2f16b824ddfe" style="width: 30%" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/04c1297f-1c88-42b6-a7df-dd090b976286" style="width: 30%" />
Highlights/Midtones/Shadows (with LUT blur enabled):
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/19aa718a-70c1-4668-8169-d68f4bd13771" style="width: 30%" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" style="width: 30%" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" style="width: 30%" />
--------------------------------
### Halftone
**Description**: Halftone converts the source image to grayscale and then performs halftoning. CMYK Halftone converts the image to CMYK and applies a per-channel halftoning to make the source image look like a magazine or newspaper. For both nodes, you can specify angles and halftone dot spacing.
**Node Link:** https://github.com/JPPhoto/halftone-node
**Example**
Input:
![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/fd5efb9f-4355-4409-a1c2-c1ca99e0cab4){: style="height:512px;width:512px"}
Halftone Output:
![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/7e606f29-e68f-4d46-b3d5-97f799a4ec2f){: style="height:512px;width:512px"}
CMYK Halftone Output:
![image](https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea){: style="height:512px;width:512px"}
--------------------------------
### Retroize
**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images.
**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/
**Retroize Output Examples**
![image](https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974)
-------------------------------- --------------------------------
### GPT2RandomPromptMaker ### GPT2RandomPromptMaker
@ -113,76 +78,49 @@ CMYK Halftone Output:
Generated Prompt: An enchanted weapon will be usable by any character regardless of their alignment. Generated Prompt: An enchanted weapon will be usable by any character regardless of their alignment.
![9acf5aef-7254-40dd-95b3-8eac431dfab0 (1)](https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c) <img src="https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c" width="200" />
-------------------------------- --------------------------------
### Load Video Frame ### Grid to Gif
**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs. **Description:** One node that turns a grid image into an image collection, one node that turns an image collection into a gif.
**Node Link:** https://github.com/helix4u/load_video_frame **Node Link:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/GridToGif.py
**Example Node Graph:** https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json **Example Node Graph:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/Grid%20to%20Gif%20Example%20Workflow.json
**Output Example:** **Output Examples**
![Example animation](https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif) <img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/input.png" width="300" />
[Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4) <img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/output.gif" width="300" />
-------------------------------- --------------------------------
### Halftone
### Oobabooga **Description**: Halftone converts the source image to grayscale and then performs halftoning. CMYK Halftone converts the image to CMYK and applies a per-channel halftoning to make the source image look like a magazine or newspaper. For both nodes, you can specify angles and halftone dot spacing.
**Description:** asks a local LLM running in Oobabooga's Text-Generation-Webui to write a prompt based on the user input. **Node Link:** https://github.com/JPPhoto/halftone-node
**Link:** https://github.com/sammyf/oobabooga-node **Example**
Input:
**Example:** <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/fd5efb9f-4355-4409-a1c2-c1ca99e0cab4" width="300" />
"describe a new mystical creature in its natural environment" Halftone Output:
*can return* <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/7e606f29-e68f-4d46-b3d5-97f799a4ec2f" width="300" />
"The mystical creature I am describing to you is called the "Glimmerwing". It is a majestic, iridescent being that inhabits the depths of the most enchanted forests and glimmering lakes. Its body is covered in shimmering scales that reflect every color of the rainbow, and it has delicate, translucent wings that sparkle like diamonds in the sunlight. The Glimmerwing's home is a crystal-clear lake, surrounded by towering trees with leaves that shimmer like jewels. In this serene environment, the Glimmerwing spends its days swimming gracefully through the water, chasing schools of glittering fish and playing with the gentle ripples of the lake's surface. CMYK Halftone Output:
As the sun sets, the Glimmerwing perches on a branch of one of the trees, spreading its wings to catch the last rays of light. The creature's scales glow softly, casting a rainbow of colors across the forest floor. The Glimmerwing sings a haunting melody, its voice echoing through the stillness of the night air. Its song is said to have the power to heal the sick and bring peace to troubled souls. Those who are lucky enough to hear the Glimmerwing's song are forever changed by its beauty and grace."
![glimmerwing_small](https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed) <img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
**Requirement**
a Text-Generation-Webui instance (might work remotely too, but I never tried it) and obviously InvokeAI 3.x
**Note**
This node works best with SDXL models, especially as the style can be described independantly of the LLM's output.
-------------------------------- --------------------------------
### Depth Map from Wavefront OBJ ### Ideal Size
**Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation. **Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations. **Node Link:** https://github.com/JPPhoto/ideal-size-node
**Node Link:** https://github.com/dwringer/depth-from-obj-node
**Example Usage:**
![depth from obj usage graph](https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg)
--------------------------------
### Generative Grammar-Based Prompt Nodes
**Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no more nonterminal terms remain in the string.
This includes 3 Nodes:
- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output)
- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading
- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts.
**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes
**Example Usage:**
![lookups usage example graph](https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg)
-------------------------------- --------------------------------
### Image and Mask Composition Pack ### Image and Mask Composition Pack
@ -209,44 +147,87 @@ This includes 15 Nodes:
**Node Link:** https://github.com/dwringer/composition-nodes **Node Link:** https://github.com/dwringer/composition-nodes
**Nodes and Output Examples:** </br><img src="https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg" width="500" />
![composition nodes usage graph](https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg)
-------------------------------- --------------------------------
### Size Stepper Nodes ### Image to Character Art Image Nodes
**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes. **Description:** Group of nodes to convert an input image into ascii/unicode art Image
A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection. **Node Link:** https://github.com/mickr777/imagetoasciiimage
**Node Link:** https://github.com/dwringer/size-stepper-nodes
**Example Usage:**
![size stepper usage graph](https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg)
--------------------------------
### Text font to Image
**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line
**Node Link:** https://github.com/mickr777/textfontimage
**Output Examples** **Output Examples**
![a3609d48-d9b7-41f0-b280-063d857986fb](https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36) <img src="https://user-images.githubusercontent.com/115216705/271817646-8e061fcc-9a2c-4fa9-bcc7-c0f7b01e9056.png" width="300" /><img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/3c4990eb-2f42-46b9-90f9-0088b939dc6a" width="300" /></br>
<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/fee7f800-a4a8-41e2-a66b-c66e4343307e" width="300" />
Results after using the depth controlnet <img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/1d9c1003-a45f-45c2-aac7-46470bb89330" width="300" />
![9133eabb-bcda-4326-831e-1b641228b178](https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a)
![4f9a3fa8-9be9-4236-8a3e-fcec66decd2a](https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc)
![babd69c4-9d60-4a55-a834-5e8397f62610](https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89)
-------------------------------- --------------------------------
### Image Picker
**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose.
**Node Link:** https://github.com/JPPhoto/image-picker-node
--------------------------------
### Load Video Frame
**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs.
**Node Link:** https://github.com/helix4u/load_video_frame
**Example Node Graph:** https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json
**Output Example:**
<img src="https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif" width="500" />
[Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4)
--------------------------------
### Make 3D
**Description:** Create compelling 3D stereo images from 2D originals.
**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d)
**Example Node Graph:** https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false
**Output Examples**
<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" />
--------------------------------
### Oobabooga
**Description:** asks a local LLM running in Oobabooga's Text-Generation-Webui to write a prompt based on the user input.
**Link:** https://github.com/sammyf/oobabooga-node
**Example:**
"describe a new mystical creature in its natural environment"
*can return*
"The mystical creature I am describing to you is called the "Glimmerwing". It is a majestic, iridescent being that inhabits the depths of the most enchanted forests and glimmering lakes. Its body is covered in shimmering scales that reflect every color of the rainbow, and it has delicate, translucent wings that sparkle like diamonds in the sunlight. The Glimmerwing's home is a crystal-clear lake, surrounded by towering trees with leaves that shimmer like jewels. In this serene environment, the Glimmerwing spends its days swimming gracefully through the water, chasing schools of glittering fish and playing with the gentle ripples of the lake's surface.
As the sun sets, the Glimmerwing perches on a branch of one of the trees, spreading its wings to catch the last rays of light. The creature's scales glow softly, casting a rainbow of colors across the forest floor. The Glimmerwing sings a haunting melody, its voice echoing through the stillness of the night air. Its song is said to have the power to heal the sick and bring peace to troubled souls. Those who are lucky enough to hear the Glimmerwing's song are forever changed by its beauty and grace."
<img src="https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed" width="300" />
**Requirement**
a Text-Generation-Webui instance (might work remotely too, but I never tried it) and obviously InvokeAI 3.x
**Note**
This node works best with SDXL models, especially as the style can be described independently of the LLM's output.
--------------------------------
### Prompt Tools ### Prompt Tools
**Description:** A set of InvokeAI nodes that add general prompt manipulation tools. These where written to accompany the PromptsFromFile node and other prompt generation nodes. **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.
1. PromptJoin - Joins to prompts into one. 1. PromptJoin - Joins to prompts into one.
2. PromptReplace - performs a search and replace on a prompt. With the option of using regex. 2. PromptReplace - performs a search and replace on a prompt. With the option of using regex.
@ -263,51 +244,83 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
**Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes **Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
-------------------------------- --------------------------------
### Retroize
**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images.
**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/
**Retroize Output Examples**
<img src="https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974" width="500" />
--------------------------------
### Size Stepper Nodes
**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes.
A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection.
**Node Link:** https://github.com/dwringer/size-stepper-nodes
**Example Usage:**
</br><img src="https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg" width="500" />
--------------------------------
### Text font to Image
**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line
**Node Link:** https://github.com/mickr777/textfontimage
**Output Examples**
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36" width="300" />
Results after using the depth controlnet
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a" width="300" />
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc" width="300" />
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89" width="300" />
--------------------------------
### Thresholding
**Description:** This node generates masks for highlights, midtones, and shadows given an input image. You can optionally specify a blur for the lookup table used in making those masks from the source image.
**Node Link:** https://github.com/JPPhoto/thresholding-node
**Examples**
Input:
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c88ada13-fb3d-484c-a4fe-947b44712632" width="300" />
Highlights/Midtones/Shadows:
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/727021c1-36ff-4ec8-90c8-105e00de986d" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0b721bfc-f051-404e-b905-2f16b824ddfe" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/04c1297f-1c88-42b6-a7df-dd090b976286" width="300" />
Highlights/Midtones/Shadows (with LUT blur enabled):
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/19aa718a-70c1-4668-8169-d68f4bd13771" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" width="300" />
--------------------------------
### XY Image to Grid and Images to Grids nodes ### XY Image to Grid and Images to Grids nodes
**Description:** Image to grid nodes and supporting tools. **Description:** Image to grid nodes and supporting tools.
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 mutilple grids will be created until it runs out of images. 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 supporoting nodes. See example node setups for more details. 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.
See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
**Node Link:** https://github.com/skunkworxdark/XYGrid_nodes **Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
-------------------------------- --------------------------------
### Image to Character Art Image Node's
**Description:** Group of nodes to convert an input image into ascii/unicode art Image
**Node Link:** https://github.com/mickr777/imagetoasciiimage
**Output Examples**
<img src="https://github.com/invoke-ai/InvokeAI/assets/115216705/8e061fcc-9a2c-4fa9-bcc7-c0f7b01e9056" width="300" />
<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/3c4990eb-2f42-46b9-90f9-0088b939dc6a" width="300" /></br>
<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/fee7f800-a4a8-41e2-a66b-c66e4343307e" width="300" />
<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/1d9c1003-a45f-45c2-aac7-46470bb89330" width="300" />
--------------------------------
### Grid to Gif
**Description:** One node that turns a grid image into an image colletion, one node that turns an image collection into a gif
**Node Link:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/GridToGif.py
**Example Node Graph:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/Grid%20to%20Gif%20Example%20Workflow.json
**Output Examples**
<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/input.png" width="300" />
<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/output.gif" width="300" />
--------------------------------
### Example Node Template ### Example Node Template
**Description:** This node allows you to do super cool things with InvokeAI. **Description:** This node allows you to do super cool things with InvokeAI.
@ -318,7 +331,7 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
**Output Examples** **Output Examples**
![Example Image](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png){: style="height:115px;width:240px"} </br><img src="https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png" width="500" />
## Disclaimer ## Disclaimer

166
invokeai/app/api/dependencies.py
View File

@ -1,35 +1,35 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import sqlite3
from logging import Logger from logging import Logger
from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.session_processor.session_processor_default import DefaultSessionProcessor
from invokeai.app.services.session_queue.session_queue_sqlite import SqliteSessionQueue
from invokeai.app.services.urls import LocalUrlService
from invokeai.backend.util.logging import InvokeAILogger from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__ from invokeai.version.invokeai_version import __version__
from ..services.default_graphs import create_system_graphs from ..services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
from ..services.graph import GraphExecutionState, LibraryGraph from ..services.board_images.board_images_default import BoardImagesService
from ..services.image_file_storage import DiskImageFileStorage from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
from ..services.invocation_queue import MemoryInvocationQueue from ..services.boards.boards_default import BoardService
from ..services.config import InvokeAIAppConfig
from ..services.image_files.image_files_disk import DiskImageFileStorage
from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
from ..services.images.images_default import ImageService
from ..services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from ..services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
from ..services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
from ..services.invocation_services import InvocationServices from ..services.invocation_services import InvocationServices
from ..services.invocation_stats import InvocationStatsService from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
from ..services.invoker import Invoker from ..services.invoker import Invoker
from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
from ..services.model_manager_service import ModelManagerService from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
from ..services.processor import DefaultInvocationProcessor from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
from ..services.sqlite import SqliteItemStorage from ..services.model_manager.model_manager_default import ModelManagerService
from ..services.thread import lock from ..services.names.names_default import SimpleNameService
from ..services.session_processor.session_processor_default import DefaultSessionProcessor
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.urls.urls_default import LocalUrlService
from .events import FastAPIEventService from .events import FastAPIEventService
@ -63,100 +63,64 @@ class ApiDependencies:
logger.info(f"Root directory = {str(config.root_path)}") logger.info(f"Root directory = {str(config.root_path)}")
logger.debug(f"Internet connectivity is {config.internet_available}") logger.debug(f"Internet connectivity is {config.internet_available}")
events = FastAPIEventService(event_handler_id)
output_folder = config.output_path output_folder = config.output_path
# TODO: build a file/path manager? db = SqliteDatabase(config, logger)
if config.use_memory_db:
db_location = ":memory:"
else:
db_path = config.db_path
db_path.parent.mkdir(parents=True, exist_ok=True)
db_location = str(db_path)
logger.info(f"Using database at {db_location}") configuration = config
db_conn = sqlite3.connect(db_location, check_same_thread=False) # TODO: figure out a better threading solution logger = logger
if config.log_sql: board_image_records = SqliteBoardImageRecordStorage(db=db)
db_conn.set_trace_callback(print) board_images = BoardImagesService()
db_conn.execute("PRAGMA foreign_keys = ON;") board_records = SqliteBoardRecordStorage(db=db)
boards = BoardService()
graph_execution_manager = SqliteItemStorage[GraphExecutionState]( events = FastAPIEventService(event_handler_id)
conn=db_conn, table_name="graph_executions", lock=lock graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
) graph_library = SqliteItemStorage[LibraryGraph](db=db, table_name="graphs")
image_files = DiskImageFileStorage(f"{output_folder}/images")
urls = LocalUrlService() image_records = SqliteImageRecordStorage(db=db)
image_record_storage = SqliteImageRecordStorage(conn=db_conn, lock=lock) images = ImageService()
image_file_storage = DiskImageFileStorage(f"{output_folder}/images") invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
names = SimpleNameService()
latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents")) latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
model_manager = ModelManagerService(config, logger)
board_record_storage = SqliteBoardRecordStorage(conn=db_conn, lock=lock) names = SimpleNameService()
board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn, lock=lock) performance_statistics = InvocationStatsService()
processor = DefaultInvocationProcessor()
boards = BoardService( queue = MemoryInvocationQueue()
services=BoardServiceDependencies( session_processor = DefaultSessionProcessor()
board_image_record_storage=board_image_record_storage, session_queue = SqliteSessionQueue(db=db)
board_record_storage=board_record_storage, urls = LocalUrlService()
image_record_storage=image_record_storage,
url=urls,
logger=logger,
)
)
board_images = BoardImagesService(
services=BoardImagesServiceDependencies(
board_image_record_storage=board_image_record_storage,
board_record_storage=board_record_storage,
image_record_storage=image_record_storage,
url=urls,
logger=logger,
)
)
images = ImageService(
services=ImageServiceDependencies(
board_image_record_storage=board_image_record_storage,
image_record_storage=image_record_storage,
image_file_storage=image_file_storage,
url=urls,
logger=logger,
names=names,
graph_execution_manager=graph_execution_manager,
)
)
services = InvocationServices( services = InvocationServices(
model_manager=ModelManagerService(config, logger), board_image_records=board_image_records,
events=events,
latents=latents,
images=images,
boards=boards,
board_images=board_images, board_images=board_images,
queue=MemoryInvocationQueue(), board_records=board_records,
graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, lock=lock, table_name="graphs"), boards=boards,
configuration=configuration,
events=events,
graph_execution_manager=graph_execution_manager, graph_execution_manager=graph_execution_manager,
processor=DefaultInvocationProcessor(), graph_library=graph_library,
configuration=config, image_files=image_files,
performance_statistics=InvocationStatsService(graph_execution_manager), image_records=image_records,
images=images,
invocation_cache=invocation_cache,
latents=latents,
logger=logger, logger=logger,
session_queue=SqliteSessionQueue(conn=db_conn, lock=lock), model_manager=model_manager,
session_processor=DefaultSessionProcessor(), names=names,
invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size), performance_statistics=performance_statistics,
processor=processor,
queue=queue,
session_processor=session_processor,
session_queue=session_queue,
urls=urls,
) )
create_system_graphs(services.graph_library) create_system_graphs(services.graph_library)
ApiDependencies.invoker = Invoker(services) ApiDependencies.invoker = Invoker(services)
try: db.clean()
lock.acquire()
db_conn.execute("VACUUM;")
db_conn.commit()
logger.info("Cleaned database")
finally:
lock.release()
@staticmethod @staticmethod
def shutdown(): def shutdown():

2
invokeai/app/api/events.py
View File

@ -7,7 +7,7 @@ from typing import Any
from fastapi_events.dispatcher import dispatch from fastapi_events.dispatcher import dispatch
from ..services.events import EventServiceBase from ..services.events.events_base import EventServiceBase
class FastAPIEventService(EventServiceBase): class FastAPIEventService(EventServiceBase):

6
invokeai/app/api/routers/boards.py
View File

@ -4,9 +4,9 @@ from fastapi import Body, HTTPException, Path, Query
from fastapi.routing import APIRouter from fastapi.routing import APIRouter
from pydantic import BaseModel, Field from pydantic import BaseModel, Field
from invokeai.app.services.board_record_storage import BoardChanges from invokeai.app.services.board_records.board_records_common import BoardChanges
from invokeai.app.services.image_record_storage import OffsetPaginatedResults from invokeai.app.services.boards.boards_common import BoardDTO
from invokeai.app.services.models.board_record import BoardDTO from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from ..dependencies import ApiDependencies from ..dependencies import ApiDependencies

25
invokeai/app/api/routers/images.py
View File

@ -8,9 +8,9 @@ from PIL import Image
from pydantic import BaseModel, Field from pydantic import BaseModel, Field
from invokeai.app.invocations.metadata import ImageMetadata from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.models.image import ImageCategory, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.services.image_record_storage import OffsetPaginatedResults from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
from invokeai.app.services.models.image_record import ImageDTO, ImageRecordChanges, ImageUrlsDTO from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from ..dependencies import ApiDependencies from ..dependencies import ApiDependencies
@ -42,7 +42,7 @@ async def upload_image(
crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"), crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
) -> ImageDTO: ) -> ImageDTO:
"""Uploads an image""" """Uploads an image"""
if not file.content_type.startswith("image"): if not file.content_type or not file.content_type.startswith("image"):
raise HTTPException(status_code=415, detail="Not an image") raise HTTPException(status_code=415, detail="Not an image")
contents = await file.read() contents = await file.read()
@ -322,3 +322,20 @@ async def unstar_images_in_list(
return ImagesUpdatedFromListResult(updated_image_names=updated_image_names) return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
except Exception: except Exception:
raise HTTPException(status_code=500, detail="Failed to unstar images") raise HTTPException(status_code=500, detail="Failed to unstar images")
class ImagesDownloaded(BaseModel):
response: Optional[str] = Field(
description="If defined, the message to display to the user when images begin downloading"
)
@images_router.post("/download", operation_id="download_images_from_list", response_model=ImagesDownloaded)
async def download_images_from_list(
image_names: list[str] = Body(description="The list of names of images to download", embed=True),
board_id: Optional[str] = Body(
default=None, description="The board from which image should be downloaded from", embed=True
),
) -> ImagesDownloaded:
# return ImagesDownloaded(response="Your images are downloading")
raise HTTPException(status_code=501, detail="Endpoint is not yet implemented")

110
invokeai/app/api/routers/models.py
View File

@ -2,11 +2,11 @@
import pathlib import pathlib
from typing import List, Literal, Optional, Union from typing import Annotated, List, Literal, Optional, Union
from fastapi import Body, Path, Query, Response from fastapi import Body, Path, Query, Response
from fastapi.routing import APIRouter from fastapi.routing import APIRouter
from pydantic import BaseModel, parse_obj_as from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
from starlette.exceptions import HTTPException from starlette.exceptions import HTTPException
from invokeai.backend import BaseModelType, ModelType from invokeai.backend import BaseModelType, ModelType
@ -23,8 +23,14 @@ from ..dependencies import ApiDependencies
models_router = APIRouter(prefix="/v1/models", tags=["models"]) models_router = APIRouter(prefix="/v1/models", tags=["models"])
UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
update_models_response_adapter = TypeAdapter(UpdateModelResponse)
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
import_models_response_adapter = TypeAdapter(ImportModelResponse)
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
convert_models_response_adapter = TypeAdapter(ConvertModelResponse)
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)] MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)] ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
@ -32,6 +38,11 @@ ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
class ModelsList(BaseModel): class ModelsList(BaseModel):
models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]] models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
model_config = ConfigDict(use_enum_values=True)
models_list_adapter = TypeAdapter(ModelsList)
@models_router.get( @models_router.get(
"/", "/",
@ -49,7 +60,7 @@ async def list_models(
models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type)) models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
else: else:
models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type) models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
models = parse_obj_as(ModelsList, {"models": models_raw}) models = models_list_adapter.validate_python({"models": models_raw})
return models return models
@ -105,11 +116,14 @@ async def update_model(
info.path = new_info.get("path") info.path = new_info.get("path")
# replace empty string values with None/null to avoid phenomenon of vae: '' # replace empty string values with None/null to avoid phenomenon of vae: ''
info_dict = info.dict() info_dict = info.model_dump()
info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()} info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
ApiDependencies.invoker.services.model_manager.update_model( ApiDependencies.invoker.services.model_manager.update_model(
model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info_dict model_name=model_name,
base_model=base_model,
model_type=model_type,
model_attributes=info_dict,
) )
model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@ -117,7 +131,7 @@ async def update_model(
base_model=base_model, base_model=base_model,
model_type=model_type, model_type=model_type,
) )
model_response = parse_obj_as(UpdateModelResponse, model_raw) model_response = update_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e: except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=str(e)) raise HTTPException(status_code=404, detail=str(e))
except ValueError as e: except ValueError as e:
@ -152,13 +166,15 @@ async def import_model(
) -> ImportModelResponse: ) -> ImportModelResponse:
"""Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically""" """Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
location = location.strip("\"' ")
items_to_import = {location} items_to_import = {location}
prediction_types = {x.value: x for x in SchedulerPredictionType} prediction_types = {x.value: x for x in SchedulerPredictionType}
logger = ApiDependencies.invoker.services.logger logger = ApiDependencies.invoker.services.logger
try: try:
installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import( installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type) items_to_import=items_to_import,
prediction_type_helper=lambda x: prediction_types.get(prediction_type),
) )
info = installed_models.get(location) info = installed_models.get(location)
@ -170,7 +186,7 @@ async def import_model(
model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.name, base_model=info.base_model, model_type=info.model_type model_name=info.name, base_model=info.base_model, model_type=info.model_type
) )
return parse_obj_as(ImportModelResponse, model_raw) return import_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e: except ModelNotFoundException as e:
logger.error(str(e)) logger.error(str(e))
@ -204,13 +220,18 @@ async def add_model(
try: try:
ApiDependencies.invoker.services.model_manager.add_model( ApiDependencies.invoker.services.model_manager.add_model(
info.model_name, info.base_model, info.model_type, model_attributes=info.dict() info.model_name,
info.base_model,
info.model_type,
model_attributes=info.model_dump(),
) )
logger.info(f"Successfully added {info.model_name}") logger.info(f"Successfully added {info.model_name}")
model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.model_name, base_model=info.base_model, model_type=info.model_type model_name=info.model_name,
base_model=info.base_model,
model_type=info.model_type,
) )
return parse_obj_as(ImportModelResponse, model_raw) return import_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e: except ModelNotFoundException as e:
logger.error(str(e)) logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e)) raise HTTPException(status_code=404, detail=str(e))
@ -222,7 +243,10 @@ async def add_model(
@models_router.delete( @models_router.delete(
"/{base_model}/{model_type}/{model_name}", "/{base_model}/{model_type}/{model_name}",
operation_id="del_model", operation_id="del_model",
responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}}, responses={
204: {"description": "Model deleted successfully"},
404: {"description": "Model not found"},
},
status_code=204, status_code=204,
response_model=None, response_model=None,
) )
@ -278,7 +302,7 @@ async def convert_model(
model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name, base_model=base_model, model_type=model_type model_name, base_model=base_model, model_type=model_type
) )
response = parse_obj_as(ConvertModelResponse, model_raw) response = convert_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e: except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}") raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
except ValueError as e: except ValueError as e:
@ -301,7 +325,8 @@ async def search_for_models(
) -> List[pathlib.Path]: ) -> List[pathlib.Path]:
if not search_path.is_dir(): if not search_path.is_dir():
raise HTTPException( raise HTTPException(
status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory" status_code=404,
detail=f"The search path '{search_path}' does not exist or is not directory",
) )
return ApiDependencies.invoker.services.model_manager.search_for_models(search_path) return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
@ -336,6 +361,26 @@ async def sync_to_config() -> bool:
return True return True
# There's some weird pydantic-fastapi behaviour that requires this to be a separate class
# TODO: After a few updates, see if it works inside the route operation handler?
class MergeModelsBody(BaseModel):
model_names: List[str] = Field(description="model name", min_length=2, max_length=3)
merged_model_name: Optional[str] = Field(description="Name of destination model")
alpha: Optional[float] = Field(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5)
interp: Optional[MergeInterpolationMethod] = Field(description="Interpolation method")
force: Optional[bool] = Field(
description="Force merging of models created with different versions of diffusers",
default=False,
)
merge_dest_directory: Optional[str] = Field(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
)
model_config = ConfigDict(protected_namespaces=())
@models_router.put( @models_router.put(
"/merge/{base_model}", "/merge/{base_model}",
operation_id="merge_models", operation_id="merge_models",
@ -348,31 +393,23 @@ async def sync_to_config() -> bool:
response_model=MergeModelResponse, response_model=MergeModelResponse,
) )
async def merge_models( async def merge_models(
body: Annotated[MergeModelsBody, Body(description="Model configuration", embed=True)],
base_model: BaseModelType = Path(description="Base model"), base_model: BaseModelType = Path(description="Base model"),
model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
merged_model_name: Optional[str] = Body(description="Name of destination model"),
alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
force: Optional[bool] = Body(
description="Force merging of models created with different versions of diffusers", default=False
),
merge_dest_directory: Optional[str] = Body(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
),
) -> MergeModelResponse: ) -> MergeModelResponse:
"""Convert a checkpoint model into a diffusers model""" """Convert a checkpoint model into a diffusers model"""
logger = ApiDependencies.invoker.services.logger logger = ApiDependencies.invoker.services.logger
try: try:
logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}") logger.info(
dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None f"Merging models: {body.model_names} into {body.merge_dest_directory or '<MODELS>'}/{body.merged_model_name}"
)
dest = pathlib.Path(body.merge_dest_directory) if body.merge_dest_directory else None
result = ApiDependencies.invoker.services.model_manager.merge_models( result = ApiDependencies.invoker.services.model_manager.merge_models(
model_names, model_names=body.model_names,
base_model, base_model=base_model,
merged_model_name=merged_model_name or "+".join(model_names), merged_model_name=body.merged_model_name or "+".join(body.model_names),
alpha=alpha, alpha=body.alpha,
interp=interp, interp=body.interp,
force=force, force=body.force,
merge_dest_directory=dest, merge_dest_directory=dest,
) )
model_raw = ApiDependencies.invoker.services.model_manager.list_model( model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@ -380,9 +417,12 @@ async def merge_models(
base_model=base_model, base_model=base_model,
model_type=ModelType.Main, model_type=ModelType.Main,
) )
response = parse_obj_as(ConvertModelResponse, model_raw) response = convert_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException: except ModelNotFoundException:
raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found") raise HTTPException(
status_code=404,
detail=f"One or more of the models '{body.model_names}' not found",
)
except ValueError as e: except ValueError as e:
raise HTTPException(status_code=400, detail=str(e)) raise HTTPException(status_code=400, detail=str(e))
return response return response

4
invokeai/app/api/routers/session_queue.py
View File

@ -18,9 +18,9 @@ from invokeai.app.services.session_queue.session_queue_common import (
SessionQueueItemDTO, SessionQueueItemDTO,
SessionQueueStatus, SessionQueueStatus,
) )
from invokeai.app.services.shared.models import CursorPaginatedResults from invokeai.app.services.shared.graph import Graph
from invokeai.app.services.shared.pagination import CursorPaginatedResults
from ...services.graph import Graph
from ..dependencies import ApiDependencies from ..dependencies import ApiDependencies
session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"]) session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])

