mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge remote-tracking branch 'origin/main' into feat/taesd
# Conflicts: # invokeai/app/invocations/latent.py
This commit is contained in:
Kevin Turner
commit
dff466244d
37
.gitignore
vendored
37
.gitignore
vendored
@ -1,23 +1,8 @@
|
||||
# ignore default image save location and model symbolic link
|
||||
.idea/
|
||||
embeddings/
|
||||
outputs/
|
||||
models/ldm/stable-diffusion-v1/model.ckpt
|
||||
**/restoration/codeformer/weights
|
||||
|
||||
# ignore user models config
|
||||
configs/models.user.yaml
|
||||
config/models.user.yml
|
||||
invokeai.init
|
||||
.version
|
||||
.last_model
|
||||
|
||||
# ignore the Anaconda/Miniconda installer used while building Docker image
|
||||
anaconda.sh
|
||||
|
||||
# ignore a directory which serves as a place for initial images
|
||||
inputs/
|
||||
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
@ -189,39 +174,17 @@ cython_debug/
|
||||
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
||||
#.idea/
|
||||
|
||||
src
|
||||
**/__pycache__/
|
||||
outputs
|
||||
|
||||
# Logs and associated folders
|
||||
# created from generated embeddings.
|
||||
logs
|
||||
testtube
|
||||
checkpoints
|
||||
# If it's a Mac
|
||||
.DS_Store
|
||||
|
||||
invokeai/frontend/yarn.lock
|
||||
invokeai/frontend/node_modules
|
||||
|
||||
# Let the frontend manage its own gitignore
|
||||
!invokeai/frontend/web/*
|
||||
|
||||
# Scratch folder
|
||||
.scratch/
|
||||
.vscode/
|
||||
gfpgan/
|
||||
models/ldm/stable-diffusion-v1/*.sha256
|
||||
|
||||
|
||||
# GFPGAN model files
|
||||
gfpgan/
|
||||
|
||||
# config file (will be created by installer)
|
||||
configs/models.yaml
|
||||
|
||||
# ignore initfile
|
||||
.invokeai
|
||||
|
||||
# ignore environment.yml and requirements.txt
|
||||
# these are links to the real files in environments-and-requirements
|
||||
|
||||
@ -43,7 +43,7 @@ Web Interface, interactive Command Line Interface, and also serves as
|
||||
the foundation for multiple commercial products.
|
||||
|
||||
**Quick links**: [[How to
|
||||
Install](https://invoke-ai.github.io/InvokeAI/#installation)] [<a
|
||||
Install](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)] [<a
|
||||
href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a
|
||||
href="https://invoke-ai.github.io/InvokeAI/">Documentation and
|
||||
Tutorials</a>] [<a
|
||||
@ -81,7 +81,7 @@ Table of Contents 📝
|
||||
## Quick Start
|
||||
|
||||
For full installation and upgrade instructions, please see:
|
||||
[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
|
||||
[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)
|
||||
|
||||
If upgrading from version 2.3, please read [Migrating a 2.3 root
|
||||
directory to 3.0](#migrating-to-3) first.
|
||||
|
||||
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@ -14,11 +14,14 @@ To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the
|
||||
#### Development
|
||||
If you’d like to help with development, please see our [development guide](contribution_guides/development.md). If you’re unfamiliar with contributing to open source projects, there is a tutorial contained within the development guide.
|
||||
|
||||
#### Nodes
|
||||
If you’d like to help with development, please see our [nodes contribution guide](/nodes/contributingNodes). If you’re unfamiliar with contributing to open source projects, there is a tutorial contained within the development guide.
|
||||
|
||||
#### Documentation
|
||||
If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documenation.md).
|
||||
If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documentation.md).
|
||||
|
||||
#### Translation
|
||||
If you'd like to help with translation, please see our [translation guide](docs/contributing/.contribution_guides/translation.md).
|
||||
If you'd like to help with translation, please see our [translation guide](contribution_guides/translation.md).
|
||||
|
||||
#### Tutorials
|
||||
Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
|
||||
|
||||
@ -270,9 +270,12 @@ new Invocation ready to be used.
|
||||
|
||||
|
||||
|
||||
# Advanced
|
||||
## Contributing Nodes
|
||||
Once you've created a Node, the next step is to share it with the community! The best way to do this is to submit a Pull Request to add the Node to the [Community Nodes](nodes/communityNodes) list. If you're not sure how to do that, take a look a at our [contributing nodes overview](contributingNodes).
|
||||
|
||||
## Custom Input Fields
|
||||
## Advanced
|
||||
|
||||
### Custom Input Fields
|
||||
|
||||
Now that you know how to create your own Invocations, let us dive into slightly
|
||||
more advanced topics.
|
||||
@ -352,7 +355,7 @@ input field.
|
||||
We will discuss the `Config` class in extra detail later in this guide and how
|
||||
you can use it to make your Invocations more robust.
|
||||
|
||||
## Custom Output Types
|
||||
### Custom Output Types
|
||||
|
||||
Like with custom inputs, sometimes you might find yourself needing custom
|
||||
outputs that InvokeAI does not provide. We can easily set one up.
|
||||
@ -396,7 +399,7 @@ All set. We now have an output type that requires what we need to create a
|
||||
blank_image. And if you noticed it, we even used the `Config` class to ensure
|
||||
the fields are required.
|
||||
|
||||
## Custom Configuration
|
||||
### Custom Configuration
|
||||
|
||||
As you might have noticed when making inputs and outputs, we used a class called
|
||||
`Config` from _pydantic_ to further customize them. Because our inputs and
|
||||
@ -492,7 +495,7 @@ later time.
|
||||
|
||||
# **[TODO]**
|
||||
|
||||
## Custom Components For Frontend
|
||||
### Custom Components For Frontend
|
||||
|
||||
Every backend input type should have a corresponding frontend component so the
|
||||
UI knows what to render when you use a particular field type.
|
||||
@ -513,7 +516,7 @@ now.
|
||||
|
||||
---
|
||||
|
||||
# OLD -- TO BE DELETED OR MOVED LATER
|
||||
<!-- # OLD -- TO BE DELETED OR MOVED LATER
|
||||
|
||||
---
|
||||
|
||||
@ -787,4 +790,5 @@ With the customization in place, the schema will now show these properties as
|
||||
required, obviating the need for extensive null checks in client code.
|
||||
|
||||
See this `pydantic` issue for discussion on this solution:
|
||||
<https://github.com/pydantic/pydantic/discussions/4577>
|
||||
<https://github.com/pydantic/pydantic/discussions/4577> -->
|
||||
|
||||
|
||||
@ -35,18 +35,17 @@ access.
|
||||
|
||||
## Backend
|
||||
|
||||
The backend is contained within the `./invokeai/backend` folder structure. To
|
||||
get started however please install the development dependencies.
|
||||
The backend is contained within the `./invokeai/backend` and `./invokeai/app` directories.
|
||||
To get started please install the development dependencies.
|
||||
|
||||
From the root of the repository run the following command. Note the use of `"`.
|
||||
|
||||
```zsh
|
||||
pip install ".[test]"
|
||||
pip install ".[dev,test]"
|
||||
```
|
||||
|
||||
This in an optional group of packages which is defined within the
|
||||
`pyproject.toml` and will be required for testing the changes you make the the
|
||||
code.
|
||||
These are optional groups of packages which are defined within the `pyproject.toml`
|
||||
and will be required for testing the changes you make to the code.
|
||||
|
||||
### Running Tests
|
||||
|
||||
@ -76,6 +75,20 @@ pytest --cov; open ./coverage/html/index.html
|
||||
|
||||
|
||||
|
||||
### Reloading Changes
|
||||
|
||||
Experimenting with changes to the Python source code is a drag if you have to re-start the server —
|
||||
and re-load those multi-gigabyte models —
|
||||
after every change.
|
||||
|
||||
For a faster development workflow, add the `--dev_reload` flag when starting the server.
|
||||
The server will watch for changes to all the Python files in the `invokeai` directory and apply those changes to the
|
||||
running server on the fly.
|
||||
|
||||
This will allow you to avoid restarting the server (and reloading models) in most cases, but there are some caveats; see
|
||||
the [jurigged documentation](https://github.com/breuleux/jurigged#caveats) for details.
|
||||
|
||||
|
||||
## Front End
|
||||
|
||||
<!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
|
||||
|
||||
@ -175,22 +175,27 @@ These configuration settings allow you to enable and disable various InvokeAI fe
|
||||
| `internet_available` | `true` | When a resource is not available locally, try to fetch it via the internet |
|
||||
| `log_tokenization` | `false` | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected |
|
||||
| `patchmatch` | `true` | Activate the "patchmatch" algorithm for improved inpainting |
|
||||
| `restore` | `true` | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) |
|
||||
|
||||
### Memory/Performance
|
||||
### Generation
|
||||
|
||||
These options tune InvokeAI's memory and performance characteristics.
|
||||
|
||||
| Setting | Default Value | Description |
|
||||
|----------|----------------|--------------|
|
||||
| `always_use_cpu` | `false` | Use the CPU to generate images, even if a GPU is available |
|
||||
| `free_gpu_mem` | `false` | Aggressively free up GPU memory after each operation; this will allow you to run in low-VRAM environments with some performance penalties |
|
||||
| `max_cache_size` | `6` | Amount of CPU RAM (in GB) to reserve for caching models in memory; more cache allows you to keep models in memory and switch among them quickly |
|
||||
| `max_vram_cache_size` | `2.75` | Amount of GPU VRAM (in GB) to reserve for caching models in VRAM; more cache speeds up generation but reduces the size of the images that can be generated. This can be set to zero to maximize the amount of memory available for generation. |
|
||||
| `precision` | `auto` | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
|
||||
| `sequential_guidance` | `false` | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss |
|
||||
| `xformers_enabled` | `true` | If the x-formers memory-efficient attention module is installed, activate it for better memory usage and generation speed|
|
||||
| `tiled_decode` | `false` | If true, then during the VAE decoding phase the image will be decoded a section at a time, reducing memory consumption at the cost of a performance hit |
|
||||
| Setting | Default Value | Description |
|
||||
|-----------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| `sequential_guidance` | `false` | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss |
|
||||
| `attention_type` | `auto` | Select the type of attention to use. One of `auto`,`normal`,`xformers`,`sliced`, or `torch-sdp` |
|
||||
| `attention_slice_size` | `auto` | When "sliced" attention is selected, set the slice size. One of `auto`, `balanced`, `max` or the integers 1-8|
|
||||
| `force_tiled_decode` | `false` | Force the VAE step to decode in tiles, reducing memory consumption at the cost of performance |
|
||||
|
||||
### Device
|
||||
|
||||
These options configure the generation execution device.
|
||||
|
||||
| Setting | Default Value | Description |
|
||||
|-----------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| `device` | `auto` | Preferred execution device. One of `auto`, `cpu`, `cuda`, `cuda:1`, `mps`. `auto` will choose the device depending on the hardware platform and the installed torch capabilities. |
|
||||
| `precision` | `auto` | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
|
||||
|
||||
|
||||
### Paths
|
||||
|
||||
|
||||
@ -1,208 +0,0 @@
|
||||
# Nodes Editor (Experimental)
|
||||
|
||||
🚨
|
||||
*The node editor is experimental. We've made it accessible because we use it to develop the application, but we have not addressed the many known rough edges. It's very easy to shoot yourself in the foot, and we cannot offer support for it until it sees full release (ETA v3.1). Everything is subject to change without warning.*
|
||||
🚨
|
||||
|
||||
The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. The node processing flow is usually done from left (inputs) to right (outputs), though linearity can become abstracted the more complex the node graph becomes. Nodes inputs and outputs are connected by dragging connectors from node to node.
|
||||
|
||||
To better understand how nodes are used, think of how an electric power bar works. It takes in one input (electricity from a wall outlet) and passes it to multiple devices through multiple outputs. Similarly, a node could have multiple inputs and outputs functioning at the same (or different) time, but all node outputs pass information onward like a power bar passes electricity. Not all outputs are compatible with all inputs, however - Each node has different constraints on how it is expecting to input/output information. In general, node outputs are colour-coded to match compatible inputs of other nodes.
|
||||
|
||||
## Anatomy of a Node
|
||||
|
||||
Individual nodes are made up of the following:
|
||||
|
||||
- Inputs: Edge points on the left side of the node window where you connect outputs from other nodes.
|
||||
- Outputs: Edge points on the right side of the node window where you connect to inputs on other nodes.
|
||||
- Options: Various options which are either manually configured, or overridden by connecting an output from another node to the input.
|
||||
|
||||
## Diffusion Overview
|
||||
|
||||
Taking the time to understand the diffusion process will help you to understand how to set up your nodes in the nodes editor.
|
||||
|
||||
There are two main spaces Stable Diffusion works in: image space and latent space.
|
||||
|
||||
Image space represents images in pixel form that you look at. Latent space represents compressed inputs. It’s in latent space that Stable Diffusion processes images. A VAE (Variational Auto Encoder) is responsible for compressing and encoding inputs into latent space, as well as decoding outputs back into image space.
|
||||
|
||||
When you generate an image using text-to-image, multiple steps occur in latent space:
|
||||
1. Random noise is generated at the chosen height and width. The noise’s characteristics are dictated by the chosen (or not chosen) seed. This noise tensor is passed into latent space. We’ll call this noise A.
|
||||
1. Using a model’s U-Net, a noise predictor examines noise A, and the words tokenized by CLIP from your prompt (conditioning). It generates its own noise tensor to predict what the final image might look like in latent space. We’ll call this noise B.
|
||||
1. Noise B is subtracted from noise A in an attempt to create a final latent image indicative of the inputs. This step is repeated for the number of sampler steps chosen.
|
||||
1. The VAE decodes the final latent image from latent space into image space.
|
||||
|
||||
image-to-image is a similar process, with only step 1 being different:
|
||||
1. The input image is decoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how much noise is added, 0 being none, and 1 being all-encompassing. We’ll call this noise A. The process is then the same as steps 2-4 in the text-to-image explanation above.
|
||||
|
||||
Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
|
||||
|
||||
A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from the latent image across the sampler steps chosen (step 3 above). Less noise is usually subtracted at higher sampler steps.
|
||||
|
||||
## Node Types (Base Nodes)
|
||||
|
||||
| Node <img width=160 align="right"> | Function |
|
||||
| ---------------------------------- | --------------------------------------------------------------------------------------|
|
||||
| Add | Adds two numbers |
|
||||
| CannyImageProcessor | Canny edge detection for ControlNet |
|
||||
| ClipSkip | Skip layers in clip text_encoder model |
|
||||
| Collect | Collects values into a collection |
|
||||
| Prompt (Compel) | Parse prompt using compel package to conditioning |
|
||||
| ContentShuffleImageProcessor | Applies content shuffle processing to image |
|
||||
| ControlNet | Collects ControlNet info to pass to other nodes |
|
||||
| CvInpaint | Simple inpaint using opencv |
|
||||
| Divide | Divides two numbers |
|
||||
| DynamicPrompt | Parses a prompt using adieyal/dynamic prompt's random or combinatorial generator |
|
||||
| FloatLinearRange | Creates a range |
|
||||
| HedImageProcessor | Applies HED edge detection to image |
|
||||
| ImageBlur | Blurs an image |
|
||||
| ImageChannel | Gets a channel from an image |
|
||||
| ImageCollection | Load a collection of images and provide it as output |
|
||||
| ImageConvert | Converts an image to a different mode |
|
||||
| ImageCrop | Crops an image to a specified box. The box can be outside of the image. |
|
||||
| ImageInverseLerp | Inverse linear interpolation of all pixels of an image |
|
||||
| ImageLerp | Linear interpolation of all pixels of an image |
|
||||
| ImageMultiply | Multiplies two images together using `PIL.ImageChops.Multiply()` |
|
||||
| ImageNSFWBlurInvocation | Detects and blurs images that may contain sexually explicit content |
|
||||
| ImagePaste | Pastes an image into another image |
|
||||
| ImageProcessor | Base class for invocations that reprocess images for ControlNet |
|
||||
| ImageResize | Resizes an image to specific dimensions |
|
||||
| ImageScale | Scales an image by a factor |
|
||||
| ImageToLatents | Scales latents by a given factor |
|
||||
| ImageWatermarkInvocation | Adds an invisible watermark to images |
|
||||
| InfillColor | Infills transparent areas of an image with a solid color |
|
||||
| InfillPatchMatch | Infills transparent areas of an image using the PatchMatch algorithm |
|
||||
| InfillTile | Infills transparent areas of an image with tiles of the image |
|
||||
| Inpaint | Generates an image using inpaint |
|
||||
| Iterate | Iterates over a list of items |
|
||||
| LatentsToImage | Generates an image from latents |
|
||||
| LatentsToLatents | Generates latents using latents as base image |
|
||||
| LeresImageProcessor | Applies leres processing to image |
|
||||
| LineartAnimeImageProcessor | Applies line art anime processing to image |
|
||||
| LineartImageProcessor | Applies line art processing to image |
|
||||
| LoadImage | Load an image and provide it as output |
|
||||
| Lora Loader | Apply selected lora to unet and text_encoder |
|
||||
| Model Loader | Loads a main model, outputting its submodels |
|
||||
| MaskFromAlpha | Extracts the alpha channel of an image as a mask |
|
||||
| MediapipeFaceProcessor | Applies mediapipe face processing to image |
|
||||
| MidasDepthImageProcessor | Applies Midas depth processing to image |
|
||||
| MlsdImageProcessor | Applied MLSD processing to image |
|
||||
| Multiply | Multiplies two numbers |
|
||||
| Noise | Generates latent noise |
|
||||
| NormalbaeImageProcessor | Applies NormalBAE processing to image |
|
||||
| OpenposeImageProcessor | Applies Openpose processing to image |
|
||||
| ParamFloat | A float parameter |
|
||||
| ParamInt | An integer parameter |
|
||||
| PidiImageProcessor | Applies PIDI processing to an image |
|
||||
| Progress Image | Displays the progress image in the Node Editor |
|
||||
| RandomInit | Outputs a single random integer |
|
||||
| RandomRange | Creates a collection of random numbers |
|
||||
| Range | Creates a range of numbers from start to stop with step |
|
||||
| RangeOfSize | Creates a range from start to start + size with step |
|
||||
| ResizeLatents | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8. |
|
||||
| RestoreFace | Restores faces in the image |
|
||||
| ScaleLatents | Scales latents by a given factor |
|
||||
| SegmentAnythingProcessor | Applies segment anything processing to image |
|
||||
| ShowImage | Displays a provided image, and passes it forward in the pipeline |
|
||||
| StepParamEasing | Experimental per-step parameter for easing for denoising steps |
|
||||
| Subtract | Subtracts two numbers |
|
||||
| TextToLatents | Generates latents from conditionings |
|
||||
| TileResampleProcessor | Bass class for invocations that preprocess images for ControlNet |
|
||||
| Upscale | Upscales an image |
|
||||
| VAE Loader | Loads a VAE model, outputting a VaeLoaderOutput |
|
||||
| ZoeDepthImageProcessor | Applies Zoe depth processing to image |
|
||||
|
||||
## Node Grouping Concepts
|
||||
|
||||
There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
|
||||
|
||||
### Noise
|
||||
|
||||
As described, an initial noise tensor is necessary for the latent diffusion process. As a result, all non-image *ToLatents nodes require a noise node input.
|
||||
|
||||
|
||||
|
||||
### Conditioning
|
||||
|
||||
As described, conditioning is necessary for the latent diffusion process, whether empty or not. As a result, all non-image *ToLatents nodes require positive and negative conditioning inputs. Conditioning is reliant on a CLIP tokenizer provided by the Model Loader node.
|
||||
|
||||
|
||||
|
||||
### Image Space & VAE
|
||||
|
||||
The ImageToLatents node doesn't require a noise node input, but requires a VAE input to convert the image from image space into latent space. In reverse, the LatentsToImage node requires a VAE input to convert from latent space back into image space.
|
||||
|
||||
|
||||
|
||||
### Defined & Random Seeds
|
||||
|
||||
It is common to want to use both the same seed (for continuity) and random seeds (for variance). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
|
||||
|
||||
|
||||
|
||||
### Control
|
||||
|
||||
Control means to guide the diffusion process to adhere to a defined input or structure. Control can be provided as input to non-image *ToLatents nodes from ControlNet nodes. ControlNet nodes usually require an image processor which converts an input image for use with ControlNet.
|
||||
|
||||
|
||||
|
||||
### LoRA
|
||||
|
||||
The Lora Loader node lets you load a LoRA (say that ten times fast) and pass it as output to both the Prompt (Compel) and non-image *ToLatents nodes. A model's CLIP tokenizer is passed through the LoRA into Prompt (Compel), where it affects conditioning. A model's U-Net is also passed through the LoRA into a non-image *ToLatents node, where it affects noise prediction.
|
||||
|
||||
|
||||
|
||||
### Scaling
|
||||
|
||||
Use the ImageScale, ScaleLatents, and Upscale nodes to upscale images and/or latent images. The chosen method differs across contexts. However, be aware that latents are already noisy and compressed at their original resolution; scaling an image could produce more detailed results.
|
||||
|
||||
|
||||
|
||||
### Iteration + Multiple Images as Input
|
||||
|
||||
Iteration is a common concept in any processing, and means to repeat a process with given input. In nodes, you're able to use the Iterate node to iterate through collections usually gathered by the Collect node. The Iterate node has many potential uses, from processing a collection of images one after another, to varying seeds across multiple image generations and more. This screenshot demonstrates how to collect several images and pass them out one at a time.
|
||||
|
||||
|
||||
|
||||
### Multiple Image Generation + Random Seeds
|
||||
|
||||
Multiple image generation in the node editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection.
|
||||
|
||||
To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
|
||||
|
||||
|
||||
|
||||
## Examples
|
||||
|
||||
With our knowledge of node grouping and the diffusion process, let’s break down some basic graphs in the nodes editor. Note that a node's options can be overridden by inputs from other nodes. These examples aren't strict rules to follow and only demonstrate some basic configurations.
|
||||
|
||||
### Basic text-to-image Node Graph
|
||||
|
||||
|
||||
|
||||
- Model Loader: A necessity to generating images (as we’ve read above). We choose our model from the dropdown. It outputs a U-Net, CLIP tokenizer, and VAE.
|
||||
- Prompt (Compel): Another necessity. Two prompt nodes are created. One will output positive conditioning (what you want, ‘dog’), one will output negative (what you don’t want, ‘cat’). They both input the CLIP tokenizer that the Model Loader node outputs.
|
||||
- Noise: Consider this noise A from step one of the text-to-image explanation above. Choose a seed number, width, and height.
|
||||
- TextToLatents: This node takes many inputs for converting and processing text & noise from image space into latent space, hence the name TextTo**Latents**. In this setup, it inputs positive and negative conditioning from the prompt nodes for processing (step 2 above). It inputs noise from the noise node for processing (steps 2 & 3 above). Lastly, it inputs a U-Net from the Model Loader node for processing (step 2 above). It outputs latents for use in the next LatentsToImage node. Choose number of sampler steps, CFG scale, and scheduler.
|
||||
- LatentsToImage: This node takes in processed latents from the TextToLatents node, and the model’s VAE from the Model Loader node which is responsible for decoding latents back into the image space, hence the name LatentsTo**Image**. This node is the last stop, and once the image is decoded, it is saved to the gallery.
|
||||
|
||||
### Basic image-to-image Node Graph
|
||||
|
||||
|
||||
|
||||
- Model Loader: Choose a model from the dropdown.
|
||||
- Prompt (Compel): Two prompt nodes. One positive (dog), one negative (dog). Same CLIP inputs from the Model Loader node as before.
|
||||
- ImageToLatents: Upload a source image directly in the node window, via drag'n'drop from the gallery, or passed in as input. The ImageToLatents node inputs the VAE from the Model Loader node to decode the chosen image from image space into latent space, hence the name ImageTo**Latents**. It outputs latents for use in the next LatentsToLatents node. It also outputs the source image's width and height for use in the next Noise node if the final image is to be the same dimensions as the source image.
|
||||
- Noise: A noise tensor is created with the width and height of the source image, and connected to the next LatentsToLatents node. Notice the width and height fields are overridden by the input from the ImageToLatents width and height outputs.
|
||||
- LatentsToLatents: The inputs and options are nearly identical to TextToLatents, except that LatentsToLatents also takes latents as an input. Considering our source image is already converted to latents in the last ImageToLatents node, and text + noise are no longer the only inputs to process, we use the LatentsToLatents node.
|
||||
- LatentsToImage: Like previously, the LatentsToImage node will use the VAE from the Model Loader as input to decode the latents from LatentsToLatents into image space, and save it to the gallery.
|
||||
|
||||
### Basic ControlNet Node Graph
|
||||
|
||||
|
||||
|
||||
- Model Loader
|
||||
- Prompt (Compel)
|
||||
- Noise: Width and height of the CannyImageProcessor ControlNet image is passed in to set the dimensions of the noise passed to TextToLatents.
|
||||
- CannyImageProcessor: The CannyImageProcessor node is used to process the source image being used as a ControlNet. Each ControlNet processor node applies control in different ways, and has some different options to configure. Width and height are passed to noise, as mentioned. The processed ControlNet image is output to the ControlNet node.
|
||||
- ControlNet: Select the type of control model. In this case, canny is chosen as the CannyImageProcessor was used to generate the ControlNet image. Configure the control node options, and pass the control output to TextToLatents.
|
||||
- TextToLatents: Similar to the basic text-to-image example, except ControlNet is passed to the control input edge point.
|
||||
- LatentsToImage
|
||||
@ -4,80 +4,6 @@ title: Prompting-Features
|
||||
|
||||
# :octicons-command-palette-24: Prompting-Features
|
||||
|
||||
## **Negative and Unconditioned Prompts**
|
||||
|
||||
Any words between a pair of square brackets will instruct Stable
|
||||
Diffusion to attempt to ban the concept from the generated image. The
|
||||
same effect is achieved by placing words in the "Negative Prompts"
|
||||
textbox in the Web UI.
|
||||
|
||||
```text
|
||||
this is a test prompt [not really] to make you understand [cool] how this works.
|
||||
```
|
||||
|
||||
In the above statement, the words 'not really cool` will be ignored by Stable
|
||||
Diffusion.
|
||||
|
||||
Here's a prompt that depicts what it does.
|
||||
|
||||
original prompt:
|
||||
|
||||
`#!bash "A fantastical translucent pony made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve"`
|
||||
|
||||
`#!bash parameters: steps=20, dimensions=512x768, CFG=7.5, Scheduler=k_euler_a, seed=1654590180`
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
That image has a woman, so if we want the horse without a rider, we can
|
||||
influence the image not to have a woman by putting [woman] in the prompt, like
|
||||
this:
|
||||
|
||||
`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman]"`
|
||||
(same parameters as above)
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
That's nice - but say we also don't want the image to be quite so blue. We can
|
||||
add "blue" to the list of negative prompts, so it's now [woman blue]:
|
||||
|
||||
`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue]"`
|
||||
(same parameters as above)
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
Getting close - but there's no sense in having a saddle when our horse doesn't
|
||||
have a rider, so we'll add one more negative prompt: [woman blue saddle].
|
||||
|
||||
`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue saddle]"`
|
||||
(same parameters as above)
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
!!! notes "Notes about this feature:"
|
||||
|
||||
* The only requirement for words to be ignored is that they are in between a pair of square brackets.
|
||||
* You can provide multiple words within the same bracket.
|
||||
* You can provide multiple brackets with multiple words in different places of your prompt. That works just fine.
|
||||
* To improve typical anatomy problems, you can add negative prompts like `[bad anatomy, extra legs, extra arms, extra fingers, poorly drawn hands, poorly drawn feet, disfigured, out of frame, tiling, bad art, deformed, mutated]`.
|
||||
|
||||
---
|
||||
|
||||
## **Prompt Syntax Features**
|
||||
|
||||
The InvokeAI prompting language has the following features:
|
||||
@ -102,9 +28,6 @@ The following syntax is recognised:
|
||||
`a tall thin man (picking (apricots)1.3)1.1`. (`+` is equivalent to 1.1, `++`
|
||||
is pow(1.1,2), `+++` is pow(1.1,3), etc; `-` means 0.9, `--` means pow(0.9,2),
|
||||
etc.)
|
||||
- attention also applies to `[unconditioning]` so
|
||||
`a tall thin man picking apricots [(ladder)0.01]` will _very gently_ nudge SD
|
||||
away from trying to draw the man on a ladder
|
||||
|
||||
You can use this to increase or decrease the amount of something. Starting from
|
||||
this prompt of `a man picking apricots from a tree`, let's see what happens if
|
||||
@ -150,7 +73,7 @@ Or, alternatively, with more man:
|
||||
| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
|
||||
|  |  |  |  |
|
||||
|
||||
### Blending between prompts
|
||||
### Prompt Blending
|
||||
|
||||
- `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
|
||||
- The existing prompt blending using `:<weight>` will continue to be supported -
|
||||
@ -168,6 +91,24 @@ Or, alternatively, with more man:
|
||||
See the section below on "Prompt Blending" for more information about how this
|
||||
works.
|
||||
|
||||
### Prompt Conjunction
|
||||
Join multiple clauses together to create a conjoined prompt. Each clause will be passed to CLIP separately.
|
||||
|
||||
For example, the prompt:
|
||||
|
||||
```bash
|
||||
"A mystical valley surround by towering granite cliffs, watercolor, warm"
|
||||
```
|
||||
|
||||
Can be used with .and():
|
||||
```bash
|
||||
("A mystical valley", "surround by towering granite cliffs", "watercolor", "warm").and()
|
||||
```
|
||||
|
||||
Each will give you different results - try them out and see what you prefer!
|
||||
|
||||
|
||||
|
||||
### Cross-Attention Control ('prompt2prompt')
|
||||
|
||||
Sometimes an image you generate is almost right, and you just want to change one
|
||||
@ -190,7 +131,7 @@ For example, consider the prompt `a cat.swap(dog) playing with a ball in the for
|
||||
|
||||
- For multiple word swaps, use parentheses: `a (fluffy cat).swap(barking dog) playing with a ball in the forest`.
|
||||
- To swap a comma, use quotes: `a ("fluffy, grey cat").swap("big, barking dog") playing with a ball in the forest`.
|
||||
- Supports options `t_start` and `t_end` (each 0-1) loosely corresponding to bloc97's `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
|
||||
- Supports options `t_start` and `t_end` (each 0-1) loosely corresponding to (bloc97's)[(https://github.com/bloc97/CrossAttentionControl)] `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
|
||||
intuitively understand. `t_start` and `t_end` are used to control on which steps cross-attention control should run. With the default values `t_start=0` and `t_end=1`, cross-attention control is active on every step of image generation. Other values can be used to turn cross-attention control off for part of the image generation process.
|
||||
- For example, if doing a diffusion with 10 steps for the prompt is `a cat.swap(dog, t_start=0.3, t_end=1.0) playing with a ball in the forest`, the first 3 steps will be run as `a cat playing with a ball in the forest`, while the last 7 steps will run as `a dog playing with a ball in the forest`, but the pixels that represent `dog` will be locked to the pixels that would have represented `cat` if the `cat` prompt had been used instead.
|
||||
- Conversely, for `a cat.swap(dog, t_start=0, t_end=0.7) playing with a ball in the forest`, the first 7 steps will run as `a dog playing with a ball in the forest` with the pixels that represent `dog` locked to the same pixels that would have represented `cat` if the `cat` prompt was being used instead. The final 3 steps will just run `a cat playing with a ball in the forest`.
|
||||
@ -201,7 +142,7 @@ Prompt2prompt `.swap()` is not compatible with xformers, which will be temporari
|
||||
The `prompt2prompt` code is based off
|
||||
[bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
|
||||
|
||||
### Escaping parantheses () and speech marks ""
|
||||
### Escaping parentheses () and speech marks ""
|
||||
|
||||
If the model you are using has parentheses () or speech marks "" as part of its
|
||||
syntax, you will need to "escape" these using a backslash, so that`(my_keyword)`
|
||||
@ -212,23 +153,16 @@ the parentheses as part of the prompt syntax and it will get confused.
|
||||
|
||||
## **Prompt Blending**
|
||||
|
||||
You may blend together different sections of the prompt to explore the AI's
|
||||
You may blend together prompts to explore the AI's
|
||||
latent semantic space and generate interesting (and often surprising!)
|
||||
variations. The syntax is:
|
||||
|
||||
```bash
|
||||
blue sphere:0.25 red cube:0.75 hybrid
|
||||
("prompt #1", "prompt #2").blend(0.25, 0.75)
|
||||
```
|
||||
|
||||
This will tell the sampler to blend 25% of the concept of a blue sphere with 75%
|
||||
of the concept of a red cube. The blend weights can use any combination of
|
||||
integers and floating point numbers, and they do not need to add up to 1.
|
||||
Everything to the left of the `:XX` up to the previous `:XX` is used for
|
||||
merging, so the overall effect is:
|
||||
|
||||
```bash
|
||||
0.25 * "blue sphere" + 0.75 * "white duck" + hybrid
|
||||
```
|
||||
This will tell the sampler to blend 25% of the concept of prompt #1 with 75%
|
||||
of the concept of prompt #2. It is recommended to keep the sum of the weights to around 1.0, but interesting things might happen if you go outside of this range.
|
||||
|
||||
Because you are exploring the "mind" of the AI, the AI's way of mixing two
|
||||
concepts may not match yours, leading to surprising effects. To illustrate, here
|
||||
@ -236,13 +170,14 @@ are three images generated using various combinations of blend weights. As
|
||||
usual, unless you fix the seed, the prompts will give you different results each
|
||||
time you run them.
|
||||
|
||||
<figure markdown>
|
||||
Let's examine how this affects image generation results:
|
||||
|
||||
### "blue sphere, red cube, hybrid"
|
||||
|
||||
</figure>
|
||||
```bash
|
||||
"blue sphere, red cube, hybrid"
|
||||
```
|
||||
|
||||
This example doesn't use melding at all and represents the default way of mixing
|
||||
This example doesn't use blending at all and represents the default way of mixing
|
||||
concepts.
|
||||
|
||||
<figure markdown>
|
||||
@ -251,55 +186,47 @@ concepts.
|
||||
|
||||
</figure>
|
||||
|
||||
It's interesting to see how the AI expressed the concept of "cube" as the four
|
||||
quadrants of the enclosing frame. If you look closely, there is depth there, so
|
||||
the enclosing frame is actually a cube.
|
||||
It's interesting to see how the AI expressed the concept of "cube" within the sphere. If you look closely, there is depth there, so the enclosing frame is actually a cube.
|
||||
|
||||
<figure markdown>
|
||||
|
||||
### "blue sphere:0.25 red cube:0.75 hybrid"
|
||||
```bash
|
||||
("blue sphere", "red cube").blend(0.25, 0.75)
|
||||
```
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
Now that's interesting. We get neither a blue sphere nor a red cube, but a red
|
||||
sphere embedded in a brick wall, which represents a melding of concepts within
|
||||
the AI's "latent space" of semantic representations. Where is Ludwig
|
||||
Wittgenstein when you need him?
|
||||
Now that's interesting. We get an image with a resemblance of a red cube, with a hint of blue shadows which represents a melding of concepts within the AI's "latent space" of semantic representations.
|
||||
|
||||
<figure markdown>
|
||||
|
||||
### "blue sphere:0.75 red cube:0.25 hybrid"
|
||||
```bash
|
||||
("blue sphere", "red cube").blend(0.75, 0.25)
|
||||
```
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
Definitely more blue-spherey. The cube is gone entirely, but it's really cool
|
||||
abstract art.
|
||||
Definitely more blue-spherey.
|
||||
|
||||
<figure markdown>
|
||||
|
||||
### "blue sphere:0.5 red cube:0.5 hybrid"
|
||||
```bash
|
||||
("blue sphere", "red cube").blend(0.5, 0.5)
|
||||
```
|
||||
</figure>
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
Whoa...! I see blue and red, but no spheres or cubes. Is the word "hybrid"
|
||||
summoning up the concept of some sort of scifi creature? Let's find out.
|
||||
|
||||
<figure markdown>
|
||||
Whoa...! I see blue and red, and if I squint, spheres and cubes.
|
||||
|
||||
### "blue sphere:0.5 red cube:0.5"
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
Indeed, removing the word "hybrid" produces an image that is more like what we'd
|
||||
expect.
|
||||
|
||||
## Dynamic Prompts
|
||||
|
||||
|
||||
@ -30,10 +30,6 @@ image output.
|
||||
### * [Image-to-Image Guide](IMG2IMG.md)
|
||||
Use a seed image to build new creations in the CLI.
|
||||
|
||||
### * [Generating Variations](VARIATIONS.md)
|
||||
Have an image you like and want to generate many more like it? Variations
|
||||
are the ticket.
|
||||
|
||||
## Model Management
|
||||
|
||||
### * [Model Installation](../installation/050_INSTALLING_MODELS.md)
|
||||
|
||||
27
docs/help/diffusion.md
Normal file
27
docs/help/diffusion.md
Normal file
@ -0,0 +1,27 @@
|
||||
Taking the time to understand the diffusion process will help you to understand how to more effectively use InvokeAI.
|
||||
|
||||
There are two main ways Stable Diffusion works - with images, and latents.
|
||||
|
||||
Image space represents images in pixel form that you look at. Latent space represents compressed inputs. It’s in latent space that Stable Diffusion processes images. A VAE (Variational Auto Encoder) is responsible for compressing and encoding inputs into latent space, as well as decoding outputs back into image space.
|
||||
|
||||
To fully understand the diffusion process, we need to understand a few more terms: UNet, CLIP, and conditioning.
|
||||
|
||||
A U-Net is a model trained on a large number of latent images with with known amounts of random noise added. This means that the U-Net can be given a slightly noisy image and it will predict the pattern of noise needed to subtract from the image in order to recover the original.
|
||||
|
||||
CLIP is a model that tokenizes and encodes text into conditioning. This conditioning guides the model during the denoising steps to produce a new image.
|
||||
|
||||
The U-Net and CLIP work together during the image generation process at each denoising step, with the U-Net removing noise in such a way that the result is similar to images in the U-Net’s training set, while CLIP guides the U-Net towards creating images that are most similar to the prompt.
|
||||
|
||||
|
||||
When you generate an image using text-to-image, multiple steps occur in latent space:
|
||||
1. Random noise is generated at the chosen height and width. The noise’s characteristics are dictated by seed. This noise tensor is passed into latent space. We’ll call this noise A.
|
||||
2. Using a model’s U-Net, a noise predictor examines noise A, and the words tokenized by CLIP from your prompt (conditioning). It generates its own noise tensor to predict what the final image might look like in latent space. We’ll call this noise B.
|
||||
3. Noise B is subtracted from noise A in an attempt to create a latent image consistent with the prompt. This step is repeated for the number of sampler steps chosen.
|
||||
4. The VAE decodes the final latent image from latent space into image space.
|
||||
|
||||
Image-to-image is a similar process, with only step 1 being different:
|
||||
1. The input image is encoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how may noise steps are added, and the amount of noise added at each step. A Denoising Strength of 0 means there are 0 steps and no noise added, resulting in an unchanged image, while a Denoising Strength of 1 results in the image being completely replaced with noise and a full set of denoising steps are performance. The process is then the same as steps 2-4 in the text-to-image process.
|
||||
|
||||
Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
|
||||
|
||||
A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from the latent image across the sampler steps chosen (step 3 above). Less noise is usually subtracted at higher sampler steps.
|
||||
@ -49,9 +49,9 @@ title: Home
|
||||
[![github stars badge]][github stars link]
|
||||
[![github forks badge]][github forks link]
|
||||
|
||||
[![CI checks on main badge]][ci checks on main link]
|
||||
<!-- [![CI checks on main badge]][ci checks on main link]
|
||||
[![CI checks on dev badge]][ci checks on dev link]
|
||||
<!-- [![latest commit to dev badge]][latest commit to dev link] -->
|
||||
[![latest commit to dev badge]][latest commit to dev link] -->
|
||||
|
||||
[![github open issues badge]][github open issues link]
|
||||
[![github open prs badge]][github open prs link]
|
||||
|
||||
@ -8,9 +8,9 @@ title: Installing Manually
|
||||
|
||||
</figure>
|
||||
|
||||
!!! warning "This is for advanced Users"
|
||||
!!! warning "This is for Advanced Users"
|
||||
|
||||
**python experience is mandatory**
|
||||
**Python experience is mandatory**
|
||||
|
||||
## Introduction
|
||||
|
||||
|
||||
@ -4,9 +4,9 @@ title: Installing with Docker
|
||||
|
||||
# :fontawesome-brands-docker: Docker
|
||||
|
||||
!!! warning "For end users"
|
||||
!!! warning "For most users"
|
||||
|
||||
We highly recommend to Install InvokeAI locally using [these instructions](index.md)
|
||||
We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md)
|
||||
|
||||
!!! tip "For developers"
|
||||
|
||||
|
||||
7
docs/javascripts/tablesort.js
Normal file
7
docs/javascripts/tablesort.js
Normal file
@ -0,0 +1,7 @@
|
||||
document$.subscribe(function() {
|
||||
var tables = document.querySelectorAll("article table:not([class])")
|
||||
tables.forEach(function(table) {
|
||||
new Tablesort(table)
|
||||
})
|
||||
})
|
||||
|
||||
68
docs/nodes/NODES.md
Normal file
68
docs/nodes/NODES.md
Normal file
@ -0,0 +1,68 @@
|
||||
# Using the Node Editor
|
||||
|
||||
The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. Nodes take in inputs on the left side of the node, and return an output on the right side of the node. A node graph is composed of multiple nodes that are connected together to create a workflow. Nodes' inputs and outputs are connected by dragging connectors from node to node. Inputs and outputs are color coded for ease of use.
|
||||
|
||||
To better understand how nodes are used, think of how an electric power bar works. It takes in one input (electricity from a wall outlet) and passes it to multiple devices through multiple outputs. Similarly, a node could have multiple inputs and outputs functioning at the same (or different) time, but all node outputs pass information onward like a power bar passes electricity. Not all outputs are compatible with all inputs, however - Each node has different constraints on how it is expecting to input/output information. In general, node outputs are colour-coded to match compatible inputs of other nodes.
|
||||
|
||||
|
||||
If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](../help/diffusion.md) Understanding how diffusion works will enable you to more easily use the Nodes Editor and build workflows to suit your needs.
|
||||
|
||||
## Important Concepts
|
||||
|
||||
There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
|
||||
|
||||
### Noise
|
||||
|
||||
An initial noise tensor is necessary for the latent diffusion process. As a result, the Denoising node requires a noise node input.
|
||||
|
||||
|
||||
|
||||
### Text Prompt Conditioning
|
||||
|
||||
Conditioning is necessary for the latent diffusion process, whether empty or not. As a result, the Denoising node requires positive and negative conditioning inputs. Conditioning is reliant on a CLIP text encoder provided by the Model Loader node.
|
||||
|
||||
|
||||
|
||||
### Image to Latents & VAE
|
||||
|
||||
The ImageToLatents node takes in a pixel image and a VAE and outputs a latents. The LatentsToImage node does the opposite, taking in a latents and a VAE and outpus a pixel image.
|
||||
|
||||
|
||||
|
||||
### Defined & Random Seeds
|
||||
|
||||
It is common to want to use both the same seed (for continuity) and random seeds (for variety). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
|
||||
|
||||
|
||||
|
||||
### ControlNet
|
||||
|
||||
The ControlNet node outputs a Control, which can be provided as input to non-image *ToLatents nodes. Depending on the type of ControlNet desired, ControlNet nodes usually require an image processor node, such as a Canny Processor or Depth Processor, which prepares an input image for use with ControlNet.
|
||||
|
||||
|
||||
|
||||
### LoRA
|
||||
|
||||
The Lora Loader node lets you load a LoRA and pass it as output.A LoRA provides fine-tunes to the UNet and text encoder weights that augment the base model’s image and text vocabularies.
|
||||
|
||||
|
||||
|
||||
### Scaling
|
||||
|
||||
Use the ImageScale, ScaleLatents, and Upscale nodes to upscale images and/or latent images. Upscaling is the process of enlarging an image and adding more detail. The chosen method differs across contexts. However, be aware that latents are already noisy and compressed at their original resolution; scaling an image could produce more detailed results.
|
||||
|
||||
|
||||
|
||||
### Iteration + Multiple Images as Input
|
||||
|
||||
Iteration is a common concept in any processing, and means to repeat a process with given input. In nodes, you're able to use the Iterate node to iterate through collections usually gathered by the Collect node. The Iterate node has many potential uses, from processing a collection of images one after another, to varying seeds across multiple image generations and more. This screenshot demonstrates how to collect several images and use them in an image generation workflow.
|
||||
|
||||
|
||||
|
||||
### Multiple Image Generation + Random Seeds
|
||||
|
||||
Multiple image generation in the node editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection.
|
||||
|
||||
To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
|
||||
|
||||
|
||||
80
docs/nodes/comfyToInvoke.md
Normal file
80
docs/nodes/comfyToInvoke.md
Normal file
@ -0,0 +1,80 @@
|
||||
# ComfyUI to InvokeAI
|
||||
|
||||
If you're coming to InvokeAI from ComfyUI, welcome! You'll find things are similar but different - the good news is that you already know how things should work, and it's just a matter of wiring them up!
|
||||
|
||||
Some things to note:
|
||||
|
||||
- InvokeAI's nodes tend to be more granular than default nodes in Comfy. This means each node in Invoke will do a specific task and you might need to use multiple nodes to achieve the same result. The added granularity improves the control you have have over your workflows.
|
||||
- InvokeAI's backend and ComfyUI's backend are very different which means Comfy workflows are not able to be imported into InvokeAI. However, we have created a [list of popular workflows](exampleWorkflows.md) for you to get started with Nodes in InvokeAI!