455
invokeai/app/api/routers/sessions.py
View File

@ -1,56 +1,50 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from typing import Annotated, Optional, Union
from fastapi import Body, HTTPException, Path, Query, Response from fastapi import HTTPException, Path
from fastapi.routing import APIRouter from fastapi.routing import APIRouter
from pydantic.fields import Field
# Importing * is bad karma but needed here for node detection from ...services.shared.graph import GraphExecutionState
from ...invocations import * # noqa: F401 F403
from ...invocations.baseinvocation import BaseInvocation
from ...services.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
from ...services.item_storage import PaginatedResults
from ..dependencies import ApiDependencies from ..dependencies import ApiDependencies
session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"]) session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
@session_router.post( # @session_router.post(
"/", # "/",
operation_id="create_session", # operation_id="create_session",
responses={ # responses={
200: {"model": GraphExecutionState}, # 200: {"model": GraphExecutionState},
400: {"description": "Invalid json"}, # 400: {"description": "Invalid json"},
}, # },
deprecated=True, # deprecated=True,
) # )
async def create_session( # async def create_session(
queue_id: str = Query(default="", description="The id of the queue to associate the session with"), # queue_id: str = Query(default="", description="The id of the queue to associate the session with"),
graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"), # graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with"),
) -> GraphExecutionState: # ) -> GraphExecutionState:
"""Creates a new session, optionally initializing it with an invocation graph""" # """Creates a new session, optionally initializing it with an invocation graph"""
session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph) # session = ApiDependencies.invoker.create_execution_state(queue_id=queue_id, graph=graph)
return session # return session
@session_router.get( # @session_router.get(
"/", # "/",
operation_id="list_sessions", # operation_id="list_sessions",
responses={200: {"model": PaginatedResults[GraphExecutionState]}}, # responses={200: {"model": PaginatedResults[GraphExecutionState]}},
deprecated=True, # deprecated=True,
) # )
async def list_sessions( # async def list_sessions(
page: int = Query(default=0, description="The page of results to get"), # page: int = Query(default=0, description="The page of results to get"),
per_page: int = Query(default=10, description="The number of results per page"), # per_page: int = Query(default=10, description="The number of results per page"),
query: str = Query(default="", description="The query string to search for"), # query: str = Query(default="", description="The query string to search for"),
) -> PaginatedResults[GraphExecutionState]: # ) -> PaginatedResults[GraphExecutionState]:
"""Gets a list of sessions, optionally searching""" # """Gets a list of sessions, optionally searching"""
if query == "": # if query == "":
result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page) # result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
else: # else:
result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page) # result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
return result # return result
@session_router.get( @session_router.get(
@ -60,7 +54,6 @@ async def list_sessions(
200: {"model": GraphExecutionState}, 200: {"model": GraphExecutionState},
404: {"description": "Session not found"}, 404: {"description": "Session not found"},
}, },
deprecated=True,
) )
async def get_session( async def get_session(
session_id: str = Path(description="The id of the session to get"), session_id: str = Path(description="The id of the session to get"),
@ -73,211 +66,211 @@ async def get_session(
return session return session
@session_router.post( # @session_router.post(
"/{session_id}/nodes", # "/{session_id}/nodes",
operation_id="add_node", # operation_id="add_node",
responses={ # responses={
200: {"model": str}, # 200: {"model": str},
400: {"description": "Invalid node or link"}, # 400: {"description": "Invalid node or link"},
404: {"description": "Session not found"}, # 404: {"description": "Session not found"},
}, # },
deprecated=True, # deprecated=True,
) # )
async def add_node( # async def add_node(
session_id: str = Path(description="The id of the session"), # session_id: str = Path(description="The id of the session"),
node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore # node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
description="The node to add" # description="The node to add"
), # ),
) -> str: # ) -> str:
"""Adds a node to the graph""" # """Adds a node to the graph"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) # session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None: # if session is None:
raise HTTPException(status_code=404) # raise HTTPException(status_code=404)
try: # try:
session.add_node(node) # session.add_node(node)
ApiDependencies.invoker.services.graph_execution_manager.set( # ApiDependencies.invoker.services.graph_execution_manager.set(
session # session
) # TODO: can this be done automatically, or add node through an API? # ) # TODO: can this be done automatically, or add node through an API?
return session.id # return session.id
except NodeAlreadyExecutedError: # except NodeAlreadyExecutedError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
except IndexError: # except IndexError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
@session_router.put( # @session_router.put(
"/{session_id}/nodes/{node_path}", # "/{session_id}/nodes/{node_path}",
operation_id="update_node", # operation_id="update_node",
responses={ # responses={
200: {"model": GraphExecutionState}, # 200: {"model": GraphExecutionState},
400: {"description": "Invalid node or link"}, # 400: {"description": "Invalid node or link"},
404: {"description": "Session not found"}, # 404: {"description": "Session not found"},
}, # },
deprecated=True, # deprecated=True,
) # )
async def update_node( # async def update_node(
session_id: str = Path(description="The id of the session"), # session_id: str = Path(description="The id of the session"),
node_path: str = Path(description="The path to the node in the graph"), # node_path: str = Path(description="The path to the node in the graph"),
node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore # node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
description="The new node" # description="The new node"
), # ),
) -> GraphExecutionState: # ) -> GraphExecutionState:
"""Updates a node in the graph and removes all linked edges""" # """Updates a node in the graph and removes all linked edges"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) # session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None: # if session is None:
raise HTTPException(status_code=404) # raise HTTPException(status_code=404)
try: # try:
session.update_node(node_path, node) # session.update_node(node_path, node)
ApiDependencies.invoker.services.graph_execution_manager.set( # ApiDependencies.invoker.services.graph_execution_manager.set(
session # session
) # TODO: can this be done automatically, or add node through an API? # ) # TODO: can this be done automatically, or add node through an API?
return session # return session
except NodeAlreadyExecutedError: # except NodeAlreadyExecutedError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
except IndexError: # except IndexError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
@session_router.delete( # @session_router.delete(
"/{session_id}/nodes/{node_path}", # "/{session_id}/nodes/{node_path}",
operation_id="delete_node", # operation_id="delete_node",
responses={ # responses={
200: {"model": GraphExecutionState}, # 200: {"model": GraphExecutionState},
400: {"description": "Invalid node or link"}, # 400: {"description": "Invalid node or link"},
404: {"description": "Session not found"}, # 404: {"description": "Session not found"},
}, # },
deprecated=True, # deprecated=True,
) # )
async def delete_node( # async def delete_node(
session_id: str = Path(description="The id of the session"), # session_id: str = Path(description="The id of the session"),
node_path: str = Path(description="The path to the node to delete"), # node_path: str = Path(description="The path to the node to delete"),
) -> GraphExecutionState: # ) -> GraphExecutionState:
"""Deletes a node in the graph and removes all linked edges""" # """Deletes a node in the graph and removes all linked edges"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) # session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None: # if session is None:
raise HTTPException(status_code=404) # raise HTTPException(status_code=404)
try: # try:
session.delete_node(node_path) # session.delete_node(node_path)
ApiDependencies.invoker.services.graph_execution_manager.set( # ApiDependencies.invoker.services.graph_execution_manager.set(
session # session
) # TODO: can this be done automatically, or add node through an API? # ) # TODO: can this be done automatically, or add node through an API?
return session # return session
except NodeAlreadyExecutedError: # except NodeAlreadyExecutedError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
except IndexError: # except IndexError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
@session_router.post( # @session_router.post(
"/{session_id}/edges", # "/{session_id}/edges",
operation_id="add_edge", # operation_id="add_edge",
responses={ # responses={
200: {"model": GraphExecutionState}, # 200: {"model": GraphExecutionState},
400: {"description": "Invalid node or link"}, # 400: {"description": "Invalid node or link"},
404: {"description": "Session not found"}, # 404: {"description": "Session not found"},
}, # },
deprecated=True, # deprecated=True,
) # )
async def add_edge( # async def add_edge(
session_id: str = Path(description="The id of the session"), # session_id: str = Path(description="The id of the session"),
edge: Edge = Body(description="The edge to add"), # edge: Edge = Body(description="The edge to add"),
) -> GraphExecutionState: # ) -> GraphExecutionState:
"""Adds an edge to the graph""" # """Adds an edge to the graph"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) # session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None: # if session is None:
raise HTTPException(status_code=404) # raise HTTPException(status_code=404)
try: # try:
session.add_edge(edge) # session.add_edge(edge)
ApiDependencies.invoker.services.graph_execution_manager.set( # ApiDependencies.invoker.services.graph_execution_manager.set(
session # session
) # TODO: can this be done automatically, or add node through an API? # ) # TODO: can this be done automatically, or add node through an API?
return session # return session
except NodeAlreadyExecutedError: # except NodeAlreadyExecutedError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
except IndexError: # except IndexError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
# TODO: the edge being in the path here is really ugly, find a better solution # # TODO: the edge being in the path here is really ugly, find a better solution
@session_router.delete( # @session_router.delete(
"/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}", # "/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}",
operation_id="delete_edge", # operation_id="delete_edge",
responses={ # responses={
200: {"model": GraphExecutionState}, # 200: {"model": GraphExecutionState},
400: {"description": "Invalid node or link"}, # 400: {"description": "Invalid node or link"},
404: {"description": "Session not found"}, # 404: {"description": "Session not found"},
}, # },
deprecated=True, # deprecated=True,
) # )
async def delete_edge( # async def delete_edge(
session_id: str = Path(description="The id of the session"), # session_id: str = Path(description="The id of the session"),
from_node_id: str = Path(description="The id of the node the edge is coming from"), # from_node_id: str = Path(description="The id of the node the edge is coming from"),
from_field: str = Path(description="The field of the node the edge is coming from"), # from_field: str = Path(description="The field of the node the edge is coming from"),
to_node_id: str = Path(description="The id of the node the edge is going to"), # to_node_id: str = Path(description="The id of the node the edge is going to"),
to_field: str = Path(description="The field of the node the edge is going to"), # to_field: str = Path(description="The field of the node the edge is going to"),
) -> GraphExecutionState: # ) -> GraphExecutionState:
"""Deletes an edge from the graph""" # """Deletes an edge from the graph"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) # session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None: # if session is None:
raise HTTPException(status_code=404) # raise HTTPException(status_code=404)
try: # try:
edge = Edge( # edge = Edge(
source=EdgeConnection(node_id=from_node_id, field=from_field), # source=EdgeConnection(node_id=from_node_id, field=from_field),
destination=EdgeConnection(node_id=to_node_id, field=to_field), # destination=EdgeConnection(node_id=to_node_id, field=to_field),
) # )
session.delete_edge(edge) # session.delete_edge(edge)
ApiDependencies.invoker.services.graph_execution_manager.set( # ApiDependencies.invoker.services.graph_execution_manager.set(
session # session
) # TODO: can this be done automatically, or add node through an API? # ) # TODO: can this be done automatically, or add node through an API?
return session # return session
except NodeAlreadyExecutedError: # except NodeAlreadyExecutedError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
except IndexError: # except IndexError:
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
@session_router.put( # @session_router.put(
"/{session_id}/invoke", # "/{session_id}/invoke",
operation_id="invoke_session", # operation_id="invoke_session",
responses={ # responses={
200: {"model": None}, # 200: {"model": None},
202: {"description": "The invocation is queued"}, # 202: {"description": "The invocation is queued"},
400: {"description": "The session has no invocations ready to invoke"}, # 400: {"description": "The session has no invocations ready to invoke"},
404: {"description": "Session not found"}, # 404: {"description": "Session not found"},
}, # },
deprecated=True, # deprecated=True,
) # )
async def invoke_session( # async def invoke_session(
queue_id: str = Query(description="The id of the queue to associate the session with"), # queue_id: str = Query(description="The id of the queue to associate the session with"),
session_id: str = Path(description="The id of the session to invoke"), # session_id: str = Path(description="The id of the session to invoke"),
all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"), # all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
) -> Response: # ) -> Response:
"""Invokes a session""" # """Invokes a session"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id) # session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
if session is None: # if session is None:
raise HTTPException(status_code=404) # raise HTTPException(status_code=404)
if session.is_complete(): # if session.is_complete():
raise HTTPException(status_code=400) # raise HTTPException(status_code=400)
ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all) # ApiDependencies.invoker.invoke(queue_id, session, invoke_all=all)
return Response(status_code=202) # return Response(status_code=202)
@session_router.delete( # @session_router.delete(
"/{session_id}/invoke", # "/{session_id}/invoke",
operation_id="cancel_session_invoke", # operation_id="cancel_session_invoke",
responses={202: {"description": "The invocation is canceled"}}, # responses={202: {"description": "The invocation is canceled"}},
deprecated=True, # deprecated=True,
) # )
async def cancel_session_invoke( # async def cancel_session_invoke(
session_id: str = Path(description="The id of the session to cancel"), # session_id: str = Path(description="The id of the session to cancel"),
) -> Response: # ) -> Response:
"""Invokes a session""" # """Invokes a session"""
ApiDependencies.invoker.cancel(session_id) # ApiDependencies.invoker.cancel(session_id)
return Response(status_code=202) # return Response(status_code=202)

3
invokeai/app/api/routers/utilities.py
View File

@ -1,4 +1,4 @@
from typing import Optional from typing import Optional, Union
from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
from fastapi import Body from fastapi import Body
@ -27,6 +27,7 @@ async def parse_dynamicprompts(
combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"), combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
) -> DynamicPromptsResponse: ) -> DynamicPromptsResponse:
"""Creates a batch process""" """Creates a batch process"""
generator: Union[RandomPromptGenerator, CombinatorialPromptGenerator]
try: try:
error: Optional[str] = None error: Optional[str] = None
if combinatorial: if combinatorial:

6
invokeai/app/api/sockets.py
View File

@ -5,7 +5,7 @@ from fastapi_events.handlers.local import local_handler
from fastapi_events.typing import Event from fastapi_events.typing import Event
from socketio import ASGIApp, AsyncServer from socketio import ASGIApp, AsyncServer
from ..services.events import EventServiceBase from ..services.events.events_base import EventServiceBase
class SocketIO: class SocketIO:
@ -30,8 +30,8 @@ class SocketIO:
async def _handle_sub_queue(self, sid, data, *args, **kwargs): async def _handle_sub_queue(self, sid, data, *args, **kwargs):
if "queue_id" in data: if "queue_id" in data:
self.__sio.enter_room(sid, data["queue_id"]) await self.__sio.enter_room(sid, data["queue_id"])
async def _handle_unsub_queue(self, sid, data, *args, **kwargs): async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
if "queue_id" in data: if "queue_id" in data:
self.__sio.enter_room(sid, data["queue_id"]) await self.__sio.enter_room(sid, data["queue_id"])

49
invokeai/app/api_app.py
View File

@ -22,7 +22,7 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
from fastapi.staticfiles import StaticFiles from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.schema import schema from pydantic.json_schema import models_json_schema
# noinspection PyUnresolvedReferences # noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import) import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
@ -51,7 +51,7 @@ mimetypes.add_type("text/css", ".css")
# Create the app # Create the app
# TODO: create this all in a method so configuration/etc. can be passed in? # TODO: create this all in a method so configuration/etc. can be passed in?
app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None) app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
# Add event handler # Add event handler
event_handler_id: int = id(app) event_handler_id: int = id(app)
@ -63,18 +63,18 @@ app.add_middleware(
socket_io = SocketIO(app) socket_io = SocketIO(app)
app.add_middleware(
CORSMiddleware,
allow_origins=app_config.allow_origins,
allow_credentials=app_config.allow_credentials,
allow_methods=app_config.allow_methods,
allow_headers=app_config.allow_headers,
)
# Add startup event to load dependencies # Add startup event to load dependencies
@app.on_event("startup") @app.on_event("startup")
async def startup_event(): async def startup_event():
app.add_middleware(
CORSMiddleware,
allow_origins=app_config.allow_origins,
allow_credentials=app_config.allow_credentials,
allow_methods=app_config.allow_methods,
allow_headers=app_config.allow_headers,
)
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger) ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
@ -85,11 +85,6 @@ async def shutdown_event():
# Include all routers # Include all routers
# TODO: REMOVE
# app.include_router(
# invocation.invocation_router,
# prefix = '/api')
app.include_router(sessions.session_router, prefix="/api") app.include_router(sessions.session_router, prefix="/api")
app.include_router(utilities.utilities_router, prefix="/api") app.include_router(utilities.utilities_router, prefix="/api")
@ -117,6 +112,7 @@ def custom_openapi():
description="An API for invoking AI image operations", description="An API for invoking AI image operations",
version="1.0.0", version="1.0.0",
routes=app.routes, routes=app.routes,
separate_input_output_schemas=False, # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
) )
# Add all outputs # Add all outputs
@ -127,29 +123,32 @@ def custom_openapi():
output_type = signature(invoker.invoke).return_annotation output_type = signature(invoker.invoke).return_annotation
output_types.add(output_type) output_types.add(output_type)
output_schemas = schema(output_types, ref_prefix="#/components/schemas/") output_schemas = models_json_schema(
for schema_key, output_schema in output_schemas["definitions"].items(): models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
output_schema["class"] = "output" )
openapi_schema["components"]["schemas"][schema_key] = output_schema for schema_key, output_schema in output_schemas[1]["$defs"].items():
# TODO: note that we assume the schema_key here is the TYPE.__name__ # TODO: note that we assume the schema_key here is the TYPE.__name__
# This could break in some cases, figure out a better way to do it # This could break in some cases, figure out a better way to do it
output_type_titles[schema_key] = output_schema["title"] output_type_titles[schema_key] = output_schema["title"]
# Add Node Editor UI helper schemas # Add Node Editor UI helper schemas
ui_config_schemas = schema([UIConfigBase, _InputField, _OutputField], ref_prefix="#/components/schemas/") ui_config_schemas = models_json_schema(
for schema_key, ui_config_schema in ui_config_schemas["definitions"].items(): [(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
ref_template="#/components/schemas/{model}",
)
for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
openapi_schema["components"]["schemas"][schema_key] = ui_config_schema openapi_schema["components"]["schemas"][schema_key] = ui_config_schema
# Add a reference to the output type to additionalProperties of the invoker schema # Add a reference to the output type to additionalProperties of the invoker schema
for invoker in all_invocations: for invoker in all_invocations:
invoker_name = invoker.__name__ invoker_name = invoker.__name__
output_type = signature(invoker.invoke).return_annotation output_type = signature(obj=invoker.invoke).return_annotation
output_type_title = output_type_titles[output_type.__name__] output_type_title = output_type_titles[output_type.__name__]
invoker_schema = openapi_schema["components"]["schemas"][invoker_name] invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"} outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
invoker_schema["output"] = outputs_ref invoker_schema["output"] = outputs_ref
invoker_schema["class"] = "invocation" invoker_schema["class"] = "invocation"
openapi_schema["components"]["schemas"][f"{output_type_title}"]["class"] = "output"
from invokeai.backend.model_management.models import get_model_config_enums from invokeai.backend.model_management.models import get_model_config_enums
@ -172,7 +171,7 @@ def custom_openapi():
return app.openapi_schema return app.openapi_schema
app.openapi = custom_openapi app.openapi = custom_openapi # type: ignore [method-assign] # this is a valid assignment
# Override API doc favicons # Override API doc favicons
app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static") app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")

11
invokeai/app/cli/commands.py
View File

@ -24,8 +24,8 @@ def add_field_argument(command_parser, name: str, field, default_override=None):
if field.default_factory is None if field.default_factory is None
else field.default_factory() else field.default_factory()
) )
if get_origin(field.type_) == Literal: if get_origin(field.annotation) == Literal:
allowed_values = get_args(field.type_) allowed_values = get_args(field.annotation)
allowed_types = set() allowed_types = set()
for val in allowed_values: for val in allowed_values:
allowed_types.add(type(val)) allowed_types.add(type(val))
@ -38,15 +38,15 @@ def add_field_argument(command_parser, name: str, field, default_override=None):
type=field_type, type=field_type,
default=default, default=default,
choices=allowed_values, choices=allowed_values,
help=field.field_info.description, help=field.description,
) )
else: else:
command_parser.add_argument( command_parser.add_argument(
f"--{name}", f"--{name}",
dest=name, dest=name,
type=field.type_, type=field.annotation,
default=default, default=default,
help=field.field_info.description, help=field.description,
) )
@ -142,7 +142,6 @@ class BaseCommand(ABC, BaseModel):
"""A CLI command""" """A CLI command"""
# All commands must include a type name like this: # All commands must include a type name like this:
# type: Literal['your_command_name'] = 'your_command_name'
@classmethod @classmethod
def get_all_subclasses(cls): def get_all_subclasses(cls):

555
invokeai/app/invocations/baseinvocation.py
View File

@ -7,28 +7,16 @@ import re
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from enum import Enum from enum import Enum
from inspect import signature from inspect import signature
from typing import ( from types import UnionType
TYPE_CHECKING, from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
AbstractSet,
Any,
Callable,
ClassVar,
Literal,
Mapping,
Optional,
Type,
TypeVar,
Union,
get_args,
get_type_hints,
)
import semver import semver
from pydantic import BaseModel, Field, validator from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator
from pydantic.fields import ModelField, Undefined from pydantic.fields import _Unset
from pydantic.typing import NoArgAnyCallable from pydantic_core import PydanticUndefined
from invokeai.app.services.config.invokeai_config import InvokeAIAppConfig from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.util.misc import uuid_string
if TYPE_CHECKING: if TYPE_CHECKING:
from ..services.invocation_services import InvocationServices from ..services.invocation_services import InvocationServices
@ -215,6 +203,11 @@ class _InputField(BaseModel):
ui_choice_labels: Optional[dict[str, str]] ui_choice_labels: Optional[dict[str, str]]
item_default: Optional[Any] item_default: Optional[Any]
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
class _OutputField(BaseModel): class _OutputField(BaseModel):
""" """
@ -228,34 +221,36 @@ class _OutputField(BaseModel):
ui_type: Optional[UIType] ui_type: Optional[UIType]
ui_order: Optional[int] ui_order: Optional[int]
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
)
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( def InputField(
*args: Any, # copied from pydantic's Field
default: Any = Undefined, default: Any = _Unset,
default_factory: Optional[NoArgAnyCallable] = None, default_factory: Callable[[], Any] | None = _Unset,
alias: Optional[str] = None, title: str | None = _Unset,
title: Optional[str] = None, description: str | None = _Unset,
description: Optional[str] = None, pattern: str | None = _Unset,
exclude: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None, strict: bool | None = _Unset,
include: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None, gt: float | None = _Unset,
const: Optional[bool] = None, ge: float | None = _Unset,
gt: Optional[float] = None, lt: float | None = _Unset,
ge: Optional[float] = None, le: float | None = _Unset,
lt: Optional[float] = None, multiple_of: float | None = _Unset,
le: Optional[float] = None, allow_inf_nan: bool | None = _Unset,
multiple_of: Optional[float] = None, max_digits: int | None = _Unset,
allow_inf_nan: Optional[bool] = None, decimal_places: int | None = _Unset,
max_digits: Optional[int] = None, min_length: int | None = _Unset,
decimal_places: Optional[int] = None, max_length: int | None = _Unset,
min_items: Optional[int] = None, # custom
max_items: Optional[int] = None,
unique_items: Optional[bool] = None,
min_length: Optional[int] = None,
max_length: Optional[int] = None,
allow_mutation: bool = True,
regex: Optional[str] = None,
discriminator: Optional[str] = None,
repr: bool = True,
input: Input = Input.Any, input: Input = Input.Any,
ui_type: Optional[UIType] = None, ui_type: Optional[UIType] = None,
ui_component: Optional[UIComponent] = None, ui_component: Optional[UIComponent] = None,
@ -263,7 +258,6 @@ def InputField(
ui_order: Optional[int] = None, ui_order: Optional[int] = None,
ui_choice_labels: Optional[dict[str, str]] = None, ui_choice_labels: Optional[dict[str, str]] = None,
item_default: Optional[Any] = None, item_default: Optional[Any] = None,
**kwargs: Any,
) -> Any: ) -> Any:
""" """
Creates an input field for an invocation. Creates an input field for an invocation.
@ -293,18 +287,26 @@ def InputField(
: 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. \ : param bool item_default: [None] Specifies the default item value, if this is a collection input. \
Ignored for non-collection fields.. Ignored for non-collection fields.
""" """
return Field(
*args, json_schema_extra_: dict[str, Any] = dict(
input=input,
ui_type=ui_type,
ui_component=ui_component,
ui_hidden=ui_hidden,
ui_order=ui_order,
item_default=item_default,
ui_choice_labels=ui_choice_labels,
)
field_args = dict(
default=default, default=default,
default_factory=default_factory, default_factory=default_factory,
alias=alias,
title=title, title=title,
description=description, description=description,
exclude=exclude, pattern=pattern,
include=include, strict=strict,
const=const,
gt=gt, gt=gt,
ge=ge, ge=ge,
lt=lt, lt=lt,
@ -313,57 +315,92 @@ def InputField(
allow_inf_nan=allow_inf_nan, allow_inf_nan=allow_inf_nan,
max_digits=max_digits, max_digits=max_digits,
decimal_places=decimal_places, decimal_places=decimal_places,
min_items=min_items,
max_items=max_items,
unique_items=unique_items,
min_length=min_length, min_length=min_length,
max_length=max_length, max_length=max_length,
allow_mutation=allow_mutation, )
regex=regex,
discriminator=discriminator, """
repr=repr, Invocation definitions have their fields typed correctly for their `invoke()` functions.
input=input, This typing is often more specific than the actual invocation definition requires, because
ui_type=ui_type, fields may have values provided only by connections.
ui_component=ui_component,
ui_hidden=ui_hidden, For example, consider an ResizeImageInvocation with an `image: ImageField` field.
ui_order=ui_order,
item_default=item_default, `image` is required during the call to `invoke()`, but when the python class is instantiated,
ui_choice_labels=ui_choice_labels, the field may not be present. This is fine, because that image field will be provided by a
**kwargs, 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
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
if default is PydanticUndefined and default_factory is PydanticUndefined:
json_schema_extra_.update(dict(orig_required=True))
else:
json_schema_extra_.update(dict(orig_required=False))
# make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined:
default_ = None if default is PydanticUndefined else default
provided_args.update(dict(default=default_))
if default is not PydanticUndefined:
# before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value
json_schema_extra_.update(dict(default=default))
json_schema_extra_.update(dict(orig_default=default))
elif default is not PydanticUndefined and default_factory is PydanticUndefined:
default_ = default
provided_args.update(dict(default=default_))
json_schema_extra_.update(dict(orig_default=default_))
elif default_factory is not PydanticUndefined:
provided_args.update(dict(default_factory=default_factory))
# TODO: cannot serialize default_factory...
# json_schema_extra_.update(dict(orig_default_factory=default_factory))
return Field(
**provided_args,
json_schema_extra=json_schema_extra_,
) )
def OutputField( def OutputField(
*args: Any, # copied from pydantic's Field
default: Any = Undefined, default: Any = _Unset,
default_factory: Optional[NoArgAnyCallable] = None, default_factory: Callable[[], Any] | None = _Unset,
alias: Optional[str] = None, title: str | None = _Unset,
title: Optional[str] = None, description: str | None = _Unset,
description: Optional[str] = None, pattern: str | None = _Unset,
exclude: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None, strict: bool | None = _Unset,
include: Optional[Union[AbstractSet[Union[int, str]], Mapping[Union[int, str], Any], Any]] = None, gt: float | None = _Unset,
const: Optional[bool] = None, ge: float | None = _Unset,
gt: Optional[float] = None, lt: float | None = _Unset,
ge: Optional[float] = None, le: float | None = _Unset,
lt: Optional[float] = None, multiple_of: float | None = _Unset,
le: Optional[float] = None, allow_inf_nan: bool | None = _Unset,
multiple_of: Optional[float] = None, max_digits: int | None = _Unset,
allow_inf_nan: Optional[bool] = None, decimal_places: int | None = _Unset,
max_digits: Optional[int] = None, min_length: int | None = _Unset,
decimal_places: Optional[int] = None, max_length: int | None = _Unset,
min_items: Optional[int] = None, # custom
max_items: Optional[int] = None,
unique_items: Optional[bool] = None,
min_length: Optional[int] = None,
max_length: Optional[int] = None,
allow_mutation: bool = True,
regex: Optional[str] = None,
discriminator: Optional[str] = None,
repr: bool = True,
ui_type: Optional[UIType] = None, ui_type: Optional[UIType] = None,
ui_hidden: bool = False, ui_hidden: bool = False,
ui_order: Optional[int] = None, ui_order: Optional[int] = None,
**kwargs: Any,
) -> Any: ) -> Any:
""" """
Creates an output field for an invocation output. Creates an output field for an invocation output.
@ -383,15 +420,12 @@ def OutputField(
: 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( return Field(
*args,
default=default, default=default,
default_factory=default_factory, default_factory=default_factory,
alias=alias,
title=title, title=title,
description=description, description=description,
exclude=exclude, pattern=pattern,
include=include, strict=strict,
const=const,
gt=gt, gt=gt,
ge=ge, ge=ge,
lt=lt, lt=lt,
@ -400,19 +434,13 @@ def OutputField(
allow_inf_nan=allow_inf_nan, allow_inf_nan=allow_inf_nan,
max_digits=max_digits, max_digits=max_digits,
decimal_places=decimal_places, decimal_places=decimal_places,
min_items=min_items,
max_items=max_items,
unique_items=unique_items,
min_length=min_length, min_length=min_length,
max_length=max_length, max_length=max_length,
allow_mutation=allow_mutation, json_schema_extra=dict(
regex=regex, ui_type=ui_type,
discriminator=discriminator, ui_hidden=ui_hidden,
repr=repr, ui_order=ui_order,
ui_type=ui_type, ),
ui_hidden=ui_hidden,
ui_order=ui_order,
**kwargs,
) )
@ -426,7 +454,13 @@ class UIConfigBase(BaseModel):
title: Optional[str] = Field(default=None, description="The node's display name") title: Optional[str] = Field(default=None, description="The node's display name")
category: Optional[str] = Field(default=None, description="The node's category") category: Optional[str] = Field(default=None, description="The node's category")
version: Optional[str] = Field( version: Optional[str] = Field(
default=None, description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".' default=None,
description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".',
)
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
) )
@ -461,23 +495,38 @@ class BaseInvocationOutput(BaseModel):
All invocation outputs must use the `@invocation_output` decorator to provide their unique type. All invocation outputs must use the `@invocation_output` decorator to provide their unique type.
""" """
@classmethod _output_classes: ClassVar[set[BaseInvocationOutput]] = set()
def get_all_subclasses_tuple(cls):
subclasses = []
toprocess = [cls]
while len(toprocess) > 0:
next = toprocess.pop(0)
next_subclasses = next.__subclasses__()
subclasses.extend(next_subclasses)
toprocess.extend(next_subclasses)
return tuple(subclasses)
class Config: @classmethod
@staticmethod def register_output(cls, output: BaseInvocationOutput) -> None:
def schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None: cls._output_classes.add(output)
if "required" not in schema or not isinstance(schema["required"], list):
schema["required"] = list() @classmethod
schema["required"].extend(["type"]) def get_outputs(cls) -> Iterable[BaseInvocationOutput]:
return cls._output_classes
@classmethod
def get_outputs_union(cls) -> UnionType:
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 map(lambda i: get_type(i), BaseInvocationOutput.get_outputs())
@staticmethod
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
# Because we use a pydantic Literal field with default value for the invocation type,
# it will be typed as optional in the OpenAPI schema. Make it required manually.
if "required" not in schema or not isinstance(schema["required"], list):
schema["required"] = list()
schema["required"].extend(["type"])
model_config = ConfigDict(
validate_assignment=True,
json_schema_serialization_defaults_required=True,
json_schema_extra=json_schema_extra,
)
class RequiredConnectionException(Exception): class RequiredConnectionException(Exception):
@ -502,104 +551,91 @@ class BaseInvocation(ABC, BaseModel):
All invocations must use the `@invocation` decorator to provide their unique type. All invocations must use the `@invocation` decorator to provide their unique type.
""" """
_invocation_classes: ClassVar[set[BaseInvocation]] = set()
@classmethod @classmethod
def get_all_subclasses(cls): def register_invocation(cls, invocation: BaseInvocation) -> None:
cls._invocation_classes.add(invocation)
@classmethod
def get_invocations_union(cls) -> UnionType:
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]:
app_config = InvokeAIAppConfig.get_config() app_config = InvokeAIAppConfig.get_config()
subclasses = [] allowed_invocations: set[BaseInvocation] = set()
toprocess = [cls] for sc in cls._invocation_classes:
while len(toprocess) > 0: invocation_type = get_type(sc)
next = toprocess.pop(0)
next_subclasses = next.__subclasses__()
subclasses.extend(next_subclasses)
toprocess.extend(next_subclasses)
allowed_invocations = []
for sc in subclasses:
is_in_allowlist = ( is_in_allowlist = (
sc.__fields__.get("type").default in app_config.allow_nodes invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True
if isinstance(app_config.allow_nodes, list)
else True
) )
is_in_denylist = ( is_in_denylist = (
sc.__fields__.get("type").default in app_config.deny_nodes invocation_type in app_config.deny_nodes if isinstance(app_config.deny_nodes, list) else False
if isinstance(app_config.deny_nodes, list)
else False
) )
if is_in_allowlist and not is_in_denylist: if is_in_allowlist and not is_in_denylist:
allowed_invocations.append(sc) allowed_invocations.add(sc)
return allowed_invocations return allowed_invocations
@classmethod @classmethod
def get_invocations(cls): def get_invocations_map(cls) -> dict[str, BaseInvocation]:
return tuple(BaseInvocation.get_all_subclasses())
@classmethod
def get_invocations_map(cls):
# Get the type strings out of the literals and into a dictionary # Get the type strings out of the literals and into a dictionary
return dict( return dict(
map( map(
lambda t: (get_args(get_type_hints(t)["type"])[0], t), lambda i: (get_type(i), i),
BaseInvocation.get_all_subclasses(), BaseInvocation.get_invocations(),
) )
) )
@classmethod @classmethod
def get_output_type(cls): def get_invocation_types(cls) -> Iterable[str]:
return map(lambda i: get_type(i), BaseInvocation.get_invocations())
@classmethod
def get_output_type(cls) -> BaseInvocationOutput:
return signature(cls.invoke).return_annotation return signature(cls.invoke).return_annotation
class Config: @staticmethod
validate_assignment = True def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
validate_all = True # Add the various UI-facing attributes to the schema. These are used to build the invocation templates.
uiconfig = getattr(model_class, "UIConfig", None)
@staticmethod if uiconfig and hasattr(uiconfig, "title"):
def schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None: schema["title"] = uiconfig.title
uiconfig = getattr(model_class, "UIConfig", None) if uiconfig and hasattr(uiconfig, "tags"):
if uiconfig and hasattr(uiconfig, "title"): schema["tags"] = uiconfig.tags
schema["title"] = uiconfig.title if uiconfig and hasattr(uiconfig, "category"):
if uiconfig and hasattr(uiconfig, "tags"): schema["category"] = uiconfig.category
schema["tags"] = uiconfig.tags if uiconfig and hasattr(uiconfig, "version"):
if uiconfig and hasattr(uiconfig, "category"): schema["version"] = uiconfig.version
schema["category"] = uiconfig.category if "required" not in schema or not isinstance(schema["required"], list):
if uiconfig and hasattr(uiconfig, "version"): schema["required"] = list()
schema["version"] = uiconfig.version schema["required"].extend(["type", "id"])
if "required" not in schema or not isinstance(schema["required"], list):
schema["required"] = list()
schema["required"].extend(["type", "id"])
@abstractmethod @abstractmethod
def invoke(self, context: InvocationContext) -> BaseInvocationOutput: def invoke(self, context: InvocationContext) -> BaseInvocationOutput:
"""Invoke with provided context and return outputs.""" """Invoke with provided context and return outputs."""
pass pass
def __init__(self, **data):
# nodes may have required fields, that can accept input from connections
# on instantiation of the model, we need to exclude these from validation
restore = dict()
try:
field_names = list(self.__fields__.keys())
for field_name in field_names:
# if the field is required and may get its value from a connection, exclude it from validation
field = self.__fields__[field_name]
_input = field.field_info.extra.get("input", None)
if _input in [Input.Connection, Input.Any] and field.required:
if field_name not in data:
restore[field_name] = self.__fields__.pop(field_name)
# instantiate the node, which will validate the data
super().__init__(**data)
finally:
# restore the removed fields
for field_name, field in restore.items():
self.__fields__[field_name] = field
def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput: def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
for field_name, field in self.__fields__.items(): for field_name, field in self.model_fields.items():
_input = field.field_info.extra.get("input", None) if not field.json_schema_extra or callable(field.json_schema_extra):
if field.required and not hasattr(self, field_name): # something has gone terribly awry, we should always have this and it should be a dict
if _input == Input.Connection: continue
raise RequiredConnectionException(self.__fields__["type"].default, field_name)
elif _input == Input.Any: # Here we handle the case where the field is optional in the pydantic class, but required
raise MissingInputException(self.__fields__["type"].default, field_name) # in the `invoke()` method.
orig_default = field.json_schema_extra.get("orig_default", PydanticUndefined)
orig_required = field.json_schema_extra.get("orig_required", True)
input_ = field.json_schema_extra.get("input", None)
if orig_default is not PydanticUndefined and not hasattr(self, field_name):
setattr(self, field_name, orig_default)
if orig_required and orig_default is PydanticUndefined and getattr(self, field_name) is None:
if input_ == Input.Connection:
raise RequiredConnectionException(self.model_fields["type"].default, field_name)
elif input_ == Input.Any:
raise MissingInputException(self.model_fields["type"].default, field_name)
# skip node cache codepath if it's disabled # skip node cache codepath if it's disabled
if context.services.configuration.node_cache_size == 0: if context.services.configuration.node_cache_size == 0:
@ -622,23 +658,31 @@ class BaseInvocation(ABC, BaseModel):
return self.invoke(context) return self.invoke(context)
def get_type(self) -> str: def get_type(self) -> str:
return self.__fields__["type"].default return self.model_fields["type"].default
id: str = Field( id: str = Field(
description="The id of this instance of an invocation. Must be unique among all instances of invocations." default_factory=uuid_string,
description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
) )
is_intermediate: bool = InputField( is_intermediate: Optional[bool] = Field(
default=False, description="Whether or not this is an intermediate invocation.", ui_type=UIType.IsIntermediate default=False,
description="Whether or not this is an intermediate invocation.",
json_schema_extra=dict(ui_type=UIType.IsIntermediate),
) )
workflow: Optional[str] = InputField( workflow: Optional[str] = Field(
default=None, default=None,
description="The workflow to save with the image", description="The workflow to save with the image",
ui_type=UIType.WorkflowField, json_schema_extra=dict(ui_type=UIType.WorkflowField),
)
use_cache: Optional[bool] = Field(
default=True,
description="Whether or not to use the cache",
) )
use_cache: bool = InputField(default=True, description="Whether or not to use the cache")
@validator("workflow", pre=True) @field_validator("workflow", mode="before")
@classmethod
def validate_workflow_is_json(cls, v): def validate_workflow_is_json(cls, v):
"""We don't have a workflow schema in the backend, so we just check that it's valid JSON"""
if v is None: if v is None:
return None return None
try: try:
@ -649,8 +693,14 @@ class BaseInvocation(ABC, BaseModel):
UIConfig: ClassVar[Type[UIConfigBase]] UIConfig: ClassVar[Type[UIConfigBase]]
model_config = ConfigDict(
validate_assignment=True,
json_schema_extra=json_schema_extra,
json_schema_serialization_defaults_required=True,
)
GenericBaseInvocation = TypeVar("GenericBaseInvocation", bound=BaseInvocation)
TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
def invocation( def invocation(
@ -660,7 +710,7 @@ def invocation(
category: Optional[str] = None, category: Optional[str] = None,
version: Optional[str] = None, version: Optional[str] = None,
use_cache: Optional[bool] = True, use_cache: Optional[bool] = True,
) -> Callable[[Type[GenericBaseInvocation]], Type[GenericBaseInvocation]]: ) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]:
""" """
Adds metadata to an invocation. Adds metadata to an invocation.
@ -672,12 +722,15 @@ def invocation(
:param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor. :param Optional[bool] use_cache: Whether or not to use the invocation cache. Defaults to True. The user may override this in the workflow editor.
""" """
def wrapper(cls: Type[GenericBaseInvocation]) -> Type[GenericBaseInvocation]: def wrapper(cls: Type[TBaseInvocation]) -> Type[TBaseInvocation]:
# Validate invocation types on creation of invocation classes # Validate invocation types on creation of invocation classes
# TODO: ensure unique? # TODO: ensure unique?
if re.compile(r"^\S+$").match(invocation_type) is None: if re.compile(r"^\S+$").match(invocation_type) is None:
raise ValueError(f'"invocation_type" must consist of non-whitespace characters, got "{invocation_type}"') raise ValueError(f'"invocation_type" must consist of non-whitespace characters, got "{invocation_type}"')
if invocation_type in BaseInvocation.get_invocation_types():
raise ValueError(f'Invocation type "{invocation_type}" already exists')
# Add OpenAPI schema extras # Add OpenAPI schema extras
uiconf_name = cls.__qualname__ + ".UIConfig" uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name: if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
@ -695,59 +748,83 @@ def invocation(
raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
cls.UIConfig.version = version cls.UIConfig.version = version
if use_cache is not None: if use_cache is not None:
cls.__fields__["use_cache"].default = use_cache cls.model_fields["use_cache"].default = use_cache
# Add the invocation type to the model.
# You'd be tempted to just add the type field and rebuild the model, like this:
# cls.model_fields.update(type=FieldInfo.from_annotated_attribute(Literal[invocation_type], invocation_type))
# cls.model_rebuild() or cls.model_rebuild(force=True)
# Unfortunately, because the `GraphInvocation` uses a forward ref in its `graph` field's annotation, this does
# not work. Instead, we have to create a new class with the type field and patch the original class with it.
# Add the invocation type to the pydantic model of the invocation
invocation_type_annotation = Literal[invocation_type] # type: ignore invocation_type_annotation = Literal[invocation_type] # type: ignore
invocation_type_field = ModelField.infer( invocation_type_field = Field(
name="type", title="type",
value=invocation_type, default=invocation_type,
annotation=invocation_type_annotation,
class_validators=None,
config=cls.__config__,
) )
cls.__fields__.update({"type": invocation_type_field})
# to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html docstring = cls.__doc__
if annotations := cls.__dict__.get("__annotations__", None): cls = create_model(
annotations.update({"type": invocation_type_annotation}) cls.__qualname__,
__base__=cls,
__module__=cls.__module__,
type=(invocation_type_annotation, invocation_type_field),
)
cls.__doc__ = docstring
# TODO: how to type this correctly? it's typed as ModelMetaclass, a private class in pydantic
BaseInvocation.register_invocation(cls) # type: ignore
return cls return cls
return wrapper return wrapper
GenericBaseInvocationOutput = TypeVar("GenericBaseInvocationOutput", bound=BaseInvocationOutput) TBaseInvocationOutput = TypeVar("TBaseInvocationOutput", bound=BaseInvocationOutput)
def invocation_output( def invocation_output(
output_type: str, output_type: str,
) -> Callable[[Type[GenericBaseInvocationOutput]], Type[GenericBaseInvocationOutput]]: ) -> Callable[[Type[TBaseInvocationOutput]], Type[TBaseInvocationOutput]]:
""" """
Adds metadata to an invocation output. Adds metadata to an invocation output.
:param str output_type: The type of the invocation output. Must be unique among all invocation outputs. :param str output_type: The type of the invocation output. Must be unique among all invocation outputs.
""" """
def wrapper(cls: Type[GenericBaseInvocationOutput]) -> Type[GenericBaseInvocationOutput]: def wrapper(cls: Type[TBaseInvocationOutput]) -> Type[TBaseInvocationOutput]:
# Validate output types on creation of invocation output classes # Validate output types on creation of invocation output classes
# TODO: ensure unique? # TODO: ensure unique?
if re.compile(r"^\S+$").match(output_type) is None: if re.compile(r"^\S+$").match(output_type) is None:
raise ValueError(f'"output_type" must consist of non-whitespace characters, got "{output_type}"') raise ValueError(f'"output_type" must consist of non-whitespace characters, got "{output_type}"')
# Add the output type to the pydantic model of the invocation output if output_type in BaseInvocationOutput.get_output_types():
output_type_annotation = Literal[output_type] # type: ignore raise ValueError(f'Invocation type "{output_type}" already exists')
output_type_field = ModelField.infer(
name="type",
value=output_type,
annotation=output_type_annotation,
class_validators=None,
config=cls.__config__,
)
cls.__fields__.update({"type": output_type_field})
# to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html # Add the output type to the model.
if annotations := cls.__dict__.get("__annotations__", None):
annotations.update({"type": output_type_annotation}) output_type_annotation = Literal[output_type] # type: ignore
output_type_field = Field(
title="type",
default=output_type,
)
docstring = cls.__doc__
cls = create_model(
cls.__qualname__,
__base__=cls,
__module__=cls.__module__,
type=(output_type_annotation, output_type_field),
)
cls.__doc__ = docstring
BaseInvocationOutput.register_output(cls) # type: ignore # TODO: how to type this correctly?
return cls return cls
return wrapper return wrapper
GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel)

8
invokeai/app/invocations/collections.py
View File

@ -2,7 +2,7 @@
import numpy as np import numpy as np
from pydantic import validator from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import IntegerCollectionOutput 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, get_random_seed
@ -20,9 +20,9 @@ class RangeInvocation(BaseInvocation):
stop: int = InputField(default=10, description="The stop of the range") stop: int = InputField(default=10, description="The stop of the range")
step: int = InputField(default=1, description="The step of the range") step: int = InputField(default=1, description="The step of the range")
@validator("stop") @field_validator("stop")
def stop_gt_start(cls, v, values): def stop_gt_start(cls, v: int, info: ValidationInfo):
if "start" in values and v <= values["start"]: if "start" in info.data and v <= info.data["start"]:
raise ValueError("stop must be greater than start") raise ValueError("stop must be greater than start")
return v return v

70
invokeai/app/invocations/compel.py
View File

@ -1,6 +1,6 @@
import re import re
from dataclasses import dataclass from dataclasses import dataclass
from typing import List, Union from typing import List, Optional, Union
import torch import torch
from compel import Compel, ReturnedEmbeddingsType from compel import Compel, ReturnedEmbeddingsType
@ -43,7 +43,13 @@ class ConditioningFieldData:
# PerpNeg = "perp_neg" # PerpNeg = "perp_neg"
@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning", version="1.0.0") @invocation(
"compel",
title="Prompt",
tags=["prompt", "compel"],
category="conditioning",
version="1.0.0",
)
class CompelInvocation(BaseInvocation): class CompelInvocation(BaseInvocation):
"""Parse prompt using compel package to conditioning.""" """Parse prompt using compel package to conditioning."""
@ -61,17 +67,19 @@ class CompelInvocation(BaseInvocation):
@torch.no_grad() @torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(), **self.clip.tokenizer.model_dump(),
context=context, context=context,
) )
text_encoder_info = context.services.model_manager.get_model( text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(), **self.clip.text_encoder.model_dump(),
context=context, context=context,
) )
def _lora_loader(): def _lora_loader():
for lora in self.clip.loras: for lora in self.clip.loras:
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context) lora_info = context.services.model_manager.get_model(
**lora.model_dump(exclude={"weight"}), context=context
)
yield (lora_info.context.model, lora.weight) yield (lora_info.context.model, lora.weight)
del lora_info del lora_info
return return
@ -160,11 +168,11 @@ class SDXLPromptInvocationBase:
zero_on_empty: bool, zero_on_empty: bool,
): ):
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(), **clip_field.tokenizer.model_dump(),
context=context, context=context,
) )
text_encoder_info = context.services.model_manager.get_model( text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(), **clip_field.text_encoder.model_dump(),
context=context, context=context,
) )
@ -172,7 +180,11 @@ class SDXLPromptInvocationBase:
if prompt == "" and zero_on_empty: if prompt == "" and zero_on_empty:
cpu_text_encoder = text_encoder_info.context.model cpu_text_encoder = text_encoder_info.context.model
c = torch.zeros( c = torch.zeros(
(1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size), (
1,
cpu_text_encoder.config.max_position_embeddings,
cpu_text_encoder.config.hidden_size,
),
dtype=text_encoder_info.context.cache.precision, dtype=text_encoder_info.context.cache.precision,
) )
if get_pooled: if get_pooled:
@ -186,7 +198,9 @@ class SDXLPromptInvocationBase:
def _lora_loader(): def _lora_loader():
for lora in clip_field.loras: for lora in clip_field.loras:
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context) lora_info = context.services.model_manager.get_model(
**lora.model_dump(exclude={"weight"}), context=context
)
yield (lora_info.context.model, lora.weight) yield (lora_info.context.model, lora.weight)
del lora_info del lora_info
return return
@ -273,8 +287,16 @@ class SDXLPromptInvocationBase:
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase): class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning.""" """Parse prompt using compel package to conditioning."""
prompt: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea) prompt: str = InputField(
style: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea) default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
)
style: str = InputField(
default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
)
original_width: int = InputField(default=1024, description="") original_width: int = InputField(default=1024, description="")
original_height: int = InputField(default=1024, description="") original_height: int = InputField(default=1024, description="")
crop_top: int = InputField(default=0, description="") crop_top: int = InputField(default=0, description="")
@ -310,7 +332,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
[ [
c1, c1,
torch.zeros( torch.zeros(
(c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]), device=c1.device, dtype=c1.dtype (c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]),
device=c1.device,
dtype=c1.dtype,
), ),
], ],
dim=1, dim=1,
@ -321,7 +345,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
[ [
c2, c2,
torch.zeros( torch.zeros(
(c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]), device=c2.device, dtype=c2.dtype (c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]),
device=c2.device,
dtype=c2.dtype,
), ),
], ],
dim=1, dim=1,
@ -359,7 +385,9 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
"""Parse prompt using compel package to conditioning.""" """Parse prompt using compel package to conditioning."""
style: str = InputField( style: str = InputField(
default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
) # TODO: ? ) # TODO: ?
original_width: int = InputField(default=1024, description="") original_width: int = InputField(default=1024, description="")
original_height: int = InputField(default=1024, description="") original_height: int = InputField(default=1024, description="")
@ -403,10 +431,16 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
class ClipSkipInvocationOutput(BaseInvocationOutput): class ClipSkipInvocationOutput(BaseInvocationOutput):
"""Clip skip node output""" """Clip skip node output"""
clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP") clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning", version="1.0.0") @invocation(
"clip_skip",
title="CLIP Skip",
tags=["clipskip", "clip", "skip"],
category="conditioning",
version="1.0.0",
)
class ClipSkipInvocation(BaseInvocation): class ClipSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model.""" """Skip layers in clip text_encoder model."""
@ -421,7 +455,9 @@ class ClipSkipInvocation(BaseInvocation):
def get_max_token_count( def get_max_token_count(
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False tokenizer,
prompt: Union[FlattenedPrompt, Blend, Conjunction],
truncate_if_too_long=False,
) -> int: ) -> int:
if type(prompt) is Blend: if type(prompt) is Blend:
blend: Blend = prompt blend: Blend = prompt

26
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -2,7 +2,7 @@
# initial implementation by Gregg Helt, 2023 # initial implementation by Gregg Helt, 2023
# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux # heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
from builtins import bool, float from builtins import bool, float
from typing import Dict, List, Literal, Optional, Union from typing import Dict, List, Literal, Union
import cv2 import cv2
import numpy as np import numpy as np
@ -24,12 +24,12 @@ from controlnet_aux import (
) )
from controlnet_aux.util import HWC3, ade_palette from controlnet_aux.util import HWC3, ade_palette
from PIL import Image from PIL import Image
from pydantic import BaseModel, Field, validator from pydantic import BaseModel, ConfigDict, Field, field_validator
from invokeai.app.invocations.primitives import ImageField, ImageOutput from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from ...backend.model_management import BaseModelType from ...backend.model_management import BaseModelType
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import ( from .baseinvocation import (
BaseInvocation, BaseInvocation,
BaseInvocationOutput, BaseInvocationOutput,
@ -57,6 +57,8 @@ class ControlNetModelField(BaseModel):
model_name: str = Field(description="Name of the ControlNet model") model_name: str = Field(description="Name of the ControlNet model")
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class ControlField(BaseModel): class ControlField(BaseModel):
image: ImageField = Field(description="The control image") image: ImageField = Field(description="The control image")
@ -71,7 +73,7 @@ class ControlField(BaseModel):
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use") control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use") resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@validator("control_weight") @field_validator("control_weight")
def validate_control_weight(cls, v): def validate_control_weight(cls, v):
"""Validate that all control weights in the valid range""" """Validate that all control weights in the valid range"""
if isinstance(v, list): if isinstance(v, list):
@ -124,9 +126,7 @@ class ControlNetInvocation(BaseInvocation):
) )
@invocation( # This invocation exists for other invocations to subclass it - do not register with @invocation!
"image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet", version="1.0.0"
)
class ImageProcessorInvocation(BaseInvocation): class ImageProcessorInvocation(BaseInvocation):
"""Base class for invocations that preprocess images for ControlNet""" """Base class for invocations that preprocess images for ControlNet"""
@ -393,9 +393,9 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res) detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res) image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
h: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `h` parameter") h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
w: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `w` parameter") w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
f: Optional[int] = InputField(default=256, ge=0, description="Content shuffle `f` parameter") f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
def run_processor(self, image): def run_processor(self, image):
content_shuffle_processor = ContentShuffleDetector() content_shuffle_processor = ContentShuffleDetector()
@ -575,14 +575,14 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
def run_processor(self, image: Image.Image): def run_processor(self, image: Image.Image):
image = image.convert("RGB") image = image.convert("RGB")
image = np.array(image, dtype=np.uint8) np_image = np.array(image, dtype=np.uint8)
height, width = image.shape[:2] height, width = np_image.shape[:2]
width_tile_size = min(self.color_map_tile_size, width) width_tile_size = min(self.color_map_tile_size, width)
height_tile_size = min(self.color_map_tile_size, height) height_tile_size = min(self.color_map_tile_size, height)
color_map = cv2.resize( color_map = cv2.resize(
image, np_image,
(width // width_tile_size, height // height_tile_size), (width // width_tile_size, height // height_tile_size),
interpolation=cv2.INTER_CUBIC, interpolation=cv2.INTER_CUBIC,
) )

2
invokeai/app/invocations/cv.py
View File

@ -6,7 +6,7 @@ import numpy
from PIL import Image, ImageOps from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ImageField, ImageOutput from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.models.image import ImageCategory, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation

114
invokeai/app/invocations/facetools.py
View File

@ -8,7 +8,7 @@ import numpy as np
from mediapipe.python.solutions.face_mesh import FaceMesh # type: ignore[import] from mediapipe.python.solutions.face_mesh import FaceMesh # type: ignore[import]
from PIL import Image, ImageDraw, ImageFilter, ImageFont, ImageOps from PIL import Image, ImageDraw, ImageFilter, ImageFont, ImageOps
from PIL.Image import Image as ImageType from PIL.Image import Image as ImageType
from pydantic import validator from pydantic import field_validator
import invokeai.assets.fonts as font_assets import invokeai.assets.fonts as font_assets
from invokeai.app.invocations.baseinvocation import ( from invokeai.app.invocations.baseinvocation import (
@ -20,7 +20,7 @@ from invokeai.app.invocations.baseinvocation import (
invocation_output, invocation_output,
) )
from invokeai.app.invocations.primitives import ImageField, ImageOutput from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.models.image import ImageCategory, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
@invocation_output("face_mask_output") @invocation_output("face_mask_output")
@ -46,6 +46,8 @@ class FaceResultData(TypedDict):
y_center: float y_center: float
mesh_width: int mesh_width: int
mesh_height: int mesh_height: int
chunk_x_offset: int
chunk_y_offset: int
class FaceResultDataWithId(FaceResultData): class FaceResultDataWithId(FaceResultData):
@ -78,6 +80,48 @@ FONT_SIZE = 32
FONT_STROKE_WIDTH = 4 FONT_STROKE_WIDTH = 4
def coalesce_faces(face1: FaceResultData, face2: FaceResultData) -> FaceResultData:
face1_x_offset = face1["chunk_x_offset"] - min(face1["chunk_x_offset"], face2["chunk_x_offset"])
face2_x_offset = face2["chunk_x_offset"] - min(face1["chunk_x_offset"], face2["chunk_x_offset"])
face1_y_offset = face1["chunk_y_offset"] - min(face1["chunk_y_offset"], face2["chunk_y_offset"])
face2_y_offset = face2["chunk_y_offset"] - min(face1["chunk_y_offset"], face2["chunk_y_offset"])
new_im_width = (
max(face1["image"].width, face2["image"].width)
+ max(face1["chunk_x_offset"], face2["chunk_x_offset"])
- min(face1["chunk_x_offset"], face2["chunk_x_offset"])
)
new_im_height = (
max(face1["image"].height, face2["image"].height)
+ max(face1["chunk_y_offset"], face2["chunk_y_offset"])
- min(face1["chunk_y_offset"], face2["chunk_y_offset"])
)
pil_image = Image.new(mode=face1["image"].mode, size=(new_im_width, new_im_height))
pil_image.paste(face1["image"], (face1_x_offset, face1_y_offset))
pil_image.paste(face2["image"], (face2_x_offset, face2_y_offset))
# Mask images are always from the origin
new_mask_im_width = max(face1["mask"].width, face2["mask"].width)
new_mask_im_height = max(face1["mask"].height, face2["mask"].height)
mask_pil = create_white_image(new_mask_im_width, new_mask_im_height)
black_image = create_black_image(face1["mask"].width, face1["mask"].height)
mask_pil.paste(black_image, (0, 0), ImageOps.invert(face1["mask"]))
black_image = create_black_image(face2["mask"].width, face2["mask"].height)
mask_pil.paste(black_image, (0, 0), ImageOps.invert(face2["mask"]))
new_face = FaceResultData(
image=pil_image,
mask=mask_pil,
x_center=max(face1["x_center"], face2["x_center"]),
y_center=max(face1["y_center"], face2["y_center"]),
mesh_width=max(face1["mesh_width"], face2["mesh_width"]),
mesh_height=max(face1["mesh_height"], face2["mesh_height"]),
chunk_x_offset=max(face1["chunk_x_offset"], face2["chunk_x_offset"]),
chunk_y_offset=max(face2["chunk_y_offset"], face2["chunk_y_offset"]),
)
return new_face
def prepare_faces_list( def prepare_faces_list(
face_result_list: list[FaceResultData], face_result_list: list[FaceResultData],
) -> list[FaceResultDataWithId]: ) -> list[FaceResultDataWithId]:
@ -91,7 +135,7 @@ def prepare_faces_list(
should_add = True should_add = True
candidate_x_center = candidate["x_center"] candidate_x_center = candidate["x_center"]
candidate_y_center = candidate["y_center"] candidate_y_center = candidate["y_center"]
for face in deduped_faces: for idx, face in enumerate(deduped_faces):
face_center_x = face["x_center"] face_center_x = face["x_center"]
face_center_y = face["y_center"] face_center_y = face["y_center"]
face_radius_w = face["mesh_width"] / 2 face_radius_w = face["mesh_width"] / 2
@ -105,6 +149,7 @@ def prepare_faces_list(
) )
if p < 1: # Inside of the already-added face's radius if p < 1: # Inside of the already-added face's radius
deduped_faces[idx] = coalesce_faces(face, candidate)
should_add = False should_add = False
break break
@ -138,7 +183,6 @@ def generate_face_box_mask(
chunk_x_offset: int = 0, chunk_x_offset: int = 0,
chunk_y_offset: int = 0, chunk_y_offset: int = 0,
draw_mesh: bool = True, draw_mesh: bool = True,
check_bounds: bool = True,
) -> list[FaceResultData]: ) -> list[FaceResultData]:
result = [] result = []
mask_pil = None mask_pil = None
@ -211,33 +255,20 @@ def generate_face_box_mask(
mask_pil = create_white_image(w + chunk_x_offset, h + chunk_y_offset) mask_pil = create_white_image(w + chunk_x_offset, h + chunk_y_offset)
mask_pil.paste(init_mask_pil, (chunk_x_offset, chunk_y_offset)) mask_pil.paste(init_mask_pil, (chunk_x_offset, chunk_y_offset))
left_side = x_center - mesh_width x_center = float(x_center)
right_side = x_center + mesh_width y_center = float(y_center)
top_side = y_center - mesh_height face = FaceResultData(
bottom_side = y_center + mesh_height image=pil_image,
im_width, im_height = pil_image.size mask=mask_pil or create_white_image(*pil_image.size),
over_w = im_width * 0.1 x_center=x_center + chunk_x_offset,
over_h = im_height * 0.1 y_center=y_center + chunk_y_offset,
if not check_bounds or ( mesh_width=mesh_width,
(left_side >= -over_w) mesh_height=mesh_height,
and (right_side < im_width + over_w) chunk_x_offset=chunk_x_offset,
and (top_side >= -over_h) chunk_y_offset=chunk_y_offset,
and (bottom_side < im_height + over_h) )
):
x_center = float(x_center)
y_center = float(y_center)
face = FaceResultData(
image=pil_image,
mask=mask_pil or create_white_image(*pil_image.size),
x_center=x_center + chunk_x_offset,
y_center=y_center + chunk_y_offset,
mesh_width=mesh_width,
mesh_height=mesh_height,
)
result.append(face) result.append(face)
else:
context.services.logger.info("FaceTools --> Face out of bounds, ignoring.")
return result return result
@ -346,7 +377,6 @@ def get_faces_list(
chunk_x_offset=0, chunk_x_offset=0,
chunk_y_offset=0, chunk_y_offset=0,
draw_mesh=draw_mesh, draw_mesh=draw_mesh,
check_bounds=False,
) )
if should_chunk or len(result) == 0: if should_chunk or len(result) == 0:
context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).") context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
@ -360,24 +390,26 @@ def get_faces_list(
if width > height: if width > height:
# Landscape - slice the image horizontally # Landscape - slice the image horizontally
fx = 0.0 fx = 0.0
steps = int(width * 2 / height) steps = int(width * 2 / height) + 1
increment = (width - height) / (steps - 1)
while fx <= (width - height): while fx <= (width - height):
x = int(fx) x = int(fx)
image_chunks.append(image.crop((x, 0, x + height - 1, height - 1))) image_chunks.append(image.crop((x, 0, x + height, height)))
x_offsets.append(x) x_offsets.append(x)
y_offsets.append(0) y_offsets.append(0)
fx += (width - height) / steps fx += increment
context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}") context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}")
elif height > width: elif height > width:
# Portrait - slice the image vertically # Portrait - slice the image vertically
fy = 0.0 fy = 0.0
steps = int(height * 2 / width) steps = int(height * 2 / width) + 1
increment = (height - width) / (steps - 1)
while fy <= (height - width): while fy <= (height - width):
y = int(fy) y = int(fy)
image_chunks.append(image.crop((0, y, width - 1, y + width - 1))) image_chunks.append(image.crop((0, y, width, y + width)))
x_offsets.append(0) x_offsets.append(0)
y_offsets.append(y) y_offsets.append(y)
fy += (height - width) / steps fy += increment
context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}") context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}")
for idx in range(len(image_chunks)): for idx in range(len(image_chunks)):
@ -404,7 +436,7 @@ def get_faces_list(
return all_faces return all_faces
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.1") @invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
class FaceOffInvocation(BaseInvocation): class FaceOffInvocation(BaseInvocation):
"""Bound, extract, and mask a face from an image using MediaPipe detection""" """Bound, extract, and mask a face from an image using MediaPipe detection"""
@ -498,7 +530,7 @@ class FaceOffInvocation(BaseInvocation):
return output return output
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.1") @invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
class FaceMaskInvocation(BaseInvocation): class FaceMaskInvocation(BaseInvocation):
"""Face mask creation using mediapipe face detection""" """Face mask creation using mediapipe face detection"""
@ -518,7 +550,7 @@ class FaceMaskInvocation(BaseInvocation):
) )
invert_mask: bool = InputField(default=False, description="Toggle to invert the mask") invert_mask: bool = InputField(default=False, description="Toggle to invert the mask")
@validator("face_ids") @field_validator("face_ids")
def validate_comma_separated_ints(cls, v) -> str: def validate_comma_separated_ints(cls, v) -> str:
comma_separated_ints_regex = re.compile(r"^\d*(,\d+)*$") comma_separated_ints_regex = re.compile(r"^\d*(,\d+)*$")
if comma_separated_ints_regex.match(v) is None: if comma_separated_ints_regex.match(v) is None:
@ -616,7 +648,7 @@ class FaceMaskInvocation(BaseInvocation):
@invocation( @invocation(
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.1" "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
) )
class FaceIdentifierInvocation(BaseInvocation): class FaceIdentifierInvocation(BaseInvocation):
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools.""" """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""