|
||||
|
||||
## Node Equivalents:
|
||||
|
||||
| Comfy UI Category | ComfyUI Node | Invoke Equivalent |
|
||||
|:---------------------------------- |:---------------------------------- | :----------------------------------|
|
||||
| Sampling |KSampler |Denoise Latents|
|
||||
| Sampling |Ksampler Advanced|Denoise Latents |
|
||||
| Loaders |Load Checkpoint | Main Model Loader _or_ SDXL Main Model Loader|
|
||||
| Loaders |Load VAE | VAE Loader |
|
||||
| Loaders |Load Lora | LoRA Loader _or_ SDXL Lora Loader|
|
||||
| Loaders |Load ControlNet Model | ControlNet|
|
||||
| Loaders |Load ControlNet Model (diff) | ControlNet|
|
||||
| Loaders |Load Style Model | Reference Only ControlNet will be coming in a future version of InvokeAI|
|
||||
| Loaders |unCLIPCheckpointLoader | N/A |
|
||||
| Loaders |GLIGENLoader | N/A |
|
||||
| Loaders |Hypernetwork Loader | N/A |
|
||||
| Loaders |Load Upscale Model | Occurs within "Upscale (RealESRGAN)"|
|
||||
|Conditioning |CLIP Text Encode (Prompt) | Compel (Prompt) or SDXL Compel (Prompt) |
|
||||
|Conditioning |CLIP Set Last Layer | CLIP Skip|
|
||||
|Conditioning |Conditioning (Average) | Use the .blend() feature of prompts |
|
||||
|Conditioning |Conditioning (Combine) | N/A |
|
||||
|Conditioning |Conditioning (Concat) | See the Prompt Tools Community Node|
|
||||
|Conditioning |Conditioning (Set Area) | N/A |
|
||||
|Conditioning |Conditioning (Set Mask) | Mask Edge |
|
||||
|Conditioning |CLIP Vision Encode | N/A |
|
||||
|Conditioning |unCLIPConditioning | N/A |
|
||||
|Conditioning |Apply ControlNet | ControlNet |
|
||||
|Conditioning |Apply ControlNet (Advanced) | ControlNet |
|
||||
|Latent |VAE Decode | Latents to Image|
|
||||
|Latent |VAE Encode | Image to Latents |
|
||||
|Latent |Empty Latent Image | Noise |
|
||||
|Latent |Upscale Latent |Resize Latents |
|
||||
|Latent |Upscale Latent By |Scale Latents |
|
||||
|Latent |Latent Composite | Blend Latents |
|
||||
|Latent |LatentCompositeMasked | N/A |
|
||||
|Image |Save Image | Image |
|
||||
|Image |Preview Image |Current |
|
||||
|Image |Load Image | Image|
|
||||
|Image |Empty Image| Blank Image |
|
||||
|Image |Invert Image | Invert Lerp Image |
|
||||
|Image |Batch Images | Link "Image" nodes into an "Image Collection" node |
|
||||
|Image |Pad Image for Outpainting | Outpainting is easily accomplished in the Unified Canvas |
|
||||
|Image |ImageCompositeMasked | Paste Image |
|
||||
|Image | Upscale Image | Resize Image |
|
||||
|Image | Upscale Image By | Upscale Image |
|
||||
|Image | Upscale Image (using Model) | Upscale Image |
|
||||
|Image | ImageBlur | Blur Image |
|
||||
|Image | ImageQuantize | N/A |
|
||||
|Image | ImageSharpen | N/A |
|
||||
|Image | Canny | Canny Processor |
|
||||
|Mask |Load Image (as Mask) | Image |
|
||||
|Mask |Convert Mask to Image | Image|
|
||||
|Mask |Convert Image to Mask | Image |
|
||||
|Mask |SolidMask | N/A |
|
||||
|Mask |InvertMask |Invert Lerp Image |
|
||||
|Mask |CropMask | Crop Image |
|
||||
|Mask |MaskComposite | Combine Mask |
|
||||
|Mask |FeatherMask | Blur Image |
|
||||
|Advanced | Load CLIP | Main Model Loader _or_ SDXL Main Model Loader|
|
||||
|Advanced | UNETLoader | Main Model Loader _or_ SDXL Main Model Loader|
|
||||
|Advanced | DualCLIPLoader | Main Model Loader _or_ SDXL Main Model Loader|
|
||||
|Advanced | Load Checkpoint | Main Model Loader _or_ SDXL Main Model Loader |
|
||||
|Advanced | ConditioningZeroOut | N/A |
|
||||
|Advanced | ConditioningSetTimestepRange | N/A |
|
||||
|Advanced | CLIPTextEncodeSDXLRefiner | Compel (Prompt) or SDXL Compel (Prompt) |
|
||||
|Advanced | CLIPTextEncodeSDXL |Compel (Prompt) or SDXL Compel (Prompt) |
|
||||
|Advanced | ModelMergeSimple | Model Merging is available in the Model Manager |
|
||||
|Advanced | ModelMergeBlocks | Model Merging is available in the Model Manager|
|
||||
|Advanced | CheckpointSave | Model saving is available in the Model Manager|
|
||||
|Advanced | CLIPMergeSimple | N/A |
|
||||
|
||||
|
||||
@ -2,17 +2,13 @@
|
||||
|
||||
These are nodes that have been developed by the community, for the community. If you're not sure what a node is, you can learn more about nodes [here](overview.md).
|
||||
|
||||
If you'd like to submit a node for the community, please refer to the [node creation overview](./overview.md#contributing-nodes).
|
||||
If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md).
|
||||
|
||||
To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations/` folder in your Invoke AI install location. Along with the node, an example node graph should be provided to help you get started with the node.
|
||||
To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations` folder in your Invoke AI install location. Along with the node, an example node graph should be provided to help you get started with the node.
|
||||
|
||||
To use a community node graph, download the the `.json` node graph file and load it into Invoke AI via the **Load Nodes** button on the Node Editor.
|
||||
|
||||
## Disclaimer
|
||||
|
||||
The nodes linked below have been developed and contributed by members of the Invoke AI community. While we strive to ensure the quality and safety of these contributions, we do not guarantee the reliability or security of the nodes. If you have issues or concerns with any of the nodes below, please raise it on GitHub or in the Discord.
|
||||
|
||||
## List of Nodes
|
||||
## Community Nodes
|
||||
|
||||
### FaceTools
|
||||
|
||||
@ -34,6 +30,33 @@ The nodes linked below have been developed and contributed by members of the Inv
|
||||
|
||||
**Node Link:** https://github.com/JPPhoto/ideal-size-node
|
||||
|
||||
<hr>
|
||||
|
||||
### 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**
|
||||
|
||||
|
||||
|
||||
--------------------------------
|
||||
### GPT2RandomPromptMaker
|
||||
|
||||
**Description:** A node for InvokeAI utilizes the GPT-2 language model to generate random prompts based on a provided seed and context.
|
||||
|
||||
**Node Link:** https://github.com/mickr777/GPT2RandomPromptMaker
|
||||
|
||||
**Output Examples**
|
||||
|
||||
Generated Prompt: An enchanted weapon will be usable by any character regardless of their alignment.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
--------------------------------
|
||||
### Example Node Template
|
||||
|
||||
@ -47,7 +70,12 @@ The nodes linked below have been developed and contributed by members of the Inv
|
||||
|
||||
{: style="height:115px;width:240px"}
|
||||
|
||||
|
||||
## Disclaimer
|
||||
|
||||
The nodes linked have been developed and contributed by members of the Invoke AI community. While we strive to ensure the quality and safety of these contributions, we do not guarantee the reliability or security of the nodes. If you have issues or concerns with any of the nodes below, please raise it on GitHub or in the Discord.
|
||||
|
||||
|
||||
## Help
|
||||
If you run into any issues with a node, please post in the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy).
|
||||
|
||||
|
||||
|
||||
27
docs/nodes/contributingNodes.md
Normal file
27
docs/nodes/contributingNodes.md
Normal file
@ -0,0 +1,27 @@
|
||||
# Contributing Nodes
|
||||
|
||||
To learn about the specifics of creating a new node, please visit our [Node creation documentation](../contributing/INVOCATIONS.md).
|
||||
|
||||
Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps:
|
||||
|
||||
- Make sure the node is contained in a new Python (.py) file
|
||||
- Submit a pull request with a link to your node in GitHub against the `nodes` branch to add the node to the [Community Nodes](Community Nodes) list
|
||||
- Make sure you are following the template below and have provided all relevant details about the node and what it does.
|
||||
- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you might be asked for permission to include it in the core project.
|
||||
|
||||
### Community Node Template
|
||||
|
||||
```markdown
|
||||
--------------------------------
|
||||
### Super Cool Node Template
|
||||
|
||||
**Description:** This node allows you to do super cool things with InvokeAI.
|
||||
|
||||
**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
|
||||
|
||||
**Example Node Graph:** https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
|
||||
|
||||
**Output Examples**
|
||||
|
||||
|
||||
```
|
||||
97
docs/nodes/defaultNodes.md
Normal file
97
docs/nodes/defaultNodes.md
Normal file
@ -0,0 +1,97 @@
|
||||
# List of Default Nodes
|
||||
|
||||
The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
|
||||
|
||||
| Node <img width=160 align="right"> | Function |
|
||||
|: ---------------------------------- | :--------------------------------------------------------------------------------------|
|
||||
|Add Integers | Adds two numbers|
|
||||
|Boolean Primitive Collection | A collection of boolean primitive values|
|
||||
|Boolean Primitive | A boolean primitive value|
|
||||
|Canny Processor | Canny edge detection for ControlNet|
|
||||
|CLIP Skip | Skip layers in clip text_encoder model.|
|
||||
|Collect | Collects values into a collection|
|
||||
|Color Correct | Shifts the colors of a target image to match the reference image, optionally using a mask to only color-correct certain regions of the target image.|
|
||||
|Color Primitive | A color primitive value|
|
||||
|Compel Prompt | Parse prompt using compel package to conditioning.|
|
||||
|Conditioning Primitive Collection | A collection of conditioning tensor primitive values|
|
||||
|Conditioning Primitive | A conditioning tensor primitive value|
|
||||
|Content Shuffle Processor | Applies content shuffle processing to image|
|
||||
|ControlNet | Collects ControlNet info to pass to other nodes|
|
||||
|OpenCV Inpaint | Simple inpaint using opencv.|
|
||||
|Denoise Latents | Denoises noisy latents to decodable images|
|
||||
|Divide Integers | Divides two numbers|
|
||||
|Dynamic Prompt | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
|
||||
|Upscale (RealESRGAN) | Upscales an image using RealESRGAN.|
|
||||
|Float Primitive Collection | A collection of float primitive values|
|
||||
|Float Primitive | A float primitive value|
|
||||
|Float Range | Creates a range|
|
||||
|HED (softedge) Processor | Applies HED edge detection to image|
|
||||
|Blur Image | Blurs an image|
|
||||
|Extract Image Channel | Gets a channel from an image.|
|
||||
|Image Primitive Collection | A collection of image primitive values|
|
||||
|Convert Image Mode | Converts an image to a different mode.|
|
||||
|Crop Image | Crops an image to a specified box. The box can be outside of the image.|
|
||||
|Image Hue Adjustment | Adjusts the Hue of an image.|
|
||||
|Inverse Lerp Image | Inverse linear interpolation of all pixels of an image|
|
||||
|Image Primitive | An image primitive value|
|
||||
|Lerp Image | Linear interpolation of all pixels of an image|
|
||||
|Image Luminosity Adjustment | Adjusts the Luminosity (Value) of an image.|
|
||||
|Multiply Images | Multiplies two images together using `PIL.ImageChops.multiply()`.|
|
||||
|Blur NSFW Image | Add blur to NSFW-flagged images|
|
||||
|Paste Image | Pastes an image into another image.|
|
||||
|ImageProcessor | Base class for invocations that preprocess images for ControlNet|
|
||||
|Resize Image | Resizes an image to specific dimensions|
|
||||
|Image Saturation Adjustment | Adjusts the Saturation of an image.|
|
||||
|Scale Image | Scales an image by a factor|
|
||||
|Image to Latents | Encodes an image into latents.|
|
||||
|Add Invisible Watermark | Add an invisible watermark to an image|
|
||||
|Solid Color Infill | Infills transparent areas of an image with a solid color|
|
||||
|PatchMatch Infill | Infills transparent areas of an image using the PatchMatch algorithm|
|
||||
|Tile Infill | Infills transparent areas of an image with tiles of the image|
|
||||
|Integer Primitive Collection | A collection of integer primitive values|
|
||||
|Integer Primitive | An integer primitive value|
|
||||
|Iterate | Iterates over a list of items|
|
||||
|Latents Primitive Collection | A collection of latents tensor primitive values|
|
||||
|Latents Primitive | A latents tensor primitive value|
|
||||
|Latents to Image | Generates an image from latents.|
|
||||
|Leres (Depth) Processor | Applies leres processing to image|
|
||||
|Lineart Anime Processor | Applies line art anime processing to image|
|
||||
|Lineart Processor | Applies line art processing to image|
|
||||
|LoRA Loader | Apply selected lora to unet and text_encoder.|
|
||||
|Main Model Loader | Loads a main model, outputting its submodels.|
|
||||
|Combine Mask | Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`.|
|
||||
|Mask Edge | Applies an edge mask to an image|
|
||||
|Mask from Alpha | Extracts the alpha channel of an image as a mask.|
|
||||
|Mediapipe Face Processor | Applies mediapipe face processing to image|
|
||||
|Midas (Depth) Processor | Applies Midas depth processing to image|
|
||||
|MLSD Processor | Applies MLSD processing to image|
|
||||
|Multiply Integers | Multiplies two numbers|
|
||||
|Noise | Generates latent noise.|
|
||||
|Normal BAE Processor | Applies NormalBae processing to image|
|
||||
|ONNX Latents to Image | Generates an image from latents.|
|
||||
|ONNX Prompt (Raw) | A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
|
||||
|ONNX Text to Latents | Generates latents from conditionings.|
|
||||
|ONNX Model Loader | Loads a main model, outputting its submodels.|
|
||||
|Openpose Processor | Applies Openpose processing to image|
|
||||
|PIDI Processor | Applies PIDI processing to image|
|
||||
|Prompts from File | Loads prompts from a text file|
|
||||
|Random Integer | Outputs a single random integer.|
|
||||
|Random Range | Creates a collection of random numbers|
|
||||
|Integer Range | Creates a range of numbers from start to stop with step|
|
||||
|Integer Range of Size | Creates a range from start to start + size with step|
|
||||
|Resize Latents | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8.|
|
||||
|SDXL Compel Prompt | Parse prompt using compel package to conditioning.|
|
||||
|SDXL LoRA Loader | Apply selected lora to unet and text_encoder.|
|
||||
|SDXL Main Model Loader | Loads an sdxl base model, outputting its submodels.|
|
||||
|SDXL Refiner Compel Prompt | Parse prompt using compel package to conditioning.|
|
||||
|SDXL Refiner Model Loader | Loads an sdxl refiner model, outputting its submodels.|
|
||||
|Scale Latents | Scales latents by a given factor.|
|
||||
|Segment Anything Processor | Applies segment anything processing to image|
|
||||
|Show Image | Displays a provided image, and passes it forward in the pipeline.|
|
||||
|Step Param Easing | Experimental per-step parameter easing for denoising steps|
|
||||
|String Primitive Collection | A collection of string primitive values|
|
||||
|String Primitive | A string primitive value|
|
||||
|Subtract Integers | Subtracts two numbers|
|
||||
|Tile Resample Processor | Tile resampler processor|
|
||||
|VAE Loader | Loads a VAE model, outputting a VaeLoaderOutput|
|
||||
|Zoe (Depth) Processor | Applies Zoe depth processing to image|
|
||||
15
docs/nodes/exampleWorkflows.md
Normal file
15
docs/nodes/exampleWorkflows.md
Normal file
@ -0,0 +1,15 @@
|
||||
# Example Workflows
|
||||
|
||||
TODO: Will update once uploading workflows is available.
|
||||
|
||||
## Text2Image
|
||||
|
||||
## Image2Image
|
||||
|
||||
## ControlNet
|
||||
|
||||
## Upscaling
|
||||
|
||||
## Inpainting / Outpainting
|
||||
|
||||
## LoRAs
|
||||
@ -1,42 +1,26 @@
|
||||
# Nodes
|
||||
|
||||
## What are Nodes?
|
||||
An Node is simply a single operation that takes in some inputs and gives
|
||||
out some outputs. We can then chain multiple nodes together to create more
|
||||
An Node is simply a single operation that takes in inputs and returns
|
||||
out outputs. Multiple nodes can be linked together to create more
|
||||
complex functionality. All InvokeAI features are added through nodes.
|
||||
|
||||
This means nodes can be used to easily extend the image generation capabilities of InvokeAI, and allow you build workflows to suit your needs.
|
||||
### Anatomy of a Node
|
||||
|
||||
You can read more about nodes and the node editor [here](../features/NODES.md).
|
||||
Individual nodes are made up of the following:
|
||||
|
||||
- Inputs: Edge points on the left side of the node window where you connect outputs from other nodes.
|
||||
- Outputs: Edge points on the right side of the node window where you connect to inputs on other nodes.
|
||||
- Options: Various options which are either manually configured, or overridden by connecting an output from another node to the input.
|
||||
|
||||
|
||||
## Downloading Nodes
|
||||
To download a new node, visit our list of [Community Nodes](communityNodes.md). These are nodes that have been created by the community, for the community.
|
||||
With nodes, you can can easily extend the image generation capabilities of InvokeAI, and allow you build workflows that suit your needs.
|
||||
|
||||
You can read more about nodes and the node editor [here](../nodes/NODES.md).
|
||||
|
||||
To get started with nodes, take a look at some of our examples for [common workflows](../nodes/exampleWorkflows.md)
|
||||
|
||||
## Downloading New Nodes
|
||||
To download a new node, visit our list of [Community Nodes](../nodes/communityNodes.md). These are nodes that have been created by the community, for the community.
|
||||
|
||||
|
||||
## Contributing Nodes
|
||||
|
||||
To learn about creating a new node, please visit our [Node creation documenation](../contributing/INVOCATIONS.md).
|
||||
|
||||
Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps:
|
||||
* Make sure the node is contained in a new Python (.py) file
|
||||
* Submit a pull request with a link to your node in GitHub against the `nodes` branch to add the node to the [Community Nodes](Community Nodes) list
|
||||
* Make sure you are following the template below and have provided all relevant details about the node and what it does.
|
||||
* A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you might be asked for permission to include it in the core project.
|
||||
|
||||
### Community Node Template
|
||||
|
||||
```markdown
|
||||
--------------------------------
|
||||
### Super Cool Node Template
|
||||
|
||||
**Description:** This node allows you to do super cool things with InvokeAI.
|
||||
|
||||
**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
|
||||
|
||||
**Example Node Graph:** https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
|
||||
|
||||
**Output Examples**
|
||||
|
||||
|
||||
```
|
||||
|
||||
@ -55,7 +55,7 @@ async def get_version() -> AppVersion:
|
||||
|
||||
@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
|
||||
async def get_config() -> AppConfig:
|
||||
infill_methods = ["tile"]
|
||||
infill_methods = ["tile", "lama"]
|
||||
if PatchMatch.patchmatch_available():
|
||||
infill_methods.append("patchmatch")
|
||||
|
||||
|
||||
@ -1,11 +1,11 @@
|
||||
# Copyright (c) 2022-2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
|
||||
import asyncio
|
||||
from inspect import signature
|
||||
|
||||
import logging
|
||||
import uvicorn
|
||||
import socket
|
||||
from inspect import signature
|
||||
from pathlib import Path
|
||||
|
||||
import uvicorn
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
|
||||
@ -13,7 +13,6 @@ from fastapi.openapi.utils import get_openapi
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from fastapi_events.handlers.local import local_handler
|
||||
from fastapi_events.middleware import EventHandlerASGIMiddleware
|
||||
from pathlib import Path
|
||||
from pydantic.schema import schema
|
||||
|
||||
from .services.config import InvokeAIAppConfig
|
||||
@ -30,9 +29,12 @@ from .api.sockets import SocketIO
|
||||
from .invocations.baseinvocation import BaseInvocation, _InputField, _OutputField, UIConfigBase
|
||||
|
||||
import torch
|
||||
|
||||
# noinspection PyUnresolvedReferences
|
||||
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
|
||||
|
||||
if torch.backends.mps.is_available():
|
||||
# noinspection PyUnresolvedReferences
|
||||
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
|
||||
|
||||
|
||||
@ -40,7 +42,6 @@ app_config = InvokeAIAppConfig.get_config()
|
||||
app_config.parse_args()
|
||||
logger = InvokeAILogger.getLogger(config=app_config)
|
||||
|
||||
|
||||
# fix for windows mimetypes registry entries being borked
|
||||
# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
|
||||
mimetypes.add_type("application/javascript", ".js")
|
||||
@ -122,6 +123,7 @@ def custom_openapi():
|
||||
|
||||
output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
|
||||
for schema_key, output_schema in output_schemas["definitions"].items():
|
||||
output_schema["class"] = "output"
|
||||
openapi_schema["components"]["schemas"][schema_key] = output_schema
|
||||
|
||||
# TODO: note that we assume the schema_key here is the TYPE.__name__
|
||||
@ -130,8 +132,8 @@ def custom_openapi():
|
||||
|
||||
# Add Node Editor UI helper schemas
|
||||
ui_config_schemas = schema([UIConfigBase, _InputField, _OutputField], ref_prefix="#/components/schemas/")
|
||||
for schema_key, output_schema in ui_config_schemas["definitions"].items():
|
||||
openapi_schema["components"]["schemas"][schema_key] = output_schema
|
||||
for schema_key, ui_config_schema in ui_config_schemas["definitions"].items():
|
||||
openapi_schema["components"]["schemas"][schema_key] = ui_config_schema
|
||||
|
||||
# Add a reference to the output type to additionalProperties of the invoker schema
|
||||
for invoker in all_invocations:
|
||||
@ -140,8 +142,8 @@ def custom_openapi():
|
||||
output_type_title = output_type_titles[output_type.__name__]
|
||||
invoker_schema = openapi_schema["components"]["schemas"][invoker_name]
|
||||
outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
|
||||
|
||||
invoker_schema["output"] = outputs_ref
|
||||
invoker_schema["class"] = "invocation"
|
||||
|
||||
from invokeai.backend.model_management.models import get_model_config_enums
|
||||
|
||||
@ -207,6 +209,17 @@ def invoke_api():
|
||||
|
||||
check_invokeai_root(app_config) # note, may exit with an exception if root not set up
|
||||
|
||||
if app_config.dev_reload:
|
||||
try:
|
||||
import jurigged
|
||||
except ImportError as e:
|
||||
logger.error(
|
||||
'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.',
|
||||
exc_info=e,
|
||||
)
|
||||
else:
|
||||
jurigged.watch(logger=InvokeAILogger.getLogger(name="jurigged").info)
|
||||
|
||||
port = find_port(app_config.port)
|
||||
if port != app_config.port:
|
||||
logger.warn(f"Port {app_config.port} in use, using port {port}")
|
||||
|
||||
@ -71,6 +71,9 @@ class FieldDescriptions:
|
||||
safe_mode = "Whether or not to use safe mode"
|
||||
scribble_mode = "Whether or not to use scribble mode"
|
||||
scale_factor = "The factor by which to scale"
|
||||
blend_alpha = (
|
||||
"Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
|
||||
)
|
||||
num_1 = "The first number"
|
||||
num_2 = "The second number"
|
||||
mask = "The mask to use for the operation"
|
||||
@ -140,6 +143,7 @@ class UIType(str, Enum):
|
||||
# region Misc
|
||||
FilePath = "FilePath"
|
||||
Enum = "enum"
|
||||
Scheduler = "Scheduler"
|
||||
# endregion
|
||||
|
||||
|
||||
@ -166,6 +170,7 @@ class _InputField(BaseModel):
|
||||
ui_hidden: bool
|
||||
ui_type: Optional[UIType]
|
||||
ui_component: Optional[UIComponent]
|
||||
ui_order: Optional[int]
|
||||
|
||||
|
||||
class _OutputField(BaseModel):
|
||||
@ -178,6 +183,7 @@ class _OutputField(BaseModel):
|
||||
|
||||
ui_hidden: bool
|
||||
ui_type: Optional[UIType]
|
||||
ui_order: Optional[int]
|
||||
|
||||
|
||||
def InputField(
|
||||
@ -211,6 +217,7 @@ def InputField(
|
||||
ui_type: Optional[UIType] = None,
|
||||
ui_component: Optional[UIComponent] = None,
|
||||
ui_hidden: bool = False,
|
||||
ui_order: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""
|
||||
@ -269,6 +276,7 @@ def InputField(
|
||||
ui_type=ui_type,
|
||||
ui_component=ui_component,
|
||||
ui_hidden=ui_hidden,
|
||||
ui_order=ui_order,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@ -302,6 +310,7 @@ def OutputField(
|
||||
repr: bool = True,
|
||||
ui_type: Optional[UIType] = None,
|
||||
ui_hidden: bool = False,
|
||||
ui_order: Optional[int] = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""
|
||||
@ -348,6 +357,7 @@ def OutputField(
|
||||
repr=repr,
|
||||
ui_type=ui_type,
|
||||
ui_hidden=ui_hidden,
|
||||
ui_order=ui_order,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@ -376,7 +386,7 @@ class BaseInvocationOutput(BaseModel):
|
||||
"""Base class for all invocation outputs"""
|
||||
|
||||
# All outputs must include a type name like this:
|
||||
# type: Literal['your_output_name']
|
||||
# type: Literal['your_output_name'] # noqa f821
|
||||
|
||||
@classmethod
|
||||
def get_all_subclasses_tuple(cls):
|
||||
@ -389,6 +399,13 @@ class BaseInvocationOutput(BaseModel):
|
||||
toprocess.extend(next_subclasses)
|
||||
return tuple(subclasses)
|
||||
|
||||
class Config:
|
||||
@staticmethod
|
||||
def schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
|
||||
if "required" not in schema or not isinstance(schema["required"], list):
|
||||
schema["required"] = list()
|
||||
schema["required"].extend(["type"])
|
||||
|
||||
|
||||
class RequiredConnectionException(Exception):
|
||||
"""Raised when an field which requires a connection did not receive a value."""
|
||||
@ -410,7 +427,7 @@ class BaseInvocation(ABC, BaseModel):
|
||||
"""
|
||||
|
||||
# All invocations must include a type name like this:
|
||||
# type: Literal['your_output_name']
|
||||
# type: Literal['your_output_name'] # noqa f821
|
||||
|
||||
@classmethod
|
||||
def get_all_subclasses(cls):
|
||||
@ -449,6 +466,9 @@ class BaseInvocation(ABC, BaseModel):
|
||||
schema["title"] = uiconfig.title
|
||||
if uiconfig and hasattr(uiconfig, "tags"):
|
||||
schema["tags"] = uiconfig.tags
|
||||
if "required" not in schema or not isinstance(schema["required"], list):
|
||||
schema["required"] = list()
|
||||
schema["required"].extend(["type", "id"])
|
||||
|
||||
@abstractmethod
|
||||
def invoke(self, context: InvocationContext) -> BaseInvocationOutput:
|
||||
@ -485,7 +505,7 @@ class BaseInvocation(ABC, BaseModel):
|
||||
raise MissingInputException(self.__fields__["type"].default, field_name)
|
||||
return self.invoke(context)
|
||||
|
||||
id: str = InputField(description="The id of this node. Must be unique among all nodes.")
|
||||
id: str = Field(description="The id of this node. Must be unique among all nodes.")
|
||||
is_intermediate: bool = InputField(
|
||||
default=False, description="Whether or not this node is an intermediate node.", input=Input.Direct
|
||||
)
|
||||
|
||||
@ -233,7 +233,7 @@ class SDXLPromptInvocationBase:
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=True, # TODO:
|
||||
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
|
||||
requires_pooled=True,
|
||||
requires_pooled=get_pooled,
|
||||
)
|
||||
|
||||
conjunction = Compel.parse_prompt_string(prompt)
|
||||
|
||||
@ -8,7 +8,7 @@ import numpy
|
||||
from PIL import Image, ImageChops, ImageFilter, ImageOps
|
||||
|
||||
from invokeai.app.invocations.metadata import CoreMetadata
|
||||
from invokeai.app.invocations.primitives import ImageField, ImageOutput
|
||||
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
|
||||
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
|
||||
from invokeai.backend.image_util.safety_checker import SafetyChecker
|
||||
|
||||
@ -41,6 +41,39 @@ class ShowImageInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@title("Blank Image")
|
||||
@tags("image")
|
||||
class BlankImageInvocation(BaseInvocation):
|
||||
"""Creates a blank image and forwards it to the pipeline"""
|
||||
|
||||
# Metadata
|
||||
type: Literal["blank_image"] = "blank_image"
|
||||
|
||||
# Inputs
|
||||
width: int = InputField(default=512, description="The width of the image")
|
||||
height: int = InputField(default=512, description="The height of the image")
|
||||
mode: Literal["RGB", "RGBA"] = InputField(default="RGB", description="The mode of the image")
|
||||
color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=255), description="The color of the image")
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = Image.new(mode=self.mode, size=(self.width, self.height), color=self.color.tuple())
|
||||
|
||||
image_dto = context.services.images.create(
|
||||
image=image,
|
||||
image_origin=ResourceOrigin.INTERNAL,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
)
|
||||
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@title("Crop Image")
|
||||
@tags("image", "crop")
|
||||
class ImageCropInvocation(BaseInvocation):
|
||||
|
||||
@ -1,23 +1,25 @@
|
||||
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
|
||||
|
||||
import math
|
||||
from typing import Literal, Optional, get_args
|
||||
|
||||
import numpy as np
|
||||
import math
|
||||
from PIL import Image, ImageOps
|
||||
from invokeai.app.invocations.primitives import ImageField, ImageOutput, ColorField
|
||||
|
||||
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
|
||||
from invokeai.app.util.misc import SEED_MAX, get_random_seed
|
||||
from invokeai.backend.image_util.lama import LaMA
|
||||
from invokeai.backend.image_util.patchmatch import PatchMatch
|
||||
|
||||
from ..models.image import ImageCategory, ResourceOrigin
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, title, tags
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
|
||||
|
||||
|
||||
def infill_methods() -> list[str]:
|
||||
methods = [
|
||||
"tile",
|
||||
"solid",
|
||||
"lama",
|
||||
]
|
||||
if PatchMatch.patchmatch_available():
|
||||
methods.insert(0, "patchmatch")
|
||||
@ -28,6 +30,11 @@ INFILL_METHODS = Literal[tuple(infill_methods())]
|
||||
DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
|
||||
|
||||
|
||||
def infill_lama(im: Image.Image) -> Image.Image:
|
||||
lama = LaMA()
|
||||
return lama(im)
|
||||
|
||||
|
||||
def infill_patchmatch(im: Image.Image) -> Image.Image:
|
||||
if im.mode != "RGBA":
|
||||
return im
|
||||
@ -90,7 +97,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
|
||||
return im
|
||||
|
||||
# Find all invalid tiles and replace with a random valid tile
|
||||
replace_count = (tiles_mask is False).sum()
|
||||
replace_count = (tiles_mask == False).sum() # noqa: E712
|
||||
rng = np.random.default_rng(seed=seed)
|
||||
tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count), :, :, :]
|
||||
|
||||
@ -218,3 +225,34 @@ class InfillPatchMatchInvocation(BaseInvocation):
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
|
||||
@title("LaMa Infill")
|
||||
@tags("image", "inpaint")
|
||||
class LaMaInfillInvocation(BaseInvocation):
|
||||
"""Infills transparent areas of an image using the LaMa model"""
|
||||
|
||||
type: Literal["infill_lama"] = "infill_lama"
|
||||
|
||||
# Inputs
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
infilled = infill_lama(image.copy())
|
||||
|
||||
image_dto = context.services.images.create(
|
||||
image=infilled,
|
||||
image_origin=ResourceOrigin.INTERNAL,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
)
|
||||
|
||||
return ImageOutput(
|
||||
image=ImageField(image_name=image_dto.image_name),
|
||||
width=image_dto.width,
|
||||
height=image_dto.height,
|
||||
)
|
||||
|
||||
@ -5,6 +5,7 @@ from functools import singledispatchmethod
|
||||
from typing import List, Literal, Optional, Union
|
||||
|
||||
import einops
|
||||
import numpy as np
|
||||
import torch
|
||||
import torchvision.transforms as T
|
||||
from diffusers import AutoencoderKL, AutoencoderTiny
|
||||
@ -108,24 +109,28 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
|
||||
# Inputs
|
||||
positive_conditioning: ConditioningField = InputField(
|
||||
description=FieldDescriptions.positive_cond, input=Input.Connection
|
||||
description=FieldDescriptions.positive_cond, input=Input.Connection, ui_order=0
|
||||
)
|
||||
negative_conditioning: ConditioningField = InputField(
|
||||
description=FieldDescriptions.negative_cond, input=Input.Connection
|
||||
description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1
|
||||
)
|
||||
noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection)
|
||||
noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection, ui_order=3)
|
||||
steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
|
||||
cfg_scale: Union[float, List[float]] = InputField(
|
||||
default=7.5, ge=1, description=FieldDescriptions.cfg_scale, ui_type=UIType.Float
|
||||
default=7.5, ge=1, description=FieldDescriptions.cfg_scale, ui_type=UIType.Float, title="CFG Scale"
|
||||
)
|
||||
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)
|
||||
scheduler: SAMPLER_NAME_VALUES = InputField(default="euler", description=FieldDescriptions.scheduler)
|
||||
unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection)
|
||||
control: Union[ControlField, list[ControlField]] = InputField(
|
||||
default=None, description=FieldDescriptions.control, input=Input.Connection
|
||||
scheduler: SAMPLER_NAME_VALUES = InputField(
|
||||
default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler
|
||||
)
|
||||
unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2)
|
||||
control: Union[ControlField, list[ControlField]] = InputField(
|
||||
default=None, description=FieldDescriptions.control, input=Input.Connection, ui_order=5
|
||||
)
|
||||
latents: Optional[LatentsField] = InputField(
|
||||
description=FieldDescriptions.latents, input=Input.Connection, ui_order=4
|
||||
)
|
||||
latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
|
||||
mask: Optional[ImageField] = InputField(
|
||||
default=None,
|
||||
description=FieldDescriptions.mask,
|
||||
@ -455,7 +460,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
|
||||
|
||||
@title("Latents to Image")
|
||||
@tags("latents", "image", "vae")
|
||||
@tags("latents", "image", "vae", "l2i")
|
||||
class LatentsToImageInvocation(BaseInvocation):
|
||||
"""Generates an image from latents."""
|
||||
|
||||
@ -652,7 +657,7 @@ class ScaleLatentsInvocation(BaseInvocation):
|
||||
|
||||
|
||||
@title("Image to Latents")
|
||||
@tags("latents", "image", "vae")
|
||||
@tags("latents", "image", "vae", "i2l")
|
||||
class ImageToLatentsInvocation(BaseInvocation):
|
||||
"""Encodes an image into latents."""
|
||||
|
||||
@ -749,3 +754,81 @@ class ImageToLatentsInvocation(BaseInvocation):
|
||||
@_encode_to_tensor.register
|
||||
def _(self, vae: AutoencoderTiny, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
|
||||
return vae.encode(image_tensor).latents
|
||||
|
||||
|
||||
@title("Blend Latents")
|
||||
@tags("latents", "blend")
|
||||
class BlendLatentsInvocation(BaseInvocation):
|
||||
"""Blend two latents using a given alpha. Latents must have same size."""
|
||||
|
||||
type: Literal["lblend"] = "lblend"
|
||||
|
||||
# Inputs
|
||||
latents_a: LatentsField = InputField(
|
||||
description=FieldDescriptions.latents,
|
||||
input=Input.Connection,
|
||||
)
|
||||
latents_b: LatentsField = InputField(
|
||||
description=FieldDescriptions.latents,
|
||||
input=Input.Connection,
|
||||
)
|
||||
alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
latents_a = context.services.latents.get(self.latents_a.latents_name)
|
||||
latents_b = context.services.latents.get(self.latents_b.latents_name)
|
||||
|
||||
if latents_a.shape != latents_b.shape:
|
||||
raise "Latents to blend must be the same size."
|
||||
|
||||
# TODO:
|
||||
device = choose_torch_device()
|
||||
|
||||
def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
|
||||
"""
|
||||
Spherical linear interpolation
|
||||
Args:
|
||||
t (float/np.ndarray): Float value between 0.0 and 1.0
|
||||
v0 (np.ndarray): Starting vector
|
||||
v1 (np.ndarray): Final vector
|
||||
DOT_THRESHOLD (float): Threshold for considering the two vectors as
|
||||
colineal. Not recommended to alter this.
|
||||
Returns:
|
||||
v2 (np.ndarray): Interpolation vector between v0 and v1
|
||||
"""
|
||||
inputs_are_torch = False
|
||||
if not isinstance(v0, np.ndarray):
|
||||
inputs_are_torch = True
|
||||
v0 = v0.detach().cpu().numpy()
|
||||
if not isinstance(v1, np.ndarray):
|
||||
inputs_are_torch = True
|
||||
v1 = v1.detach().cpu().numpy()
|
||||
|
||||
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
|
||||
if np.abs(dot) > DOT_THRESHOLD:
|
||||
v2 = (1 - t) * v0 + t * v1
|
||||
else:
|
||||
theta_0 = np.arccos(dot)
|
||||
sin_theta_0 = np.sin(theta_0)
|
||||
theta_t = theta_0 * t
|
||||
sin_theta_t = np.sin(theta_t)
|
||||
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
|
||||
s1 = sin_theta_t / sin_theta_0
|
||||
v2 = s0 * v0 + s1 * v1
|
||||
|
||||
if inputs_are_torch:
|
||||
v2 = torch.from_numpy(v2).to(device)
|
||||
|
||||
return v2
|
||||
|
||||
# blend
|
||||
blended_latents = slerp(self.alpha, latents_a, latents_b)
|
||||
|
||||
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
|
||||
blended_latents = blended_latents.to("cpu")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
name = f"{context.graph_execution_state_id}__{self.id}"
|
||||
# context.services.latents.set(name, resized_latents)
|
||||
context.services.latents.save(name, blended_latents)
|
||||
return build_latents_output(latents_name=name, latents=blended_latents)
|
||||
|
||||
@ -21,7 +21,7 @@ class AddInvocation(BaseInvocation):
|
||||
b: int = InputField(default=0, description=FieldDescriptions.num_2)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntegerOutput:
|
||||
return IntegerOutput(a=self.a + self.b)
|
||||
return IntegerOutput(value=self.a + self.b)
|
||||
|
||||
|
||||
@title("Subtract Integers")
|
||||
@ -36,7 +36,7 @@ class SubtractInvocation(BaseInvocation):
|
||||
b: int = InputField(default=0, description=FieldDescriptions.num_2)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntegerOutput:
|
||||
return IntegerOutput(a=self.a - self.b)
|
||||
return IntegerOutput(value=self.a - self.b)
|
||||
|
||||
|
||||
@title("Multiply Integers")
|
||||
@ -51,7 +51,7 @@ class MultiplyInvocation(BaseInvocation):
|
||||
b: int = InputField(default=0, description=FieldDescriptions.num_2)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntegerOutput:
|
||||
return IntegerOutput(a=self.a * self.b)
|
||||
return IntegerOutput(value=self.a * self.b)
|
||||
|
||||
|
||||
@title("Divide Integers")
|
||||
@ -66,7 +66,7 @@ class DivideInvocation(BaseInvocation):
|
||||
b: int = InputField(default=0, description=FieldDescriptions.num_2)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntegerOutput:
|
||||
return IntegerOutput(a=int(self.a / self.b))
|
||||
return IntegerOutput(value=int(self.a / self.b))
|
||||
|
||||
|
||||
@title("Random Integer")
|
||||
@ -81,4 +81,4 @@ class RandomIntInvocation(BaseInvocation):
|
||||
high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntegerOutput:
|
||||
return IntegerOutput(a=np.random.randint(self.low, self.high))
|
||||
return IntegerOutput(value=np.random.randint(self.low, self.high))
|
||||
|
||||
@ -32,6 +32,7 @@ class CoreMetadata(BaseModelExcludeNull):
|
||||
generation_mode: str = Field(
|
||||
description="The generation mode that output this image",
|
||||
)
|
||||
created_by: Optional[str] = Field(description="The name of the creator of the image")
|
||||
positive_prompt: str = Field(description="The positive prompt parameter")
|
||||
negative_prompt: str = Field(description="The negative prompt parameter")
|
||||
width: int = Field(description="The width parameter")
|
||||
|
||||
@ -72,7 +72,7 @@ class LoRAModelField(BaseModel):
|
||||
base_model: BaseModelType = Field(description="Base model")
|
||||
|
||||
|
||||
@title("Main Model Loader")
|
||||
@title("Main Model")
|
||||
@tags("model")
|
||||
class MainModelLoaderInvocation(BaseInvocation):
|
||||
"""Loads a main model, outputting its submodels."""
|
||||
@ -179,7 +179,7 @@ class LoraLoaderOutput(BaseInvocationOutput):
|
||||
# fmt: on
|
||||
|
||||
|
||||
@title("LoRA Loader")
|
||||
@title("LoRA")
|
||||
@tags("lora", "model")
|
||||
class LoraLoaderInvocation(BaseInvocation):
|
||||
"""Apply selected lora to unet and text_encoder."""
|
||||
@ -257,7 +257,7 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
|
||||
# fmt: on
|
||||
|
||||
|
||||
@title("SDXL LoRA Loader")
|
||||
@title("SDXL LoRA")
|
||||
@tags("sdxl", "lora", "model")
|
||||
class SDXLLoraLoaderInvocation(BaseInvocation):
|
||||
"""Apply selected lora to unet and text_encoder."""
|
||||
@ -356,7 +356,7 @@ class VaeLoaderOutput(BaseInvocationOutput):
|
||||
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
|
||||
|
||||
|
||||
@title("VAE Loader")
|
||||
@title("VAE")
|
||||
@tags("vae", "model")
|
||||
class VaeLoaderInvocation(BaseInvocation):
|
||||
"""Loads a VAE model, outputting a VaeLoaderOutput"""
|
||||
|
||||
@ -169,7 +169,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
|
||||
ui_type=UIType.Float,
|
||||
)
|
||||
scheduler: SAMPLER_NAME_VALUES = InputField(
|
||||
default="euler", description=FieldDescriptions.scheduler, input=Input.Direct
|
||||
default="euler", description=FieldDescriptions.scheduler, input=Input.Direct, ui_type=UIType.Scheduler
|
||||
)
|
||||
precision: PRECISION_VALUES = InputField(default="tensor(float16)", description=FieldDescriptions.precision)
|
||||
unet: UNetField = InputField(
|
||||
@ -406,7 +406,7 @@ class OnnxModelField(BaseModel):
|
||||
model_type: ModelType = Field(description="Model Type")
|
||||
|
||||
|
||||
@title("ONNX Model Loader")
|
||||
@title("ONNX Main Model")
|
||||
@tags("onnx", "model")
|
||||
class OnnxModelLoaderInvocation(BaseInvocation):
|
||||
"""Loads a main model, outputting its submodels."""
|
||||
|
||||
@ -2,8 +2,8 @@
|
||||
|
||||
from typing import Literal, Optional, Tuple
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
import torch
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
@ -33,7 +33,7 @@ class BooleanOutput(BaseInvocationOutput):
|
||||
"""Base class for nodes that output a single boolean"""
|
||||
|
||||
type: Literal["boolean_output"] = "boolean_output"
|
||||
a: bool = OutputField(description="The output boolean")
|
||||
value: bool = OutputField(description="The output boolean")
|
||||
|
||||
|
||||
class BooleanCollectionOutput(BaseInvocationOutput):
|
||||
@ -42,9 +42,7 @@ class BooleanCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["boolean_collection_output"] = "boolean_collection_output"
|
||||
|
||||
# Outputs
|
||||
collection: list[bool] = OutputField(
|
||||
default_factory=list, description="The output boolean collection", ui_type=UIType.BooleanCollection
|
||||
)
|
||||
collection: list[bool] = OutputField(description="The output boolean collection", ui_type=UIType.BooleanCollection)
|
||||
|
||||
|
||||
@title("Boolean Primitive")
|
||||
@ -55,10 +53,10 @@ class BooleanInvocation(BaseInvocation):
|
||||
type: Literal["boolean"] = "boolean"
|
||||
|
||||
# Inputs
|
||||
a: bool = InputField(default=False, description="The boolean value")
|
||||
value: bool = InputField(default=False, description="The boolean value")
|
||||
|
||||
def invoke(self, context: InvocationContext) -> BooleanOutput:
|
||||
return BooleanOutput(a=self.a)
|
||||
return BooleanOutput(value=self.value)
|
||||
|
||||
|
||||
@title("Boolean Primitive Collection")
|
||||
@ -70,7 +68,7 @@ class BooleanCollectionInvocation(BaseInvocation):
|
||||
|
||||
# Inputs
|
||||
collection: list[bool] = InputField(
|
||||
default=False, description="The collection of boolean values", ui_type=UIType.BooleanCollection
|
||||
default_factory=list, description="The collection of boolean values", ui_type=UIType.BooleanCollection
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
|
||||
@ -86,7 +84,7 @@ class IntegerOutput(BaseInvocationOutput):
|
||||
"""Base class for nodes that output a single integer"""
|
||||
|
||||
type: Literal["integer_output"] = "integer_output"
|
||||
a: int = OutputField(description="The output integer")
|
||||
value: int = OutputField(description="The output integer")
|
||||
|
||||
|
||||
class IntegerCollectionOutput(BaseInvocationOutput):
|
||||
@ -95,9 +93,7 @@ class IntegerCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["integer_collection_output"] = "integer_collection_output"
|
||||
|
||||
# Outputs
|
||||
collection: list[int] = OutputField(
|
||||
default_factory=list, description="The int collection", ui_type=UIType.IntegerCollection
|
||||
)
|
||||
collection: list[int] = OutputField(description="The int collection", ui_type=UIType.IntegerCollection)
|
||||
|
||||
|
||||
@title("Integer Primitive")
|
||||
@ -108,10 +104,10 @@ class IntegerInvocation(BaseInvocation):
|
||||
type: Literal["integer"] = "integer"
|
||||
|
||||
# Inputs
|
||||
a: int = InputField(default=0, description="The integer value")
|
||||
value: int = InputField(default=0, description="The integer value")
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntegerOutput:
|
||||
return IntegerOutput(a=self.a)
|
||||
return IntegerOutput(value=self.value)
|
||||
|
||||
|
||||
@title("Integer Primitive Collection")
|
||||
@ -139,7 +135,7 @@ class FloatOutput(BaseInvocationOutput):
|
||||
"""Base class for nodes that output a single float"""
|
||||
|
||||
type: Literal["float_output"] = "float_output"
|
||||
a: float = OutputField(description="The output float")
|
||||
value: float = OutputField(description="The output float")
|
||||
|
||||
|
||||
class FloatCollectionOutput(BaseInvocationOutput):
|
||||
@ -148,9 +144,7 @@ class FloatCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["float_collection_output"] = "float_collection_output"
|
||||
|
||||
# Outputs
|
||||
collection: list[float] = OutputField(
|
||||
default_factory=list, description="The float collection", ui_type=UIType.FloatCollection
|
||||
)
|
||||
collection: list[float] = OutputField(description="The float collection", ui_type=UIType.FloatCollection)
|
||||
|
||||
|
||||
@title("Float Primitive")
|
||||
@ -161,10 +155,10 @@ class FloatInvocation(BaseInvocation):
|
||||
type: Literal["float"] = "float"
|
||||
|
||||
# Inputs
|
||||
param: float = InputField(default=0.0, description="The float value")
|
||||
value: float = InputField(default=0.0, description="The float value")
|
||||
|
||||
def invoke(self, context: InvocationContext) -> FloatOutput:
|
||||
return FloatOutput(a=self.param)
|
||||
return FloatOutput(value=self.value)
|
||||
|
||||
|
||||
@title("Float Primitive Collection")
|
||||
@ -176,7 +170,7 @@ class FloatCollectionInvocation(BaseInvocation):
|
||||
|
||||
# Inputs
|
||||
collection: list[float] = InputField(
|
||||
default=0, description="The collection of float values", ui_type=UIType.FloatCollection
|
||||
default_factory=list, description="The collection of float values", ui_type=UIType.FloatCollection
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
|
||||
@ -192,7 +186,7 @@ class StringOutput(BaseInvocationOutput):
|
||||
"""Base class for nodes that output a single string"""
|
||||
|
||||
type: Literal["string_output"] = "string_output"
|
||||
text: str = OutputField(description="The output string")
|
||||
value: str = OutputField(description="The output string")
|
||||
|
||||
|
||||
class StringCollectionOutput(BaseInvocationOutput):
|
||||
@ -201,9 +195,7 @@ class StringCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["string_collection_output"] = "string_collection_output"
|
||||
|
||||
# Outputs
|
||||
collection: list[str] = OutputField(
|
||||
default_factory=list, description="The output strings", ui_type=UIType.StringCollection
|
||||
)
|
||||
collection: list[str] = OutputField(description="The output strings", ui_type=UIType.StringCollection)
|
||||
|
||||
|
||||
@title("String Primitive")
|
||||
@ -214,10 +206,10 @@ class StringInvocation(BaseInvocation):
|
||||
type: Literal["string"] = "string"
|
||||
|
||||
# Inputs
|
||||
text: str = InputField(default="", description="The string value", ui_component=UIComponent.Textarea)
|
||||
value: str = InputField(default="", description="The string value", ui_component=UIComponent.Textarea)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> StringOutput:
|
||||
return StringOutput(text=self.text)
|
||||
return StringOutput(value=self.value)
|
||||
|
||||
|
||||
@title("String Primitive Collection")
|
||||
@ -229,7 +221,7 @@ class StringCollectionInvocation(BaseInvocation):
|
||||
|
||||
# Inputs
|
||||
collection: list[str] = InputField(
|
||||
default=0, description="The collection of string values", ui_type=UIType.StringCollection
|
||||
default_factory=list, description="The collection of string values", ui_type=UIType.StringCollection
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> StringCollectionOutput:
|
||||
@ -262,9 +254,7 @@ class ImageCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["image_collection_output"] = "image_collection_output"
|
||||
|
||||
# Outputs
|
||||
collection: list[ImageField] = OutputField(
|
||||
default_factory=list, description="The output images", ui_type=UIType.ImageCollection
|
||||
)
|
||||
collection: list[ImageField] = OutputField(description="The output images", ui_type=UIType.ImageCollection)
|
||||
|
||||
|
||||
@title("Image Primitive")
|
||||
@ -334,7 +324,6 @@ class LatentsCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["latents_collection_output"] = "latents_collection_output"
|
||||
|
||||
collection: list[LatentsField] = OutputField(
|
||||
default_factory=list,
|
||||
description=FieldDescriptions.latents,
|
||||
ui_type=UIType.LatentsCollection,
|
||||
)
|
||||
@ -365,7 +354,7 @@ class LatentsCollectionInvocation(BaseInvocation):
|
||||
|
||||
# Inputs
|
||||
collection: list[LatentsField] = InputField(
|
||||
default=0, description="The collection of latents tensors", ui_type=UIType.LatentsCollection
|
||||
description="The collection of latents tensors", ui_type=UIType.LatentsCollection
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> LatentsCollectionOutput:
|
||||
@ -410,9 +399,7 @@ class ColorCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["color_collection_output"] = "color_collection_output"
|
||||
|
||||
# Outputs
|
||||
collection: list[ColorField] = OutputField(
|
||||
default_factory=list, description="The output colors", ui_type=UIType.ColorCollection
|
||||
)
|
||||
collection: list[ColorField] = OutputField(description="The output colors", ui_type=UIType.ColorCollection)
|
||||
|
||||
|
||||
@title("Color Primitive")
|
||||
@ -455,7 +442,6 @@ class ConditioningCollectionOutput(BaseInvocationOutput):
|
||||
|
||||
# Outputs
|
||||
collection: list[ConditioningField] = OutputField(
|
||||
default_factory=list,
|
||||
description="The output conditioning tensors",
|
||||
ui_type=UIType.ConditioningCollection,
|
||||
)
|
||||
|
||||
@ -37,7 +37,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
|
||||
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
|
||||
|
||||
|
||||
@title("SDXL Main Model Loader")
|
||||
@title("SDXL Main Model")
|
||||
@tags("model", "sdxl")
|
||||
class SDXLModelLoaderInvocation(BaseInvocation):
|
||||
"""Loads an sdxl base model, outputting its submodels."""