156
invokeai/app/invocations/image.py
View File

@ -9,10 +9,10 @@ from PIL import Image, ImageChops, ImageFilter, ImageOps
from invokeai.app.invocations.metadata import CoreMetadata from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker from invokeai.backend.image_util.safety_checker import SafetyChecker
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
@ -36,7 +36,13 @@ class ShowImageInvocation(BaseInvocation):
) )
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0") @invocation(
"blank_image",
title="Blank Image",
tags=["image"],
category="image",
version="1.0.0",
)
class BlankImageInvocation(BaseInvocation): class BlankImageInvocation(BaseInvocation):
"""Creates a blank image and forwards it to the pipeline""" """Creates a blank image and forwards it to the pipeline"""
@ -65,7 +71,13 @@ class BlankImageInvocation(BaseInvocation):
) )
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0") @invocation(
"img_crop",
title="Crop Image",
tags=["image", "crop"],
category="image",
version="1.0.0",
)
class ImageCropInvocation(BaseInvocation): class ImageCropInvocation(BaseInvocation):
"""Crops an image to a specified box. The box can be outside of the image.""" """Crops an image to a specified box. The box can be outside of the image."""
@ -98,7 +110,13 @@ class ImageCropInvocation(BaseInvocation):
) )
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1") @invocation(
"img_paste",
title="Paste Image",
tags=["image", "paste"],
category="image",
version="1.0.1",
)
class ImagePasteInvocation(BaseInvocation): class ImagePasteInvocation(BaseInvocation):
"""Pastes an image into another image.""" """Pastes an image into another image."""
@ -151,7 +169,13 @@ class ImagePasteInvocation(BaseInvocation):
) )
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0") @invocation(
"tomask",
title="Mask from Alpha",
tags=["image", "mask"],
category="image",
version="1.0.0",
)
class MaskFromAlphaInvocation(BaseInvocation): class MaskFromAlphaInvocation(BaseInvocation):
"""Extracts the alpha channel of an image as a mask.""" """Extracts the alpha channel of an image as a mask."""
@ -182,7 +206,13 @@ class MaskFromAlphaInvocation(BaseInvocation):
) )
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0") @invocation(
"img_mul",
title="Multiply Images",
tags=["image", "multiply"],
category="image",
version="1.0.0",
)
class ImageMultiplyInvocation(BaseInvocation): class ImageMultiplyInvocation(BaseInvocation):
"""Multiplies two images together using `PIL.ImageChops.multiply()`.""" """Multiplies two images together using `PIL.ImageChops.multiply()`."""
@ -215,7 +245,13 @@ class ImageMultiplyInvocation(BaseInvocation):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"] IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0") @invocation(
"img_chan",
title="Extract Image Channel",
tags=["image", "channel"],
category="image",
version="1.0.0",
)
class ImageChannelInvocation(BaseInvocation): class ImageChannelInvocation(BaseInvocation):
"""Gets a channel from an image.""" """Gets a channel from an image."""
@ -247,7 +283,13 @@ class ImageChannelInvocation(BaseInvocation):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"] 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="1.0.0") @invocation(
"img_conv",
title="Convert Image Mode",
tags=["image", "convert"],
category="image",
version="1.0.0",
)
class ImageConvertInvocation(BaseInvocation): class ImageConvertInvocation(BaseInvocation):
"""Converts an image to a different mode.""" """Converts an image to a different mode."""
@ -276,7 +318,13 @@ class ImageConvertInvocation(BaseInvocation):
) )
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0") @invocation(
"img_blur",
title="Blur Image",
tags=["image", "blur"],
category="image",
version="1.0.0",
)
class ImageBlurInvocation(BaseInvocation): class ImageBlurInvocation(BaseInvocation):
"""Blurs an image""" """Blurs an image"""
@ -330,7 +378,13 @@ PIL_RESAMPLING_MAP = {
} }
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0") @invocation(
"img_resize",
title="Resize Image",
tags=["image", "resize"],
category="image",
version="1.0.0",
)
class ImageResizeInvocation(BaseInvocation): class ImageResizeInvocation(BaseInvocation):
"""Resizes an image to specific dimensions""" """Resizes an image to specific dimensions"""
@ -359,7 +413,7 @@ class ImageResizeInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow, workflow=self.workflow,
) )
@ -370,7 +424,13 @@ class ImageResizeInvocation(BaseInvocation):
) )
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0") @invocation(
"img_scale",
title="Scale Image",
tags=["image", "scale"],
category="image",
version="1.0.0",
)
class ImageScaleInvocation(BaseInvocation): class ImageScaleInvocation(BaseInvocation):
"""Scales an image by a factor""" """Scales an image by a factor"""
@ -411,7 +471,13 @@ class ImageScaleInvocation(BaseInvocation):
) )
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0") @invocation(
"img_lerp",
title="Lerp Image",
tags=["image", "lerp"],
category="image",
version="1.0.0",
)
class ImageLerpInvocation(BaseInvocation): class ImageLerpInvocation(BaseInvocation):
"""Linear interpolation of all pixels of an image""" """Linear interpolation of all pixels of an image"""
@ -444,7 +510,13 @@ class ImageLerpInvocation(BaseInvocation):
) )
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0") @invocation(
"img_ilerp",
title="Inverse Lerp Image",
tags=["image", "ilerp"],
category="image",
version="1.0.0",
)
class ImageInverseLerpInvocation(BaseInvocation): class ImageInverseLerpInvocation(BaseInvocation):
"""Inverse linear interpolation of all pixels of an image""" """Inverse linear interpolation of all pixels of an image"""
@ -456,7 +528,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
image = context.services.images.get_pil_image(self.image.image_name) image = context.services.images.get_pil_image(self.image.image_name)
image_arr = numpy.asarray(image, dtype=numpy.float32) image_arr = numpy.asarray(image, dtype=numpy.float32)
image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255 image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255 # type: ignore [assignment]
ilerp_image = Image.fromarray(numpy.uint8(image_arr)) ilerp_image = Image.fromarray(numpy.uint8(image_arr))
@ -477,7 +549,13 @@ class ImageInverseLerpInvocation(BaseInvocation):
) )
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0") @invocation(
"img_nsfw",
title="Blur NSFW Image",
tags=["image", "nsfw"],
category="image",
version="1.0.0",
)
class ImageNSFWBlurInvocation(BaseInvocation): class ImageNSFWBlurInvocation(BaseInvocation):
"""Add blur to NSFW-flagged images""" """Add blur to NSFW-flagged images"""
@ -505,7 +583,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow, workflow=self.workflow,
) )
@ -515,7 +593,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
height=image_dto.height, height=image_dto.height,
) )
def _get_caution_img(self) -> Image: def _get_caution_img(self) -> Image.Image:
import invokeai.app.assets.images as image_assets import invokeai.app.assets.images as image_assets
caution = Image.open(Path(image_assets.__path__[0]) / "caution.png") caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
@ -523,7 +601,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
@invocation( @invocation(
"img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image", version="1.0.0" "img_watermark",
title="Add Invisible Watermark",
tags=["image", "watermark"],
category="image",
version="1.0.0",
) )
class ImageWatermarkInvocation(BaseInvocation): class ImageWatermarkInvocation(BaseInvocation):
"""Add an invisible watermark to an image""" """Add an invisible watermark to an image"""
@ -544,7 +626,7 @@ class ImageWatermarkInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow, workflow=self.workflow,
) )
@ -555,7 +637,13 @@ class ImageWatermarkInvocation(BaseInvocation):
) )
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0") @invocation(
"mask_edge",
title="Mask Edge",
tags=["image", "mask", "inpaint"],
category="image",
version="1.0.0",
)
class MaskEdgeInvocation(BaseInvocation): class MaskEdgeInvocation(BaseInvocation):
"""Applies an edge mask to an image""" """Applies an edge mask to an image"""
@ -601,7 +689,11 @@ class MaskEdgeInvocation(BaseInvocation):
@invocation( @invocation(
"mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image", version="1.0.0" "mask_combine",
title="Combine Masks",
tags=["image", "mask", "multiply"],
category="image",
version="1.0.0",
) )
class MaskCombineInvocation(BaseInvocation): class MaskCombineInvocation(BaseInvocation):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.""" """Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
@ -632,7 +724,13 @@ class MaskCombineInvocation(BaseInvocation):
) )
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0") @invocation(
"color_correct",
title="Color Correct",
tags=["image", "color"],
category="image",
version="1.0.0",
)
class ColorCorrectInvocation(BaseInvocation): class ColorCorrectInvocation(BaseInvocation):
""" """
Shifts the colors of a target image to match the reference image, optionally Shifts the colors of a target image to match the reference image, optionally
@ -742,7 +840,13 @@ class ColorCorrectInvocation(BaseInvocation):
) )
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0") @invocation(
"img_hue_adjust",
title="Adjust Image Hue",
tags=["image", "hue"],
category="image",
version="1.0.0",
)
class ImageHueAdjustmentInvocation(BaseInvocation): class ImageHueAdjustmentInvocation(BaseInvocation):
"""Adjusts the Hue of an image.""" """Adjusts the Hue of an image."""
@ -980,7 +1084,7 @@ class SaveImageInvocation(BaseInvocation):
image: ImageField = InputField(description=FieldDescriptions.image) image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct) board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
metadata: CoreMetadata = InputField( metadata: Optional[CoreMetadata] = InputField(
default=None, default=None,
description=FieldDescriptions.core_metadata, description=FieldDescriptions.core_metadata,
ui_hidden=True, ui_hidden=True,
@ -997,7 +1101,7 @@ class SaveImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow, workflow=self.workflow,
) )

2
invokeai/app/invocations/infill.py
View File

@ -7,12 +7,12 @@ import numpy as np
from PIL import Image, ImageOps from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput 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, get_random_seed
from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch from invokeai.backend.image_util.patchmatch import PatchMatch
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES

6
invokeai/app/invocations/ip_adapter.py
View File

@ -2,7 +2,7 @@ import os
from builtins import float from builtins import float
from typing import List, Union from typing import List, Union
from pydantic import BaseModel, Field from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import ( from invokeai.app.invocations.baseinvocation import (
BaseInvocation, BaseInvocation,
@ -25,11 +25,15 @@ class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model") model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class CLIPVisionModelField(BaseModel): class CLIPVisionModelField(BaseModel):
model_name: str = Field(description="Name of the CLIP Vision image encoder model") model_name: str = Field(description="Name of the CLIP Vision image encoder model")
base_model: BaseModelType = Field(description="Base model (usually 'Any')") base_model: BaseModelType = Field(description="Base model (usually 'Any')")
model_config = ConfigDict(protected_namespaces=())
class IPAdapterField(BaseModel): class IPAdapterField(BaseModel):
image: ImageField = Field(description="The IP-Adapter image prompt.") image: ImageField = Field(description="The IP-Adapter image prompt.")

151
invokeai/app/invocations/latent.py
View File

@ -19,7 +19,7 @@ from diffusers.models.attention_processor import (
) )
from diffusers.schedulers import DPMSolverSDEScheduler from diffusers.schedulers import DPMSolverSDEScheduler
from diffusers.schedulers import SchedulerMixin as Scheduler from diffusers.schedulers import SchedulerMixin as Scheduler
from pydantic import validator from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.ip_adapter import IPAdapterField from invokeai.app.invocations.ip_adapter import IPAdapterField
@ -34,6 +34,7 @@ from invokeai.app.invocations.primitives import (
build_latents_output, build_latents_output,
) )
from invokeai.app.invocations.t2i_adapter import T2IAdapterField from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.controlnet_utils import prepare_control_image from invokeai.app.util.controlnet_utils import prepare_control_image
from invokeai.app.util.step_callback import stable_diffusion_step_callback from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
@ -54,7 +55,6 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import choose_precision, choose_torch_device from ...backend.util.devices import choose_precision, choose_torch_device
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import ( from .baseinvocation import (
BaseInvocation, BaseInvocation,
BaseInvocationOutput, BaseInvocationOutput,
@ -84,12 +84,20 @@ class SchedulerOutput(BaseInvocationOutput):
scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler) scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents", version="1.0.0") @invocation(
"scheduler",
title="Scheduler",
tags=["scheduler"],
category="latents",
version="1.0.0",
)
class SchedulerInvocation(BaseInvocation): class SchedulerInvocation(BaseInvocation):
"""Selects a scheduler.""" """Selects a scheduler."""
scheduler: SAMPLER_NAME_VALUES = InputField( scheduler: SAMPLER_NAME_VALUES = InputField(
default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler default="euler",
description=FieldDescriptions.scheduler,
ui_type=UIType.Scheduler,
) )
def invoke(self, context: InvocationContext) -> SchedulerOutput: def invoke(self, context: InvocationContext) -> SchedulerOutput:
@ -97,7 +105,11 @@ class SchedulerInvocation(BaseInvocation):
@invocation( @invocation(
"create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents", version="1.0.0" "create_denoise_mask",
title="Create Denoise Mask",
tags=["mask", "denoise"],
category="latents",
version="1.0.0",
) )
class CreateDenoiseMaskInvocation(BaseInvocation): class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run.""" """Creates mask for denoising model run."""
@ -106,7 +118,11 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1) image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2) mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3) tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32, ui_order=4) fp32: bool = InputField(
default=DEFAULT_PRECISION == "float32",
description=FieldDescriptions.fp32,
ui_order=4,
)
def prep_mask_tensor(self, mask_image): def prep_mask_tensor(self, mask_image):
if mask_image.mode != "L": if mask_image.mode != "L":
@ -134,7 +150,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
if image is not None: if image is not None:
vae_info = context.services.model_manager.get_model( vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), **self.vae.vae.model_dump(),
context=context, context=context,
) )
@ -167,7 +183,7 @@ def get_scheduler(
) -> Scheduler: ) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"]) scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
orig_scheduler_info = context.services.model_manager.get_model( orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.dict(), **scheduler_info.model_dump(),
context=context, context=context,
) )
with orig_scheduler_info as orig_scheduler: with orig_scheduler_info as orig_scheduler:
@ -209,34 +225,64 @@ class DenoiseLatentsInvocation(BaseInvocation):
negative_conditioning: ConditioningField = InputField( negative_conditioning: ConditioningField = InputField(
description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1 description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1
) )
noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection, ui_order=3) noise: Optional[LatentsField] = InputField(
default=None,
description=FieldDescriptions.noise,
input=Input.Connection,
ui_order=3,
)
steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps) steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
cfg_scale: Union[float, List[float]] = InputField( cfg_scale: Union[float, List[float]] = InputField(
default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale" default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
) )
denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start) denoising_start: float = InputField(
default=0.0,
ge=0,
le=1,
description=FieldDescriptions.denoising_start,
)
denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end) denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
scheduler: SAMPLER_NAME_VALUES = InputField( scheduler: SAMPLER_NAME_VALUES = InputField(
default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler default="euler",
description=FieldDescriptions.scheduler,
ui_type=UIType.Scheduler,
) )
unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2) unet: UNetField = InputField(
control: Union[ControlField, list[ControlField]] = InputField( description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
ui_order=2,
)
control: Optional[Union[ControlField, list[ControlField]]] = InputField(
default=None, default=None,
input=Input.Connection, input=Input.Connection,
ui_order=5, ui_order=5,
) )
ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField( ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6 description=FieldDescriptions.ip_adapter,
title="IP-Adapter",
default=None,
input=Input.Connection,
ui_order=6,
) )
t2i_adapter: Union[T2IAdapterField, list[T2IAdapterField]] = InputField( t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]] = InputField(
description=FieldDescriptions.t2i_adapter, title="T2I-Adapter", default=None, input=Input.Connection, ui_order=7 description=FieldDescriptions.t2i_adapter,
title="T2I-Adapter",
default=None,
input=Input.Connection,
ui_order=7,
)
latents: Optional[LatentsField] = InputField(
default=None, description=FieldDescriptions.latents, input=Input.Connection
) )
latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
denoise_mask: Optional[DenoiseMaskField] = InputField( denoise_mask: Optional[DenoiseMaskField] = InputField(
default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=8 default=None,
description=FieldDescriptions.mask,
input=Input.Connection,
ui_order=8,
) )
@validator("cfg_scale") @field_validator("cfg_scale")
def ge_one(cls, v): def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1""" """validate that all cfg_scale values are >= 1"""
if isinstance(v, list): if isinstance(v, list):
@ -259,7 +305,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback( stable_diffusion_step_callback(
context=context, context=context,
intermediate_state=intermediate_state, intermediate_state=intermediate_state,
node=self.dict(), node=self.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
base_model=base_model, base_model=base_model,
) )
@ -451,9 +497,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments. # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
with image_encoder_model_info as image_encoder_model: with image_encoder_model_info as image_encoder_model:
# Get image embeddings from CLIP and ImageProjModel. # Get image embeddings from CLIP and ImageProjModel.
image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds( (
input_image, image_encoder_model image_prompt_embeds,
) uncond_image_prompt_embeds,
) = ip_adapter_model.get_image_embeds(input_image, image_encoder_model)
conditioning_data.ip_adapter_conditioning.append( conditioning_data.ip_adapter_conditioning.append(
IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds) IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
) )
@ -628,7 +675,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
# TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets, # TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
# below. Investigate whether this is appropriate. # below. Investigate whether this is appropriate.
t2i_adapter_data = self.run_t2i_adapters( t2i_adapter_data = self.run_t2i_adapters(
context, self.t2i_adapter, latents.shape, do_classifier_free_guidance=True context,
self.t2i_adapter,
latents.shape,
do_classifier_free_guidance=True,
) )
# Get the source node id (we are invoking the prepared node) # Get the source node id (we are invoking the prepared node)
@ -641,7 +691,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
def _lora_loader(): def _lora_loader():
for lora in self.unet.loras: for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model( lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), **lora.model_dump(exclude={"weight"}),
context=context, context=context,
) )
yield (lora_info.context.model, lora.weight) yield (lora_info.context.model, lora.weight)
@ -649,7 +699,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
return return
unet_info = context.services.model_manager.get_model( unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), **self.unet.unet.model_dump(),
context=context, context=context,
) )
with ( with (
@ -701,7 +751,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
denoising_end=self.denoising_end, denoising_end=self.denoising_end,
) )
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings( (
result_latents,
result_attention_map_saver,
) = pipeline.latents_from_embeddings(
latents=latents, latents=latents,
timesteps=timesteps, timesteps=timesteps,
init_timestep=init_timestep, init_timestep=init_timestep,
@ -729,7 +782,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
@invocation( @invocation(
"l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents", version="1.0.0" "l2i",
title="Latents to Image",
tags=["latents", "image", "vae", "l2i"],
category="latents",
version="1.0.0",
) )
class LatentsToImageInvocation(BaseInvocation): class LatentsToImageInvocation(BaseInvocation):
"""Generates an image from latents.""" """Generates an image from latents."""
@ -744,7 +801,7 @@ class LatentsToImageInvocation(BaseInvocation):
) )
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled) tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32) fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
metadata: CoreMetadata = InputField( metadata: Optional[CoreMetadata] = InputField(
default=None, default=None,
description=FieldDescriptions.core_metadata, description=FieldDescriptions.core_metadata,
ui_hidden=True, ui_hidden=True,
@ -755,7 +812,7 @@ class LatentsToImageInvocation(BaseInvocation):
latents = context.services.latents.get(self.latents.latents_name) latents = context.services.latents.get(self.latents.latents_name)
vae_info = context.services.model_manager.get_model( vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), **self.vae.vae.model_dump(),
context=context, context=context,
) )
@ -817,7 +874,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow, workflow=self.workflow,
) )
@ -831,7 +888,13 @@ class LatentsToImageInvocation(BaseInvocation):
LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"] LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents", version="1.0.0") @invocation(
"lresize",
title="Resize Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.0",
)
class ResizeLatentsInvocation(BaseInvocation): class ResizeLatentsInvocation(BaseInvocation):
"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.""" """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@ -877,7 +940,13 @@ class ResizeLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed) return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents", version="1.0.0") @invocation(
"lscale",
title="Scale Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.0",
)
class ScaleLatentsInvocation(BaseInvocation): class ScaleLatentsInvocation(BaseInvocation):
"""Scales latents by a given factor.""" """Scales latents by a given factor."""
@ -916,7 +985,11 @@ class ScaleLatentsInvocation(BaseInvocation):
@invocation( @invocation(
"i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents", version="1.0.0" "i2l",
title="Image to Latents",
tags=["latents", "image", "vae", "i2l"],
category="latents",
version="1.0.0",
) )
class ImageToLatentsInvocation(BaseInvocation): class ImageToLatentsInvocation(BaseInvocation):
"""Encodes an image into latents.""" """Encodes an image into latents."""
@ -980,7 +1053,7 @@ class ImageToLatentsInvocation(BaseInvocation):
image = context.services.images.get_pil_image(self.image.image_name) image = context.services.images.get_pil_image(self.image.image_name)
vae_info = context.services.model_manager.get_model( vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), **self.vae.vae.model_dump(),
context=context, context=context,
) )
@ -1008,7 +1081,13 @@ class ImageToLatentsInvocation(BaseInvocation):
return vae.encode(image_tensor).latents return vae.encode(image_tensor).latents
@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0") @invocation(
"lblend",
title="Blend Latents",
tags=["latents", "blend"],
category="latents",
version="1.0.0",
)
class BlendLatentsInvocation(BaseInvocation): class BlendLatentsInvocation(BaseInvocation):
"""Blend two latents using a given alpha. Latents must have same size.""" """Blend two latents using a given alpha. Latents must have same size."""

15
invokeai/app/invocations/math.py
View File

@ -3,7 +3,7 @@
from typing import Literal from typing import Literal
import numpy as np import numpy as np
from pydantic import validator from pydantic import field_validator
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
@ -72,7 +72,14 @@ class RandomIntInvocation(BaseInvocation):
return IntegerOutput(value=np.random.randint(self.low, self.high)) return IntegerOutput(value=np.random.randint(self.low, self.high))
@invocation("rand_float", title="Random Float", tags=["math", "float", "random"], category="math", version="1.0.0") @invocation(
"rand_float",
title="Random Float",
tags=["math", "float", "random"],
category="math",
version="1.0.1",
use_cache=False,
)
class RandomFloatInvocation(BaseInvocation): class RandomFloatInvocation(BaseInvocation):
"""Outputs a single random float""" """Outputs a single random float"""
@ -178,7 +185,7 @@ class IntegerMathInvocation(BaseInvocation):
a: int = InputField(default=0, description=FieldDescriptions.num_1) a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2) b: int = InputField(default=0, description=FieldDescriptions.num_2)
@validator("b") @field_validator("b")
def no_unrepresentable_results(cls, v, values): def no_unrepresentable_results(cls, v, values):
if values["operation"] == "DIV" and v == 0: if values["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero") raise ValueError("Cannot divide by zero")
@ -252,7 +259,7 @@ class FloatMathInvocation(BaseInvocation):
a: float = InputField(default=0, description=FieldDescriptions.num_1) a: float = InputField(default=0, description=FieldDescriptions.num_1)
b: float = InputField(default=0, description=FieldDescriptions.num_2) b: float = InputField(default=0, description=FieldDescriptions.num_2)
@validator("b") @field_validator("b")
def no_unrepresentable_results(cls, v, values): def no_unrepresentable_results(cls, v, values):
if values["operation"] == "DIV" and v == 0: if values["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero") raise ValueError("Cannot divide by zero")

90
invokeai/app/invocations/metadata.py
View File

@ -44,28 +44,31 @@ class CoreMetadata(BaseModelExcludeNull):
"""Core generation metadata for an image generated in InvokeAI.""" """Core generation metadata for an image generated in InvokeAI."""
app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image") app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
generation_mode: str = Field( generation_mode: Optional[str] = Field(
default=None,
description="The generation mode that output this image", description="The generation mode that output this image",
) )
created_by: Optional[str] = Field(description="The name of the creator of the image") created_by: Optional[str] = Field(default=None, description="The name of the creator of the image")
positive_prompt: str = Field(description="The positive prompt parameter") positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
negative_prompt: str = Field(description="The negative prompt parameter") negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
width: int = Field(description="The width parameter") width: Optional[int] = Field(default=None, description="The width parameter")
height: int = Field(description="The height parameter") height: Optional[int] = Field(default=None, description="The height parameter")
seed: int = Field(description="The seed used for noise generation") seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
rand_device: str = Field(description="The device used for random number generation") rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
cfg_scale: float = Field(description="The classifier-free guidance scale parameter") cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
steps: int = Field(description="The number of steps used for inference") steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
scheduler: str = Field(description="The scheduler used for inference") scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
clip_skip: Optional[int] = Field( clip_skip: Optional[int] = Field(
default=None, default=None,
description="The number of skipped CLIP layers", description="The number of skipped CLIP layers",
) )
model: MainModelField = Field(description="The main model used for inference") model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
controlnets: list[ControlField] = Field(description="The ControlNets used for inference") controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
ipAdapters: list[IPAdapterMetadataField] = Field(description="The IP Adapters used for inference") ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference") default=None, description="The IP Adapters used for inference"
loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference") )
t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
loras: Optional[list[LoRAMetadataField]] = Field(default=None, description="The LoRAs used for inference")
vae: Optional[VAEModelField] = Field( vae: Optional[VAEModelField] = Field(
default=None, default=None,
description="The VAE used for decoding, if the main model's default was not used", description="The VAE used for decoding, if the main model's default was not used",
@ -122,27 +125,34 @@ class MetadataAccumulatorOutput(BaseInvocationOutput):
class MetadataAccumulatorInvocation(BaseInvocation): class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object""" """Outputs a Core Metadata Object"""
generation_mode: str = InputField( generation_mode: Optional[str] = InputField(
default=None,
description="The generation mode that output this image", description="The generation mode that output this image",
) )
positive_prompt: str = InputField(description="The positive prompt parameter") positive_prompt: Optional[str] = InputField(default=None, description="The positive prompt parameter")
negative_prompt: str = InputField(description="The negative prompt parameter") negative_prompt: Optional[str] = InputField(default=None, description="The negative prompt parameter")
width: int = InputField(description="The width parameter") width: Optional[int] = InputField(default=None, description="The width parameter")
height: int = InputField(description="The height parameter") height: Optional[int] = InputField(default=None, description="The height parameter")
seed: int = InputField(description="The seed used for noise generation") seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
rand_device: str = InputField(description="The device used for random number generation") rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
cfg_scale: float = InputField(description="The classifier-free guidance scale parameter") cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
steps: int = InputField(description="The number of steps used for inference") steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
scheduler: str = InputField(description="The scheduler used for inference") scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
clip_skip: Optional[int] = Field( clip_skip: Optional[int] = InputField(
default=None, default=None,
description="The number of skipped CLIP layers", description="The number of skipped CLIP layers",
) )
model: MainModelField = InputField(description="The main model used for inference") model: Optional[MainModelField] = InputField(default=None, description="The main model used for inference")
controlnets: list[ControlField] = InputField(description="The ControlNets used for inference") controlnets: Optional[list[ControlField]] = InputField(
ipAdapters: list[IPAdapterMetadataField] = InputField(description="The IP Adapters used for inference") default=None, description="The ControlNets used for inference"
t2iAdapters: list[T2IAdapterField] = Field(description="The IP Adapters used for inference") )
loras: list[LoRAMetadataField] = InputField(description="The LoRAs used for inference") ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
default=None, description="The IP Adapters used for inference"
)
t2iAdapters: Optional[list[T2IAdapterField]] = InputField(
default=None, description="The IP Adapters used for inference"
)
loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference")
strength: Optional[float] = InputField( strength: Optional[float] = InputField(
default=None, default=None,
description="The strength used for latents-to-latents", description="The strength used for latents-to-latents",
@ -156,6 +166,20 @@ class MetadataAccumulatorInvocation(BaseInvocation):
description="The VAE used for decoding, if the main model's default was not used", description="The VAE used for decoding, if the main model's default was not used",
) )
# High resolution fix metadata.
hrf_width: Optional[int] = InputField(
default=None,
description="The high resolution fix height and width multipler.",
)
hrf_height: Optional[int] = InputField(
default=None,
description="The high resolution fix height and width multipler.",
)
hrf_strength: Optional[float] = InputField(
default=None,
description="The high resolution fix img2img strength used in the upscale pass.",
)
# SDXL # SDXL
positive_style_prompt: Optional[str] = InputField( positive_style_prompt: Optional[str] = InputField(
default=None, default=None,
@ -199,4 +223,4 @@ class MetadataAccumulatorInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput: def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
"""Collects and outputs a CoreMetadata object""" """Collects and outputs a CoreMetadata object"""
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict())) return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))

78
invokeai/app/invocations/model.py
View File

@ -1,7 +1,7 @@
import copy import copy
from typing import List, Optional from typing import List, Optional
from pydantic import BaseModel, Field from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.shared import FreeUConfig from invokeai.app.invocations.shared import FreeUConfig
@ -26,6 +26,8 @@ class ModelInfo(BaseModel):
model_type: ModelType = Field(description="Info to load submodel") model_type: ModelType = Field(description="Info to load submodel")
submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel") submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
model_config = ConfigDict(protected_namespaces=())
class LoraInfo(ModelInfo): class LoraInfo(ModelInfo):
weight: float = Field(description="Lora's weight which to use when apply to model") weight: float = Field(description="Lora's weight which to use when apply to model")
@ -87,6 +89,8 @@ class MainModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Model Type") model_type: ModelType = Field(description="Model Type")
model_config = ConfigDict(protected_namespaces=())
class LoRAModelField(BaseModel): class LoRAModelField(BaseModel):
"""LoRA model field""" """LoRA model field"""
@ -94,8 +98,16 @@ class LoRAModelField(BaseModel):
model_name: str = Field(description="Name of the LoRA model") model_name: str = Field(description="Name of the LoRA model")
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
@invocation("main_model_loader", title="Main Model", tags=["model"], category="model", version="1.0.0")
@invocation(
"main_model_loader",
title="Main Model",
tags=["model"],
category="model",
version="1.0.0",
)
class MainModelLoaderInvocation(BaseInvocation): class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels.""" """Loads a main model, outputting its submodels."""
@ -202,10 +214,16 @@ class LoraLoaderInvocation(BaseInvocation):
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA") lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight) weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField( unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet" default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
) )
clip: Optional[ClipField] = InputField( clip: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP" default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP",
) )
def invoke(self, context: InvocationContext) -> LoraLoaderOutput: def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
@ -266,20 +284,35 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2") clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model", version="1.0.0") @invocation(
"sdxl_lora_loader",
title="SDXL LoRA",
tags=["lora", "model"],
category="model",
version="1.0.0",
)
class SDXLLoraLoaderInvocation(BaseInvocation): class SDXLLoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder.""" """Apply selected lora to unet and text_encoder."""
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA") lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight) weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField( unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet" default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
) )
clip: Optional[ClipField] = InputField( clip: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1" default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP 1",
) )
clip2: Optional[ClipField] = InputField( clip2: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2" default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP 2",
) )
def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput: def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
@ -352,13 +385,18 @@ class VAEModelField(BaseModel):
model_name: str = Field(description="Name of the model") model_name: str = Field(description="Name of the model")
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0") @invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
class VaeLoaderInvocation(BaseInvocation): class VaeLoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput""" """Loads a VAE model, outputting a VaeLoaderOutput"""
vae_model: VAEModelField = InputField( vae_model: VAEModelField = InputField(
description=FieldDescriptions.vae_model, input=Input.Direct, ui_type=UIType.VaeModel, title="VAE" description=FieldDescriptions.vae_model,
input=Input.Direct,
ui_type=UIType.VaeModel,
title="VAE",
) )
def invoke(self, context: InvocationContext) -> VAEOutput: def invoke(self, context: InvocationContext) -> VAEOutput:
@ -387,19 +425,31 @@ class VaeLoaderInvocation(BaseInvocation):
class SeamlessModeOutput(BaseInvocationOutput): class SeamlessModeOutput(BaseInvocationOutput):
"""Modified Seamless Model output""" """Modified Seamless Model output"""
unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet") unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE") vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model", version="1.0.0") @invocation(
"seamless",
title="Seamless",
tags=["seamless", "model"],
category="model",
version="1.0.0",
)
class SeamlessModeInvocation(BaseInvocation): class SeamlessModeInvocation(BaseInvocation):
"""Applies the seamless transformation to the Model UNet and VAE.""" """Applies the seamless transformation to the Model UNet and VAE."""
unet: Optional[UNetField] = InputField( unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet" default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
) )
vae: Optional[VaeField] = InputField( vae: Optional[VaeField] = InputField(
default=None, description=FieldDescriptions.vae_model, input=Input.Connection, title="VAE" default=None,
description=FieldDescriptions.vae_model,
input=Input.Connection,
title="VAE",
) )
seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless") seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless") seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")

14
invokeai/app/invocations/noise.py
View File

@ -2,7 +2,7 @@
import torch import torch
from pydantic import validator from pydantic import field_validator
from invokeai.app.invocations.latent import LatentsField from invokeai.app.invocations.latent import LatentsField
from invokeai.app.util.misc import SEED_MAX, get_random_seed from invokeai.app.util.misc import SEED_MAX, get_random_seed
@ -65,7 +65,7 @@ Nodes
class NoiseOutput(BaseInvocationOutput): class NoiseOutput(BaseInvocationOutput):
"""Invocation noise output""" """Invocation noise output"""
noise: LatentsField = OutputField(default=None, description=FieldDescriptions.noise) noise: LatentsField = OutputField(description=FieldDescriptions.noise)
width: int = OutputField(description=FieldDescriptions.width) width: int = OutputField(description=FieldDescriptions.width)
height: int = OutputField(description=FieldDescriptions.height) height: int = OutputField(description=FieldDescriptions.height)
@ -78,7 +78,13 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
) )
@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents", version="1.0.0") @invocation(
"noise",
title="Noise",
tags=["latents", "noise"],
category="latents",
version="1.0.0",
)
class NoiseInvocation(BaseInvocation): class NoiseInvocation(BaseInvocation):
"""Generates latent noise.""" """Generates latent noise."""
@ -105,7 +111,7 @@ class NoiseInvocation(BaseInvocation):
description="Use CPU for noise generation (for reproducible results across platforms)", description="Use CPU for noise generation (for reproducible results across platforms)",
) )
@validator("seed", pre=True) @field_validator("seed", mode="before")
def modulo_seed(cls, v): def modulo_seed(cls, v):
"""Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range.""" """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
return v % (SEED_MAX + 1) return v % (SEED_MAX + 1)

32
invokeai/app/invocations/onnx.py
View File

@ -9,18 +9,18 @@ from typing import List, Literal, Optional, Union
import numpy as np import numpy as np
import torch import torch
from diffusers.image_processor import VaeImageProcessor from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, Field, validator from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm from tqdm import tqdm
from invokeai.app.invocations.metadata import CoreMetadata from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.step_callback import stable_diffusion_step_callback from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend import BaseModelType, ModelType, SubModelType from invokeai.backend import BaseModelType, ModelType, SubModelType
from ...backend.model_management import ONNXModelPatcher from ...backend.model_management import ONNXModelPatcher
from ...backend.stable_diffusion import PipelineIntermediateState from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.util import choose_torch_device from ...backend.util import choose_torch_device
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import ( from .baseinvocation import (
BaseInvocation, BaseInvocation,
BaseInvocationOutput, BaseInvocationOutput,
@ -63,14 +63,17 @@ class ONNXPromptInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> ConditioningOutput: def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model( tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(), **self.clip.tokenizer.model_dump(),
) )
text_encoder_info = context.services.model_manager.get_model( text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(), **self.clip.text_encoder.model_dump(),
) )
with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder: # , ExitStack() as stack: with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder: # , ExitStack() as stack:
loras = [ loras = [
(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) (
context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
lora.weight,
)
for lora in self.clip.loras for lora in self.clip.loras
] ]
@ -175,14 +178,14 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
description=FieldDescriptions.unet, description=FieldDescriptions.unet,
input=Input.Connection, input=Input.Connection,
) )
control: Optional[Union[ControlField, list[ControlField]]] = InputField( control: Union[ControlField, list[ControlField]] = InputField(
default=None, default=None,
description=FieldDescriptions.control, description=FieldDescriptions.control,
) )
# seamless: bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", ) # seamless: bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'") # seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'")
@validator("cfg_scale") @field_validator("cfg_scale")
def ge_one(cls, v): def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1""" """validate that all cfg_scale values are >= 1"""
if isinstance(v, list): if isinstance(v, list):
@ -241,7 +244,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback( stable_diffusion_step_callback(
context=context, context=context,
intermediate_state=intermediate_state, intermediate_state=intermediate_state,
node=self.dict(), node=self.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
) )
@ -254,12 +257,15 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
eta=0.0, eta=0.0,
) )
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict()) unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump())
with unet_info as unet: # , ExitStack() as stack: with unet_info as unet: # , ExitStack() as stack:
# loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras] # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
loras = [ loras = [
(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) (
context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
lora.weight,
)
for lora in self.unet.loras for lora in self.unet.loras
] ]
@ -346,7 +352,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}") raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
vae_info = context.services.model_manager.get_model( vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), **self.vae.vae.model_dump(),
) )
# clear memory as vae decode can request a lot # clear memory as vae decode can request a lot
@ -375,7 +381,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id, node_id=self.id,
session_id=context.graph_execution_state_id, session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate, is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None, metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow, workflow=self.workflow,
) )
@ -403,6 +409,8 @@ class OnnxModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Model Type") model_type: ModelType = Field(description="Model Type")
model_config = ConfigDict(protected_namespaces=())
@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0") @invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0")
class OnnxModelLoaderInvocation(BaseInvocation): class OnnxModelLoaderInvocation(BaseInvocation):