|
||||
@ -122,7 +122,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@title("SDXL Refiner Model Loader")
|
||||
@title("SDXL Refiner Model")
|
||||
@tags("model", "sdxl", "refiner")
|
||||
class SDXLRefinerModelLoaderInvocation(BaseInvocation):
|
||||
"""Loads an sdxl refiner model, outputting its submodels."""
|
||||
|
||||
8
invokeai/app/services/config/__init__.py
Normal file
8
invokeai/app/services/config/__init__.py
Normal file
@ -0,0 +1,8 @@
|
||||
"""
|
||||
Init file for InvokeAI configure package
|
||||
"""
|
||||
|
||||
from .invokeai_config import ( # noqa F401
|
||||
InvokeAIAppConfig,
|
||||
get_invokeai_config,
|
||||
)
|
||||
239
invokeai/app/services/config/base.py
Normal file
239
invokeai/app/services/config/base.py
Normal file
@ -0,0 +1,239 @@
|
||||
# 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 os
|
||||
import pydoc
|
||||
import sys
|
||||
from argparse import ArgumentParser
|
||||
from omegaconf import OmegaConf, DictConfig, ListConfig
|
||||
from pathlib import Path
|
||||
from pydantic import BaseSettings
|
||||
from typing import ClassVar, Dict, List, Literal, Union, get_origin, get_type_hints, get_args
|
||||
|
||||
|
||||
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):
|
||||
"""
|
||||
Runtime configuration settings in which default values are
|
||||
read from an omegaconf .yaml file.
|
||||
"""
|
||||
|
||||
initconf: ClassVar[DictConfig] = None
|
||||
argparse_groups: ClassVar[Dict] = {}
|
||||
|
||||
def parse_args(self, argv: list = sys.argv[1:]):
|
||||
parser = self.get_parser()
|
||||
opt = parser.parse_args(argv)
|
||||
for name in self.__fields__:
|
||||
if name not in self._excluded():
|
||||
value = getattr(opt, name)
|
||||
if isinstance(value, ListConfig):
|
||||
value = list(value)
|
||||
elif isinstance(value, DictConfig):
|
||||
value = dict(value)
|
||||
setattr(self, name, value)
|
||||
|
||||
def to_yaml(self) -> str:
|
||||
"""
|
||||
Return a YAML string representing our settings. This can be used
|
||||
as the contents of `invokeai.yaml` to restore settings later.
|
||||
"""
|
||||
cls = self.__class__
|
||||
type = get_args(get_type_hints(cls)["type"])[0]
|
||||
field_dict = dict({type: dict()})
|
||||
for name, field in self.__fields__.items():
|
||||
if name in cls._excluded_from_yaml():
|
||||
continue
|
||||
category = field.field_info.extra.get("category") or "Uncategorized"
|
||||
value = getattr(self, name)
|
||||
if category not in field_dict[type]:
|
||||
field_dict[type][category] = dict()
|
||||
# keep paths as strings to make it easier to read
|
||||
field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
|
||||
conf = OmegaConf.create(field_dict)
|
||||
return OmegaConf.to_yaml(conf)
|
||||
|
||||
@classmethod
|
||||
def add_parser_arguments(cls, parser):
|
||||
if "type" in get_type_hints(cls):
|
||||
settings_stanza = get_args(get_type_hints(cls)["type"])[0]
|
||||
else:
|
||||
settings_stanza = "Uncategorized"
|
||||
|
||||
env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
|
||||
|
||||
initconf = (
|
||||
cls.initconf.get(settings_stanza)
|
||||
if cls.initconf and settings_stanza in cls.initconf
|
||||
else OmegaConf.create()
|
||||
)
|
||||
|
||||
# create an upcase version of the environment in
|
||||
# order to achieve case-insensitive environment
|
||||
# variables (the way Windows does)
|
||||
upcase_environ = dict()
|
||||
for key, value in os.environ.items():
|
||||
upcase_environ[key.upper()] = value
|
||||
|
||||
fields = cls.__fields__
|
||||
cls.argparse_groups = {}
|
||||
|
||||
for name, field in fields.items():
|
||||
if name not in cls._excluded():
|
||||
current_default = field.default
|
||||
|
||||
category = field.field_info.extra.get("category", "Uncategorized")
|
||||
env_name = env_prefix + "_" + name
|
||||
if category in initconf and name in initconf.get(category):
|
||||
field.default = initconf.get(category).get(name)
|
||||
if env_name.upper() in upcase_environ:
|
||||
field.default = upcase_environ[env_name.upper()]
|
||||
cls.add_field_argument(parser, name, field)
|
||||
|
||||
field.default = current_default
|
||||
|
||||
@classmethod
|
||||
def cmd_name(self, command_field: str = "type") -> str:
|
||||
hints = get_type_hints(self)
|
||||
if command_field in hints:
|
||||
return get_args(hints[command_field])[0]
|
||||
else:
|
||||
return "Uncategorized"
|
||||
|
||||
@classmethod
|
||||
def get_parser(cls) -> ArgumentParser:
|
||||
parser = PagingArgumentParser(
|
||||
prog=cls.cmd_name(),
|
||||
description=cls.__doc__,
|
||||
)
|
||||
cls.add_parser_arguments(parser)
|
||||
return parser
|
||||
|
||||
@classmethod
|
||||
def add_subparser(cls, parser: argparse.ArgumentParser):
|
||||
parser.add_parser(cls.cmd_name(), help=cls.__doc__)
|
||||
|
||||
@classmethod
|
||||
def _excluded(self) -> List[str]:
|
||||
# internal fields that shouldn't be exposed as command line options
|
||||
return ["type", "initconf"]
|
||||
|
||||
@classmethod
|
||||
def _excluded_from_yaml(self) -> List[str]:
|
||||
# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
|
||||
return [
|
||||
"type",
|
||||
"initconf",
|
||||
"version",
|
||||
"from_file",
|
||||
"model",
|
||||
"root",
|
||||
"max_cache_size",
|
||||
"max_vram_cache_size",
|
||||
"always_use_cpu",
|
||||
"free_gpu_mem",
|
||||
"xformers_enabled",
|
||||
"tiled_decode",
|
||||
]
|
||||
|
||||
class Config:
|
||||
env_file_encoding = "utf-8"
|
||||
arbitrary_types_allowed = True
|
||||
case_sensitive = True
|
||||
|
||||
@classmethod
|
||||
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
|
||||
field_type = get_type_hints(cls).get(name)
|
||||
default = (
|
||||
default_override
|
||||
if default_override is not None
|
||||
else field.default
|
||||
if field.default_factory is None
|
||||
else field.default_factory()
|
||||
)
|
||||
if category := field.field_info.extra.get("category"):
|
||||
if category not in cls.argparse_groups:
|
||||
cls.argparse_groups[category] = command_parser.add_argument_group(category)
|
||||
argparse_group = cls.argparse_groups[category]
|
||||
else:
|
||||
argparse_group = command_parser
|
||||
|
||||
if get_origin(field_type) == Literal:
|
||||
allowed_values = get_args(field.type_)
|
||||
allowed_types = set()
|
||||
for val in allowed_values:
|
||||
allowed_types.add(type(val))
|
||||
allowed_types_list = list(allowed_types)
|
||||
field_type = allowed_types_list[0] if len(allowed_types) == 1 else int_or_float_or_str
|
||||
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field_type,
|
||||
default=default,
|
||||
choices=allowed_values,
|
||||
help=field.field_info.description,
|
||||
)
|
||||
|
||||
elif get_origin(field_type) == Union:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=int_or_float_or_str,
|
||||
default=default,
|
||||
help=field.field_info.description,
|
||||
)
|
||||
|
||||
elif get_origin(field_type) == list:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
nargs="*",
|
||||
type=field.type_,
|
||||
default=default,
|
||||
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
|
||||
help=field.field_info.description,
|
||||
)
|
||||
else:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field.type_,
|
||||
default=default,
|
||||
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
|
||||
help=field.field_info.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)
|
||||
@ -10,37 +10,49 @@ categories returned by `invokeai --help`. The file looks like this:
|
||||
[file: invokeai.yaml]
|
||||
|
||||
InvokeAI:
|
||||
Paths:
|
||||
root: /home/lstein/invokeai-main
|
||||
conf_path: configs/models.yaml
|
||||
legacy_conf_dir: configs/stable-diffusion
|
||||
outdir: outputs
|
||||
autoimport_dir: null
|
||||
Models:
|
||||
model: stable-diffusion-1.5
|
||||
embeddings: true
|
||||
Memory/Performance:
|
||||
xformers_enabled: false
|
||||
sequential_guidance: false
|
||||
precision: float16
|
||||
max_cache_size: 6
|
||||
max_vram_cache_size: 0.5
|
||||
always_use_cpu: false
|
||||
free_gpu_mem: false
|
||||
Features:
|
||||
esrgan: true
|
||||
patchmatch: true
|
||||
internet_available: true
|
||||
log_tokenization: false
|
||||
Web Server:
|
||||
host: 127.0.0.1
|
||||
port: 8081
|
||||
port: 9090
|
||||
allow_origins: []
|
||||
allow_credentials: true
|
||||
allow_methods:
|
||||
- '*'
|
||||
allow_headers:
|
||||
- '*'
|
||||
Features:
|
||||
esrgan: true
|
||||
internet_available: true
|
||||
log_tokenization: false
|
||||
patchmatch: true
|
||||
ignore_missing_core_models: false
|
||||
Paths:
|
||||
autoimport_dir: autoimport
|
||||
lora_dir: null
|
||||
embedding_dir: null
|
||||
controlnet_dir: null
|
||||
conf_path: configs/models.yaml
|
||||
models_dir: models
|
||||
legacy_conf_dir: configs/stable-diffusion
|
||||
db_dir: databases
|
||||
outdir: /home/lstein/invokeai-main/outputs
|
||||
use_memory_db: false
|
||||
Logging:
|
||||
log_handlers:
|
||||
- console
|
||||
log_format: plain
|
||||
log_level: info
|
||||
Model Cache:
|
||||
ram: 13.5
|
||||
vram: 0.25
|
||||
lazy_offload: true
|
||||
Device:
|
||||
device: auto
|
||||
precision: auto
|
||||
Generation:
|
||||
sequential_guidance: false
|
||||
attention_type: xformers
|
||||
attention_slice_size: auto
|
||||
force_tiled_decode: false
|
||||
|
||||
The default name of the configuration file is `invokeai.yaml`, located
|
||||
in INVOKEAI_ROOT. You can replace supersede this by providing any
|
||||
@ -54,24 +66,23 @@ InvokeAIAppConfig.parse_args() will parse the contents of `sys.argv`
|
||||
at initialization time. You may pass a list of strings in the optional
|
||||
`argv` argument to use instead of the system argv:
|
||||
|
||||
conf.parse_args(argv=['--xformers_enabled'])
|
||||
conf.parse_args(argv=['--log_tokenization'])
|
||||
|
||||
It is also possible to set a value at initialization time. However, if
|
||||
you call parse_args() it may be overwritten.
|
||||
|
||||
conf = InvokeAIAppConfig(xformers_enabled=True)
|
||||
conf.parse_args(argv=['--no-xformers'])
|
||||
conf.xformers_enabled
|
||||
conf = InvokeAIAppConfig(log_tokenization=True)
|
||||
conf.parse_args(argv=['--no-log_tokenization'])
|
||||
conf.log_tokenization
|
||||
# False
|
||||
|
||||
|
||||
To avoid this, use `get_config()` to retrieve the application-wide
|
||||
configuration object. This will retain any properties set at object
|
||||
creation time:
|
||||
|
||||
conf = InvokeAIAppConfig.get_config(xformers_enabled=True)
|
||||
conf.parse_args(argv=['--no-xformers'])
|
||||
conf.xformers_enabled
|
||||
conf = InvokeAIAppConfig.get_config(log_tokenization=True)
|
||||
conf.parse_args(argv=['--no-log_tokenization'])
|
||||
conf.log_tokenization
|
||||
# True
|
||||
|
||||
Any setting can be overwritten by setting an environment variable of
|
||||
@ -93,7 +104,7 @@ Typical usage at the top level file:
|
||||
# get global configuration and print its cache size
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
conf.parse_args()
|
||||
print(conf.max_cache_size)
|
||||
print(conf.ram_cache_size)
|
||||
|
||||
Typical usage in a backend module:
|
||||
|
||||
@ -101,8 +112,7 @@ Typical usage in a backend module:
|
||||
|
||||
# get global configuration and print its cache size value
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
print(conf.max_cache_size)
|
||||
|
||||
print(conf.ram_cache_size)
|
||||
|
||||
Computed properties:
|
||||
|
||||
@ -159,15 +169,15 @@ two configs are kept in separate sections of the config file:
|
||||
|
||||
"""
|
||||
from __future__ import annotations
|
||||
import argparse
|
||||
import pydoc
|
||||
|
||||
import os
|
||||
import sys
|
||||
from argparse import ArgumentParser
|
||||
from omegaconf import OmegaConf, DictConfig, ListConfig
|
||||
from pathlib import Path
|
||||
from pydantic import BaseSettings, Field, parse_obj_as
|
||||
from typing import ClassVar, Dict, List, Literal, Union, get_origin, get_type_hints, get_args
|
||||
from typing import ClassVar, Dict, List, Literal, Union, get_type_hints, Optional
|
||||
|
||||
from omegaconf import OmegaConf, DictConfig
|
||||
from pydantic import Field, parse_obj_as
|
||||
|
||||
from .base import InvokeAISettings
|
||||
|
||||
INIT_FILE = Path("invokeai.yaml")
|
||||
DB_FILE = Path("invokeai.db")
|
||||
@ -175,195 +185,6 @@ LEGACY_INIT_FILE = Path("invokeai.init")
|
||||
DEFAULT_MAX_VRAM = 0.5
|
||||
|
||||
|
||||
class InvokeAISettings(BaseSettings):
|
||||
"""
|
||||
Runtime configuration settings in which default values are
|
||||
read from an omegaconf .yaml file.
|
||||
"""
|
||||
|
||||
initconf: ClassVar[DictConfig] = None
|
||||
argparse_groups: ClassVar[Dict] = {}
|
||||
|
||||
def parse_args(self, argv: list = sys.argv[1:]):
|
||||
parser = self.get_parser()
|
||||
opt = parser.parse_args(argv)
|
||||
for name in self.__fields__:
|
||||
if name not in self._excluded():
|
||||
value = getattr(opt, name)
|
||||
if isinstance(value, ListConfig):
|
||||
value = list(value)
|
||||
elif isinstance(value, DictConfig):
|
||||
value = dict(value)
|
||||
setattr(self, name, value)
|
||||
|
||||
def to_yaml(self) -> str:
|
||||
"""
|
||||
Return a YAML string representing our settings. This can be used
|
||||
as the contents of `invokeai.yaml` to restore settings later.
|
||||
"""
|
||||
cls = self.__class__
|
||||
type = get_args(get_type_hints(cls)["type"])[0]
|
||||
field_dict = dict({type: dict()})
|
||||
for name, field in self.__fields__.items():
|
||||
if name in cls._excluded_from_yaml():
|
||||
continue
|
||||
category = field.field_info.extra.get("category") or "Uncategorized"
|
||||
value = getattr(self, name)
|
||||
if category not in field_dict[type]:
|
||||
field_dict[type][category] = dict()
|
||||
# keep paths as strings to make it easier to read
|
||||
field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
|
||||
conf = OmegaConf.create(field_dict)
|
||||
return OmegaConf.to_yaml(conf)
|
||||
|
||||
@classmethod
|
||||
def add_parser_arguments(cls, parser):
|
||||
if "type" in get_type_hints(cls):
|
||||
settings_stanza = get_args(get_type_hints(cls)["type"])[0]
|
||||
else:
|
||||
settings_stanza = "Uncategorized"
|
||||
|
||||
env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
|
||||
|
||||
initconf = (
|
||||
cls.initconf.get(settings_stanza)
|
||||
if cls.initconf and settings_stanza in cls.initconf
|
||||
else OmegaConf.create()
|
||||
)
|
||||
|
||||
# create an upcase version of the environment in
|
||||
# order to achieve case-insensitive environment
|
||||
# variables (the way Windows does)
|
||||
upcase_environ = dict()
|
||||
for key, value in os.environ.items():
|
||||
upcase_environ[key.upper()] = value
|
||||
|
||||
fields = cls.__fields__
|
||||
cls.argparse_groups = {}
|
||||
|
||||
for name, field in fields.items():
|
||||
if name not in cls._excluded():
|
||||
current_default = field.default
|
||||
|
||||
category = field.field_info.extra.get("category", "Uncategorized")
|
||||
env_name = env_prefix + "_" + name
|
||||
if category in initconf and name in initconf.get(category):
|
||||
field.default = initconf.get(category).get(name)
|
||||
if env_name.upper() in upcase_environ:
|
||||
field.default = upcase_environ[env_name.upper()]
|
||||
cls.add_field_argument(parser, name, field)
|
||||
|
||||
field.default = current_default
|
||||
|
||||
@classmethod
|
||||
def cmd_name(self, command_field: str = "type") -> str:
|
||||
hints = get_type_hints(self)
|
||||
if command_field in hints:
|
||||
return get_args(hints[command_field])[0]
|
||||
else:
|
||||
return "Uncategorized"
|
||||
|
||||
@classmethod
|
||||
def get_parser(cls) -> ArgumentParser:
|
||||
parser = PagingArgumentParser(
|
||||
prog=cls.cmd_name(),
|
||||
description=cls.__doc__,
|
||||
)
|
||||
cls.add_parser_arguments(parser)
|
||||
return parser
|
||||
|
||||
@classmethod
|
||||
def add_subparser(cls, parser: argparse.ArgumentParser):
|
||||
parser.add_parser(cls.cmd_name(), help=cls.__doc__)
|
||||
|
||||
@classmethod
|
||||
def _excluded(self) -> List[str]:
|
||||
# internal fields that shouldn't be exposed as command line options
|
||||
return ["type", "initconf"]
|
||||
|
||||
@classmethod
|
||||
def _excluded_from_yaml(self) -> List[str]:
|
||||
# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
|
||||
return [
|
||||
"type",
|
||||
"initconf",
|
||||
"version",
|
||||
"from_file",
|
||||
"model",
|
||||
"root",
|
||||
]
|
||||
|
||||
class Config:
|
||||
env_file_encoding = "utf-8"
|
||||
arbitrary_types_allowed = True
|
||||
case_sensitive = True
|
||||
|
||||
@classmethod
|
||||
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
|
||||
field_type = get_type_hints(cls).get(name)
|
||||
default = (
|
||||
default_override
|
||||
if default_override is not None
|
||||
else field.default
|
||||
if field.default_factory is None
|
||||
else field.default_factory()
|
||||
)
|
||||
if category := field.field_info.extra.get("category"):
|
||||
if category not in cls.argparse_groups:
|
||||
cls.argparse_groups[category] = command_parser.add_argument_group(category)
|
||||
argparse_group = cls.argparse_groups[category]
|
||||
else:
|
||||
argparse_group = command_parser
|
||||
|
||||
if get_origin(field_type) == Literal:
|
||||
allowed_values = get_args(field.type_)
|
||||
allowed_types = set()
|
||||
for val in allowed_values:
|
||||
allowed_types.add(type(val))
|
||||
allowed_types_list = list(allowed_types)
|
||||
field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list] # type: ignore
|
||||
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field_type,
|
||||
default=default,
|
||||
choices=allowed_values,
|
||||
help=field.field_info.description,
|
||||
)
|
||||
|
||||
elif get_origin(field_type) == list:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
nargs="*",
|
||||
type=field.type_,
|
||||
default=default,
|
||||
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
|
||||
help=field.field_info.description,
|
||||
)
|
||||
else:
|
||||
argparse_group.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field.type_,
|
||||
default=default,
|
||||
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
|
||||
help=field.field_info.description,
|
||||
)
|
||||
|
||||
|
||||
def _find_root() -> Path:
|
||||
venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
|
||||
if os.environ.get("INVOKEAI_ROOT"):
|
||||
root = Path(os.environ["INVOKEAI_ROOT"])
|
||||
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
|
||||
root = (venv.parent).resolve()
|
||||
else:
|
||||
root = Path("~/invokeai").expanduser().resolve()
|
||||
return root
|
||||
|
||||
|
||||
class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""
|
||||
Generate images using Stable Diffusion. Use "invokeai" to launch
|
||||
@ -378,6 +199,8 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
|
||||
# fmt: off
|
||||
type: Literal["InvokeAI"] = "InvokeAI"
|
||||
|
||||
# WEB
|
||||
host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
|
||||
port : int = Field(default=9090, description="Port to bind to", category='Web Server')
|
||||
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server')
|
||||
@ -385,20 +208,14 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server')
|
||||
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server')
|
||||
|
||||
# FEATURES
|
||||
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
|
||||
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
|
||||
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
|
||||
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
|
||||
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
|
||||
|
||||
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
|
||||
free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
|
||||
max_cache_size : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
|
||||
max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
|
||||
precision : Literal['auto', 'float16', 'float32', 'autocast'] = Field(default='auto', description='Floating point precision', category='Memory/Performance')
|
||||
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
|
||||
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
|
||||
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
|
||||
|
||||
# PATHS
|
||||
root : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths')
|
||||
autoimport_dir : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
|
||||
lora_dir : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
|
||||
@ -409,16 +226,43 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
|
||||
db_dir : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths')
|
||||
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
|
||||
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
|
||||
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
|
||||
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
|
||||
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
|
||||
|
||||
# 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")
|
||||
# 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_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging")
|
||||
|
||||
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", category="Development")
|
||||
|
||||
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
|
||||
|
||||
# CACHE
|
||||
ram : Union[float, Literal["auto"]] = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number or 'auto')", category="Model Cache", )
|
||||
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", )
|
||||
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", )
|
||||
|
||||
# DEVICE
|
||||
device : Literal[tuple(["auto", "cpu", "cuda", "cuda:1", "mps"])] = Field(default="auto", description="Generation device", category="Device", )
|
||||
precision: Literal[tuple(["auto", "float16", "float32", "autocast"])] = Field(default="auto", description="Floating point precision", category="Device", )
|
||||
|
||||
# GENERATION
|
||||
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category="Generation", )
|
||||
attention_type : Literal[tuple(["auto", "normal", "xformers", "sliced", "torch-sdp"])] = Field(default="auto", description="Attention type", category="Generation", )
|
||||
attention_slice_size: Literal[tuple(["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8])] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
|
||||
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
|
||||
|
||||
# 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')
|
||||
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
|
||||
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_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
|
||||
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
|
||||
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
|
||||
|
||||
# See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
|
||||
# fmt: on
|
||||
|
||||
class Config:
|
||||
@ -541,11 +385,6 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""Return true if precision set to float32"""
|
||||
return self.precision == "float32"
|
||||
|
||||
@property
|
||||
def disable_xformers(self) -> bool:
|
||||
"""Return true if xformers_enabled is false"""
|
||||
return not self.xformers_enabled
|
||||
|
||||
@property
|
||||
def try_patchmatch(self) -> bool:
|
||||
"""Return true if patchmatch true"""
|
||||
@ -561,6 +400,27 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""invisible watermark node is always active and disabled from Web UIe"""
|
||||
return True
|
||||
|
||||
@property
|
||||
def ram_cache_size(self) -> float:
|
||||
return self.max_cache_size or self.ram
|
||||
|
||||
@property
|
||||
def vram_cache_size(self) -> float:
|
||||
return self.max_vram_cache_size or self.vram
|
||||
|
||||
@property
|
||||
def use_cpu(self) -> bool:
|
||||
return self.always_use_cpu or self.device == "cpu"
|
||||
|
||||
@property
|
||||
def disable_xformers(self) -> bool:
|
||||
"""
|
||||
Return true if enable_xformers is false (reversed logic)
|
||||
and attention type is not set to xformers.
|
||||
"""
|
||||
disabled_in_config = not self.xformers_enabled
|
||||
return disabled_in_config and self.attention_type != "xformers"
|
||||
|
||||
@staticmethod
|
||||
def find_root() -> Path:
|
||||
"""
|
||||
@ -570,19 +430,19 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
return _find_root()
|
||||
|
||||
|
||||
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 get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
|
||||
"""
|
||||
Legacy function which returns InvokeAIAppConfig.get_config()
|
||||
"""
|
||||
return InvokeAIAppConfig.get_config(**kwargs)
|
||||
|
||||
|
||||
def _find_root() -> Path:
|
||||
venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
|
||||
if os.environ.get("INVOKEAI_ROOT"):
|
||||
root = Path(os.environ["INVOKEAI_ROOT"])
|
||||
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
|
||||
root = (venv.parent).resolve()
|
||||
else:
|
||||
root = Path("~/invokeai").expanduser().resolve()
|
||||
return root
|
||||
@ -17,9 +17,9 @@ def create_text_to_image() -> LibraryGraph:
|
||||
description="Converts text to an image",
|
||||
graph=Graph(
|
||||
nodes={
|
||||
"width": IntegerInvocation(id="width", a=512),
|
||||
"height": IntegerInvocation(id="height", a=512),
|
||||
"seed": IntegerInvocation(id="seed", a=-1),
|
||||
"width": IntegerInvocation(id="width", value=512),
|
||||
"height": IntegerInvocation(id="height", value=512),
|
||||
"seed": IntegerInvocation(id="seed", value=-1),
|
||||
"3": NoiseInvocation(id="3"),
|
||||
"4": CompelInvocation(id="4"),
|
||||
"5": CompelInvocation(id="5"),
|
||||
@ -29,15 +29,15 @@ def create_text_to_image() -> LibraryGraph:
|
||||
},
|
||||
edges=[
|
||||
Edge(
|
||||
source=EdgeConnection(node_id="width", field="a"),
|
||||
source=EdgeConnection(node_id="width", field="value"),
|
||||
destination=EdgeConnection(node_id="3", field="width"),
|
||||
),
|
||||
Edge(
|
||||
source=EdgeConnection(node_id="height", field="a"),
|
||||
source=EdgeConnection(node_id="height", field="value"),
|
||||
destination=EdgeConnection(node_id="3", field="height"),
|
||||
),
|
||||
Edge(
|
||||
source=EdgeConnection(node_id="seed", field="a"),
|
||||
source=EdgeConnection(node_id="seed", field="value"),
|
||||
destination=EdgeConnection(node_id="3", field="seed"),
|
||||
),
|
||||
Edge(
|
||||
@ -65,9 +65,9 @@ def create_text_to_image() -> LibraryGraph:
|
||||
exposed_inputs=[
|
||||
ExposedNodeInput(node_path="4", field="prompt", alias="positive_prompt"),
|
||||
ExposedNodeInput(node_path="5", field="prompt", alias="negative_prompt"),
|
||||
ExposedNodeInput(node_path="width", field="a", alias="width"),
|
||||
ExposedNodeInput(node_path="height", field="a", alias="height"),
|
||||
ExposedNodeInput(node_path="seed", field="a", alias="seed"),
|
||||
ExposedNodeInput(node_path="width", field="value", alias="width"),
|
||||
ExposedNodeInput(node_path="height", field="value", alias="height"),
|
||||
ExposedNodeInput(node_path="seed", field="value", alias="seed"),
|
||||
],
|
||||
exposed_outputs=[ExposedNodeOutput(node_path="8", field="image", alias="image")],
|
||||
)
|
||||
|
||||
@ -49,9 +49,36 @@ from invokeai.backend.model_management.model_cache import CacheStats
|
||||
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"]):
|
||||
"""
|
||||
@ -94,8 +121,6 @@ class InvocationStatsServiceBase(ABC):
|
||||
invocation_type: str,
|
||||
time_used: float,
|
||||
vram_used: float,
|
||||
ram_used: float,
|
||||
ram_changed: float,
|
||||
):
|
||||
"""
|
||||
Add timing information on execution of a node. Usually
|
||||
@ -104,8 +129,6 @@ class InvocationStatsServiceBase(ABC):
|
||||
: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)
|
||||
:param ram_used: Current RAM available (GB)
|
||||
:param ram_changed: Change in RAM usage over course of the run (GB)
|
||||
"""
|
||||
pass
|
||||
|
||||
@ -116,25 +139,19 @@ class InvocationStatsServiceBase(ABC):
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def update_mem_stats(
|
||||
self,
|
||||
ram_used: float,
|
||||
ram_changed: float,
|
||||
):
|
||||
"""
|
||||
Update the collector with RAM memory usage info.
|
||||
|
||||
@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)
|
||||
:param ram_used: How much RAM is currently in use.
|
||||
:param ram_changed: How much RAM changed since last generation.
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class InvocationStatsService(InvocationStatsServiceBase):
|
||||
@ -152,12 +169,12 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
class StatsContext:
|
||||
"""Context manager for collecting statistics."""
|
||||
|
||||
invocation: BaseInvocation = None
|
||||
collector: "InvocationStatsServiceBase" = None
|
||||
graph_id: str = None
|
||||
start_time: int = 0
|
||||
ram_used: int = 0
|
||||
model_manager: ModelManagerService = None
|
||||
invocation: BaseInvocation
|
||||
collector: "InvocationStatsServiceBase"
|
||||
graph_id: str
|
||||
start_time: float
|
||||
ram_used: int
|
||||
model_manager: ModelManagerService
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@ -170,7 +187,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
self.invocation = invocation
|
||||
self.collector = collector
|
||||
self.graph_id = graph_id
|
||||
self.start_time = 0
|
||||
self.start_time = 0.0
|
||||
self.ram_used = 0
|
||||
self.model_manager = model_manager
|
||||
|
||||
@ -191,7 +208,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
)
|
||||
self.collector.update_invocation_stats(
|
||||
graph_id=self.graph_id,
|
||||
invocation_type=self.invocation.type,
|
||||
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,
|
||||
vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0,
|
||||
)
|
||||
@ -202,11 +219,6 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
graph_execution_state_id: str,
|
||||
model_manager: ModelManagerService,
|
||||
) -> StatsContext:
|
||||
"""
|
||||
Return a context object that will capture the statistics.
|
||||
:param invocation: BaseInvocation object from the current graph.
|
||||
:param graph_execution_state: GraphExecutionState object from the current session.
|
||||
"""
|
||||
if not self._stats.get(graph_execution_state_id): # first time we're seeing this
|
||||
self._stats[graph_execution_state_id] = NodeLog()
|
||||
self._cache_stats[graph_execution_state_id] = CacheStats()
|
||||
@ -217,7 +229,6 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
self._stats = {}
|
||||
|
||||
def reset_stats(self, graph_execution_id: str):
|
||||
"""Zero the statistics for the indicated graph."""
|
||||
try:
|
||||
self._stats.pop(graph_execution_id)
|
||||
except KeyError:
|
||||
@ -228,12 +239,6 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
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.
|
||||
"""
|
||||
self.ram_used = ram_used
|
||||
self.ram_changed = ram_changed
|
||||
|
||||
@ -244,16 +249,6 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
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)
|
||||
:param ram_used: Current RAM available (GB)
|
||||
:param ram_changed: Change in RAM usage over course of the run (GB)
|
||||
"""
|
||||
if not self._stats[graph_id].nodes.get(invocation_type):
|
||||
self._stats[graph_id].nodes[invocation_type] = NodeStats()
|
||||
stats = self._stats[graph_id].nodes[invocation_type]
|
||||
@ -262,14 +257,15 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
stats.max_vram = max(stats.max_vram, vram_used)
|
||||
|
||||
def log_stats(self):
|
||||
"""
|
||||
Send the statistics to the system logger at the info level.
|
||||
Stats will only be printed when the execution of the graph
|
||||
is complete.
|
||||
"""
|
||||
completed = set()
|
||||
errored = set()
|
||||
for graph_id, node_log in self._stats.items():
|
||||
current_graph_state = self.graph_execution_manager.get(graph_id)
|
||||
try:
|
||||
current_graph_state = self.graph_execution_manager.get(graph_id)
|
||||
except Exception:
|
||||
errored.add(graph_id)
|
||||
continue
|
||||
|
||||
if not current_graph_state.is_complete():
|
||||
continue
|
||||
|
||||
@ -302,3 +298,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
|
||||
for graph_id in completed:
|
||||
del self._stats[graph_id]
|
||||
del self._cache_stats[graph_id]
|
||||
|
||||
for graph_id in errored:
|
||||
del self._stats[graph_id]
|
||||
del self._cache_stats[graph_id]
|
||||
|
||||
@ -330,8 +330,8 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
# configuration value. If present, then the
|
||||
# cache size is set to 2.5 GB times
|
||||
# the number of max_loaded_models. Otherwise
|
||||
# use new `max_cache_size` config setting
|
||||
max_cache_size = config.max_cache_size if hasattr(config, "max_cache_size") else config.max_loaded_models * 2.5
|
||||
# use new `ram_cache_size` config setting
|
||||
max_cache_size = config.ram_cache_size
|
||||
|
||||
logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")
|
||||
|
||||
|
||||
56
invokeai/backend/image_util/lama.py
Normal file
56
invokeai/backend/image_util/lama.py
Normal file
@ -0,0 +1,56 @@
|
||||
import gc
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.backend.util.devices import choose_torch_device
|
||||
|
||||
|
||||
def norm_img(np_img):
|
||||
if len(np_img.shape) == 2:
|
||||
np_img = np_img[:, :, np.newaxis]
|
||||
np_img = np.transpose(np_img, (2, 0, 1))
|
||||
np_img = np_img.astype("float32") / 255
|
||||
return np_img
|
||||
|
||||
|
||||
def load_jit_model(url_or_path, device):
|
||||
model_path = url_or_path
|
||||
print(f"Loading model from: {model_path}")
|
||||
model = torch.jit.load(model_path, map_location="cpu").to(device)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
class LaMA:
|
||||
def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
|
||||
device = choose_torch_device()
|
||||
model_location = get_invokeai_config().models_path / "core/misc/lama/lama.pt"
|
||||
model = load_jit_model(model_location, device)
|
||||
|
||||
image = np.asarray(input_image.convert("RGB"))
|
||||
image = norm_img(image)
|
||||
|
||||
mask = input_image.split()[-1]
|
||||
mask = np.asarray(mask)
|
||||
mask = np.invert(mask)
|
||||
mask = norm_img(mask)
|
||||
|
||||
mask = (mask > 0) * 1
|
||||
image = torch.from_numpy(image).unsqueeze(0).to(device)
|
||||
mask = torch.from_numpy(mask).unsqueeze(0).to(device)
|
||||
|
||||
with torch.inference_mode():
|
||||
infilled_image = model(image, mask)
|
||||
|
||||
infilled_image = infilled_image[0].permute(1, 2, 0).detach().cpu().numpy()
|
||||
infilled_image = np.clip(infilled_image * 255, 0, 255).astype("uint8")
|
||||
infilled_image = Image.fromarray(infilled_image)
|
||||
|
||||
del model
|
||||
gc.collect()
|
||||
|
||||
return infilled_image
|
||||
@ -21,6 +21,7 @@ from argparse import Namespace
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from shutil import get_terminal_size
|
||||
from typing import get_type_hints, get_args, Any
|
||||
from urllib import request
|
||||
|
||||
import npyscreen
|
||||
@ -49,7 +50,8 @@ from invokeai.frontend.install.model_install import addModelsForm, process_and_e
|
||||
|
||||
# TO DO - Move all the frontend code into invokeai.frontend.install
|
||||
from invokeai.frontend.install.widgets import (
|
||||
SingleSelectColumns,
|
||||
SingleSelectColumnsSimple,
|
||||
MultiSelectColumns,
|
||||
CenteredButtonPress,
|
||||
FileBox,
|
||||
set_min_terminal_size,
|
||||
@ -71,6 +73,10 @@ warnings.filterwarnings("ignore")
|
||||
transformers.logging.set_verbosity_error()
|
||||
|
||||
|
||||
def get_literal_fields(field) -> list[Any]:
|
||||
return get_args(get_type_hints(InvokeAIAppConfig).get(field))
|
||||
|
||||
|
||||
# --------------------------globals-----------------------
|
||||
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
@ -80,7 +86,11 @@ Model_dir = "models"
|
||||
Default_config_file = config.model_conf_path
|
||||
SD_Configs = config.legacy_conf_path
|
||||
|
||||
PRECISION_CHOICES = ["auto", "float16", "float32"]
|
||||
PRECISION_CHOICES = get_literal_fields("precision")
|
||||
DEVICE_CHOICES = get_literal_fields("device")
|
||||
ATTENTION_CHOICES = get_literal_fields("attention_type")
|
||||
ATTENTION_SLICE_CHOICES = get_literal_fields("attention_slice_size")
|
||||
GENERATION_OPT_CHOICES = ["sequential_guidance", "force_tiled_decode", "lazy_offload"]
|
||||
GB = 1073741824 # GB in bytes
|
||||
HAS_CUDA = torch.cuda.is_available()
|
||||
_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
|
||||
@ -311,6 +321,7 @@ class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.
|
||||
Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
"""
|
||||
self.nextrely -= 1
|
||||
for i in textwrap.wrap(label, width=window_width - 6):
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.FixedText,
|
||||
@ -337,76 +348,127 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
use_two_lines=False,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely += 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="GPU Management",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.free_gpu_mem = self.add_widget_intelligent(
|
||||
npyscreen.Checkbox,
|
||||
name="Free GPU memory after each generation",
|
||||
value=old_opts.free_gpu_mem,
|
||||
max_width=45,
|
||||
relx=5,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.xformers_enabled = self.add_widget_intelligent(
|
||||
npyscreen.Checkbox,
|
||||
name="Enable xformers support",
|
||||
value=old_opts.xformers_enabled,
|
||||
max_width=30,
|
||||
relx=50,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.always_use_cpu = self.add_widget_intelligent(
|
||||
npyscreen.Checkbox,
|
||||
name="Force CPU to be used on GPU systems",
|
||||
value=old_opts.always_use_cpu,
|
||||
relx=80,
|
||||
scroll_exit=True,
|
||||
)
|
||||
|
||||
# old settings for defaults
|
||||
precision = old_opts.precision or ("float32" if program_opts.full_precision else "auto")
|
||||
device = old_opts.device
|
||||
attention_type = old_opts.attention_type
|
||||
attention_slice_size = old_opts.attention_slice_size
|
||||
self.nextrely += 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="Floating Point Precision",
|
||||
name="Image Generation Options:",
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 2
|
||||
self.generation_options = self.add_widget_intelligent(
|
||||
MultiSelectColumns,
|
||||
columns=3,
|
||||
values=GENERATION_OPT_CHOICES,
|
||||
value=[GENERATION_OPT_CHOICES.index(x) for x in GENERATION_OPT_CHOICES if getattr(old_opts, x)],
|
||||
relx=30,
|
||||
max_height=2,
|
||||
max_width=80,
|
||||
scroll_exit=True,
|
||||
)
|
||||
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="Floating Point Precision:",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.nextrely -= 2
|
||||
self.precision = self.add_widget_intelligent(
|
||||
SingleSelectColumns,
|
||||
columns=3,
|
||||
SingleSelectColumnsSimple,
|
||||
columns=len(PRECISION_CHOICES),
|
||||
name="Precision",
|
||||
values=PRECISION_CHOICES,
|
||||
value=PRECISION_CHOICES.index(precision),
|
||||
begin_entry_at=3,
|
||||
max_height=2,
|
||||
relx=30,
|
||||
max_width=56,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="Generation Device:",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 2
|
||||
self.device = self.add_widget_intelligent(
|
||||
SingleSelectColumnsSimple,
|
||||
columns=len(DEVICE_CHOICES),
|
||||
values=DEVICE_CHOICES,
|
||||
value=[DEVICE_CHOICES.index(device)],
|
||||
begin_entry_at=3,
|
||||
relx=30,
|
||||
max_height=2,
|
||||
max_width=60,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="Attention Type:",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 2
|
||||
self.attention_type = self.add_widget_intelligent(
|
||||
SingleSelectColumnsSimple,
|
||||
columns=len(ATTENTION_CHOICES),
|
||||
values=ATTENTION_CHOICES,
|
||||
value=[ATTENTION_CHOICES.index(attention_type)],
|
||||
begin_entry_at=3,
|
||||
max_height=2,
|
||||
relx=30,
|
||||
max_width=80,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely += 1
|
||||
self.attention_type.on_changed = self.show_hide_slice_sizes
|
||||
self.attention_slice_label = self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="Attention Slice Size:",
|
||||
relx=5,
|
||||
editable=False,
|
||||
hidden=attention_type != "sliced",
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 2
|
||||
self.attention_slice_size = self.add_widget_intelligent(
|
||||
SingleSelectColumnsSimple,
|
||||
columns=len(ATTENTION_SLICE_CHOICES),
|
||||
values=ATTENTION_SLICE_CHOICES,
|
||||
value=[ATTENTION_SLICE_CHOICES.index(attention_slice_size)],
|
||||
relx=30,
|
||||
hidden=attention_type != "sliced",
|
||||
max_height=2,
|
||||
max_width=110,
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
|
||||
name="Model RAM cache size (GB). Make this at least large enough to hold a single full model.",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.max_cache_size = self.add_widget_intelligent(
|
||||
self.ram = self.add_widget_intelligent(
|
||||
npyscreen.Slider,
|
||||
value=clip(old_opts.max_cache_size, range=(3.0, MAX_RAM), step=0.5),
|
||||
value=clip(old_opts.ram_cache_size, range=(3.0, MAX_RAM), step=0.5),
|
||||
out_of=round(MAX_RAM),
|
||||
lowest=0.0,
|
||||
step=0.5,
|
||||
@ -417,16 +479,16 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
self.nextrely += 1
|
||||
self.add_widget_intelligent(
|
||||
npyscreen.TitleFixedText,
|
||||
name="VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
|
||||
name="Model VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
|
||||
begin_entry_at=0,
|
||||
editable=False,
|
||||
color="CONTROL",
|
||||
scroll_exit=True,
|
||||
)
|
||||
self.nextrely -= 1
|
||||
self.max_vram_cache_size = self.add_widget_intelligent(
|
||||
self.vram = self.add_widget_intelligent(
|
||||
npyscreen.Slider,
|
||||
value=clip(old_opts.max_vram_cache_size, range=(0, MAX_VRAM), step=0.25),
|
||||
value=clip(old_opts.vram_cache_size, range=(0, MAX_VRAM), step=0.25),
|
||||
out_of=round(MAX_VRAM * 2) / 2,
|
||||
lowest=0.0,
|
||||
relx=8,
|
||||
@ -434,7 +496,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
|
||||
scroll_exit=True,
|
||||
)
|
||||
else:
|
||||
self.max_vram_cache_size = DummyWidgetValue.zero
|
||||
self.vram_cache_size = DummyWidgetValue.zero
|
||||
self.nextrely += 1
|
||||
self.outdir = self.add_widget_intelligent(
|
||||
FileBox,
|
||||
@ -490,6 +552,11 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
|
||||
when_pressed_function=self.on_ok,
|
||||
)
|
||||
|
||||
def show_hide_slice_sizes(self, value):
|
||||
show = ATTENTION_CHOICES[value[0]] == "sliced"
|
||||
self.attention_slice_label.hidden = not show
|
||||
self.attention_slice_size.hidden = not show
|
||||
|
||||
def on_ok(self):
|
||||
options = self.marshall_arguments()
|
||||
if self.validate_field_values(options):
|
||||
@ -523,12 +590,9 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
|
||||
new_opts = Namespace()
|
||||
|
||||
for attr in [
|
||||
"ram",
|
||||
"vram",
|
||||
"outdir",
|
||||
"free_gpu_mem",
|
||||
"max_cache_size",
|
||||
"max_vram_cache_size",
|
||||
"xformers_enabled",
|
||||
"always_use_cpu",
|
||||
]:
|
||||
setattr(new_opts, attr, getattr(self, attr).value)
|
||||
|
||||
@ -541,6 +605,12 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
|
||||
new_opts.hf_token = self.hf_token.value
|
||||
new_opts.license_acceptance = self.license_acceptance.value
|
||||
new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
|
||||
new_opts.device = DEVICE_CHOICES[self.device.value[0]]
|
||||
new_opts.attention_type = ATTENTION_CHOICES[self.attention_type.value[0]]
|
||||
new_opts.attention_slice_size = ATTENTION_SLICE_CHOICES[self.attention_slice_size.value[0]]
|
||||
generation_options = [GENERATION_OPT_CHOICES[x] for x in self.generation_options.value]
|
||||
for v in GENERATION_OPT_CHOICES:
|
||||
setattr(new_opts, v, v in generation_options)
|
||||
|
||||
return new_opts
|
||||
|
||||
@ -635,8 +705,6 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
|
||||
path = dest / "core"
|
||||
path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
maybe_create_models_yaml(root)
|
||||
|
||||
|
||||
def maybe_create_models_yaml(root: Path):
|
||||
models_yaml = root / "configs" / "models.yaml"
|
||||
|
||||
@ -341,7 +341,8 @@ class ModelManager(object):
|
||||
self.logger = logger
|
||||
self.cache = ModelCache(
|
||||
max_cache_size=max_cache_size,
|
||||
max_vram_cache_size=self.app_config.max_vram_cache_size,
|
||||
max_vram_cache_size=self.app_config.vram_cache_size,
|
||||
lazy_offloading=self.app_config.lazy_offload,
|
||||
execution_device=device_type,
|
||||
precision=precision,
|
||||
sequential_offload=sequential_offload,
|
||||
|
||||
@ -33,7 +33,7 @@ from .diffusion import (
|
||||
PostprocessingSettings,
|
||||
BasicConditioningInfo,
|
||||
)
|
||||
from ..util import normalize_device
|
||||
from ..util import normalize_device, auto_detect_slice_size
|
||||
|
||||
|
||||
@dataclass
|
||||
@ -291,6 +291,24 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
if xformers is available, use it, otherwise use sliced attention.