31
invokeai/app/invocations/param_easing.py
View File

@ -44,13 +44,22 @@ from invokeai.app.invocations.primitives import FloatCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@invocation("float_range", title="Float Range", tags=["math", "range"], category="math", version="1.0.0") @invocation(
"float_range",
title="Float Range",
tags=["math", "range"],
category="math",
version="1.0.0",
)
class FloatLinearRangeInvocation(BaseInvocation): class FloatLinearRangeInvocation(BaseInvocation):
"""Creates a range""" """Creates a range"""
start: float = InputField(default=5, description="The first value of the range") start: float = InputField(default=5, description="The first value of the range")
stop: float = InputField(default=10, description="The last value of the range") stop: float = InputField(default=10, description="The last value of the range")
steps: int = InputField(default=30, description="number of values to interpolate over (including start and stop)") steps: int = InputField(
default=30,
description="number of values to interpolate over (including start and stop)",
)
def invoke(self, context: InvocationContext) -> FloatCollectionOutput: def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
param_list = list(np.linspace(self.start, self.stop, self.steps)) param_list = list(np.linspace(self.start, self.stop, self.steps))
@ -95,7 +104,13 @@ EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
# actually I think for now could just use CollectionOutput (which is list[Any] # actually I think for now could just use CollectionOutput (which is list[Any]
@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step", version="1.0.0") @invocation(
"step_param_easing",
title="Step Param Easing",
tags=["step", "easing"],
category="step",
version="1.0.0",
)
class StepParamEasingInvocation(BaseInvocation): class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps""" """Experimental per-step parameter easing for denoising steps"""
@ -159,7 +174,9 @@ class StepParamEasingInvocation(BaseInvocation):
context.services.logger.debug("base easing duration: " + str(base_easing_duration)) context.services.logger.debug("base easing duration: " + str(base_easing_duration))
even_num_steps = num_easing_steps % 2 == 0 # even number of steps even_num_steps = num_easing_steps % 2 == 0 # even number of steps
easing_function = easing_class( easing_function = easing_class(
start=self.start_value, end=self.end_value, duration=base_easing_duration - 1 start=self.start_value,
end=self.end_value,
duration=base_easing_duration - 1,
) )
base_easing_vals = list() base_easing_vals = list()
for step_index in range(base_easing_duration): for step_index in range(base_easing_duration):
@ -199,7 +216,11 @@ class StepParamEasingInvocation(BaseInvocation):
# #
else: # no mirroring (default) else: # no mirroring (default)
easing_function = easing_class(start=self.start_value, end=self.end_value, duration=num_easing_steps - 1) easing_function = easing_class(
start=self.start_value,
end=self.end_value,
duration=num_easing_steps - 1,
)
for step_index in range(num_easing_steps): for step_index in range(num_easing_steps):
step_val = easing_function.ease(step_index) step_val = easing_function.ease(step_index)
easing_list.append(step_val) easing_list.append(step_val)

31
invokeai/app/invocations/prompt.py
View File

@ -3,7 +3,7 @@ from typing import Optional, Union
import numpy as np import numpy as np
from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
from pydantic import validator from pydantic import field_validator
from invokeai.app.invocations.primitives import StringCollectionOutput from invokeai.app.invocations.primitives import StringCollectionOutput
@ -21,7 +21,10 @@ from .baseinvocation import BaseInvocation, InputField, InvocationContext, UICom
class DynamicPromptInvocation(BaseInvocation): class DynamicPromptInvocation(BaseInvocation):
"""Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator""" """Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""
prompt: str = InputField(description="The prompt to parse with dynamicprompts", ui_component=UIComponent.Textarea) prompt: str = InputField(
description="The prompt to parse with dynamicprompts",
ui_component=UIComponent.Textarea,
)
max_prompts: int = InputField(default=1, description="The number of prompts to generate") max_prompts: int = InputField(default=1, description="The number of prompts to generate")
combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator") combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator")
@ -36,21 +39,31 @@ class DynamicPromptInvocation(BaseInvocation):
return StringCollectionOutput(collection=prompts) return StringCollectionOutput(collection=prompts)
@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt", version="1.0.0") @invocation(
"prompt_from_file",
title="Prompts from File",
tags=["prompt", "file"],
category="prompt",
version="1.0.0",
)
class PromptsFromFileInvocation(BaseInvocation): class PromptsFromFileInvocation(BaseInvocation):
"""Loads prompts from a text file""" """Loads prompts from a text file"""
file_path: str = InputField(description="Path to prompt text file") file_path: str = InputField(description="Path to prompt text file")
pre_prompt: Optional[str] = InputField( pre_prompt: Optional[str] = InputField(
default=None, description="String to prepend to each prompt", ui_component=UIComponent.Textarea default=None,
description="String to prepend to each prompt",
ui_component=UIComponent.Textarea,
) )
post_prompt: Optional[str] = InputField( post_prompt: Optional[str] = InputField(
default=None, description="String to append to each prompt", ui_component=UIComponent.Textarea default=None,
description="String to append to each prompt",
ui_component=UIComponent.Textarea,
) )
start_line: int = InputField(default=1, ge=1, description="Line in the file to start start from") start_line: int = InputField(default=1, ge=1, description="Line in the file to start start from")
max_prompts: int = InputField(default=1, ge=0, description="Max lines to read from file (0=all)") max_prompts: int = InputField(default=1, ge=0, description="Max lines to read from file (0=all)")
@validator("file_path") @field_validator("file_path")
def file_path_exists(cls, v): def file_path_exists(cls, v):
if not exists(v): if not exists(v):
raise ValueError(FileNotFoundError) raise ValueError(FileNotFoundError)
@ -79,6 +92,10 @@ class PromptsFromFileInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> StringCollectionOutput: def invoke(self, context: InvocationContext) -> StringCollectionOutput:
prompts = self.promptsFromFile( prompts = self.promptsFromFile(
self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts self.file_path,
self.pre_prompt,
self.post_prompt,
self.start_line,
self.max_prompts,
) )
return StringCollectionOutput(collection=prompts) return StringCollectionOutput(collection=prompts)

4
invokeai/app/invocations/t2i_adapter.py
View File

@ -1,6 +1,6 @@
from typing import Union from typing import Union
from pydantic import BaseModel, Field from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import ( from invokeai.app.invocations.baseinvocation import (
BaseInvocation, BaseInvocation,
@ -23,6 +23,8 @@ class T2IAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the T2I-Adapter model") model_name: str = Field(description="Name of the T2I-Adapter model")
base_model: BaseModelType = Field(description="Base model") base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class T2IAdapterField(BaseModel): class T2IAdapterField(BaseModel):
image: ImageField = Field(description="The T2I-Adapter image prompt.") image: ImageField = Field(description="The T2I-Adapter image prompt.")

5
invokeai/app/invocations/upscale.py
View File

@ -7,10 +7,11 @@ import numpy as np
import torch import torch
from basicsr.archs.rrdbnet_arch import RRDBNet from basicsr.archs.rrdbnet_arch import RRDBNet
from PIL import Image from PIL import Image
from pydantic import ConfigDict
from realesrgan import RealESRGANer from realesrgan import RealESRGANer
from invokeai.app.invocations.primitives import ImageField, ImageOutput from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.models.image import ImageCategory, ResourceOrigin from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.util.devices import choose_torch_device from invokeai.backend.util.devices import choose_torch_device
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@ -38,6 +39,8 @@ class ESRGANInvocation(BaseInvocation):
default=400, ge=0, description="Tile size for tiled ESRGAN upscaling (0=tiling disabled)" default=400, ge=0, description="Tile size for tiled ESRGAN upscaling (0=tiling disabled)"
) )
model_config = ConfigDict(protected_namespaces=())
def invoke(self, context: InvocationContext) -> ImageOutput: def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name) image = context.services.images.get_pil_image(self.image.image_name)
models_path = context.services.configuration.models_path models_path = context.services.configuration.models_path

4
invokeai/app/models/exceptions.py
View File

@ -1,4 +0,0 @@
class CanceledException(Exception):
"""Execution canceled by user."""
pass

71
invokeai/app/models/image.py
View File

@ -1,71 +0,0 @@
from enum import Enum
from pydantic import BaseModel, Field
from invokeai.app.util.metaenum import MetaEnum
class ProgressImage(BaseModel):
"""The progress image sent intermittently during processing"""
width: int = Field(description="The effective width of the image in pixels")
height: int = Field(description="The effective height of the image in pixels")
dataURL: str = Field(description="The image data as a b64 data URL")
class ResourceOrigin(str, Enum, metaclass=MetaEnum):
"""The origin of a resource (eg image).
- INTERNAL: The resource was created by the application.
- EXTERNAL: The resource was not created by the application.
This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
"""
INTERNAL = "internal"
"""The resource was created by the application."""
EXTERNAL = "external"
"""The resource was not created by the application.
This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
"""
class InvalidOriginException(ValueError):
"""Raised when a provided value is not a valid ResourceOrigin.
Subclasses `ValueError`.
"""
def __init__(self, message="Invalid resource origin."):
super().__init__(message)
class ImageCategory(str, Enum, metaclass=MetaEnum):
"""The category of an image.
- GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose.
- MASK: The image is a mask image.
- CONTROL: The image is a ControlNet control image.
- USER: The image is a user-provide image.
- OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes.
"""
GENERAL = "general"
"""GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose."""
MASK = "mask"
"""MASK: The image is a mask image."""
CONTROL = "control"
"""CONTROL: The image is a ControlNet control image."""
USER = "user"
"""USER: The image is a user-provide image."""
OTHER = "other"
"""OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
class InvalidImageCategoryException(ValueError):
"""Raised when a provided value is not a valid ImageCategory.
Subclasses `ValueError`.
"""
def __init__(self, message="Invalid image category."):
super().__init__(message)

0
invokeai/app/models/__init__.py → invokeai/app/services/board_image_records/__init__.py
View File

47
invokeai/app/services/board_image_records/board_image_records_base.py Normal file
View File

@ -0,0 +1,47 @@
from abc import ABC, abstractmethod
from typing import Optional
class BoardImageRecordStorageBase(ABC):
"""Abstract base class for the one-to-many board-image relationship record storage."""
@abstractmethod
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
"""Adds an image to a board."""
pass
@abstractmethod
def remove_image_from_board(
self,
image_name: str,
) -> None:
"""Removes an image from a board."""
pass
@abstractmethod
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
"""Gets all board images for a board, as a list of the image names."""
pass
@abstractmethod
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's board id, if it has one."""
pass
@abstractmethod
def get_image_count_for_board(
self,
board_id: str,
) -> int:
"""Gets the number of images for a board."""
pass

61
invokeai/app/services/board_image_record_storage.py → invokeai/app/services/board_image_records/board_image_records_sqlite.py
View File

@ -1,69 +1,24 @@
import sqlite3 import sqlite3
import threading import threading
from abc import ABC, abstractmethod
from typing import Optional, cast from typing import Optional, cast
from invokeai.app.services.image_record_storage import OffsetPaginatedResults from invokeai.app.services.image_records.image_records_common import ImageRecord, deserialize_image_record
from invokeai.app.services.models.image_record import ImageRecord, deserialize_image_record from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
from .board_image_records_base import BoardImageRecordStorageBase
class BoardImageRecordStorageBase(ABC):
"""Abstract base class for the one-to-many board-image relationship record storage."""
@abstractmethod
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
"""Adds an image to a board."""
pass
@abstractmethod
def remove_image_from_board(
self,
image_name: str,
) -> None:
"""Removes an image from a board."""
pass
@abstractmethod
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
"""Gets all board images for a board, as a list of the image names."""
pass
@abstractmethod
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's board id, if it has one."""
pass
@abstractmethod
def get_image_count_for_board(
self,
board_id: str,
) -> int:
"""Gets the number of images for a board."""
pass
class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase): class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
_conn: sqlite3.Connection _conn: sqlite3.Connection
_cursor: sqlite3.Cursor _cursor: sqlite3.Cursor
_lock: threading.Lock _lock: threading.RLock
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None: def __init__(self, db: SqliteDatabase) -> None:
super().__init__() super().__init__()
self._conn = conn self._lock = db.lock
# Enable row factory to get rows as dictionaries (must be done before making the cursor!) self._conn = db.conn
self._conn.row_factory = sqlite3.Row
self._cursor = self._conn.cursor() self._cursor = self._conn.cursor()
self._lock = lock
try: try:
self._lock.acquire() self._lock.acquire()

112
invokeai/app/services/board_images.py
View File

@ -1,112 +0,0 @@
from abc import ABC, abstractmethod
from logging import Logger
from typing import Optional
from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
from invokeai.app.services.board_record_storage import BoardRecord, BoardRecordStorageBase
from invokeai.app.services.image_record_storage import ImageRecordStorageBase
from invokeai.app.services.models.board_record import BoardDTO
from invokeai.app.services.urls import UrlServiceBase
class BoardImagesServiceABC(ABC):
"""High-level service for board-image relationship management."""
@abstractmethod
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
"""Adds an image to a board."""
pass
@abstractmethod
def remove_image_from_board(
self,
image_name: str,
) -> None:
"""Removes an image from a board."""
pass
@abstractmethod
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
"""Gets all board images for a board, as a list of the image names."""
pass
@abstractmethod
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's board id, if it has one."""
pass
class BoardImagesServiceDependencies:
"""Service dependencies for the BoardImagesService."""
board_image_records: BoardImageRecordStorageBase
board_records: BoardRecordStorageBase
image_records: ImageRecordStorageBase
urls: UrlServiceBase
logger: Logger
def __init__(
self,
board_image_record_storage: BoardImageRecordStorageBase,
image_record_storage: ImageRecordStorageBase,
board_record_storage: BoardRecordStorageBase,
url: UrlServiceBase,
logger: Logger,
):
self.board_image_records = board_image_record_storage
self.image_records = image_record_storage
self.board_records = board_record_storage
self.urls = url
self.logger = logger
class BoardImagesService(BoardImagesServiceABC):
_services: BoardImagesServiceDependencies
def __init__(self, services: BoardImagesServiceDependencies):
self._services = services
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
self._services.board_image_records.add_image_to_board(board_id, image_name)
def remove_image_from_board(
self,
image_name: str,
) -> None:
self._services.board_image_records.remove_image_from_board(image_name)
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
return self._services.board_image_records.get_all_board_image_names_for_board(board_id)
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
board_id = self._services.board_image_records.get_board_for_image(image_name)
return board_id
def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
"""Converts a board record to a board DTO."""
return BoardDTO(
**board_record.dict(exclude={"cover_image_name"}),
cover_image_name=cover_image_name,
image_count=image_count,
)

0
invokeai/app/services/board_images/__init__.py Normal file
View File

39
invokeai/app/services/board_images/board_images_base.py Normal file
View File

@ -0,0 +1,39 @@
from abc import ABC, abstractmethod
from typing import Optional
class BoardImagesServiceABC(ABC):
"""High-level service for board-image relationship management."""
@abstractmethod
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
"""Adds an image to a board."""
pass
@abstractmethod
def remove_image_from_board(
self,
image_name: str,
) -> None:
"""Removes an image from a board."""
pass
@abstractmethod
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
"""Gets all board images for a board, as a list of the image names."""
pass
@abstractmethod
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's board id, if it has one."""
pass

0
invokeai/app/services/models/board_image.py → invokeai/app/services/board_images/board_images_common.py
View File

38
invokeai/app/services/board_images/board_images_default.py Normal file
View File

@ -0,0 +1,38 @@
from typing import Optional
from invokeai.app.services.invoker import Invoker
from .board_images_base import BoardImagesServiceABC
class BoardImagesService(BoardImagesServiceABC):
__invoker: Invoker
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
self.__invoker.services.board_image_records.add_image_to_board(board_id, image_name)
def remove_image_from_board(
self,
image_name: str,
) -> None:
self.__invoker.services.board_image_records.remove_image_from_board(image_name)
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
return self.__invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
board_id = self.__invoker.services.board_image_records.get_board_for_image(image_name)
return board_id

55
invokeai/app/services/board_records/board_records_base.py Normal file
View File

@ -0,0 +1,55 @@
from abc import ABC, abstractmethod
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .board_records_common import BoardChanges, BoardRecord
class BoardRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the board record store."""
@abstractmethod
def delete(self, board_id: str) -> None:
"""Deletes a board record."""
pass
@abstractmethod
def save(
self,
board_name: str,
) -> BoardRecord:
"""Saves a board record."""
pass
@abstractmethod
def get(
self,
board_id: str,
) -> BoardRecord:
"""Gets a board record."""
pass
@abstractmethod
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardRecord:
"""Updates a board record."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
) -> OffsetPaginatedResults[BoardRecord]:
"""Gets many board records."""
pass
@abstractmethod
def get_all(
self,
) -> list[BoardRecord]:
"""Gets all board records."""
pass

41
invokeai/app/services/models/board_record.py → invokeai/app/services/board_records/board_records_common.py
View File

@ -1,7 +1,7 @@
from datetime import datetime from datetime import datetime
from typing import Optional, Union from typing import Optional, Union
from pydantic import Field from pydantic import BaseModel, Field
from invokeai.app.util.misc import get_iso_timestamp from invokeai.app.util.misc import get_iso_timestamp
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
@ -18,21 +18,12 @@ class BoardRecord(BaseModelExcludeNull):
"""The created timestamp of the image.""" """The created timestamp of the image."""
updated_at: Union[datetime, str] = Field(description="The updated timestamp of the board.") updated_at: Union[datetime, str] = Field(description="The updated timestamp of the board.")
"""The updated timestamp of the image.""" """The updated timestamp of the image."""
deleted_at: Union[datetime, str, None] = Field(description="The deleted timestamp of the board.") deleted_at: Optional[Union[datetime, str]] = Field(default=None, description="The deleted timestamp of the board.")
"""The updated timestamp of the image.""" """The updated timestamp of the image."""
cover_image_name: Optional[str] = Field(description="The name of the cover image of the board.") cover_image_name: Optional[str] = Field(default=None, description="The name of the cover image of the board.")
"""The name of the cover image of the board.""" """The name of the cover image of the board."""
class BoardDTO(BoardRecord):
"""Deserialized board record with cover image URL and image count."""
cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
"""The URL of the thumbnail of the most recent image in the board."""
image_count: int = Field(description="The number of images in the board.")
"""The number of images in the board."""
def deserialize_board_record(board_dict: dict) -> BoardRecord: def deserialize_board_record(board_dict: dict) -> BoardRecord:
"""Deserializes a board record.""" """Deserializes a board record."""
@ -53,3 +44,29 @@ def deserialize_board_record(board_dict: dict) -> BoardRecord:
updated_at=updated_at, updated_at=updated_at,
deleted_at=deleted_at, deleted_at=deleted_at,
) )
class BoardChanges(BaseModel, extra="forbid"):
board_name: Optional[str] = Field(default=None, description="The board's new name.")
cover_image_name: Optional[str] = Field(default=None, description="The name of the board's new cover image.")
class BoardRecordNotFoundException(Exception):
"""Raised when an board record is not found."""
def __init__(self, message="Board record not found"):
super().__init__(message)
class BoardRecordSaveException(Exception):
"""Raised when an board record cannot be saved."""
def __init__(self, message="Board record not saved"):
super().__init__(message)
class BoardRecordDeleteException(Exception):
"""Raised when an board record cannot be deleted."""
def __init__(self, message="Board record not deleted"):
super().__init__(message)

103
invokeai/app/services/board_record_storage.py → invokeai/app/services/board_records/board_records_sqlite.py
View File

@ -1,103 +1,32 @@
import sqlite3 import sqlite3
import threading import threading
from abc import ABC, abstractmethod from typing import Union, cast
from typing import Optional, Union, cast
from pydantic import BaseModel, Extra, Field from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
from invokeai.app.services.image_record_storage import OffsetPaginatedResults
from invokeai.app.services.models.board_record import BoardRecord, deserialize_board_record
from invokeai.app.util.misc import uuid_string from invokeai.app.util.misc import uuid_string
from .board_records_base import BoardRecordStorageBase
class BoardChanges(BaseModel, extra=Extra.forbid): from .board_records_common import (
board_name: Optional[str] = Field(description="The board's new name.") BoardChanges,
cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.") BoardRecord,
BoardRecordDeleteException,
BoardRecordNotFoundException,
class BoardRecordNotFoundException(Exception): BoardRecordSaveException,
"""Raised when an board record is not found.""" deserialize_board_record,
)
def __init__(self, message="Board record not found"):
super().__init__(message)
class BoardRecordSaveException(Exception):
"""Raised when an board record cannot be saved."""
def __init__(self, message="Board record not saved"):
super().__init__(message)
class BoardRecordDeleteException(Exception):
"""Raised when an board record cannot be deleted."""
def __init__(self, message="Board record not deleted"):
super().__init__(message)
class BoardRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the board record store."""
@abstractmethod
def delete(self, board_id: str) -> None:
"""Deletes a board record."""
pass
@abstractmethod
def save(
self,
board_name: str,
) -> BoardRecord:
"""Saves a board record."""
pass
@abstractmethod
def get(
self,
board_id: str,
) -> BoardRecord:
"""Gets a board record."""
pass
@abstractmethod
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardRecord:
"""Updates a board record."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
) -> OffsetPaginatedResults[BoardRecord]:
"""Gets many board records."""
pass
@abstractmethod
def get_all(
self,
) -> list[BoardRecord]:
"""Gets all board records."""
pass
class SqliteBoardRecordStorage(BoardRecordStorageBase): class SqliteBoardRecordStorage(BoardRecordStorageBase):
_conn: sqlite3.Connection _conn: sqlite3.Connection
_cursor: sqlite3.Cursor _cursor: sqlite3.Cursor
_lock: threading.Lock _lock: threading.RLock
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None: def __init__(self, db: SqliteDatabase) -> None:
super().__init__() super().__init__()
self._conn = conn self._lock = db.lock
# Enable row factory to get rows as dictionaries (must be done before making the cursor!) self._conn = db.conn
self._conn.row_factory = sqlite3.Row
self._cursor = self._conn.cursor() self._cursor = self._conn.cursor()
self._lock = lock
try: try:
self._lock.acquire() self._lock.acquire()

158
invokeai/app/services/boards.py
View File

@ -1,158 +0,0 @@
from abc import ABC, abstractmethod
from logging import Logger
from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
from invokeai.app.services.board_images import board_record_to_dto
from invokeai.app.services.board_record_storage import BoardChanges, BoardRecordStorageBase
from invokeai.app.services.image_record_storage import ImageRecordStorageBase, OffsetPaginatedResults
from invokeai.app.services.models.board_record import BoardDTO
from invokeai.app.services.urls import UrlServiceBase
class BoardServiceABC(ABC):
"""High-level service for board management."""
@abstractmethod
def create(
self,
board_name: str,
) -> BoardDTO:
"""Creates a board."""
pass
@abstractmethod
def get_dto(
self,
board_id: str,
) -> BoardDTO:
"""Gets a board."""
pass
@abstractmethod
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardDTO:
"""Updates a board."""
pass
@abstractmethod
def delete(
self,
board_id: str,
) -> None:
"""Deletes a board."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
) -> OffsetPaginatedResults[BoardDTO]:
"""Gets many boards."""
pass
@abstractmethod
def get_all(
self,
) -> list[BoardDTO]:
"""Gets all boards."""
pass
class BoardServiceDependencies:
"""Service dependencies for the BoardService."""
board_image_records: BoardImageRecordStorageBase
board_records: BoardRecordStorageBase
image_records: ImageRecordStorageBase
urls: UrlServiceBase
logger: Logger
def __init__(
self,
board_image_record_storage: BoardImageRecordStorageBase,
image_record_storage: ImageRecordStorageBase,
board_record_storage: BoardRecordStorageBase,
url: UrlServiceBase,
logger: Logger,
):
self.board_image_records = board_image_record_storage
self.image_records = image_record_storage
self.board_records = board_record_storage
self.urls = url
self.logger = logger
class BoardService(BoardServiceABC):
_services: BoardServiceDependencies
def __init__(self, services: BoardServiceDependencies):
self._services = services
def create(
self,
board_name: str,
) -> BoardDTO:
board_record = self._services.board_records.save(board_name)
return board_record_to_dto(board_record, None, 0)
def get_dto(self, board_id: str) -> BoardDTO:
board_record = self._services.board_records.get(board_id)
cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardDTO:
board_record = self._services.board_records.update(board_id, changes)
cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def delete(self, board_id: str) -> None:
self._services.board_records.delete(board_id)
def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
board_records = self._services.board_records.get_many(offset, limit)
board_dtos = []
for r in board_records.items:
cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
def get_all(self) -> list[BoardDTO]:
board_records = self._services.board_records.get_all()
board_dtos = []
for r in board_records:
cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return board_dtos

0
invokeai/app/services/boards/__init__.py Normal file
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59
invokeai/app/services/boards/boards_base.py Normal file
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@ -0,0 +1,59 @@
from abc import ABC, abstractmethod
from invokeai.app.services.board_records.board_records_common import BoardChanges
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .boards_common import BoardDTO
class BoardServiceABC(ABC):
"""High-level service for board management."""
@abstractmethod
def create(
self,
board_name: str,
) -> BoardDTO:
"""Creates a board."""
pass
@abstractmethod
def get_dto(
self,
board_id: str,
) -> BoardDTO:
"""Gets a board."""
pass
@abstractmethod
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardDTO:
"""Updates a board."""
pass
@abstractmethod
def delete(
self,
board_id: str,
) -> None:
"""Deletes a board."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
) -> OffsetPaginatedResults[BoardDTO]:
"""Gets many boards."""
pass
@abstractmethod
def get_all(
self,
) -> list[BoardDTO]:
"""Gets all boards."""
pass

23
invokeai/app/services/boards/boards_common.py Normal file
View File

@ -0,0 +1,23 @@
from typing import Optional
from pydantic import Field
from ..board_records.board_records_common import BoardRecord
class BoardDTO(BoardRecord):
"""Deserialized board record with cover image URL and image count."""
cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
"""The URL of the thumbnail of the most recent image in the board."""
image_count: int = Field(description="The number of images in the board.")
"""The number of images in the board."""
def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
"""Converts a board record to a board DTO."""
return BoardDTO(
**board_record.model_dump(exclude={"cover_image_name"}),
cover_image_name=cover_image_name,
image_count=image_count,
)

79
invokeai/app/services/boards/boards_default.py Normal file
View File

@ -0,0 +1,79 @@
from invokeai.app.services.board_records.board_records_common import BoardChanges
from invokeai.app.services.boards.boards_common import BoardDTO
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .boards_base import BoardServiceABC
from .boards_common import board_record_to_dto
class BoardService(BoardServiceABC):
__invoker: Invoker
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
def create(
self,
board_name: str,
) -> BoardDTO:
board_record = self.__invoker.services.board_records.save(board_name)
return board_record_to_dto(board_record, None, 0)
def get_dto(self, board_id: str) -> BoardDTO:
board_record = self.__invoker.services.board_records.get(board_id)
cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardDTO:
board_record = self.__invoker.services.board_records.update(board_id, changes)
cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def delete(self, board_id: str) -> None:
self.__invoker.services.board_records.delete(board_id)
def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
board_records = self.__invoker.services.board_records.get_many(offset, limit)
board_dtos = []
for r in board_records.items:
cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
def get_all(self) -> list[BoardDTO]:
board_records = self.__invoker.services.board_records.get_all()
board_dtos = []
for r in board_records:
cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return board_dtos

4
invokeai/app/services/config/__init__.py
View File

@ -2,5 +2,5 @@
Init file for InvokeAI configure package Init file for InvokeAI configure package
""" """
from .base import PagingArgumentParser # noqa F401 from .config_base import PagingArgumentParser # noqa F401
from .invokeai_config import InvokeAIAppConfig, get_invokeai_config # noqa F401 from .config_default import InvokeAIAppConfig, get_invokeai_config # noqa F401

74
invokeai/app/services/config/base.py → invokeai/app/services/config/config_base.py
View File

@ -12,25 +12,15 @@ from __future__ import annotations
import argparse import argparse
import os import os
import pydoc
import sys import sys
from argparse import ArgumentParser from argparse import ArgumentParser
from pathlib import Path from pathlib import Path
from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
from omegaconf import DictConfig, ListConfig, OmegaConf from omegaconf import DictConfig, ListConfig, OmegaConf
from pydantic import BaseSettings from pydantic_settings import BaseSettings, SettingsConfigDict
from invokeai.app.services.config.config_common import PagingArgumentParser, int_or_float_or_str
class PagingArgumentParser(argparse.ArgumentParser):
"""
A custom ArgumentParser that uses pydoc to page its output.
It also supports reading defaults from an init file.
"""
def print_help(self, file=None):
text = self.format_help()
pydoc.pager(text)
class InvokeAISettings(BaseSettings): class InvokeAISettings(BaseSettings):
@ -42,12 +32,14 @@ class InvokeAISettings(BaseSettings):
initconf: ClassVar[Optional[DictConfig]] = None initconf: ClassVar[Optional[DictConfig]] = None
argparse_groups: ClassVar[Dict] = {} argparse_groups: ClassVar[Dict] = {}
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:]): def parse_args(self, argv: Optional[list] = sys.argv[1:]):
parser = self.get_parser() parser = self.get_parser()
opt, unknown_opts = parser.parse_known_args(argv) opt, unknown_opts = parser.parse_known_args(argv)
if len(unknown_opts) > 0: if len(unknown_opts) > 0:
print("Unknown args:", unknown_opts) print("Unknown args:", unknown_opts)
for name in self.__fields__: for name in self.model_fields:
if name not in self._excluded(): if name not in self._excluded():
value = getattr(opt, name) value = getattr(opt, name)
if isinstance(value, ListConfig): if isinstance(value, ListConfig):
@ -64,10 +56,12 @@ class InvokeAISettings(BaseSettings):
cls = self.__class__ cls = self.__class__
type = get_args(get_type_hints(cls)["type"])[0] type = get_args(get_type_hints(cls)["type"])[0]
field_dict = dict({type: dict()}) field_dict = dict({type: dict()})
for name, field in self.__fields__.items(): for name, field in self.model_fields.items():
if name in cls._excluded_from_yaml(): if name in cls._excluded_from_yaml():
continue continue
category = field.field_info.extra.get("category") or "Uncategorized" category = (
field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
)
value = getattr(self, name) value = getattr(self, name)
if category not in field_dict[type]: if category not in field_dict[type]:
field_dict[type][category] = dict() field_dict[type][category] = dict()
@ -83,7 +77,7 @@ class InvokeAISettings(BaseSettings):
else: else:
settings_stanza = "Uncategorized" settings_stanza = "Uncategorized"
env_prefix = getattr(cls.Config, "env_prefix", None) env_prefix = getattr(cls.model_config, "env_prefix", None)
env_prefix = env_prefix if env_prefix is not None else settings_stanza.upper() env_prefix = env_prefix if env_prefix is not None else settings_stanza.upper()
initconf = ( initconf = (
@ -99,14 +93,18 @@ class InvokeAISettings(BaseSettings):
for key, value in os.environ.items(): for key, value in os.environ.items():
upcase_environ[key.upper()] = value upcase_environ[key.upper()] = value
fields = cls.__fields__ fields = cls.model_fields
cls.argparse_groups = {} cls.argparse_groups = {}
for name, field in fields.items(): for name, field in fields.items():
if name not in cls._excluded(): if name not in cls._excluded():
current_default = field.default current_default = field.default
category = field.field_info.extra.get("category", "Uncategorized") category = (
field.json_schema_extra.get("category", "Uncategorized")
if field.json_schema_extra
else "Uncategorized"
)
env_name = env_prefix + "_" + name env_name = env_prefix + "_" + name
if category in initconf and name in initconf.get(category): if category in initconf and name in initconf.get(category):
field.default = initconf.get(category).get(name) field.default = initconf.get(category).get(name)
@ -156,11 +154,6 @@ class InvokeAISettings(BaseSettings):
"tiled_decode", "tiled_decode",
] ]
class Config:
env_file_encoding = "utf-8"
arbitrary_types_allowed = True
case_sensitive = True
@classmethod @classmethod
def add_field_argument(cls, command_parser, name: str, field, default_override=None): def add_field_argument(cls, command_parser, name: str, field, default_override=None):
field_type = get_type_hints(cls).get(name) field_type = get_type_hints(cls).get(name)
@ -171,7 +164,7 @@ class InvokeAISettings(BaseSettings):
if field.default_factory is None if field.default_factory is None
else field.default_factory() else field.default_factory()
) )
if category := field.field_info.extra.get("category"): if category := (field.json_schema_extra.get("category", None) if field.json_schema_extra else None):
if category not in cls.argparse_groups: if category not in cls.argparse_groups:
cls.argparse_groups[category] = command_parser.add_argument_group(category) cls.argparse_groups[category] = command_parser.add_argument_group(category)
argparse_group = cls.argparse_groups[category] argparse_group = cls.argparse_groups[category]
@ -179,7 +172,7 @@ class InvokeAISettings(BaseSettings):
argparse_group = command_parser argparse_group = command_parser
if get_origin(field_type) == Literal: if get_origin(field_type) == Literal:
allowed_values = get_args(field.type_) allowed_values = get_args(field.annotation)
allowed_types = set() allowed_types = set()
for val in allowed_values: for val in allowed_values:
allowed_types.add(type(val)) allowed_types.add(type(val))
@ -192,7 +185,7 @@ class InvokeAISettings(BaseSettings):
type=field_type, type=field_type,
default=default, default=default,
choices=allowed_values, choices=allowed_values,
help=field.field_info.description, help=field.description,
) )
elif get_origin(field_type) == Union: elif get_origin(field_type) == Union:
@ -201,7 +194,7 @@ class InvokeAISettings(BaseSettings):
dest=name, dest=name,
type=int_or_float_or_str, type=int_or_float_or_str,
default=default, default=default,
help=field.field_info.description, help=field.description,
) )
elif get_origin(field_type) == list: elif get_origin(field_type) == list:
@ -209,32 +202,17 @@ class InvokeAISettings(BaseSettings):
f"--{name}", f"--{name}",
dest=name, dest=name,
nargs="*", nargs="*",
type=field.type_, type=field.annotation,
default=default, default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store", action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
help=field.field_info.description, help=field.description,
) )
else: else:
argparse_group.add_argument( argparse_group.add_argument(
f"--{name}", f"--{name}",
dest=name, dest=name,
type=field.type_, type=field.annotation,
default=default, default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store", action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
help=field.field_info.description, help=field.description,
) )
def int_or_float_or_str(value: str) -> Union[int, float, str]:
"""
Workaround for argparse type checking.
"""
try:
return int(value)
except Exception as e: # noqa F841
pass
try:
return float(value)
except Exception as e: # noqa F841
pass
return str(value)

41
invokeai/app/services/config/config_common.py Normal file
View File

@ -0,0 +1,41 @@
# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
"""
Base class for the InvokeAI configuration system.
It defines a type of pydantic BaseSettings object that
is able to read and write from an omegaconf-based config file,
with overriding of settings from environment variables and/or
the command line.
"""
from __future__ import annotations
import argparse
import pydoc
from typing import Union
class PagingArgumentParser(argparse.ArgumentParser):
"""
A custom ArgumentParser that uses pydoc to page its output.
It also supports reading defaults from an init file.
"""
def print_help(self, file=None):
text = self.format_help()
pydoc.pager(text)
def int_or_float_or_str(value: str) -> Union[int, float, str]:
"""
Workaround for argparse type checking.
"""
try:
return int(value)
except Exception as e: # noqa F841
pass
try:
return float(value)
except Exception as e: # noqa F841
pass
return str(value)

140
invokeai/app/services/config/invokeai_config.py → invokeai/app/services/config/config_default.py
View File

@ -144,8 +144,8 @@ which is set to the desired top-level name. For example, to create a
class InvokeBatch(InvokeAISettings): class InvokeBatch(InvokeAISettings):
type: Literal["InvokeBatch"] = "InvokeBatch" type: Literal["InvokeBatch"] = "InvokeBatch"
node_count : int = Field(default=1, description="Number of nodes to run on", category='Resources') node_count : int = Field(default=1, description="Number of nodes to run on", json_schema_extra=dict(category='Resources'))
cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", category='Resources') cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", json_schema_extra=dict(category='Resources'))
This will now read and write from the "InvokeBatch" section of the This will now read and write from the "InvokeBatch" section of the
config file, look for environment variables named INVOKEBATCH_*, and config file, look for environment variables named INVOKEBATCH_*, and
@ -175,9 +175,10 @@ from pathlib import Path
from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hints from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
from omegaconf import DictConfig, OmegaConf from omegaconf import DictConfig, OmegaConf
from pydantic import Field, parse_obj_as from pydantic import Field, TypeAdapter
from pydantic_settings import SettingsConfigDict
from .base import InvokeAISettings from .config_base import InvokeAISettings
INIT_FILE = Path("invokeai.yaml") INIT_FILE = Path("invokeai.yaml")
DB_FILE = Path("invokeai.db") DB_FILE = Path("invokeai.db")
@ -185,6 +186,21 @@ LEGACY_INIT_FILE = Path("invokeai.init")
DEFAULT_MAX_VRAM = 0.5 DEFAULT_MAX_VRAM = 0.5
class Categories(object):
WebServer = dict(category="Web Server")
Features = dict(category="Features")
Paths = dict(category="Paths")
Logging = dict(category="Logging")
Development = dict(category="Development")
Other = dict(category="Other")
ModelCache = dict(category="Model Cache")
Device = dict(category="Device")
Generation = dict(category="Generation")
Queue = dict(category="Queue")
Nodes = dict(category="Nodes")
MemoryPerformance = dict(category="Memory/Performance")
class InvokeAIAppConfig(InvokeAISettings): class InvokeAIAppConfig(InvokeAISettings):
""" """
Generate images using Stable Diffusion. Use "invokeai" to launch Generate images using Stable Diffusion. Use "invokeai" to launch
@ -201,86 +217,88 @@ class InvokeAIAppConfig(InvokeAISettings):
type: Literal["InvokeAI"] = "InvokeAI" type: Literal["InvokeAI"] = "InvokeAI"
# WEB # WEB
host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server') host : str = Field(default="127.0.0.1", description="IP address to bind to", json_schema_extra=Categories.WebServer)
port : int = Field(default=9090, description="Port to bind to", category='Web Server') port : int = Field(default=9090, description="Port to bind to", json_schema_extra=Categories.WebServer)
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server') allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", json_schema_extra=Categories.WebServer)
allow_credentials : bool = Field(default=True, description="Allow CORS credentials", category='Web Server') allow_credentials : bool = Field(default=True, description="Allow CORS credentials", json_schema_extra=Categories.WebServer)
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server') allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", json_schema_extra=Categories.WebServer)
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server') allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", json_schema_extra=Categories.WebServer)
# FEATURES # FEATURES
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features') esrgan : bool = Field(default=True, description="Enable/disable upscaling code", json_schema_extra=Categories.Features)
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features') internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", json_schema_extra=Categories.Features)
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features') log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", json_schema_extra=Categories.Features)
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features') patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", json_schema_extra=Categories.Features)
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features') ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', json_schema_extra=Categories.Features)
# PATHS # PATHS
root : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths') root : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
autoimport_dir : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='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 : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='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 : Path = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='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 : Path = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', category='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 : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths') conf_path : Optional[Path] = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
models_dir : Path = Field(default='models', description='Path to the models directory', category='Paths') models_dir : Optional[Path] = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='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 : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths') db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths') outdir : Optional[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', category='Paths') use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='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)
# LOGGING # LOGGING
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging") log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', json_schema_extra=Categories.Logging)
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging") log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', json_schema_extra=Categories.Logging)
log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging") log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", json_schema_extra=Categories.Logging)
log_sql : bool = Field(default=False, description="Log SQL queries", category="Logging") log_sql : bool = Field(default=False, description="Log SQL queries", json_schema_extra=Categories.Logging)
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", category="Development") dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", json_schema_extra=Categories.Development)
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other") version : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
# CACHE # CACHE
ram : Union[float, Literal["auto"]] = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number or 'auto')", category="Model Cache", ) ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
vram : Union[float, Literal["auto"]] = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number or 'auto')", category="Model Cache", ) vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", ) lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
# DEVICE # DEVICE
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", category="Device", ) device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", category="Device", ) precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
# GENERATION # GENERATION
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category="Generation", ) sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", json_schema_extra=Categories.Generation)
attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", category="Generation", ) attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", json_schema_extra=Categories.Generation)
attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", ) attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', json_schema_extra=Categories.Generation)
force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",) force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Generation)
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",) png_compress_level : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation)
png_compress_level : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", category="Generation", )
# QUEUE # QUEUE
max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", ) max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", json_schema_extra=Categories.Queue)
# NODES # NODES
allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", category="Nodes") allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", json_schema_extra=Categories.Nodes)
deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", category="Nodes") deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", json_schema_extra=Categories.Nodes)
node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", category="Nodes", ) node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", json_schema_extra=Categories.Nodes)
# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES # 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.", category='Memory/Performance') 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.", category='Memory/Performance') 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", category='Memory/Performance') 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", category='Memory/Performance') 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", category='Memory/Performance') 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)", category='Memory/Performance') 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)
# See InvokeAIAppConfig subclass below for CACHE and DEVICE categories # See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
# fmt: on # fmt: on
class Config: model_config = SettingsConfigDict(validate_assignment=True, env_prefix="INVOKEAI")
validate_assignment = True
env_prefix = "INVOKEAI"
def parse_args(self, argv: Optional[list[str]] = None, conf: Optional[DictConfig] = None, clobber=False): def parse_args(
self,
argv: Optional[list[str]] = None,
conf: Optional[DictConfig] = None,
clobber=False,
):
""" """
Update settings with contents of init file, environment, and Update settings with contents of init file, environment, and
command-line settings. command-line settings.
@ -308,7 +326,11 @@ class InvokeAIAppConfig(InvokeAISettings):
if self.singleton_init and not clobber: if self.singleton_init and not clobber:
hints = get_type_hints(self.__class__) hints = get_type_hints(self.__class__)
for k in self.singleton_init: for k in self.singleton_init:
setattr(self, k, parse_obj_as(hints[k], self.singleton_init[k])) setattr(
self,
k,
TypeAdapter(hints[k]).validate_python(self.singleton_init[k]),
)
@classmethod @classmethod
def get_config(cls, **kwargs) -> InvokeAIAppConfig: def get_config(cls, **kwargs) -> InvokeAIAppConfig:

0
invokeai/app/services/events/__init__.py Normal file
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11
invokeai/app/services/events.py → invokeai/app/services/events/events_base.py
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@ -2,8 +2,7 @@
from typing import Any, Optional from typing import Any, Optional
from invokeai.app.models.image import ProgressImage from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
from invokeai.app.services.model_manager_service import BaseModelType, ModelInfo, ModelType, SubModelType
from invokeai.app.services.session_queue.session_queue_common import ( from invokeai.app.services.session_queue.session_queue_common import (
BatchStatus, BatchStatus,
EnqueueBatchResult, EnqueueBatchResult,
@ -11,6 +10,8 @@ from invokeai.app.services.session_queue.session_queue_common import (
SessionQueueStatus, SessionQueueStatus,
) )
from invokeai.app.util.misc import get_timestamp from invokeai.app.util.misc import get_timestamp
from invokeai.backend.model_management.model_manager import ModelInfo
from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
class EventServiceBase: class EventServiceBase:
@ -54,7 +55,7 @@ class EventServiceBase:
graph_execution_state_id=graph_execution_state_id, graph_execution_state_id=graph_execution_state_id,
node_id=node.get("id"), node_id=node.get("id"),
source_node_id=source_node_id, source_node_id=source_node_id,
progress_image=progress_image.dict() if progress_image is not None else None, progress_image=progress_image.model_dump() if progress_image is not None else None,
step=step, step=step,
order=order, order=order,
total_steps=total_steps, total_steps=total_steps,
@ -290,8 +291,8 @@ class EventServiceBase:
started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None, started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None, completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
), ),
batch_status=batch_status.dict(), batch_status=batch_status.model_dump(),
queue_status=queue_status.dict(), queue_status=queue_status.model_dump(),
), ),
) )

0
invokeai/app/services/image_files/__init__.py Normal file
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43
invokeai/app/services/image_files/image_files_base.py Normal file
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@ -0,0 +1,43 @@
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Optional
from PIL.Image import Image as PILImageType
class ImageFileStorageBase(ABC):
"""Low-level service responsible for storing and retrieving image files."""
@abstractmethod
def get(self, image_name: str) -> PILImageType:
"""Retrieves an image as PIL Image."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> Path:
"""Gets the internal path to an image or thumbnail."""
pass
# TODO: We need to validate paths before starlette makes the FileResponse, else we get a
# 500 internal server error. I don't like having this method on the service.
@abstractmethod
def validate_path(self, path: str) -> bool:
"""Validates the path given for an image or thumbnail."""
pass
@abstractmethod
def save(
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = 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."""
pass
@abstractmethod
def delete(self, image_name: str) -> None:
"""Deletes an image and its thumbnail (if one exists)."""
pass

20
invokeai/app/services/image_files/image_files_common.py Normal file
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@ -0,0 +1,20 @@
# TODO: Should these excpetions subclass existing python exceptions?
class ImageFileNotFoundException(Exception):
"""Raised when an image file is not found in storage."""
def __init__(self, message="Image file not found"):
super().__init__(message)
class ImageFileSaveException(Exception):
"""Raised when an image cannot be saved."""
def __init__(self, message="Image file not saved"):
super().__init__(message)
class ImageFileDeleteException(Exception):
"""Raised when an image cannot be deleted."""
def __init__(self, message="Image file not deleted"):
super().__init__(message)

77
invokeai/app/services/image_file_storage.py → invokeai/app/services/image_files/image_files_disk.py
View File

@ -1,6 +1,5 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import json import json
from abc import ABC, abstractmethod
from pathlib import Path from pathlib import Path
from queue import Queue from queue import Queue
from typing import Dict, Optional, Union from typing import Dict, Optional, Union
@ -9,68 +8,11 @@ from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType from PIL.Image import Image as PILImageType
from send2trash import send2trash from send2trash import send2trash
from invokeai.app.services.config.invokeai_config import InvokeAIAppConfig from invokeai.app.services.invoker import Invoker
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
from .image_files_base import ImageFileStorageBase
# TODO: Should these excpetions subclass existing python exceptions? from .image_files_common import ImageFileDeleteException, ImageFileNotFoundException, ImageFileSaveException
class ImageFileNotFoundException(Exception):
"""Raised when an image file is not found in storage."""
def __init__(self, message="Image file not found"):
super().__init__(message)
class ImageFileSaveException(Exception):
"""Raised when an image cannot be saved."""
def __init__(self, message="Image file not saved"):
super().__init__(message)
class ImageFileDeleteException(Exception):
"""Raised when an image cannot be deleted."""
def __init__(self, message="Image file not deleted"):
super().__init__(message)
class ImageFileStorageBase(ABC):
"""Low-level service responsible for storing and retrieving image files."""
@abstractmethod
def get(self, image_name: str) -> PILImageType:
"""Retrieves an image as PIL Image."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets the internal path to an image or thumbnail."""
pass
# TODO: We need to validate paths before starlette makes the FileResponse, else we get a
# 500 internal server error. I don't like having this method on the service.
@abstractmethod
def validate_path(self, path: str) -> bool:
"""Validates the path given for an image or thumbnail."""
pass
@abstractmethod
def save(
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = 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."""
pass
@abstractmethod
def delete(self, image_name: str) -> None:
"""Deletes an image and its thumbnail (if one exists)."""
pass
class DiskImageFileStorage(ImageFileStorageBase): class DiskImageFileStorage(ImageFileStorageBase):
@ -80,7 +22,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
__cache_ids: Queue # TODO: this is an incredibly naive cache __cache_ids: Queue # TODO: this is an incredibly naive cache
__cache: Dict[Path, PILImageType] __cache: Dict[Path, PILImageType]
__max_cache_size: int __max_cache_size: int
__compress_level: int __invoker: Invoker
def __init__(self, output_folder: Union[str, Path]): def __init__(self, output_folder: Union[str, Path]):
self.__cache = dict() self.__cache = dict()
@ -89,10 +31,12 @@ class DiskImageFileStorage(ImageFileStorageBase):
self.__output_folder: Path = output_folder if isinstance(output_folder, Path) else Path(output_folder) self.__output_folder: Path = output_folder if isinstance(output_folder, Path) else Path(output_folder)
self.__thumbnails_folder = self.__output_folder / "thumbnails" self.__thumbnails_folder = self.__output_folder / "thumbnails"
self.__compress_level = InvokeAIAppConfig.get_config().png_compress_level
# Validate required output folders at launch # Validate required output folders at launch
self.__validate_storage_folders() self.__validate_storage_folders()
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
def get(self, image_name: str) -> PILImageType: def get(self, image_name: str) -> PILImageType:
try: try:
image_path = self.get_path(image_name) image_path = self.get_path(image_name)
@ -136,7 +80,12 @@ class DiskImageFileStorage(ImageFileStorageBase):
if original_workflow is not None: if original_workflow is not None:
pnginfo.add_text("invokeai_workflow", original_workflow) pnginfo.add_text("invokeai_workflow", original_workflow)
image.save(image_path, "PNG", pnginfo=pnginfo, compress_level=self.__compress_level) image.save(
image_path,
"PNG",
pnginfo=pnginfo,
compress_level=self.__invoker.services.configuration.png_compress_level,
)
thumbnail_name = get_thumbnail_name(image_name) thumbnail_name = get_thumbnail_name(image_name)
thumbnail_path = self.get_path(thumbnail_name, thumbnail=True) thumbnail_path = self.get_path(thumbnail_name, thumbnail=True)

0
invokeai/app/services/image_records/__init__.py Normal file
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84
invokeai/app/services/image_records/image_records_base.py Normal file
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@ -0,0 +1,84 @@
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Optional
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
class ImageRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the image record store."""
# TODO: Implement an `update()` method
@abstractmethod
def get(self, image_name: str) -> ImageRecord:
"""Gets an image record."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]:
"""Gets an image's metadata'."""
pass
@abstractmethod
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> None:
"""Updates an image record."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageRecord]:
"""Gets a page of image records."""
pass
# TODO: The database has a nullable `deleted_at` column, currently unused.
# Should we implement soft deletes? Would need coordination with ImageFileStorage.
@abstractmethod
def delete(self, image_name: str) -> None:
"""Deletes an image record."""
pass
@abstractmethod
def delete_many(self, image_names: list[str]) -> None:
"""Deletes many image records."""
pass
@abstractmethod
def delete_intermediates(self) -> list[str]:
"""Deletes all intermediate image records, returning a list of deleted image names."""
pass
@abstractmethod
def save(
self,
image_name: str,
image_origin: ResourceOrigin,
image_category: ImageCategory,
width: int,
height: int,
is_intermediate: Optional[bool] = False,
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
) -> datetime:
"""Saves an image record."""
pass
@abstractmethod
def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
"""Gets the most recent image for a board."""
pass

149
invokeai/app/services/models/image_record.py → invokeai/app/services/image_records/image_records_common.py
View File

@ -1,13 +1,117 @@
# TODO: Should these excpetions subclass existing python exceptions?
import datetime import datetime
from enum import Enum
from typing import Optional, Union from typing import Optional, Union
from pydantic import Extra, Field, StrictBool, StrictStr from pydantic import Field, StrictBool, StrictStr
from invokeai.app.models.image import ImageCategory, ResourceOrigin from invokeai.app.util.metaenum import MetaEnum
from invokeai.app.util.misc import get_iso_timestamp from invokeai.app.util.misc import get_iso_timestamp
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
class ResourceOrigin(str, Enum, metaclass=MetaEnum):
"""The origin of a resource (eg image).
- INTERNAL: The resource was created by the application.
- EXTERNAL: The resource was not created by the application.
This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
"""
INTERNAL = "internal"
"""The resource was created by the application."""
EXTERNAL = "external"
"""The resource was not created by the application.
This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
"""
class InvalidOriginException(ValueError):
"""Raised when a provided value is not a valid ResourceOrigin.
Subclasses `ValueError`.
"""
def __init__(self, message="Invalid resource origin."):
super().__init__(message)
class ImageCategory(str, Enum, metaclass=MetaEnum):
"""The category of an image.
- GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose.
- MASK: The image is a mask image.
- CONTROL: The image is a ControlNet control image.
- USER: The image is a user-provide image.
- OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes.
"""
GENERAL = "general"
"""GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose."""
MASK = "mask"
"""MASK: The image is a mask image."""
CONTROL = "control"
"""CONTROL: The image is a ControlNet control image."""
USER = "user"
"""USER: The image is a user-provide image."""
OTHER = "other"
"""OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
class InvalidImageCategoryException(ValueError):
"""Raised when a provided value is not a valid ImageCategory.
Subclasses `ValueError`.
"""
def __init__(self, message="Invalid image category."):
super().__init__(message)
class ImageRecordNotFoundException(Exception):
"""Raised when an image record is not found."""
def __init__(self, message="Image record not found"):
super().__init__(message)
class ImageRecordSaveException(Exception):
"""Raised when an image record cannot be saved."""
def __init__(self, message="Image record not saved"):
super().__init__(message)
class ImageRecordDeleteException(Exception):
"""Raised when an image record cannot be deleted."""
def __init__(self, message="Image record not deleted"):
super().__init__(message)
IMAGE_DTO_COLS = ", ".join(
list(
map(
lambda c: "images." + c,
[
"image_name",
"image_origin",
"image_category",
"width",
"height",
"session_id",
"node_id",
"is_intermediate",
"created_at",
"updated_at",
"deleted_at",
"starred",
],
)
)
)
class ImageRecord(BaseModelExcludeNull): class ImageRecord(BaseModelExcludeNull):
"""Deserialized image record without metadata.""" """Deserialized image record without metadata."""
@ -25,7 +129,9 @@ class ImageRecord(BaseModelExcludeNull):
"""The created timestamp of the image.""" """The created timestamp of the image."""
updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the image.") updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the image.")
"""The updated timestamp of the image.""" """The updated timestamp of the image."""
deleted_at: Union[datetime.datetime, str, None] = Field(description="The deleted timestamp of the image.") deleted_at: Optional[Union[datetime.datetime, str]] = Field(
default=None, description="The deleted timestamp of the image."
)
"""The deleted timestamp of the image.""" """The deleted timestamp of the image."""
is_intermediate: bool = Field(description="Whether this is an intermediate image.") is_intermediate: bool = Field(description="Whether this is an intermediate image.")
"""Whether this is an intermediate image.""" """Whether this is an intermediate image."""
@ -43,7 +149,7 @@ class ImageRecord(BaseModelExcludeNull):
"""Whether this image is starred.""" """Whether this image is starred."""
class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid): class ImageRecordChanges(BaseModelExcludeNull, extra="allow"):
"""A set of changes to apply to an image record. """A set of changes to apply to an image record.
Only limited changes are valid: Only limited changes are valid:
@ -66,41 +172,6 @@ class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid):
"""The image's new `starred` state.""" """The image's new `starred` state."""
class ImageUrlsDTO(BaseModelExcludeNull):
"""The URLs for an image and its thumbnail."""
image_name: str = Field(description="The unique name of the image.")
"""The unique name of the image."""
image_url: str = Field(description="The URL of the image.")
"""The URL of the image."""
thumbnail_url: str = Field(description="The URL of the image's thumbnail.")
"""The URL of the image's thumbnail."""
class ImageDTO(ImageRecord, ImageUrlsDTO):
"""Deserialized image record, enriched for the frontend."""
board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.")
"""The id of the board the image belongs to, if one exists."""
pass
def image_record_to_dto(
image_record: ImageRecord,
image_url: str,
thumbnail_url: str,
board_id: Optional[str],
) -> ImageDTO:
"""Converts an image record to an image DTO."""
return ImageDTO(
**image_record.dict(),
image_url=image_url,
thumbnail_url=thumbnail_url,
board_id=board_id,
)
def deserialize_image_record(image_dict: dict) -> ImageRecord: def deserialize_image_record(image_dict: dict) -> ImageRecord:
"""Deserializes an image record.""" """Deserializes an image record."""

182
invokeai/app/services/image_record_storage.py → invokeai/app/services/image_records/image_records_sqlite.py
View File

@ -1,164 +1,36 @@
import json import json
import sqlite3 import sqlite3
import threading import threading
from abc import ABC, abstractmethod
from datetime import datetime from datetime import datetime
from typing import Generic, Optional, TypeVar, cast from typing import Optional, Union, cast
from pydantic import BaseModel, Field from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from pydantic.generics import GenericModel from invokeai.app.services.shared.sqlite import SqliteDatabase
from invokeai.app.models.image import ImageCategory, ResourceOrigin from .image_records_base import ImageRecordStorageBase
from invokeai.app.services.models.image_record import ImageRecord, ImageRecordChanges, deserialize_image_record from .image_records_common import (
IMAGE_DTO_COLS,
T = TypeVar("T", bound=BaseModel) ImageCategory,
ImageRecord,
ImageRecordChanges,
class OffsetPaginatedResults(GenericModel, Generic[T]): ImageRecordDeleteException,
"""Offset-paginated results""" ImageRecordNotFoundException,
ImageRecordSaveException,
# fmt: off ResourceOrigin,
items: list[T] = Field(description="Items") deserialize_image_record,
offset: int = Field(description="Offset from which to retrieve items")
limit: int = Field(description="Limit of items to get")
total: int = Field(description="Total number of items in result")
# fmt: on
# TODO: Should these excpetions subclass existing python exceptions?
class ImageRecordNotFoundException(Exception):
"""Raised when an image record is not found."""
def __init__(self, message="Image record not found"):
super().__init__(message)
class ImageRecordSaveException(Exception):
"""Raised when an image record cannot be saved."""
def __init__(self, message="Image record not saved"):
super().__init__(message)
class ImageRecordDeleteException(Exception):
"""Raised when an image record cannot be deleted."""
def __init__(self, message="Image record not deleted"):
super().__init__(message)
IMAGE_DTO_COLS = ", ".join(
list(
map(
lambda c: "images." + c,
[
"image_name",
"image_origin",
"image_category",
"width",
"height",
"session_id",
"node_id",
"is_intermediate",
"created_at",
"updated_at",
"deleted_at",
"starred",
],
)
)
) )
class ImageRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the image record store."""
# TODO: Implement an `update()` method
@abstractmethod
def get(self, image_name: str) -> ImageRecord:
"""Gets an image record."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]:
"""Gets an image's metadata'."""
pass
@abstractmethod
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> None:
"""Updates an image record."""
pass
@abstractmethod
def get_many(
self,
offset: Optional[int] = None,
limit: Optional[int] = None,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageRecord]:
"""Gets a page of image records."""
pass
# TODO: The database has a nullable `deleted_at` column, currently unused.
# Should we implement soft deletes? Would need coordination with ImageFileStorage.
@abstractmethod
def delete(self, image_name: str) -> None:
"""Deletes an image record."""
pass
@abstractmethod
def delete_many(self, image_names: list[str]) -> None:
"""Deletes many image records."""
pass
@abstractmethod
def delete_intermediates(self) -> list[str]:
"""Deletes all intermediate image records, returning a list of deleted image names."""
pass
@abstractmethod
def save(
self,
image_name: str,
image_origin: ResourceOrigin,
image_category: ImageCategory,
width: int,
height: int,
session_id: Optional[str],
node_id: Optional[str],
metadata: Optional[dict],
is_intermediate: bool = False,
starred: bool = False,
) -> datetime:
"""Saves an image record."""
pass
@abstractmethod
def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
"""Gets the most recent image for a board."""
pass
class SqliteImageRecordStorage(ImageRecordStorageBase): class SqliteImageRecordStorage(ImageRecordStorageBase):
_conn: sqlite3.Connection _conn: sqlite3.Connection
_cursor: sqlite3.Cursor _cursor: sqlite3.Cursor
_lock: threading.Lock _lock: threading.RLock
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None: def __init__(self, db: SqliteDatabase) -> None:
super().__init__() super().__init__()
self._conn = conn self._lock = db.lock
# Enable row factory to get rows as dictionaries (must be done before making the cursor!) self._conn = db.conn
self._conn.row_factory = sqlite3.Row
self._cursor = self._conn.cursor() self._cursor = self._conn.cursor()
self._lock = lock
try: try:
self._lock.acquire() self._lock.acquire()
@ -245,7 +117,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
""" """
) )
def get(self, image_name: str) -> Optional[ImageRecord]: def get(self, image_name: str) -> ImageRecord:
try: try:
self._lock.acquire() self._lock.acquire()
@ -351,8 +223,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
def get_many( def get_many(
self, self,
offset: Optional[int] = None, offset: int = 0,
limit: Optional[int] = None, limit: int = 10,
image_origin: Optional[ResourceOrigin] = None, image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None, categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None, is_intermediate: Optional[bool] = None,
@ -377,7 +249,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
""" """
query_conditions = "" query_conditions = ""
query_params = [] query_params: list[Union[int, str, bool]] = []
if image_origin is not None: if image_origin is not None:
query_conditions += """--sql query_conditions += """--sql
@ -515,13 +387,13 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
image_name: str, image_name: str,
image_origin: ResourceOrigin, image_origin: ResourceOrigin,
image_category: ImageCategory, image_category: ImageCategory,
session_id: Optional[str],
width: int, width: int,
height: int, height: int,
node_id: Optional[str], is_intermediate: Optional[bool] = False,
metadata: Optional[dict], starred: Optional[bool] = False,
is_intermediate: bool = False, session_id: Optional[str] = None,
starred: bool = False, node_id: Optional[str] = None,
metadata: Optional[dict] = None,
) -> datetime: ) -> datetime:
try: try:
metadata_json = None if metadata is None else json.dumps(metadata) metadata_json = None if metadata is None else json.dumps(metadata)