|
||||
"""
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
if config.attention_type == "xformers":
|
||||
self.enable_xformers_memory_efficient_attention()
|
||||
return
|
||||
elif config.attention_type == "sliced":
|
||||
slice_size = config.attention_slice_size
|
||||
if slice_size == "auto":
|
||||
slice_size = auto_detect_slice_size(latents)
|
||||
elif slice_size == "balanced":
|
||||
slice_size = "auto"
|
||||
self.enable_attention_slicing(slice_size=slice_size)
|
||||
return
|
||||
elif config.attention_type == "normal":
|
||||
self.disable_attention_slicing()
|
||||
return
|
||||
elif config.attention_type == "torch-sdp":
|
||||
raise Exception("torch-sdp attention slicing not yet implemented")
|
||||
|
||||
# the remainder if this code is called when attention_type=='auto'
|
||||
if self.unet.device.type == "cuda":
|
||||
if is_xformers_available() and not config.disable_xformers:
|
||||
self.enable_xformers_memory_efficient_attention()
|
||||
|
||||
@ -11,4 +11,11 @@ from .devices import ( # noqa: F401
|
||||
torch_dtype,
|
||||
)
|
||||
from .log import write_log # noqa: F401
|
||||
from .util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name, Chdir # noqa: F401
|
||||
from .util import ( # noqa: F401
|
||||
ask_user,
|
||||
download_with_resume,
|
||||
instantiate_from_config,
|
||||
url_attachment_name,
|
||||
Chdir,
|
||||
)
|
||||
from .attention import auto_detect_slice_size # noqa: F401
|
||||
|
||||
32
invokeai/backend/util/attention.py
Normal file
32
invokeai/backend/util/attention.py
Normal file
@ -0,0 +1,32 @@
|
||||
# Copyright (c) 2023 Lincoln Stein and the InvokeAI Team
|
||||
"""
|
||||
Utility routine used for autodetection of optimal slice size
|
||||
for attention mechanism.
|
||||
"""
|
||||
import torch
|
||||
import psutil
|
||||
|
||||
|
||||
def auto_detect_slice_size(latents: torch.Tensor) -> str:
|
||||
bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
|
||||
max_size_required_for_baddbmm = (
|
||||
16
|
||||
* latents.size(dim=2)
|
||||
* latents.size(dim=3)
|
||||
* latents.size(dim=2)
|
||||
* latents.size(dim=3)
|
||||
* bytes_per_element_needed_for_baddbmm_duplication
|
||||
)
|
||||
if latents.device.type in {"cpu", "mps"}:
|
||||
mem_free = psutil.virtual_memory().free
|
||||
elif latents.device.type == "cuda":
|
||||
mem_free, _ = torch.cuda.mem_get_info(latents.device)
|
||||
else:
|
||||
raise ValueError(f"unrecognized device {latents.device}")
|
||||
|
||||
if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0):
|
||||
return "max"
|
||||
elif torch.backends.mps.is_available():
|
||||
return "max"
|
||||
else:
|
||||
return "balanced"
|
||||
@ -17,13 +17,17 @@ config = InvokeAIAppConfig.get_config()
|
||||
|
||||
def choose_torch_device() -> torch.device:
|
||||
"""Convenience routine for guessing which GPU device to run model on"""
|
||||
if config.always_use_cpu:
|
||||
if config.use_cpu: # legacy setting - force CPU
|
||||
return CPU_DEVICE
|
||||
if torch.cuda.is_available():
|
||||
return torch.device("cuda")
|
||||
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||||
return torch.device("mps")
|
||||
return CPU_DEVICE
|
||||
elif config.device == "auto":
|
||||
if torch.cuda.is_available():
|
||||
return torch.device("cuda")
|
||||
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||||
return torch.device("mps")
|
||||
else:
|
||||
return CPU_DEVICE
|
||||
else:
|
||||
return torch.device(config.device)
|
||||
|
||||
|
||||
def choose_precision(device: torch.device) -> str:
|
||||
|
||||
@ -17,8 +17,8 @@ from shutil import get_terminal_size
|
||||
from curses import BUTTON2_CLICKED, BUTTON3_CLICKED
|
||||
|
||||
# minimum size for UIs
|
||||
MIN_COLS = 130
|
||||
MIN_LINES = 38
|
||||
MIN_COLS = 150
|
||||
MIN_LINES = 40
|
||||
|
||||
|
||||
class WindowTooSmallException(Exception):
|
||||
@ -177,6 +177,8 @@ class FloatTitleSlider(npyscreen.TitleText):
|
||||
|
||||
|
||||
class SelectColumnBase:
|
||||
"""Base class for selection widget arranged in columns."""
|
||||
|
||||
def make_contained_widgets(self):
|
||||
self._my_widgets = []
|
||||
column_width = self.width // self.columns
|
||||
@ -253,6 +255,7 @@ class MultiSelectColumns(SelectColumnBase, npyscreen.MultiSelect):
|
||||
class SingleSelectWithChanged(npyscreen.SelectOne):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.on_changed = None
|
||||
|
||||
def h_select(self, ch):
|
||||
super().h_select(ch)
|
||||
@ -260,7 +263,9 @@ class SingleSelectWithChanged(npyscreen.SelectOne):
|
||||
self.on_changed(self.value)
|
||||
|
||||
|
||||
class SingleSelectColumns(SelectColumnBase, SingleSelectWithChanged):
|
||||
class SingleSelectColumnsSimple(SelectColumnBase, SingleSelectWithChanged):
|
||||
"""Row of radio buttons. Spacebar to select."""
|
||||
|
||||
def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
|
||||
self.columns = columns
|
||||
self.value_cnt = len(values)
|
||||
@ -268,15 +273,19 @@ class SingleSelectColumns(SelectColumnBase, SingleSelectWithChanged):
|
||||
self.on_changed = None
|
||||
super().__init__(screen, values=values, **keywords)
|
||||
|
||||
def when_value_edited(self):
|
||||
self.h_select(self.cursor_line)
|
||||
def h_cursor_line_right(self, ch):
|
||||
self.h_exit_down("bye bye")
|
||||
|
||||
def h_cursor_line_left(self, ch):
|
||||
self.h_exit_up("bye bye")
|
||||
|
||||
|
||||
class SingleSelectColumns(SingleSelectColumnsSimple):
|
||||
"""Row of radio buttons. When tabbing over a selection, it is auto selected."""
|
||||
|
||||
def when_cursor_moved(self):
|
||||
self.h_select(self.cursor_line)
|
||||
|
||||
def h_cursor_line_right(self, ch):
|
||||
self.h_exit_down("bye bye")
|
||||
|
||||
|
||||
class TextBoxInner(npyscreen.MultiLineEdit):
|
||||
def __init__(self, *args, **kwargs):
|
||||
@ -324,55 +333,6 @@ class TextBoxInner(npyscreen.MultiLineEdit):
|
||||
if bstate & (BUTTON2_CLICKED | BUTTON3_CLICKED):
|
||||
self.h_paste()
|
||||
|
||||
# def update(self, clear=True):
|
||||
# if clear:
|
||||
# self.clear()
|
||||
|
||||
# HEIGHT = self.height
|
||||
# WIDTH = self.width
|
||||
# # draw box.
|
||||
# self.parent.curses_pad.hline(self.rely, self.relx, curses.ACS_HLINE, WIDTH)
|
||||
# self.parent.curses_pad.hline(
|
||||
# self.rely + HEIGHT, self.relx, curses.ACS_HLINE, WIDTH
|
||||
# )
|
||||
# self.parent.curses_pad.vline(
|
||||
# self.rely, self.relx, curses.ACS_VLINE, self.height
|
||||
# )
|
||||
# self.parent.curses_pad.vline(
|
||||
# self.rely, self.relx + WIDTH, curses.ACS_VLINE, HEIGHT
|
||||
# )
|
||||
|
||||
# # draw corners
|
||||
# self.parent.curses_pad.addch(
|
||||
# self.rely,
|
||||
# self.relx,
|
||||
# curses.ACS_ULCORNER,
|
||||
# )
|
||||
# self.parent.curses_pad.addch(
|
||||
# self.rely,
|
||||
# self.relx + WIDTH,
|
||||
# curses.ACS_URCORNER,
|
||||
# )
|
||||
# self.parent.curses_pad.addch(
|
||||
# self.rely + HEIGHT,
|
||||
# self.relx,
|
||||
# curses.ACS_LLCORNER,
|
||||
# )
|
||||
# self.parent.curses_pad.addch(
|
||||
# self.rely + HEIGHT,
|
||||
# self.relx + WIDTH,
|
||||
# curses.ACS_LRCORNER,
|
||||
# )
|
||||
|
||||
# # fool our superclass into thinking drawing area is smaller - this is really hacky but it seems to work
|
||||
# (relx, rely, height, width) = (self.relx, self.rely, self.height, self.width)
|
||||
# self.relx += 1
|
||||
# self.rely += 1
|
||||
# self.height -= 1
|
||||
# self.width -= 1
|
||||
# super().update(clear=False)
|
||||
# (self.relx, self.rely, self.height, self.width) = (relx, rely, height, width)
|
||||
|
||||
|
||||
class TextBox(npyscreen.BoxTitle):
|
||||
_contained_widget = TextBoxInner
|
||||
|
||||
@ -9,8 +9,8 @@ module.exports = {
|
||||
'plugin:@typescript-eslint/recommended',
|
||||
'plugin:react/recommended',
|
||||
'plugin:react-hooks/recommended',
|
||||
'plugin:prettier/recommended',
|
||||
'plugin:react/jsx-runtime',
|
||||
'prettier',
|
||||
],
|
||||
parser: '@typescript-eslint/parser',
|
||||
parserOptions: {
|
||||
@ -23,6 +23,11 @@ module.exports = {
|
||||
plugins: ['react', '@typescript-eslint', 'eslint-plugin-react-hooks'],
|
||||
root: true,
|
||||
rules: {
|
||||
curly: 'error',
|
||||
'react/jsx-curly-brace-presence': [
|
||||
'error',
|
||||
{ props: 'never', children: 'never' },
|
||||
],
|
||||
'react-hooks/exhaustive-deps': 'error',
|
||||
'no-var': 'error',
|
||||
'brace-style': 'error',
|
||||
@ -34,7 +39,6 @@ module.exports = {
|
||||
'warn',
|
||||
{ varsIgnorePattern: '^_', argsIgnorePattern: '^_' },
|
||||
],
|
||||
'prettier/prettier': ['error', { endOfLine: 'auto' }],
|
||||
'@typescript-eslint/ban-ts-comment': 'warn',
|
||||
'@typescript-eslint/no-explicit-any': 'warn',
|
||||
'@typescript-eslint/no-empty-interface': [
|
||||
|
||||
@ -29,12 +29,13 @@
|
||||
"lint:eslint": "eslint --max-warnings=0 .",
|
||||
"lint:prettier": "prettier --check .",
|
||||
"lint:tsc": "tsc --noEmit",
|
||||
"lint": "yarn run lint:eslint && yarn run lint:prettier && yarn run lint:tsc && yarn run lint:madge",
|
||||
"lint": "concurrently -g -n eslint,prettier,tsc,madge -c cyan,green,magenta,yellow \"yarn run lint:eslint\" \"yarn run lint:prettier\" \"yarn run lint:tsc\" \"yarn run lint:madge\"",
|
||||
"fix": "eslint --fix . && prettier --loglevel warn --write . && tsc --noEmit",
|
||||
"lint-staged": "lint-staged",
|
||||
"postinstall": "patch-package && yarn run theme",
|
||||
"theme": "chakra-cli tokens src/theme/theme.ts",
|
||||
"theme:watch": "chakra-cli tokens src/theme/theme.ts --watch"
|
||||
"theme:watch": "chakra-cli tokens src/theme/theme.ts --watch",
|
||||
"up": "yarn upgrade-interactive --latest"
|
||||
},
|
||||
"madge": {
|
||||
"detectiveOptions": {
|
||||
@ -54,7 +55,7 @@
|
||||
},
|
||||
"dependencies": {
|
||||
"@chakra-ui/anatomy": "^2.2.0",
|
||||
"@chakra-ui/icons": "^2.0.19",
|
||||
"@chakra-ui/icons": "^2.1.0",
|
||||
"@chakra-ui/react": "^2.8.0",
|
||||
"@chakra-ui/styled-system": "^2.9.1",
|
||||
"@chakra-ui/theme-tools": "^2.1.0",
|
||||
@ -65,55 +66,55 @@
|
||||
"@emotion/react": "^11.11.1",
|
||||
"@emotion/styled": "^11.11.0",
|
||||
"@floating-ui/react-dom": "^2.0.1",
|
||||
"@fontsource-variable/inter": "^5.0.3",
|
||||
"@fontsource/inter": "^5.0.3",
|
||||
"@mantine/core": "^6.0.14",
|
||||
"@mantine/form": "^6.0.15",
|
||||
"@mantine/hooks": "^6.0.14",
|
||||
"@fontsource-variable/inter": "^5.0.8",
|
||||
"@fontsource/inter": "^5.0.8",
|
||||
"@mantine/core": "^6.0.19",
|
||||
"@mantine/form": "^6.0.19",
|
||||
"@mantine/hooks": "^6.0.19",
|
||||
"@nanostores/react": "^0.7.1",
|
||||
"@reduxjs/toolkit": "^1.9.5",
|
||||
"@roarr/browser-log-writer": "^1.1.5",
|
||||
"chakra-ui-contextmenu": "^1.0.5",
|
||||
"dateformat": "^5.0.3",
|
||||
"downshift": "^7.6.0",
|
||||
"formik": "^2.4.2",
|
||||
"framer-motion": "^10.12.17",
|
||||
"formik": "^2.4.3",
|
||||
"framer-motion": "^10.16.1",
|
||||
"fuse.js": "^6.6.2",
|
||||
"i18next": "^23.2.3",
|
||||
"i18next": "^23.4.4",
|
||||
"i18next-browser-languagedetector": "^7.0.2",
|
||||
"i18next-http-backend": "^2.2.1",
|
||||
"konva": "^9.2.0",
|
||||
"lodash-es": "^4.17.21",
|
||||
"nanostores": "^0.9.2",
|
||||
"openapi-fetch": "^0.6.1",
|
||||
"new-github-issue-url": "^1.0.0",
|
||||
"openapi-fetch": "^0.7.4",
|
||||
"overlayscrollbars": "^2.2.0",
|
||||
"overlayscrollbars-react": "^0.5.0",
|
||||
"patch-package": "^7.0.0",
|
||||
"patch-package": "^8.0.0",
|
||||
"query-string": "^8.1.0",
|
||||
"re-resizable": "^6.9.9",
|
||||
"react": "^18.2.0",
|
||||
"react-colorful": "^5.6.1",
|
||||
"react-dom": "^18.2.0",
|
||||
"react-dropzone": "^14.2.3",
|
||||
"react-hotkeys-hook": "4.4.0",
|
||||
"react-i18next": "^13.0.1",
|
||||
"react-error-boundary": "^4.0.11",
|
||||
"react-hotkeys-hook": "4.4.1",
|
||||
"react-i18next": "^13.1.2",
|
||||
"react-icons": "^4.10.1",
|
||||
"react-konva": "^18.2.10",
|
||||
"react-redux": "^8.1.1",
|
||||
"react-resizable-panels": "^0.0.52",
|
||||
"react-redux": "^8.1.2",
|
||||
"react-resizable-panels": "^0.0.55",
|
||||
"react-use": "^17.4.0",
|
||||
"react-virtuoso": "^4.3.11",
|
||||
"react-virtuoso": "^4.5.0",
|
||||
"react-zoom-pan-pinch": "^3.0.8",
|
||||
"reactflow": "^11.7.4",
|
||||
"reactflow": "^11.8.3",
|
||||
"redux-dynamic-middlewares": "^2.2.0",
|
||||
"redux-remember": "^3.3.1",
|
||||
"roarr": "^7.15.0",
|
||||
"serialize-error": "^11.0.0",
|
||||
"socket.io-client": "^4.7.0",
|
||||
"redux-remember": "^4.0.1",
|
||||
"roarr": "^7.15.1",
|
||||
"serialize-error": "^11.0.1",
|
||||
"socket.io-client": "^4.7.2",
|
||||
"use-debounce": "^9.0.4",
|
||||
"use-image": "^1.1.1",
|
||||
"uuid": "^9.0.0",
|
||||
"zod": "^3.21.4"
|
||||
"zod": "^3.22.2",
|
||||
"zod-validation-error": "^1.5.0"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@chakra-ui/cli": "^2.4.0",
|
||||
@ -126,38 +127,36 @@
|
||||
"@chakra-ui/cli": "^2.4.1",
|
||||
"@types/dateformat": "^5.0.0",
|
||||
"@types/lodash-es": "^4.14.194",
|
||||
"@types/node": "^20.3.1",
|
||||
"@types/react": "^18.2.14",
|
||||
"@types/node": "^20.5.1",
|
||||
"@types/react": "^18.2.20",
|
||||
"@types/react-dom": "^18.2.6",
|
||||
"@types/react-redux": "^7.1.25",
|
||||
"@types/react-transition-group": "^4.4.6",
|
||||
"@types/uuid": "^9.0.2",
|
||||
"@typescript-eslint/eslint-plugin": "^5.60.0",
|
||||
"@typescript-eslint/parser": "^5.60.0",
|
||||
"@typescript-eslint/eslint-plugin": "^6.4.1",
|
||||
"@typescript-eslint/parser": "^6.4.1",
|
||||
"@vitejs/plugin-react-swc": "^3.3.2",
|
||||
"axios": "^1.4.0",
|
||||
"babel-plugin-transform-imports": "^2.0.0",
|
||||
"concurrently": "^8.2.0",
|
||||
"eslint": "^8.43.0",
|
||||
"eslint-config-prettier": "^8.8.0",
|
||||
"eslint-plugin-prettier": "^4.2.1",
|
||||
"eslint-plugin-react": "^7.32.2",
|
||||
"eslint": "^8.47.0",
|
||||
"eslint-config-prettier": "^9.0.0",
|
||||
"eslint-plugin-prettier": "^5.0.0",
|
||||
"eslint-plugin-react": "^7.33.2",
|
||||
"eslint-plugin-react-hooks": "^4.6.0",
|
||||
"form-data": "^4.0.0",
|
||||
"husky": "^8.0.3",
|
||||
"lint-staged": "^13.2.2",
|
||||
"lint-staged": "^14.0.1",
|
||||
"madge": "^6.1.0",
|
||||
"openapi-types": "^12.1.3",
|
||||
"openapi-typescript": "^6.2.8",
|
||||
"openapi-typescript-codegen": "^0.24.0",
|
||||
"openapi-typescript": "^6.5.2",
|
||||
"postinstall-postinstall": "^2.1.0",
|
||||
"prettier": "^2.8.8",
|
||||
"prettier": "^3.0.2",
|
||||
"rollup-plugin-visualizer": "^5.9.2",
|
||||
"terser": "^5.18.1",
|
||||
"ts-toolbelt": "^9.6.0",
|
||||
"vite": "^4.3.9",
|
||||
"vite-plugin-css-injected-by-js": "^3.1.1",
|
||||
"vite-plugin-dts": "^2.3.0",
|
||||
"vite": "^4.4.9",
|
||||
"vite-plugin-css-injected-by-js": "^3.3.0",
|
||||
"vite-plugin-dts": "^3.5.2",
|
||||
"vite-plugin-eslint": "^1.8.1",
|
||||
"vite-tsconfig-paths": "^4.2.0",
|
||||
"yarn": "^1.22.19"
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
"toggleAutoscroll": "Toggle autoscroll",
|
||||
"toggleLogViewer": "Toggle Log Viewer",
|
||||
"showGallery": "Show Gallery",
|
||||
"showOptionsPanel": "Show Options Panel",
|
||||
"showOptionsPanel": "Show Side Panel",
|
||||
"menu": "Menu"
|
||||
},
|
||||
"common": {
|
||||
@ -52,7 +52,7 @@
|
||||
"img2img": "Image To Image",
|
||||
"unifiedCanvas": "Unified Canvas",
|
||||
"linear": "Linear",
|
||||
"nodes": "Node Editor",
|
||||
"nodes": "Workflow Editor",
|
||||
"batch": "Batch Manager",
|
||||
"modelManager": "Model Manager",
|
||||
"postprocessing": "Post Processing",
|
||||
@ -95,7 +95,6 @@
|
||||
"statusModelConverted": "Model Converted",
|
||||
"statusMergingModels": "Merging Models",
|
||||
"statusMergedModels": "Models Merged",
|
||||
"pinOptionsPanel": "Pin Options Panel",
|
||||
"loading": "Loading",
|
||||
"loadingInvokeAI": "Loading Invoke AI",
|
||||
"random": "Random",
|
||||
@ -116,7 +115,6 @@
|
||||
"maintainAspectRatio": "Maintain Aspect Ratio",
|
||||
"autoSwitchNewImages": "Auto-Switch to New Images",
|
||||
"singleColumnLayout": "Single Column Layout",
|
||||
"pinGallery": "Pin Gallery",
|
||||
"allImagesLoaded": "All Images Loaded",
|
||||
"loadMore": "Load More",
|
||||
"noImagesInGallery": "No Images to Display",
|
||||
@ -133,6 +131,7 @@
|
||||
"generalHotkeys": "General Hotkeys",
|
||||
"galleryHotkeys": "Gallery Hotkeys",
|
||||
"unifiedCanvasHotkeys": "Unified Canvas Hotkeys",
|
||||
"nodesHotkeys": "Nodes Hotkeys",
|
||||
"invoke": {
|
||||
"title": "Invoke",
|
||||
"desc": "Generate an image"
|
||||
@ -332,6 +331,10 @@
|
||||
"acceptStagingImage": {
|
||||
"title": "Accept Staging Image",
|
||||
"desc": "Accept Current Staging Area Image"
|
||||
},
|
||||
"addNodes": {
|
||||
"title": "Add Nodes",
|
||||
"desc": "Opens the add node menu"
|
||||
}
|
||||
},
|
||||
"modelManager": {
|
||||
@ -506,12 +509,9 @@
|
||||
"maskAdjustmentsHeader": "Mask Adjustments",
|
||||
"maskBlur": "Mask Blur",
|
||||
"maskBlurMethod": "Mask Blur Method",
|
||||
"seamPaintingHeader": "Seam Painting",
|
||||
"seamSize": "Seam Size",
|
||||
"seamBlur": "Seam Blur",
|
||||
"seamSteps": "Seam Steps",
|
||||
"seamStrength": "Seam Strength",
|
||||
"seamThreshold": "Seam Threshold",
|
||||
"coherencePassHeader": "Coherence Pass",
|
||||
"coherenceSteps": "Coherence Pass Steps",
|
||||
"coherenceStrength": "Coherence Pass Strength",
|
||||
"seamLowThreshold": "Low",
|
||||
"seamHighThreshold": "High",
|
||||
"scaleBeforeProcessing": "Scale Before Processing",
|
||||
@ -572,7 +572,7 @@
|
||||
"resetWebUI": "Reset Web UI",
|
||||
"resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
|
||||
"resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",
|
||||
"resetComplete": "Web UI has been reset. Refresh the page to reload.",
|
||||
"resetComplete": "Web UI has been reset.",
|
||||
"consoleLogLevel": "Log Level",
|
||||
"shouldLogToConsole": "Console Logging",
|
||||
"developer": "Developer",
|
||||
@ -715,11 +715,12 @@
|
||||
"swapSizes": "Swap Sizes"
|
||||
},
|
||||
"nodes": {
|
||||
"reloadSchema": "Reload Schema",
|
||||
"saveGraph": "Save Graph",
|
||||
"loadGraph": "Load Graph (saved from Node Editor) (Do not copy-paste metadata)",
|
||||
"clearGraph": "Clear Graph",
|
||||
"clearGraphDesc": "Are you sure you want to clear all nodes?",
|
||||
"reloadNodeTemplates": "Reload Node Templates",
|
||||
"saveWorkflow": "Save Workflow",
|
||||
"loadWorkflow": "Load Workflow",
|
||||
"resetWorkflow": "Reset Workflow",
|
||||
"resetWorkflowDesc": "Are you sure you want to reset this workflow?",
|
||||
"resetWorkflowDesc2": "Resetting the workflow will clear all nodes, edges and workflow details.",
|
||||
"zoomInNodes": "Zoom In",
|
||||
"zoomOutNodes": "Zoom Out",
|
||||
"fitViewportNodes": "Fit View",
|
||||
|
||||
@ -27,22 +27,10 @@ async function main() {
|
||||
* field accepts connection input. If it does, we can make the field optional.