449
invokeai/app/services/images.py
View File

@ -1,449 +0,0 @@
from abc import ABC, abstractmethod
from logging import Logger
from typing import TYPE_CHECKING, Callable, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.models.image import (
ImageCategory,
InvalidImageCategoryException,
InvalidOriginException,
ResourceOrigin,
)
from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
from invokeai.app.services.image_file_storage import (
ImageFileDeleteException,
ImageFileNotFoundException,
ImageFileSaveException,
ImageFileStorageBase,
)
from invokeai.app.services.image_record_storage import (
ImageRecordDeleteException,
ImageRecordNotFoundException,
ImageRecordSaveException,
ImageRecordStorageBase,
OffsetPaginatedResults,
)
from invokeai.app.services.item_storage import ItemStorageABC
from invokeai.app.services.models.image_record import ImageDTO, ImageRecord, ImageRecordChanges, image_record_to_dto
from invokeai.app.services.resource_name import NameServiceBase
from invokeai.app.services.urls import UrlServiceBase
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
if TYPE_CHECKING:
from invokeai.app.services.graph import GraphExecutionState
class ImageServiceABC(ABC):
"""High-level service for image management."""
_on_changed_callbacks: list[Callable[[ImageDTO], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = list()
self._on_deleted_callbacks = list()
def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
"""Register a callback for when an image is changed"""
self._on_changed_callbacks.append(on_changed)
def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
"""Register a callback for when an image is deleted"""
self._on_deleted_callbacks.append(on_deleted)
def _on_changed(self, item: ImageDTO) -> None:
for callback in self._on_changed_callbacks:
callback(item)
def _on_deleted(self, item_id: str) -> None:
for callback in self._on_deleted_callbacks:
callback(item_id)
@abstractmethod
def create(
self,
image: PILImageType,
image_origin: ResourceOrigin,
image_category: ImageCategory,
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: bool = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
"""Creates an image, storing the file and its metadata."""
pass
@abstractmethod
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> ImageDTO:
"""Updates an image."""
pass
@abstractmethod
def get_pil_image(self, image_name: str) -> PILImageType:
"""Gets an image as a PIL image."""
pass
@abstractmethod
def get_record(self, image_name: str) -> ImageRecord:
"""Gets an image record."""
pass
@abstractmethod
def get_dto(self, image_name: str) -> ImageDTO:
"""Gets an image DTO."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata:
"""Gets an image's metadata."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's path."""
pass
@abstractmethod
def validate_path(self, path: str) -> bool:
"""Validates an image's path."""
pass
@abstractmethod
def get_url(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's or thumbnail's URL."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageDTO]:
"""Gets a paginated list of image DTOs."""
pass
@abstractmethod
def delete(self, image_name: str):
"""Deletes an image."""
pass
@abstractmethod
def delete_intermediates(self) -> int:
"""Deletes all intermediate images."""
pass
@abstractmethod
def delete_images_on_board(self, board_id: str):
"""Deletes all images on a board."""
pass
class ImageServiceDependencies:
"""Service dependencies for the ImageService."""
image_records: ImageRecordStorageBase
image_files: ImageFileStorageBase
board_image_records: BoardImageRecordStorageBase
urls: UrlServiceBase
logger: Logger
names: NameServiceBase
graph_execution_manager: ItemStorageABC["GraphExecutionState"]
def __init__(
self,
image_record_storage: ImageRecordStorageBase,
image_file_storage: ImageFileStorageBase,
board_image_record_storage: BoardImageRecordStorageBase,
url: UrlServiceBase,
logger: Logger,
names: NameServiceBase,
graph_execution_manager: ItemStorageABC["GraphExecutionState"],
):
self.image_records = image_record_storage
self.image_files = image_file_storage
self.board_image_records = board_image_record_storage
self.urls = url
self.logger = logger
self.names = names
self.graph_execution_manager = graph_execution_manager
class ImageService(ImageServiceABC):
_services: ImageServiceDependencies
def __init__(self, services: ImageServiceDependencies):
super().__init__()
self._services = services
def create(
self,
image: PILImageType,
image_origin: ResourceOrigin,
image_category: ImageCategory,
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: bool = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
if image_origin not in ResourceOrigin:
raise InvalidOriginException
if image_category not in ImageCategory:
raise InvalidImageCategoryException
image_name = self._services.names.create_image_name()
# TODO: Do we want to store the graph in the image at all? I don't think so...
# graph = None
# if session_id is not None:
# session_raw = self._services.graph_execution_manager.get_raw(session_id)
# if session_raw is not None:
# try:
# graph = get_metadata_graph_from_raw_session(session_raw)
# except Exception as e:
# self._services.logger.warn(f"Failed to parse session graph: {e}")
# graph = None
(width, height) = image.size
try:
# TODO: Consider using a transaction here to ensure consistency between storage and database
self._services.image_records.save(
# Non-nullable fields
image_name=image_name,
image_origin=image_origin,
image_category=image_category,
width=width,
height=height,
# Meta fields
is_intermediate=is_intermediate,
# Nullable fields
node_id=node_id,
metadata=metadata,
session_id=session_id,
)
if board_id is not None:
self._services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
self._services.image_files.save(image_name=image_name, image=image, metadata=metadata, workflow=workflow)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self._services.logger.error("Failed to save image record")
raise
except ImageFileSaveException:
self._services.logger.error("Failed to save image file")
raise
except Exception as e:
self._services.logger.error(f"Problem saving image record and file: {str(e)}")
raise e
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> ImageDTO:
try:
self._services.image_records.update(image_name, changes)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self._services.logger.error("Failed to update image record")
raise
except Exception as e:
self._services.logger.error("Problem updating image record")
raise e
def get_pil_image(self, image_name: str) -> PILImageType:
try:
return self._services.image_files.get(image_name)
except ImageFileNotFoundException:
self._services.logger.error("Failed to get image file")
raise
except Exception as e:
self._services.logger.error("Problem getting image file")
raise e
def get_record(self, image_name: str) -> ImageRecord:
try:
return self._services.image_records.get(image_name)
except ImageRecordNotFoundException:
self._services.logger.error("Image record not found")
raise
except Exception as e:
self._services.logger.error("Problem getting image record")
raise e
def get_dto(self, image_name: str) -> ImageDTO:
try:
image_record = self._services.image_records.get(image_name)
image_dto = image_record_to_dto(
image_record,
self._services.urls.get_image_url(image_name),
self._services.urls.get_image_url(image_name, True),
self._services.board_image_records.get_board_for_image(image_name),
)
return image_dto
except ImageRecordNotFoundException:
self._services.logger.error("Image record not found")
raise
except Exception as e:
self._services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
try:
image_record = self._services.image_records.get(image_name)
metadata = self._services.image_records.get_metadata(image_name)
if not image_record.session_id:
return ImageMetadata(metadata=metadata)
session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id)
graph = None
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self._services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
return ImageMetadata(graph=graph, metadata=metadata)
except ImageRecordNotFoundException:
self._services.logger.error("Image record not found")
raise
except Exception as e:
self._services.logger.error("Problem getting image DTO")
raise e
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
try:
return self._services.image_files.get_path(image_name, thumbnail)
except Exception as e:
self._services.logger.error("Problem getting image path")
raise e
def validate_path(self, path: str) -> bool:
try:
return self._services.image_files.validate_path(path)
except Exception as e:
self._services.logger.error("Problem validating image path")
raise e
def get_url(self, image_name: str, thumbnail: bool = False) -> str:
try:
return self._services.urls.get_image_url(image_name, thumbnail)
except Exception as e:
self._services.logger.error("Problem getting image path")
raise e
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageDTO]:
try:
results = self._services.image_records.get_many(
offset,
limit,
image_origin,
categories,
is_intermediate,
board_id,
)
image_dtos = list(
map(
lambda r: image_record_to_dto(
r,
self._services.urls.get_image_url(r.image_name),
self._services.urls.get_image_url(r.image_name, True),
self._services.board_image_records.get_board_for_image(r.image_name),
),
results.items,
)
)
return OffsetPaginatedResults[ImageDTO](
items=image_dtos,
offset=results.offset,
limit=results.limit,
total=results.total,
)
except Exception as e:
self._services.logger.error("Problem getting paginated image DTOs")
raise e
def delete(self, image_name: str):
try:
self._services.image_files.delete(image_name)
self._services.image_records.delete(image_name)
self._on_deleted(image_name)
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image record")
raise
except ImageFileDeleteException:
self._services.logger.error("Failed to delete image file")
raise
except Exception as e:
self._services.logger.error("Problem deleting image record and file")
raise e
def delete_images_on_board(self, board_id: str):
try:
image_names = self._services.board_image_records.get_all_board_image_names_for_board(board_id)
for image_name in image_names:
self._services.image_files.delete(image_name)
self._services.image_records.delete_many(image_names)
for image_name in image_names:
self._on_deleted(image_name)
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image records")
raise
except ImageFileDeleteException:
self._services.logger.error("Failed to delete image files")
raise
except Exception as e:
self._services.logger.error("Problem deleting image records and files")
raise e
def delete_intermediates(self) -> int:
try:
image_names = self._services.image_records.delete_intermediates()
count = len(image_names)
for image_name in image_names:
self._services.image_files.delete(image_name)
self._on_deleted(image_name)
return count
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image records")
raise
except ImageFileDeleteException:
self._services.logger.error("Failed to delete image files")
raise
except Exception as e:
self._services.logger.error("Problem deleting image records and files")
raise e

0
invokeai/app/services/images/__init__.py Normal file
View File

129
invokeai/app/services/images/images_base.py Normal file
View File

@ -0,0 +1,129 @@
from abc import ABC, abstractmethod
from typing import Callable, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.services.image_records.image_records_common import (
ImageCategory,
ImageRecord,
ImageRecordChanges,
ResourceOrigin,
)
from invokeai.app.services.images.images_common import ImageDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
class ImageServiceABC(ABC):
"""High-level service for image management."""
_on_changed_callbacks: list[Callable[[ImageDTO], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = list()
self._on_deleted_callbacks = list()
def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
"""Register a callback for when an image is changed"""
self._on_changed_callbacks.append(on_changed)
def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
"""Register a callback for when an image is deleted"""
self._on_deleted_callbacks.append(on_deleted)
def _on_changed(self, item: ImageDTO) -> None:
for callback in self._on_changed_callbacks:
callback(item)
def _on_deleted(self, item_id: str) -> None:
for callback in self._on_deleted_callbacks:
callback(item_id)
@abstractmethod
def create(
self,
image: PILImageType,
image_origin: ResourceOrigin,
image_category: ImageCategory,
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
"""Creates an image, storing the file and its metadata."""
pass
@abstractmethod
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> ImageDTO:
"""Updates an image."""
pass
@abstractmethod
def get_pil_image(self, image_name: str) -> PILImageType:
"""Gets an image as a PIL image."""
pass
@abstractmethod
def get_record(self, image_name: str) -> ImageRecord:
"""Gets an image record."""
pass
@abstractmethod
def get_dto(self, image_name: str) -> ImageDTO:
"""Gets an image DTO."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata:
"""Gets an image's metadata."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's path."""
pass
@abstractmethod
def validate_path(self, path: str) -> bool:
"""Validates an image's path."""
pass
@abstractmethod
def get_url(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's or thumbnail's URL."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageDTO]:
"""Gets a paginated list of image DTOs."""
pass
@abstractmethod
def delete(self, image_name: str):
"""Deletes an image."""
pass
@abstractmethod
def delete_intermediates(self) -> int:
"""Deletes all intermediate images."""
pass
@abstractmethod
def delete_images_on_board(self, board_id: str):
"""Deletes all images on a board."""
pass

43
invokeai/app/services/images/images_common.py Normal file
View File

@ -0,0 +1,43 @@
from typing import Optional
from pydantic import Field
from invokeai.app.services.image_records.image_records_common import ImageRecord
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
class ImageUrlsDTO(BaseModelExcludeNull):
"""The URLs for an image and its thumbnail."""
image_name: str = Field(description="The unique name of the image.")
"""The unique name of the image."""
image_url: str = Field(description="The URL of the image.")
"""The URL of the image."""
thumbnail_url: str = Field(description="The URL of the image's thumbnail.")
"""The URL of the image's thumbnail."""
class ImageDTO(ImageRecord, ImageUrlsDTO):
"""Deserialized image record, enriched for the frontend."""
board_id: Optional[str] = Field(
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."""
pass
def image_record_to_dto(
image_record: ImageRecord,
image_url: str,
thumbnail_url: str,
board_id: Optional[str],
) -> ImageDTO:
"""Converts an image record to an image DTO."""
return ImageDTO(
**image_record.model_dump(),
image_url=image_url,
thumbnail_url=thumbnail_url,
board_id=board_id,
)

286
invokeai/app/services/images/images_default.py Normal file
View File

@ -0,0 +1,286 @@
from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
from ..image_files.image_files_common import (
ImageFileDeleteException,
ImageFileNotFoundException,
ImageFileSaveException,
)
from ..image_records.image_records_common import (
ImageCategory,
ImageRecord,
ImageRecordChanges,
ImageRecordDeleteException,
ImageRecordNotFoundException,
ImageRecordSaveException,
InvalidImageCategoryException,
InvalidOriginException,
ResourceOrigin,
)
from .images_base import ImageServiceABC
from .images_common import ImageDTO, image_record_to_dto
class ImageService(ImageServiceABC):
__invoker: Invoker
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
def create(
self,
image: PILImageType,
image_origin: ResourceOrigin,
image_category: ImageCategory,
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
if image_origin not in ResourceOrigin:
raise InvalidOriginException
if image_category not in ImageCategory:
raise InvalidImageCategoryException
image_name = self.__invoker.services.names.create_image_name()
(width, height) = image.size
try:
# TODO: Consider using a transaction here to ensure consistency between storage and database
self.__invoker.services.image_records.save(
# Non-nullable fields
image_name=image_name,
image_origin=image_origin,
image_category=image_category,
width=width,
height=height,
# Meta fields
is_intermediate=is_intermediate,
# Nullable fields
node_id=node_id,
metadata=metadata,
session_id=session_id,
)
if board_id is not None:
self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
self.__invoker.services.image_files.save(
image_name=image_name, image=image, metadata=metadata, workflow=workflow
)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self.__invoker.services.logger.error("Failed to save image record")
raise
except ImageFileSaveException:
self.__invoker.services.logger.error("Failed to save image file")
raise
except Exception as e:
self.__invoker.services.logger.error(f"Problem saving image record and file: {str(e)}")
raise e
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> ImageDTO:
try:
self.__invoker.services.image_records.update(image_name, changes)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self.__invoker.services.logger.error("Failed to update image record")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem updating image record")
raise e
def get_pil_image(self, image_name: str) -> PILImageType:
try:
return self.__invoker.services.image_files.get(image_name)
except ImageFileNotFoundException:
self.__invoker.services.logger.error("Failed to get image file")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image file")
raise e
def get_record(self, image_name: str) -> ImageRecord:
try:
return self.__invoker.services.image_records.get(image_name)
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image record")
raise e
def get_dto(self, image_name: str) -> ImageDTO:
try:
image_record = self.__invoker.services.image_records.get(image_name)
image_dto = image_record_to_dto(
image_record,
self.__invoker.services.urls.get_image_url(image_name),
self.__invoker.services.urls.get_image_url(image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(image_name),
)
return image_dto
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> ImageMetadata:
try:
image_record = self.__invoker.services.image_records.get(image_name)
metadata = self.__invoker.services.image_records.get_metadata(image_name)
if not image_record.session_id:
return ImageMetadata(metadata=metadata)
session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id)
graph = None
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
return ImageMetadata(graph=graph, metadata=metadata)
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
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:
return str(self.__invoker.services.image_files.get_path(image_name, thumbnail))
except Exception as e:
self.__invoker.services.logger.error("Problem getting image path")
raise e
def validate_path(self, path: str) -> bool:
try:
return self.__invoker.services.image_files.validate_path(path)
except Exception as e:
self.__invoker.services.logger.error("Problem validating image path")
raise e
def get_url(self, image_name: str, thumbnail: bool = False) -> str:
try:
return self.__invoker.services.urls.get_image_url(image_name, thumbnail)
except Exception as e:
self.__invoker.services.logger.error("Problem getting image path")
raise e
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageDTO]:
try:
results = self.__invoker.services.image_records.get_many(
offset,
limit,
image_origin,
categories,
is_intermediate,
board_id,
)
image_dtos = list(
map(
lambda r: image_record_to_dto(
r,
self.__invoker.services.urls.get_image_url(r.image_name),
self.__invoker.services.urls.get_image_url(r.image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
),
results.items,
)
)
return OffsetPaginatedResults[ImageDTO](
items=image_dtos,
offset=results.offset,
limit=results.limit,
total=results.total,
)
except Exception as e:
self.__invoker.services.logger.error("Problem getting paginated image DTOs")
raise e
def delete(self, image_name: str):
try:
self.__invoker.services.image_files.delete(image_name)
self.__invoker.services.image_records.delete(image_name)
self._on_deleted(image_name)
except ImageRecordDeleteException:
self.__invoker.services.logger.error("Failed to delete image record")
raise
except ImageFileDeleteException:
self.__invoker.services.logger.error("Failed to delete image file")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem deleting image record and file")
raise e
def delete_images_on_board(self, board_id: str):
try:
image_names = self.__invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
for image_name in image_names:
self.__invoker.services.image_files.delete(image_name)
self.__invoker.services.image_records.delete_many(image_names)
for image_name in image_names:
self._on_deleted(image_name)
except ImageRecordDeleteException:
self.__invoker.services.logger.error("Failed to delete image records")
raise
except ImageFileDeleteException:
self.__invoker.services.logger.error("Failed to delete image files")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem deleting image records and files")
raise e
def delete_intermediates(self) -> int:
try:
image_names = self.__invoker.services.image_records.delete_intermediates()
count = len(image_names)
for image_name in image_names:
self.__invoker.services.image_files.delete(image_name)
self._on_deleted(image_name)
return count
except ImageRecordDeleteException:
self.__invoker.services.logger.error("Failed to delete image records")
raise
except ImageFileDeleteException:
self.__invoker.services.logger.error("Failed to delete image files")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem deleting image records and files")
raise e

9
invokeai/app/services/invocation_cache/invocation_cache_memory.py
View File

@ -58,7 +58,12 @@ class MemoryInvocationCache(InvocationCacheBase):
# If the cache is full, we need to remove the least used # If the cache is full, we need to remove the least used
number_to_delete = len(self._cache) + 1 - self._max_cache_size number_to_delete = len(self._cache) + 1 - self._max_cache_size
self._delete_oldest_access(number_to_delete) self._delete_oldest_access(number_to_delete)
self._cache[key] = CachedItem(invocation_output, invocation_output.json()) self._cache[key] = CachedItem(
invocation_output,
invocation_output.model_dump_json(
warnings=False, exclude_defaults=True, exclude_unset=True, include={"type"}
),
)
def _delete_oldest_access(self, number_to_delete: int) -> None: def _delete_oldest_access(self, number_to_delete: int) -> None:
number_to_delete = min(number_to_delete, len(self._cache)) number_to_delete = min(number_to_delete, len(self._cache))
@ -85,7 +90,7 @@ class MemoryInvocationCache(InvocationCacheBase):
@staticmethod @staticmethod
def create_key(invocation: BaseInvocation) -> int: def create_key(invocation: BaseInvocation) -> int:
return hash(invocation.json(exclude={"id"})) return hash(invocation.model_dump_json(exclude={"id"}, warnings=False))
def disable(self) -> None: def disable(self) -> None:
with self._lock: with self._lock:

0
invokeai/app/services/invocation_processor/__init__.py Normal file
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5
invokeai/app/services/invocation_processor/invocation_processor_base.py Normal file
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@ -0,0 +1,5 @@
from abc import ABC
class InvocationProcessorABC(ABC):
pass

15
invokeai/app/services/invocation_processor/invocation_processor_common.py Normal file
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@ -0,0 +1,15 @@
from pydantic import BaseModel, Field
class ProgressImage(BaseModel):
"""The progress image sent intermittently during processing"""
width: int = Field(description="The effective width of the image in pixels")
height: int = Field(description="The effective height of the image in pixels")
dataURL: str = Field(description="The image data as a b64 data URL")
class CanceledException(Exception):
"""Execution canceled by user."""
pass

30
invokeai/app/services/processor.py → invokeai/app/services/invocation_processor/invocation_processor_default.py
View File

@ -4,12 +4,12 @@ from threading import BoundedSemaphore, Event, Thread
from typing import Optional from typing import Optional
import invokeai.backend.util.logging as logger import invokeai.backend.util.logging as logger
from invokeai.app.invocations.baseinvocation import InvocationContext
from invokeai.app.services.invocation_queue.invocation_queue_common import InvocationQueueItem
from ..invocations.baseinvocation import InvocationContext from ..invoker import Invoker
from ..models.exceptions import CanceledException from .invocation_processor_base import InvocationProcessorABC
from .invocation_queue import InvocationQueueItem from .invocation_processor_common import CanceledException
from .invocation_stats import InvocationStatsServiceBase
from .invoker import InvocationProcessorABC, Invoker
class DefaultInvocationProcessor(InvocationProcessorABC): class DefaultInvocationProcessor(InvocationProcessorABC):
@ -37,7 +37,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
def __process(self, stop_event: Event): def __process(self, stop_event: Event):
try: try:
self.__threadLimit.acquire() self.__threadLimit.acquire()
statistics: InvocationStatsServiceBase = self.__invoker.services.performance_statistics
queue_item: Optional[InvocationQueueItem] = None queue_item: Optional[InvocationQueueItem] = None
while not stop_event.is_set(): while not stop_event.is_set():
@ -90,15 +89,14 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id, queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id, queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id, graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(), node=invocation.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
) )
# Invoke # Invoke
try: try:
graph_id = graph_execution_state.id graph_id = graph_execution_state.id
model_manager = self.__invoker.services.model_manager with self.__invoker.services.performance_statistics.collect_stats(invocation, graph_id):
with statistics.collect_stats(invocation, graph_id, model_manager):
# use the internal invoke_internal(), which wraps the node's invoke() method, # use the internal invoke_internal(), which wraps the node's invoke() method,
# which handles a few things: # which handles a few things:
# - nodes that require a value, but get it only from a connection # - nodes that require a value, but get it only from a connection
@ -129,17 +127,17 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id, queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id, queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id, graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(), node=invocation.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
result=outputs.dict(), result=outputs.model_dump(),
) )
statistics.log_stats() self.__invoker.services.performance_statistics.log_stats()
except KeyboardInterrupt: except KeyboardInterrupt:
pass pass
except CanceledException: except CanceledException:
statistics.reset_stats(graph_execution_state.id) self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
pass pass
except Exception as e: except Exception as e:
@ -159,12 +157,12 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id, queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id, queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id, graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(), node=invocation.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
error_type=e.__class__.__name__, error_type=e.__class__.__name__,
error=error, error=error,
) )
statistics.reset_stats(graph_execution_state.id) self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
pass pass
# Check queue to see if this is canceled, and skip if so # Check queue to see if this is canceled, and skip if so
@ -189,7 +187,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id, queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id, queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id, graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(), node=invocation.model_dump(),
source_node_id=source_node_id, source_node_id=source_node_id,
error_type=e.__class__.__name__, error_type=e.__class__.__name__,
error=traceback.format_exc(), error=traceback.format_exc(),

0
invokeai/app/services/invocation_queue/__init__.py Normal file
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26
invokeai/app/services/invocation_queue/invocation_queue_base.py Normal file
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@ -0,0 +1,26 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from abc import ABC, abstractmethod
from typing import Optional
from .invocation_queue_common import InvocationQueueItem
class InvocationQueueABC(ABC):
"""Abstract base class for all invocation queues"""
@abstractmethod
def get(self) -> InvocationQueueItem:
pass
@abstractmethod
def put(self, item: Optional[InvocationQueueItem]) -> None:
pass
@abstractmethod
def cancel(self, graph_execution_state_id: str) -> None:
pass
@abstractmethod
def is_canceled(self, graph_execution_state_id: str) -> bool:
pass

19
invokeai/app/services/invocation_queue/invocation_queue_common.py Normal file
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@ -0,0 +1,19 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import time
from pydantic import BaseModel, Field
class InvocationQueueItem(BaseModel):
graph_execution_state_id: str = Field(description="The ID of the graph execution state")
invocation_id: str = Field(description="The ID of the node being invoked")
session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
session_queue_item_id: int = Field(
description="The ID of session queue item from which this invocation queue item came"
)
session_queue_batch_id: str = Field(
description="The ID of the session batch from which this invocation queue item came"
)
invoke_all: bool = Field(default=False)
timestamp: float = Field(default_factory=time.time)

38
invokeai/app/services/invocation_queue.py → invokeai/app/services/invocation_queue/invocation_queue_memory.py
View File

@ -1,45 +1,11 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import time import time
from abc import ABC, abstractmethod
from queue import Queue from queue import Queue
from typing import Optional from typing import Optional
from pydantic import BaseModel, Field from .invocation_queue_base import InvocationQueueABC
from .invocation_queue_common import InvocationQueueItem
class InvocationQueueItem(BaseModel):
graph_execution_state_id: str = Field(description="The ID of the graph execution state")
invocation_id: str = Field(description="The ID of the node being invoked")
session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
session_queue_item_id: int = Field(
description="The ID of session queue item from which this invocation queue item came"
)
session_queue_batch_id: str = Field(
description="The ID of the session batch from which this invocation queue item came"
)
invoke_all: bool = Field(default=False)
timestamp: float = Field(default_factory=time.time)
class InvocationQueueABC(ABC):
"""Abstract base class for all invocation queues"""
@abstractmethod
def get(self) -> InvocationQueueItem:
pass
@abstractmethod
def put(self, item: Optional[InvocationQueueItem]) -> None:
pass
@abstractmethod
def cancel(self, graph_execution_state_id: str) -> None:
pass
@abstractmethod
def is_canceled(self, graph_execution_state_id: str) -> bool:
pass
class MemoryInvocationQueue(InvocationQueueABC): class MemoryInvocationQueue(InvocationQueueABC):

54
invokeai/app/services/invocation_services.py
View File

@ -6,21 +6,27 @@ from typing import TYPE_CHECKING
if TYPE_CHECKING: if TYPE_CHECKING:
from logging import Logger from logging import Logger
from invokeai.app.services.board_images import BoardImagesServiceABC from .board_image_records.board_image_records_base import BoardImageRecordStorageBase
from invokeai.app.services.boards import BoardServiceABC from .board_images.board_images_base import BoardImagesServiceABC
from invokeai.app.services.config import InvokeAIAppConfig from .board_records.board_records_base import BoardRecordStorageBase
from invokeai.app.services.events import EventServiceBase from .boards.boards_base import BoardServiceABC
from invokeai.app.services.graph import GraphExecutionState, LibraryGraph from .config import InvokeAIAppConfig
from invokeai.app.services.images import ImageServiceABC from .events.events_base import EventServiceBase
from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase from .image_files.image_files_base import ImageFileStorageBase
from invokeai.app.services.invocation_queue import InvocationQueueABC from .image_records.image_records_base import ImageRecordStorageBase
from invokeai.app.services.invocation_stats import InvocationStatsServiceBase from .images.images_base import ImageServiceABC
from invokeai.app.services.invoker import InvocationProcessorABC from .invocation_cache.invocation_cache_base import InvocationCacheBase
from invokeai.app.services.item_storage import ItemStorageABC from .invocation_processor.invocation_processor_base import InvocationProcessorABC
from invokeai.app.services.latent_storage import LatentsStorageBase from .invocation_queue.invocation_queue_base import InvocationQueueABC
from invokeai.app.services.model_manager_service import ModelManagerServiceBase from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
from invokeai.app.services.session_processor.session_processor_base import SessionProcessorBase from .item_storage.item_storage_base import ItemStorageABC
from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase from .latents_storage.latents_storage_base import LatentsStorageBase
from .model_manager.model_manager_base import ModelManagerServiceBase
from .names.names_base import NameServiceBase
from .session_processor.session_processor_base import SessionProcessorBase
from .session_queue.session_queue_base import SessionQueueBase
from .shared.graph import GraphExecutionState, LibraryGraph
from .urls.urls_base import UrlServiceBase
class InvocationServices: class InvocationServices:
@ -28,12 +34,16 @@ class InvocationServices:
# TODO: Just forward-declared everything due to circular dependencies. Fix structure. # TODO: Just forward-declared everything due to circular dependencies. Fix structure.
board_images: "BoardImagesServiceABC" board_images: "BoardImagesServiceABC"
board_image_record_storage: "BoardImageRecordStorageBase"
boards: "BoardServiceABC" boards: "BoardServiceABC"
board_records: "BoardRecordStorageBase"
configuration: "InvokeAIAppConfig" configuration: "InvokeAIAppConfig"
events: "EventServiceBase" events: "EventServiceBase"
graph_execution_manager: "ItemStorageABC[GraphExecutionState]" graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
graph_library: "ItemStorageABC[LibraryGraph]" graph_library: "ItemStorageABC[LibraryGraph]"
images: "ImageServiceABC" images: "ImageServiceABC"
image_records: "ImageRecordStorageBase"
image_files: "ImageFileStorageBase"
latents: "LatentsStorageBase" latents: "LatentsStorageBase"
logger: "Logger" logger: "Logger"
model_manager: "ModelManagerServiceBase" model_manager: "ModelManagerServiceBase"
@ -43,16 +53,22 @@ class InvocationServices:
session_queue: "SessionQueueBase" session_queue: "SessionQueueBase"
session_processor: "SessionProcessorBase" session_processor: "SessionProcessorBase"
invocation_cache: "InvocationCacheBase" invocation_cache: "InvocationCacheBase"
names: "NameServiceBase"
urls: "UrlServiceBase"
def __init__( def __init__(
self, self,
board_images: "BoardImagesServiceABC", board_images: "BoardImagesServiceABC",
board_image_records: "BoardImageRecordStorageBase",
boards: "BoardServiceABC", boards: "BoardServiceABC",
board_records: "BoardRecordStorageBase",
configuration: "InvokeAIAppConfig", configuration: "InvokeAIAppConfig",
events: "EventServiceBase", events: "EventServiceBase",
graph_execution_manager: "ItemStorageABC[GraphExecutionState]", graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
graph_library: "ItemStorageABC[LibraryGraph]", graph_library: "ItemStorageABC[LibraryGraph]",
images: "ImageServiceABC", images: "ImageServiceABC",
image_files: "ImageFileStorageBase",
image_records: "ImageRecordStorageBase",
latents: "LatentsStorageBase", latents: "LatentsStorageBase",
logger: "Logger", logger: "Logger",
model_manager: "ModelManagerServiceBase", model_manager: "ModelManagerServiceBase",
@ -62,14 +78,20 @@ class InvocationServices:
session_queue: "SessionQueueBase", session_queue: "SessionQueueBase",
session_processor: "SessionProcessorBase", session_processor: "SessionProcessorBase",
invocation_cache: "InvocationCacheBase", invocation_cache: "InvocationCacheBase",
names: "NameServiceBase",
urls: "UrlServiceBase",
): ):
self.board_images = board_images self.board_images = board_images
self.board_image_records = board_image_records
self.boards = boards self.boards = boards
self.board_records = board_records
self.configuration = configuration self.configuration = configuration
self.events = events self.events = events
self.graph_execution_manager = graph_execution_manager self.graph_execution_manager = graph_execution_manager
self.graph_library = graph_library self.graph_library = graph_library
self.images = images self.images = images
self.image_files = image_files
self.image_records = image_records
self.latents = latents self.latents = latents
self.logger = logger self.logger = logger
self.model_manager = model_manager self.model_manager = model_manager
@ -79,3 +101,5 @@ class InvocationServices:
self.session_queue = session_queue self.session_queue = session_queue
self.session_processor = session_processor self.session_processor = session_processor
self.invocation_cache = invocation_cache self.invocation_cache = invocation_cache
self.names = names
self.urls = urls

0
invokeai/app/services/invocation_stats/__init__.py Normal file
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121
invokeai/app/services/invocation_stats/invocation_stats_base.py Normal file
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@ -0,0 +1,121 @@
# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
"""Utility to collect execution time and GPU usage stats on invocations in flight
Usage:
statistics = InvocationStatsService(graph_execution_manager)
with statistics.collect_stats(invocation, graph_execution_state.id):
... execute graphs...
statistics.log_stats()
Typical output:
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader 1 0.005s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip 1 0.004s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel 2 0.512s 0.26G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l 1 3.541s 1.93G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i 1 0.679s 0.58G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 4.749s
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
writes to the system log is stored in InvocationServices.performance_statistics.
"""
from abc import ABC, abstractmethod
from contextlib import AbstractContextManager
from typing import Dict
from invokeai.app.invocations.baseinvocation import BaseInvocation
from invokeai.backend.model_management.model_cache import CacheStats
from .invocation_stats_common import NodeLog
class InvocationStatsServiceBase(ABC):
"Abstract base class for recording node memory/time performance statistics"
# {graph_id => NodeLog}
_stats: Dict[str, NodeLog]
_cache_stats: Dict[str, CacheStats]
ram_used: float
ram_changed: float
@abstractmethod
def __init__(self):
"""
Initialize the InvocationStatsService and reset counters to zero
"""
pass
@abstractmethod
def collect_stats(
self,
invocation: BaseInvocation,
graph_execution_state_id: str,
) -> AbstractContextManager:
"""
Return a context object that will capture the statistics on the execution
of invocaation. Use with: to place around the part of the code that executes the invocation.
:param invocation: BaseInvocation object from the current graph.
:param graph_execution_state_id: The id of the current session.
"""
pass
@abstractmethod
def reset_stats(self, graph_execution_state_id: str):
"""
Reset all statistics for the indicated graph
:param graph_execution_state_id
"""
pass
@abstractmethod
def reset_all_stats(self):
"""Zero all statistics"""
pass
@abstractmethod
def update_invocation_stats(
self,
graph_id: str,
invocation_type: str,
time_used: float,
vram_used: float,
):
"""
Add timing information on execution of a node. Usually
used internally.
:param graph_id: ID of the graph that is currently executing
:param invocation_type: String literal type of the node
:param time_used: Time used by node's exection (sec)
:param vram_used: Maximum VRAM used during exection (GB)
"""
pass
@abstractmethod
def log_stats(self):
"""
Write out the accumulated statistics to the log or somewhere else.
"""
pass
@abstractmethod
def update_mem_stats(
self,
ram_used: float,
ram_changed: float,
):
"""
Update the collector with RAM memory usage info.
:param ram_used: How much RAM is currently in use.
:param ram_changed: How much RAM changed since last generation.
"""
pass

25
invokeai/app/services/invocation_stats/invocation_stats_common.py Normal file
View File

@ -0,0 +1,25 @@
from dataclasses import dataclass, field
from typing import Dict
# size of GIG in bytes
GIG = 1073741824
@dataclass
class NodeStats:
"""Class for tracking execution stats of an invocation node"""
calls: int = 0
time_used: float = 0.0 # seconds
max_vram: float = 0.0 # GB
cache_hits: int = 0
cache_misses: int = 0
cache_high_watermark: int = 0
@dataclass
class NodeLog:
"""Class for tracking node usage"""
# {node_type => NodeStats}
nodes: Dict[str, NodeStats] = field(default_factory=dict)

169
invokeai/app/services/invocation_stats.py → invokeai/app/services/invocation_stats/invocation_stats_default.py
View File

@ -1,171 +1,35 @@
# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
"""Utility to collect execution time and GPU usage stats on invocations in flight
Usage:
statistics = InvocationStatsService(graph_execution_manager)
with statistics.collect_stats(invocation, graph_execution_state.id):
... execute graphs...
statistics.log_stats()
Typical output:
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader 1 0.005s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip 1 0.004s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel 2 0.512s 0.26G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l 1 3.541s 1.93G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i 1 0.679s 0.58G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 4.749s
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
writes to the system log is stored in InvocationServices.performance_statistics.
"""
import time import time
from abc import ABC, abstractmethod
from contextlib import AbstractContextManager
from dataclasses import dataclass, field
from typing import Dict from typing import Dict
import psutil import psutil
import torch import torch
import invokeai.backend.util.logging as logger import invokeai.backend.util.logging as logger
from invokeai.app.invocations.baseinvocation import BaseInvocation
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.model_manager.model_manager_base import ModelManagerServiceBase
from invokeai.backend.model_management.model_cache import CacheStats from invokeai.backend.model_management.model_cache import CacheStats
from ..invocations.baseinvocation import BaseInvocation from .invocation_stats_base import InvocationStatsServiceBase
from .graph import GraphExecutionState from .invocation_stats_common import GIG, NodeLog, NodeStats
from .item_storage import ItemStorageABC
from .model_manager_service import ModelManagerService
# size of GIG in bytes
GIG = 1073741824
@dataclass
class NodeStats:
"""Class for tracking execution stats of an invocation node"""
calls: int = 0
time_used: float = 0.0 # seconds
max_vram: float = 0.0 # GB
cache_hits: int = 0
cache_misses: int = 0
cache_high_watermark: int = 0
@dataclass
class NodeLog:
"""Class for tracking node usage"""
# {node_type => NodeStats}
nodes: Dict[str, NodeStats] = field(default_factory=dict)
class InvocationStatsServiceBase(ABC):
"Abstract base class for recording node memory/time performance statistics"
graph_execution_manager: ItemStorageABC["GraphExecutionState"]
# {graph_id => NodeLog}
_stats: Dict[str, NodeLog]
_cache_stats: Dict[str, CacheStats]
ram_used: float
ram_changed: float
@abstractmethod
def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]):
"""
Initialize the InvocationStatsService and reset counters to zero
:param graph_execution_manager: Graph execution manager for this session
"""
pass
@abstractmethod
def collect_stats(
self,
invocation: BaseInvocation,
graph_execution_state_id: str,
) -> AbstractContextManager:
"""
Return a context object that will capture the statistics on the execution
of invocaation. Use with: to place around the part of the code that executes the invocation.
:param invocation: BaseInvocation object from the current graph.
:param graph_execution_state: GraphExecutionState object from the current session.
"""
pass
@abstractmethod
def reset_stats(self, graph_execution_state_id: str):
"""
Reset all statistics for the indicated graph
:param graph_execution_state_id
"""
pass
@abstractmethod
def reset_all_stats(self):
"""Zero all statistics"""
pass
@abstractmethod
def update_invocation_stats(
self,
graph_id: str,
invocation_type: str,
time_used: float,
vram_used: float,
):
"""
Add timing information on execution of a node. Usually
used internally.
:param graph_id: ID of the graph that is currently executing
:param invocation_type: String literal type of the node
:param time_used: Time used by node's exection (sec)
:param vram_used: Maximum VRAM used during exection (GB)
"""
pass
@abstractmethod
def log_stats(self):
"""
Write out the accumulated statistics to the log or somewhere else.
"""
pass
@abstractmethod
def update_mem_stats(
self,
ram_used: float,
ram_changed: float,
):
"""
Update the collector with RAM memory usage info.
:param ram_used: How much RAM is currently in use.
:param ram_changed: How much RAM changed since last generation.
"""
pass
class InvocationStatsService(InvocationStatsServiceBase): class InvocationStatsService(InvocationStatsServiceBase):
"""Accumulate performance information about a running graph. Collects time spent in each node, """Accumulate performance information about a running graph. Collects time spent in each node,
as well as the maximum and current VRAM utilisation for CUDA systems""" as well as the maximum and current VRAM utilisation for CUDA systems"""
def __init__(self, graph_execution_manager: ItemStorageABC["GraphExecutionState"]): _invoker: Invoker
self.graph_execution_manager = graph_execution_manager
def __init__(self):
# {graph_id => NodeLog} # {graph_id => NodeLog}
self._stats: Dict[str, NodeLog] = {} self._stats: Dict[str, NodeLog] = {}
self._cache_stats: Dict[str, CacheStats] = {} self._cache_stats: Dict[str, CacheStats] = {}
self.ram_used: float = 0.0 self.ram_used: float = 0.0
self.ram_changed: float = 0.0 self.ram_changed: float = 0.0
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
class StatsContext: class StatsContext:
"""Context manager for collecting statistics.""" """Context manager for collecting statistics."""
@ -174,13 +38,13 @@ class InvocationStatsService(InvocationStatsServiceBase):
graph_id: str graph_id: str
start_time: float start_time: float
ram_used: int ram_used: int
model_manager: ModelManagerService model_manager: ModelManagerServiceBase
def __init__( def __init__(
self, self,
invocation: BaseInvocation, invocation: BaseInvocation,
graph_id: str, graph_id: str,
model_manager: ModelManagerService, model_manager: ModelManagerServiceBase,
collector: "InvocationStatsServiceBase", collector: "InvocationStatsServiceBase",
): ):
"""Initialize statistics for this run.""" """Initialize statistics for this run."""
@ -208,7 +72,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
) )
self.collector.update_invocation_stats( self.collector.update_invocation_stats(
graph_id=self.graph_id, graph_id=self.graph_id,
invocation_type=self.invocation.type, # type: ignore - `type` is not on the `BaseInvocation` model, but *is* on all invocations invocation_type=self.invocation.type, # type: ignore # `type` is not on the `BaseInvocation` model, but *is* on all invocations
time_used=time.time() - self.start_time, time_used=time.time() - self.start_time,
vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0, vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0,
) )
@ -217,12 +81,11 @@ class InvocationStatsService(InvocationStatsServiceBase):
self, self,
invocation: BaseInvocation, invocation: BaseInvocation,
graph_execution_state_id: str, graph_execution_state_id: str,
model_manager: ModelManagerService,
) -> StatsContext: ) -> StatsContext:
if not self._stats.get(graph_execution_state_id): # first time we're seeing this if not self._stats.get(graph_execution_state_id): # first time we're seeing this
self._stats[graph_execution_state_id] = NodeLog() self._stats[graph_execution_state_id] = NodeLog()
self._cache_stats[graph_execution_state_id] = CacheStats() self._cache_stats[graph_execution_state_id] = CacheStats()
return self.StatsContext(invocation, graph_execution_state_id, model_manager, self) return self.StatsContext(invocation, graph_execution_state_id, self._invoker.services.model_manager, self)
def reset_all_stats(self): def reset_all_stats(self):
"""Zero all statistics""" """Zero all statistics"""
@ -261,7 +124,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
errored = set() errored = set()
for graph_id, node_log in self._stats.items(): for graph_id, node_log in self._stats.items():
try: try:
current_graph_state = self.graph_execution_manager.get(graph_id) current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
except Exception: except Exception:
errored.add(graph_id) errored.add(graph_id)
continue continue

9
invokeai/app/services/invoker.py
View File

@ -1,11 +1,10 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from abc import ABC
from typing import Optional from typing import Optional
from .graph import Graph, GraphExecutionState from .invocation_queue.invocation_queue_common import InvocationQueueItem
from .invocation_queue import InvocationQueueItem
from .invocation_services import InvocationServices from .invocation_services import InvocationServices
from .shared.graph import Graph, GraphExecutionState
class Invoker: class Invoker:
@ -84,7 +83,3 @@ class Invoker:
self.__stop_service(getattr(self.services, service)) self.__stop_service(getattr(self.services, service))
self.services.queue.put(None) self.services.queue.put(None)
class InvocationProcessorABC(ABC):
pass

0
invokeai/app/services/item_storage/__init__.py Normal file
View File

19
invokeai/app/services/item_storage.py → invokeai/app/services/item_storage/item_storage_base.py
View File

@ -1,25 +1,16 @@
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from typing import Callable, Generic, Optional, TypeVar from typing import Callable, Generic, Optional, TypeVar
from pydantic import BaseModel, Field from pydantic import BaseModel
from pydantic.generics import GenericModel
from invokeai.app.services.shared.pagination import PaginatedResults
T = TypeVar("T", bound=BaseModel) T = TypeVar("T", bound=BaseModel)
class PaginatedResults(GenericModel, Generic[T]):
"""Paginated results"""
# fmt: off
items: list[T] = Field(description="Items")
page: int = Field(description="Current Page")
pages: int = Field(description="Total number of pages")
per_page: int = Field(description="Number of items per page")
total: int = Field(description="Total number of items in result")
# fmt: on
class ItemStorageABC(ABC, Generic[T]): class ItemStorageABC(ABC, Generic[T]):
"""Provides storage for a single type of item. The type must be a Pydantic model."""
_on_changed_callbacks: list[Callable[[T], None]] _on_changed_callbacks: list[Callable[[T], None]]
_on_deleted_callbacks: list[Callable[[str], None]] _on_deleted_callbacks: list[Callable[[str], None]]

31
invokeai/app/services/sqlite.py → invokeai/app/services/item_storage/item_storage_sqlite.py
View File

@ -2,30 +2,33 @@ import sqlite3
import threading import threading
from typing import Generic, Optional, TypeVar, get_args from typing import Generic, Optional, TypeVar, get_args
from pydantic import BaseModel, parse_raw_as from pydantic import BaseModel, TypeAdapter
from .item_storage import ItemStorageABC, PaginatedResults from invokeai.app.services.shared.pagination import PaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
from .item_storage_base import ItemStorageABC
T = TypeVar("T", bound=BaseModel) T = TypeVar("T", bound=BaseModel)
sqlite_memory = ":memory:"
class SqliteItemStorage(ItemStorageABC, Generic[T]): class SqliteItemStorage(ItemStorageABC, Generic[T]):
_table_name: str _table_name: str
_conn: sqlite3.Connection _conn: sqlite3.Connection
_cursor: sqlite3.Cursor _cursor: sqlite3.Cursor
_id_field: str _id_field: str
_lock: threading.Lock _lock: threading.RLock
_adapter: Optional[TypeAdapter[T]]
def __init__(self, conn: sqlite3.Connection, table_name: str, lock: threading.Lock, id_field: str = "id"): def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
super().__init__() super().__init__()
self._lock = db.lock
self._conn = db.conn
self._table_name = table_name self._table_name = table_name
self._id_field = id_field # TODO: validate that T has this field self._id_field = id_field # TODO: validate that T has this field
self._lock = lock
self._conn = conn
self._cursor = self._conn.cursor() self._cursor = self._conn.cursor()
self._adapter: Optional[TypeAdapter[T]] = None
self._create_table() self._create_table()
@ -44,15 +47,21 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
self._lock.release() self._lock.release()
def _parse_item(self, item: str) -> T: def _parse_item(self, item: str) -> T:
item_type = get_args(self.__orig_class__)[0] if self._adapter is None:
return parse_raw_as(item_type, item) """
We don't get access to `__orig_class__` in `__init__()`, and we need this before start(), so
we can create it when it is first needed instead.
__orig_class__ is technically an implementation detail of the typing module, not a supported API
"""
self._adapter = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined]
return self._adapter.validate_json(item)
def set(self, item: T): def set(self, item: T):
try: try:
self._lock.acquire() self._lock.acquire()
self._cursor.execute( self._cursor.execute(
f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""", f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""",
(item.json(),), (item.model_dump_json(warnings=False, exclude_none=True),),
) )
self._conn.commit() self._conn.commit()
finally: finally:

119
invokeai/app/services/latent_storage.py
View File

@ -1,119 +0,0 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from abc import ABC, abstractmethod
from pathlib import Path
from queue import Queue
from typing import Callable, Dict, Optional, Union
import torch
class LatentsStorageBase(ABC):
"""Responsible for storing and retrieving latents."""
_on_changed_callbacks: list[Callable[[torch.Tensor], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = list()
self._on_deleted_callbacks = list()
@abstractmethod
def get(self, name: str) -> torch.Tensor:
pass
@abstractmethod
def save(self, name: str, data: torch.Tensor) -> None:
pass
@abstractmethod
def delete(self, name: str) -> None:
pass
def on_changed(self, on_changed: Callable[[torch.Tensor], None]) -> None:
"""Register a callback for when an item is changed"""
self._on_changed_callbacks.append(on_changed)
def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
"""Register a callback for when an item is deleted"""
self._on_deleted_callbacks.append(on_deleted)
def _on_changed(self, item: torch.Tensor) -> None:
for callback in self._on_changed_callbacks:
callback(item)
def _on_deleted(self, item_id: str) -> None:
for callback in self._on_deleted_callbacks:
callback(item_id)
class ForwardCacheLatentsStorage(LatentsStorageBase):
"""Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
__cache: Dict[str, torch.Tensor]
__cache_ids: Queue
__max_cache_size: int
__underlying_storage: LatentsStorageBase
def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
super().__init__()
self.__underlying_storage = underlying_storage
self.__cache = dict()
self.__cache_ids = Queue()
self.__max_cache_size = max_cache_size
def get(self, name: str) -> torch.Tensor:
cache_item = self.__get_cache(name)
if cache_item is not None:
return cache_item
latent = self.__underlying_storage.get(name)
self.__set_cache(name, latent)
return latent
def save(self, name: str, data: torch.Tensor) -> None:
self.__underlying_storage.save(name, data)
self.__set_cache(name, data)
self._on_changed(data)
def delete(self, name: str) -> None:
self.__underlying_storage.delete(name)
if name in self.__cache:
del self.__cache[name]
self._on_deleted(name)
def __get_cache(self, name: str) -> Optional[torch.Tensor]:
return None if name not in self.__cache else self.__cache[name]
def __set_cache(self, name: str, data: torch.Tensor):
if name not in self.__cache:
self.__cache[name] = data
self.__cache_ids.put(name)
if self.__cache_ids.qsize() > self.__max_cache_size:
self.__cache.pop(self.__cache_ids.get())
class DiskLatentsStorage(LatentsStorageBase):
"""Stores latents in a folder on disk without caching"""
__output_folder: Union[str, Path]
def __init__(self, output_folder: Union[str, Path]):
self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
self.__output_folder.mkdir(parents=True, exist_ok=True)
def get(self, name: str) -> torch.Tensor:
latent_path = self.get_path(name)
return torch.load(latent_path)
def save(self, name: str, data: torch.Tensor) -> None:
self.__output_folder.mkdir(parents=True, exist_ok=True)
latent_path = self.get_path(name)
torch.save(data, latent_path)
def delete(self, name: str) -> None:
latent_path = self.get_path(name)
latent_path.unlink()
def get_path(self, name: str) -> Path:
return self.__output_folder / name

0
invokeai/app/services/latents_storage/__init__.py Normal file
View File

45
invokeai/app/services/latents_storage/latents_storage_base.py Normal file
View File

@ -0,0 +1,45 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from abc import ABC, abstractmethod
from typing import Callable
import torch
class LatentsStorageBase(ABC):
"""Responsible for storing and retrieving latents."""
_on_changed_callbacks: list[Callable[[torch.Tensor], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = list()
self._on_deleted_callbacks = list()
@abstractmethod
def get(self, name: str) -> torch.Tensor:
pass
@abstractmethod
def save(self, name: str, data: torch.Tensor) -> None:
pass
@abstractmethod
def delete(self, name: str) -> None:
pass
def on_changed(self, on_changed: Callable[[torch.Tensor], None]) -> None:
"""Register a callback for when an item is changed"""
self._on_changed_callbacks.append(on_changed)
def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
"""Register a callback for when an item is deleted"""
self._on_deleted_callbacks.append(on_deleted)
def _on_changed(self, item: torch.Tensor) -> None:
for callback in self._on_changed_callbacks:
callback(item)
def _on_deleted(self, item_id: str) -> None:
for callback in self._on_deleted_callbacks:
callback(item_id)

34
invokeai/app/services/latents_storage/latents_storage_disk.py Normal file
View File

@ -0,0 +1,34 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from pathlib import Path
from typing import Union
import torch
from .latents_storage_base import LatentsStorageBase
class DiskLatentsStorage(LatentsStorageBase):
"""Stores latents in a folder on disk without caching"""
__output_folder: Path
def __init__(self, output_folder: Union[str, Path]):
self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
self.__output_folder.mkdir(parents=True, exist_ok=True)
def get(self, name: str) -> torch.Tensor:
latent_path = self.get_path(name)
return torch.load(latent_path)
def save(self, name: str, data: torch.Tensor) -> None:
self.__output_folder.mkdir(parents=True, exist_ok=True)
latent_path = self.get_path(name)
torch.save(data, latent_path)
def delete(self, name: str) -> None:
latent_path = self.get_path(name)
latent_path.unlink()
def get_path(self, name: str) -> Path:
return self.__output_folder / name

54
invokeai/app/services/latents_storage/latents_storage_forward_cache.py Normal file
View File

@ -0,0 +1,54 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from queue import Queue
from typing import Dict, Optional
import torch
from .latents_storage_base import LatentsStorageBase
class ForwardCacheLatentsStorage(LatentsStorageBase):
"""Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
__cache: Dict[str, torch.Tensor]
__cache_ids: Queue
__max_cache_size: int
__underlying_storage: LatentsStorageBase
def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
super().__init__()
self.__underlying_storage = underlying_storage
self.__cache = dict()
self.__cache_ids = Queue()
self.__max_cache_size = max_cache_size
def get(self, name: str) -> torch.Tensor:
cache_item = self.__get_cache(name)
if cache_item is not None:
return cache_item
latent = self.__underlying_storage.get(name)
self.__set_cache(name, latent)
return latent
def save(self, name: str, data: torch.Tensor) -> None:
self.__underlying_storage.save(name, data)
self.__set_cache(name, data)
self._on_changed(data)
def delete(self, name: str) -> None:
self.__underlying_storage.delete(name)
if name in self.__cache:
del self.__cache[name]
self._on_deleted(name)
def __get_cache(self, name: str) -> Optional[torch.Tensor]:
return None if name not in self.__cache else self.__cache[name]
def __set_cache(self, name: str, data: torch.Tensor):
if name not in self.__cache:
self.__cache[name] = data
self.__cache_ids.put(name)
if self.__cache_ids.qsize() > self.__max_cache_size:
self.__cache.pop(self.__cache_ids.get())

0
invokeai/app/services/model_manager/__init__.py Normal file
View File

286
invokeai/app/services/model_manager/model_manager_base.py Normal file
View File

@ -0,0 +1,286 @@
# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
from __future__ import annotations
from abc import ABC, abstractmethod
from logging import Logger
from pathlib import Path
from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
from pydantic import Field
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.backend.model_management import (
AddModelResult,
BaseModelType,
MergeInterpolationMethod,
ModelInfo,
ModelType,
SchedulerPredictionType,
SubModelType,
)
from invokeai.backend.model_management.model_cache import CacheStats
if TYPE_CHECKING:
from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext
class ModelManagerServiceBase(ABC):
"""Responsible for managing models on disk and in memory"""
@abstractmethod
def __init__(
self,
config: InvokeAIAppConfig,
logger: Logger,
):
"""
Initialize with the path to the models.yaml config file.
Optional parameters are the torch device type, precision, max_models,
and sequential_offload boolean. Note that the default device
type and precision are set up for a CUDA system running at half precision.
"""
pass
@abstractmethod
def get_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
submodel: Optional[SubModelType] = None,
node: Optional[BaseInvocation] = None,
context: Optional[InvocationContext] = None,
) -> ModelInfo:
"""Retrieve the indicated model with name and type.
submodel can be used to get a part (such as the vae)
of a diffusers pipeline."""
pass
@property
@abstractmethod
def logger(self):
pass
@abstractmethod
def model_exists(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
) -> bool:
pass
@abstractmethod
def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
"""
Given a model name returns a dict-like (OmegaConf) object describing it.
Uses the exact format as the omegaconf stanza.
"""
pass
@abstractmethod
def list_models(self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None) -> dict:
"""
Return a dict of models in the format:
{ model_type1:
{ model_name1: {'status': 'active'|'cached'|'not loaded',
'model_name' : name,
'model_type' : SDModelType,
'description': description,
'format': 'folder'|'safetensors'|'ckpt'
},
model_name2: { etc }
},
model_type2:
{ model_name_n: etc
}
"""
pass
@abstractmethod
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
"""
Return information about the model using the same format as list_models()
"""
pass
@abstractmethod
def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
"""
Returns a list of all the model names known.
"""
pass
@abstractmethod
def add_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
model_attributes: dict,
clobber: bool = False,
) -> AddModelResult:
"""
Update the named model with a dictionary of attributes. Will fail with an
assertion error if the name already exists. Pass clobber=True to overwrite.
On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk.
"""
pass
@abstractmethod
def update_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
model_attributes: dict,
) -> AddModelResult:
"""
Update the named model with a dictionary of attributes. Will fail with a
ModelNotFoundException if the name does not already exist.
On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk.
"""
pass
@abstractmethod
def del_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
):
"""
Delete the named model from configuration. If delete_files is true,
then the underlying weight file or diffusers directory will be deleted
as well. Call commit() to write to disk.
"""
pass
@abstractmethod
def rename_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str,
):
"""
Rename the indicated model.
"""
pass
@abstractmethod
def list_checkpoint_configs(self) -> List[Path]:
"""
List the checkpoint config paths from ROOT/configs/stable-diffusion.
"""
pass
@abstractmethod
def convert_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: Literal[ModelType.Main, ModelType.Vae],
) -> AddModelResult:
"""
Convert a checkpoint file into a diffusers folder, deleting the cached
version and deleting the original checkpoint file if it is in the models
directory.
:param model_name: Name of the model to convert
:param base_model: Base model type
:param model_type: Type of model ['vae' or 'main']
This will raise a ValueError unless the model is not a checkpoint. It will
also raise a ValueError in the event that there is a similarly-named diffusers
directory already in place.
"""
pass
@abstractmethod
def heuristic_import(
self,
items_to_import: set[str],
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
) -> dict[str, AddModelResult]:
"""Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.
:param items_to_import: Set of strings corresponding to models to be imported.
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
The prediction type helper is necessary to distinguish between
models based on Stable Diffusion 2 Base (requiring
SchedulerPredictionType.Epsilson) and Stable Diffusion 768
(requiring SchedulerPredictionType.VPrediction). It is
generally impossible to do this programmatically, so the
prediction_type_helper usually asks the user to choose.
The result is a set of successfully installed models. Each element
of the set is a dict corresponding to the newly-created OmegaConf stanza for
that model.
"""
pass
@abstractmethod
def merge_models(
self,
model_names: List[str] = Field(
default=None, min_length=2, max_length=3, description="List of model names to merge"
),
base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged"
),
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
alpha: Optional[float] = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
merge_dest_directory: Optional[Path] = None,
) -> AddModelResult:
"""
Merge two to three diffusrs pipeline models and save as a new model.
:param model_names: List of 2-3 models to merge
:param base_model: Base model to use for all models
:param merged_model_name: Name of destination merged model
:param alpha: Alpha strength to apply to 2d and 3d model
:param interp: Interpolation method. None (default)
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
"""
pass
@abstractmethod
def search_for_models(self, directory: Path) -> List[Path]:
"""
Return list of all models found in the designated directory.
"""
pass
@abstractmethod
def sync_to_config(self):
"""
Re-read models.yaml, rescan the models directory, and reimport models
in the autoimport directories. Call after making changes outside the
model manager API.
"""
pass
@abstractmethod
def collect_cache_stats(self, cache_stats: CacheStats):
"""
Reset model cache statistics for graph with graph_id.
"""
pass
@abstractmethod
def commit(self, conf_file: Optional[Path] = None) -> None:
"""
Write current configuration out to the indicated file.
If no conf_file is provided, then replaces the
original file/database used to initialize the object.
"""
pass

274
invokeai/app/services/model_manager_service.py → invokeai/app/services/model_manager/model_manager_default.py
View File

@ -2,16 +2,15 @@
from __future__ import annotations from __future__ import annotations
from abc import ABC, abstractmethod
from logging import Logger from logging import Logger
from pathlib import Path from pathlib import Path
from types import ModuleType
from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
import torch import torch
from pydantic import Field from pydantic import Field
from invokeai.app.models.exceptions import CanceledException from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
from invokeai.backend.model_management import ( from invokeai.backend.model_management import (
AddModelResult, AddModelResult,
BaseModelType, BaseModelType,
@ -26,273 +25,12 @@ from invokeai.backend.model_management import (
) )
from invokeai.backend.model_management.model_cache import CacheStats from invokeai.backend.model_management.model_cache import CacheStats
from invokeai.backend.model_management.model_search import FindModels from invokeai.backend.model_management.model_search import FindModels
from invokeai.backend.util import choose_precision, choose_torch_device
from ...backend.util import choose_precision, choose_torch_device from .model_manager_base import ModelManagerServiceBase
from .config import InvokeAIAppConfig
if TYPE_CHECKING: if TYPE_CHECKING:
from ..invocations.baseinvocation import BaseInvocation, InvocationContext from invokeai.app.invocations.baseinvocation import InvocationContext
class ModelManagerServiceBase(ABC):
"""Responsible for managing models on disk and in memory"""
@abstractmethod
def __init__(
self,
config: InvokeAIAppConfig,
logger: ModuleType,
):
"""
Initialize with the path to the models.yaml config file.
Optional parameters are the torch device type, precision, max_models,
and sequential_offload boolean. Note that the default device
type and precision are set up for a CUDA system running at half precision.
"""
pass
@abstractmethod
def get_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
submodel: Optional[SubModelType] = None,
node: Optional[BaseInvocation] = None,
context: Optional[InvocationContext] = None,
) -> ModelInfo:
"""Retrieve the indicated model with name and type.
submodel can be used to get a part (such as the vae)
of a diffusers pipeline."""
pass
@property
@abstractmethod
def logger(self):
pass
@abstractmethod
def model_exists(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
) -> bool:
pass
@abstractmethod
def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
"""
Given a model name returns a dict-like (OmegaConf) object describing it.
Uses the exact format as the omegaconf stanza.
"""
pass
@abstractmethod
def list_models(self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None) -> dict:
"""
Return a dict of models in the format:
{ model_type1:
{ model_name1: {'status': 'active'|'cached'|'not loaded',
'model_name' : name,
'model_type' : SDModelType,
'description': description,
'format': 'folder'|'safetensors'|'ckpt'
},
model_name2: { etc }
},
model_type2:
{ model_name_n: etc
}
"""
pass
@abstractmethod
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
"""
Return information about the model using the same format as list_models()
"""
pass
@abstractmethod
def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
"""
Returns a list of all the model names known.
"""
pass
@abstractmethod
def add_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
model_attributes: dict,
clobber: bool = False,
) -> AddModelResult:
"""
Update the named model with a dictionary of attributes. Will fail with an
assertion error if the name already exists. Pass clobber=True to overwrite.
On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk.
"""
pass
@abstractmethod
def update_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
model_attributes: dict,
) -> AddModelResult:
"""
Update the named model with a dictionary of attributes. Will fail with a
ModelNotFoundException if the name does not already exist.
On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk.
"""
pass
@abstractmethod
def del_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
):
"""
Delete the named model from configuration. If delete_files is true,
then the underlying weight file or diffusers directory will be deleted
as well. Call commit() to write to disk.
"""
pass
@abstractmethod
def rename_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str,
):
"""
Rename the indicated model.
"""
pass
@abstractmethod
def list_checkpoint_configs(self) -> List[Path]:
"""
List the checkpoint config paths from ROOT/configs/stable-diffusion.
"""
pass
@abstractmethod
def convert_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: Literal[ModelType.Main, ModelType.Vae],
) -> AddModelResult:
"""
Convert a checkpoint file into a diffusers folder, deleting the cached
version and deleting the original checkpoint file if it is in the models
directory.
:param model_name: Name of the model to convert
:param base_model: Base model type
:param model_type: Type of model ['vae' or 'main']
This will raise a ValueError unless the model is not a checkpoint. It will
also raise a ValueError in the event that there is a similarly-named diffusers
directory already in place.
"""
pass
@abstractmethod
def heuristic_import(
self,
items_to_import: set[str],
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
) -> dict[str, AddModelResult]:
"""Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.
:param items_to_import: Set of strings corresponding to models to be imported.
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
The prediction type helper is necessary to distinguish between
models based on Stable Diffusion 2 Base (requiring
SchedulerPredictionType.Epsilson) and Stable Diffusion 768
(requiring SchedulerPredictionType.VPrediction). It is
generally impossible to do this programmatically, so the
prediction_type_helper usually asks the user to choose.
The result is a set of successfully installed models. Each element
of the set is a dict corresponding to the newly-created OmegaConf stanza for
that model.
"""
pass
@abstractmethod
def merge_models(
self,
model_names: List[str] = Field(
default=None, min_items=2, max_items=3, description="List of model names to merge"
),
base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged"
),
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
alpha: Optional[float] = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
merge_dest_directory: Optional[Path] = None,
) -> AddModelResult:
"""
Merge two to three diffusrs pipeline models and save as a new model.
:param model_names: List of 2-3 models to merge
:param base_model: Base model to use for all models
:param merged_model_name: Name of destination merged model
:param alpha: Alpha strength to apply to 2d and 3d model
:param interp: Interpolation method. None (default)
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
"""
pass
@abstractmethod
def search_for_models(self, directory: Path) -> List[Path]:
"""
Return list of all models found in the designated directory.
"""
pass
@abstractmethod
def sync_to_config(self):
"""
Re-read models.yaml, rescan the models directory, and reimport models
in the autoimport directories. Call after making changes outside the
model manager API.
"""
pass
@abstractmethod
def collect_cache_stats(self, cache_stats: CacheStats):
"""
Reset model cache statistics for graph with graph_id.
"""
pass
@abstractmethod
def commit(self, conf_file: Optional[Path] = None) -> None:
"""
Write current configuration out to the indicated file.
If no conf_file is provided, then replaces the
original file/database used to initialize the object.
"""
pass
# simple implementation # simple implementation
@ -589,7 +327,7 @@ class ModelManagerService(ModelManagerServiceBase):
def merge_models( def merge_models(
self, self,
model_names: List[str] = Field( model_names: List[str] = Field(
default=None, min_items=2, max_items=3, description="List of model names to merge" default=None, min_length=2, max_length=3, description="List of model names to merge"
), ),
base_model: Union[BaseModelType, str] = Field( base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged" default=None, description="Base model shared by all models to be merged"

0
invokeai/app/services/names/__init__.py Normal file
View File

11
invokeai/app/services/names/names_base.py Normal file
View File

@ -0,0 +1,11 @@
from abc import ABC, abstractmethod
class NameServiceBase(ABC):
"""Low-level service responsible for naming resources (images, latents, etc)."""
# TODO: Add customizable naming schemes
@abstractmethod
def create_image_name(self) -> str:
"""Creates a name for an image."""
pass

8
invokeai/app/services/names/names_common.py Normal file
View File

@ -0,0 +1,8 @@
from enum import Enum, EnumMeta
class ResourceType(str, Enum, metaclass=EnumMeta):
"""Enum for resource types."""
IMAGE = "image"
LATENT = "latent"

13
invokeai/app/services/names/names_default.py Normal file
View File

@ -0,0 +1,13 @@
from invokeai.app.util.misc import uuid_string
from .names_base import NameServiceBase
class SimpleNameService(NameServiceBase):
"""Creates image names from UUIDs."""
# TODO: Add customizable naming schemes
def create_image_name(self) -> str:
uuid_str = uuid_string()
filename = f"{uuid_str}.png"
return filename

31
invokeai/app/services/resource_name.py
View File

@ -1,31 +0,0 @@
from abc import ABC, abstractmethod
from enum import Enum, EnumMeta
from invokeai.app.util.misc import uuid_string
class ResourceType(str, Enum, metaclass=EnumMeta):
"""Enum for resource types."""
IMAGE = "image"
LATENT = "latent"
class NameServiceBase(ABC):
"""Low-level service responsible for naming resources (images, latents, etc)."""
# TODO: Add customizable naming schemes
@abstractmethod
def create_image_name(self) -> str:
"""Creates a name for an image."""
pass
class SimpleNameService(NameServiceBase):
"""Creates image names from UUIDs."""
# TODO: Add customizable naming schemes
def create_image_name(self) -> str:
uuid_str = uuid_string()
filename = f"{uuid_str}.png"
return filename

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