|
||||
*/
|
||||
|
||||
// Check if we are generating types for an invocation
|
||||
const isInvocationPath = metadata.path.match(
|
||||
/^#\/components\/schemas\/\w*Invocation$/
|
||||
);
|
||||
|
||||
const hasInvocationProperties =
|
||||
schemaObject.properties &&
|
||||
['id', 'is_intermediate', 'type'].every(
|
||||
(prop) => prop in schemaObject.properties
|
||||
);
|
||||
|
||||
if (isInvocationPath && hasInvocationProperties) {
|
||||
if ('class' in schemaObject && schemaObject.class === 'invocation') {
|
||||
// We only want to make fields optional if they are required
|
||||
if (!Array.isArray(schemaObject?.required)) {
|
||||
schemaObject.required = ['id', 'type'];
|
||||
return;
|
||||
schemaObject.required = [];
|
||||
}
|
||||
|
||||
schemaObject.required.forEach((prop) => {
|
||||
@ -61,19 +49,13 @@ async function main() {
|
||||
);
|
||||
}
|
||||
});
|
||||
|
||||
schemaObject.required = [
|
||||
...new Set(schemaObject.required.concat(['id', 'type'])),
|
||||
];
|
||||
|
||||
return;
|
||||
}
|
||||
// if (
|
||||
// 'input' in schemaObject &&
|
||||
// (schemaObject.input === 'any' || schemaObject.input === 'connection')
|
||||
// ) {
|
||||
// schemaObject.required = false;
|
||||
// }
|
||||
|
||||
// Check if we are generating types for an invocation output
|
||||
if ('class' in schemaObject && schemaObject.class === 'output') {
|
||||
// modify output types
|
||||
}
|
||||
},
|
||||
});
|
||||
fs.writeFileSync(OUTPUT_FILE, types);
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
import { Flex, Grid, Portal } from '@chakra-ui/react';
|
||||
import { Flex, Grid } from '@chakra-ui/react';
|
||||
import { useLogger } from 'app/logging/useLogger';
|
||||
import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
@ -6,17 +6,15 @@ import { PartialAppConfig } from 'app/types/invokeai';
|
||||
import ImageUploader from 'common/components/ImageUploader';
|
||||
import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
|
||||
import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
|
||||
import GalleryDrawer from 'features/gallery/components/GalleryPanel';
|
||||
import SiteHeader from 'features/system/components/SiteHeader';
|
||||
import { configChanged } from 'features/system/store/configSlice';
|
||||
import { languageSelector } from 'features/system/store/systemSelectors';
|
||||
import FloatingGalleryButton from 'features/ui/components/FloatingGalleryButton';
|
||||
import FloatingParametersPanelButtons from 'features/ui/components/FloatingParametersPanelButtons';
|
||||
import InvokeTabs from 'features/ui/components/InvokeTabs';
|
||||
import ParametersDrawer from 'features/ui/components/ParametersDrawer';
|
||||
import i18n from 'i18n';
|
||||
import { size } from 'lodash-es';
|
||||
import { ReactNode, memo, useEffect } from 'react';
|
||||
import { ReactNode, memo, useCallback, useEffect } from 'react';
|
||||
import { ErrorBoundary } from 'react-error-boundary';
|
||||
import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
|
||||
import GlobalHotkeys from './GlobalHotkeys';
|
||||
import Toaster from './Toaster';
|
||||
|
||||
@ -30,8 +28,13 @@ interface Props {
|
||||
const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
|
||||
const language = useAppSelector(languageSelector);
|
||||
|
||||
const logger = useLogger();
|
||||
const logger = useLogger('system');
|
||||
const dispatch = useAppDispatch();
|
||||
const handleReset = useCallback(() => {
|
||||
localStorage.clear();
|
||||
location.reload();
|
||||
return false;
|
||||
}, []);
|
||||
|
||||
useEffect(() => {
|
||||
i18n.changeLanguage(language);
|
||||
@ -39,7 +42,7 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
|
||||
|
||||
useEffect(() => {
|
||||
if (size(config)) {
|
||||
logger.info({ namespace: 'App', config }, 'Received config');
|
||||
logger.info({ config }, 'Received config');
|
||||
dispatch(configChanged(config));
|
||||
}
|
||||
}, [dispatch, config, logger]);
|
||||
@ -49,7 +52,10 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
|
||||
}, [dispatch]);
|
||||
|
||||
return (
|
||||
<>
|
||||
<ErrorBoundary
|
||||
onReset={handleReset}
|
||||
FallbackComponent={AppErrorBoundaryFallback}
|
||||
>
|
||||
<Grid w="100vw" h="100vh" position="relative" overflow="hidden">
|
||||
<ImageUploader>
|
||||
<Grid
|
||||
@ -73,21 +79,12 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
|
||||
</Flex>
|
||||
</Grid>
|
||||
</ImageUploader>
|
||||
|
||||
<GalleryDrawer />
|
||||
<ParametersDrawer />
|
||||
<Portal>
|
||||
<FloatingParametersPanelButtons />
|
||||
</Portal>
|
||||
<Portal>
|
||||
<FloatingGalleryButton />
|
||||
</Portal>
|
||||
</Grid>
|
||||
<DeleteImageModal />
|
||||
<ChangeBoardModal />
|
||||
<Toaster />
|
||||
<GlobalHotkeys />
|
||||
</>
|
||||
</ErrorBoundary>
|
||||
);
|
||||
};
|
||||
|
||||
|
||||
@ -0,0 +1,97 @@
|
||||
import { Flex, Heading, Link, Text, useToast } from '@chakra-ui/react';
|
||||
import IAIButton from 'common/components/IAIButton';
|
||||
import newGithubIssueUrl from 'new-github-issue-url';
|
||||
import { memo, useCallback, useMemo } from 'react';
|
||||
import { FaCopy, FaExternalLinkAlt } from 'react-icons/fa';
|
||||
import { FaArrowRotateLeft } from 'react-icons/fa6';
|
||||
import { serializeError } from 'serialize-error';
|
||||
|
||||
type Props = {
|
||||
error: Error;
|
||||
resetErrorBoundary: () => void;
|
||||
};
|
||||
|
||||
const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
|
||||
const toast = useToast();
|
||||
|
||||
const handleCopy = useCallback(() => {
|
||||
const text = JSON.stringify(serializeError(error), null, 2);
|
||||
navigator.clipboard.writeText(`\`\`\`\n${text}\n\`\`\``);
|
||||
toast({
|
||||
title: 'Error Copied',
|
||||
});
|
||||
}, [error, toast]);
|
||||
|
||||
const url = useMemo(
|
||||
() =>
|
||||
newGithubIssueUrl({
|
||||
user: 'invoke-ai',
|
||||
repo: 'InvokeAI',
|
||||
template: 'BUG_REPORT.yml',
|
||||
title: `[bug]: ${error.name}: ${error.message}`,
|
||||
}),
|
||||
[error.message, error.name]
|
||||
);
|
||||
return (
|
||||
<Flex
|
||||
layerStyle="body"
|
||||
sx={{
|
||||
w: '100vw',
|
||||
h: '100vh',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
p: 4,
|
||||
}}
|
||||
>
|
||||
<Flex
|
||||
layerStyle="first"
|
||||
sx={{
|
||||
flexDir: 'column',
|
||||
borderRadius: 'base',
|
||||
justifyContent: 'center',
|
||||
gap: 8,
|
||||
p: 16,
|
||||
}}
|
||||
>
|
||||
<Heading>Something went wrong</Heading>
|
||||
<Flex
|
||||
layerStyle="second"
|
||||
sx={{
|
||||
px: 8,
|
||||
py: 4,
|
||||
borderRadius: 'base',
|
||||
gap: 4,
|
||||
justifyContent: 'space-between',
|
||||
alignItems: 'center',
|
||||
}}
|
||||
>
|
||||
<Text
|
||||
sx={{
|
||||
fontWeight: 600,
|
||||
color: 'error.500',
|
||||
_dark: { color: 'error.400' },
|
||||
}}
|
||||
>
|
||||
{error.name}: {error.message}
|
||||
</Text>
|
||||
</Flex>
|
||||
<Flex sx={{ gap: 4 }}>
|
||||
<IAIButton
|
||||
leftIcon={<FaArrowRotateLeft />}
|
||||
onClick={resetErrorBoundary}
|
||||
>
|
||||
Reset UI
|
||||
</IAIButton>
|
||||
<IAIButton leftIcon={<FaCopy />} onClick={handleCopy}>
|
||||
Copy Error
|
||||
</IAIButton>
|
||||
<Link href={url} isExternal>
|
||||
<IAIButton leftIcon={<FaExternalLinkAlt />}>Create Issue</IAIButton>
|
||||
</Link>
|
||||
</Flex>
|
||||
</Flex>
|
||||
</Flex>
|
||||
);
|
||||
};
|
||||
|
||||
export default memo(AppErrorBoundaryFallback);
|
||||
@ -1,30 +1,21 @@
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { stateSelector } from 'app/store/store';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { requestCanvasRescale } from 'features/canvas/store/thunks/requestCanvasScale';
|
||||
import {
|
||||
ctrlKeyPressed,
|
||||
metaKeyPressed,
|
||||
shiftKeyPressed,
|
||||
} from 'features/ui/store/hotkeysSlice';
|
||||
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
|
||||
import {
|
||||
setActiveTab,
|
||||
toggleGalleryPanel,
|
||||
toggleParametersPanel,
|
||||
togglePinGalleryPanel,
|
||||
togglePinParametersPanel,
|
||||
} from 'features/ui/store/uiSlice';
|
||||
import { setActiveTab } from 'features/ui/store/uiSlice';
|
||||
import { isEqual } from 'lodash-es';
|
||||
import React, { memo } from 'react';
|
||||
import { isHotkeyPressed, useHotkeys } from 'react-hotkeys-hook';
|
||||
|
||||
const globalHotkeysSelector = createSelector(
|
||||
[stateSelector],
|
||||
({ hotkeys, ui }) => {
|
||||
({ hotkeys }) => {
|
||||
const { shift, ctrl, meta } = hotkeys;
|
||||
const { shouldPinParametersPanel, shouldPinGallery } = ui;
|
||||
return { shift, ctrl, meta, shouldPinGallery, shouldPinParametersPanel };
|
||||
return { shift, ctrl, meta };
|
||||
},
|
||||
{
|
||||
memoizeOptions: {
|
||||
@ -41,9 +32,7 @@ const globalHotkeysSelector = createSelector(
|
||||
*/
|
||||
const GlobalHotkeys: React.FC = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
const { shift, ctrl, meta, shouldPinParametersPanel, shouldPinGallery } =
|
||||
useAppSelector(globalHotkeysSelector);
|
||||
const activeTabName = useAppSelector(activeTabNameSelector);
|
||||
const { shift, ctrl, meta } = useAppSelector(globalHotkeysSelector);
|
||||
|
||||
useHotkeys(
|
||||
'*',
|
||||
@ -68,34 +57,6 @@ const GlobalHotkeys: React.FC = () => {
|
||||
[shift, ctrl, meta]
|
||||
);
|
||||
|
||||
useHotkeys('o', () => {
|
||||
dispatch(toggleParametersPanel());
|
||||
if (activeTabName === 'unifiedCanvas' && shouldPinParametersPanel) {
|
||||
dispatch(requestCanvasRescale());
|
||||
}
|
||||
});
|
||||
|
||||
useHotkeys(['shift+o'], () => {
|
||||
dispatch(togglePinParametersPanel());
|
||||
if (activeTabName === 'unifiedCanvas') {
|
||||
dispatch(requestCanvasRescale());
|
||||
}
|
||||
});
|
||||
|
||||
useHotkeys('g', () => {
|
||||
dispatch(toggleGalleryPanel());
|
||||
if (activeTabName === 'unifiedCanvas' && shouldPinGallery) {
|
||||
dispatch(requestCanvasRescale());
|
||||
}
|
||||
});
|
||||
|
||||
useHotkeys(['shift+g'], () => {
|
||||
dispatch(togglePinGalleryPanel());
|
||||
if (activeTabName === 'unifiedCanvas') {
|
||||
dispatch(requestCanvasRescale());
|
||||
}
|
||||
});
|
||||
|
||||
useHotkeys('1', () => {
|
||||
dispatch(setActiveTab('txt2img'));
|
||||
});
|
||||
@ -112,6 +73,10 @@ const GlobalHotkeys: React.FC = () => {
|
||||
dispatch(setActiveTab('nodes'));
|
||||
});
|
||||
|
||||
useHotkeys('5', () => {
|
||||
dispatch(setActiveTab('modelManager'));
|
||||
});
|
||||
|
||||
return null;
|
||||
};
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@ import {
|
||||
createLocalStorageManager,
|
||||
extendTheme,
|
||||
} from '@chakra-ui/react';
|
||||
import { ReactNode, useEffect, useMemo } from 'react';
|
||||
import { ReactNode, memo, useEffect, useMemo } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { theme as invokeAITheme } from 'theme/theme';
|
||||
|
||||
@ -46,4 +46,4 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
|
||||
);
|
||||
}
|
||||
|
||||
export default ThemeLocaleProvider;
|
||||
export default memo(ThemeLocaleProvider);
|
||||
|
||||
@ -3,7 +3,7 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { toastQueueSelector } from 'features/system/store/systemSelectors';
|
||||
import { addToast, clearToastQueue } from 'features/system/store/systemSlice';
|
||||
import { MakeToastArg, makeToast } from 'features/system/util/makeToast';
|
||||
import { useCallback, useEffect } from 'react';
|
||||
import { memo, useCallback, useEffect } from 'react';
|
||||
|
||||
/**
|
||||
* Logical component. Watches the toast queue and makes toasts when the queue is not empty.
|
||||
@ -44,4 +44,4 @@ export const useAppToaster = () => {
|
||||
return toaster;
|
||||
};
|
||||
|
||||
export default Toaster;
|
||||
export default memo(Toaster);
|
||||
|
||||
@ -9,7 +9,7 @@ export const log = Roarr.child(BASE_CONTEXT);
|
||||
|
||||
export const $logger = atom<Logger>(Roarr.child(BASE_CONTEXT));
|
||||
|
||||
type LoggerNamespace =
|
||||
export type LoggerNamespace =
|
||||
| 'images'
|
||||
| 'models'
|
||||
| 'config'
|
||||
|
||||
@ -1,12 +1,17 @@
|
||||
import { useStore } from '@nanostores/react';
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { createLogWriter } from '@roarr/browser-log-writer';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { systemSelector } from 'features/system/store/systemSelectors';
|
||||
import { isEqual } from 'lodash-es';
|
||||
import { useEffect } from 'react';
|
||||
import { useEffect, useMemo } from 'react';
|
||||
import { ROARR, Roarr } from 'roarr';
|
||||
import { $logger, BASE_CONTEXT, LOG_LEVEL_MAP } from './logger';
|
||||
import {
|
||||
$logger,
|
||||
BASE_CONTEXT,
|
||||
LOG_LEVEL_MAP,
|
||||
LoggerNamespace,
|
||||
logger,
|
||||
} from './logger';
|
||||
|
||||
const selector = createSelector(
|
||||
systemSelector,
|
||||
@ -25,7 +30,7 @@ const selector = createSelector(
|
||||
}
|
||||
);
|
||||
|
||||
export const useLogger = () => {
|
||||
export const useLogger = (namespace: LoggerNamespace) => {
|
||||
const { consoleLogLevel, shouldLogToConsole } = useAppSelector(selector);
|
||||
|
||||
// The provided Roarr browser log writer uses localStorage to config logging to console
|
||||
@ -57,7 +62,7 @@ export const useLogger = () => {
|
||||
$logger.set(Roarr.child(newContext));
|
||||
}, []);
|
||||
|
||||
const logger = useStore($logger);
|
||||
const log = useMemo(() => logger(namespace), [namespace]);
|
||||
|
||||
return logger;
|
||||
return log;
|
||||
};
|
||||
|
||||
@ -1,13 +1,17 @@
|
||||
import { logger } from 'app/logging/logger';
|
||||
import { resetCanvas } from 'features/canvas/store/canvasSlice';
|
||||
import { controlNetReset } from 'features/controlNet/store/controlNetSlice';
|
||||
import {
|
||||
controlNetImageChanged,
|
||||
controlNetProcessedImageChanged,
|
||||
} from 'features/controlNet/store/controlNetSlice';
|
||||
import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
|
||||
import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
|
||||
import { selectListImagesBaseQueryArgs } from 'features/gallery/store/gallerySelectors';
|
||||
import { imageSelected } from 'features/gallery/store/gallerySlice';
|
||||
import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
|
||||
import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
|
||||
import { isInvocationNode } from 'features/nodes/types/types';
|
||||
import { clearInitialImage } from 'features/parameters/store/generationSlice';
|
||||
import { clamp } from 'lodash-es';
|
||||
import { clamp, forEach } from 'lodash-es';
|
||||
import { api } from 'services/api';
|
||||
import { imagesApi } from 'services/api/endpoints/images';
|
||||
import { imagesAdapter } from 'services/api/util';
|
||||
@ -73,22 +77,61 @@ export const addRequestedSingleImageDeletionListener = () => {
|
||||
}
|
||||
|
||||
// We need to reset the features where the image is in use - none of these work if their image(s) don't exist
|
||||
|
||||
if (imageUsage.isCanvasImage) {
|
||||
dispatch(resetCanvas());
|
||||
}
|
||||
|
||||
if (imageUsage.isControlNetImage) {
|
||||
dispatch(controlNetReset());
|
||||
}
|
||||
imageDTOs.forEach((imageDTO) => {
|
||||
// reset init image if we deleted it
|
||||
if (
|
||||
getState().generation.initialImage?.imageName === imageDTO.image_name
|
||||
) {
|
||||
dispatch(clearInitialImage());
|
||||
}
|
||||
|
||||
if (imageUsage.isInitialImage) {
|
||||
dispatch(clearInitialImage());
|
||||
}
|
||||
// reset controlNets that use the deleted images
|
||||
forEach(getState().controlNet.controlNets, (controlNet) => {
|
||||
if (
|
||||
controlNet.controlImage === imageDTO.image_name ||
|
||||
controlNet.processedControlImage === imageDTO.image_name
|
||||
) {
|
||||
dispatch(
|
||||
controlNetImageChanged({
|
||||
controlNetId: controlNet.controlNetId,
|
||||
controlImage: null,
|
||||
})
|
||||
);
|
||||
dispatch(
|
||||
controlNetProcessedImageChanged({
|
||||
controlNetId: controlNet.controlNetId,
|
||||
processedControlImage: null,
|
||||
})
|
||||
);
|
||||
}
|
||||
});
|
||||
|
||||
if (imageUsage.isNodesImage) {
|
||||
dispatch(nodeEditorReset());
|
||||
}
|
||||
// reset nodes that use the deleted images
|
||||
getState().nodes.nodes.forEach((node) => {
|
||||
if (!isInvocationNode(node)) {
|
||||
return;
|
||||
}
|
||||
|
||||
forEach(node.data.inputs, (input) => {
|
||||
if (
|
||||
input.type === 'ImageField' &&
|
||||
input.value?.image_name === imageDTO.image_name
|
||||
) {
|
||||
dispatch(
|
||||
fieldImageValueChanged({
|
||||
nodeId: node.data.id,
|
||||
fieldName: input.name,
|
||||
value: undefined,
|
||||
})
|
||||
);
|
||||
}
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
// Delete from server
|
||||
const { requestId } = dispatch(
|
||||
@ -154,17 +197,58 @@ export const addRequestedMultipleImageDeletionListener = () => {
|
||||
dispatch(resetCanvas());
|
||||
}
|
||||
|
||||
if (imagesUsage.some((i) => i.isControlNetImage)) {
|
||||
dispatch(controlNetReset());
|
||||
}
|
||||
imageDTOs.forEach((imageDTO) => {
|
||||
// reset init image if we deleted it
|
||||
if (
|
||||
getState().generation.initialImage?.imageName ===
|
||||
imageDTO.image_name
|
||||
) {
|
||||
dispatch(clearInitialImage());
|
||||
}
|
||||
|
||||
if (imagesUsage.some((i) => i.isInitialImage)) {
|
||||
dispatch(clearInitialImage());
|
||||
}
|
||||
// reset controlNets that use the deleted images
|
||||
forEach(getState().controlNet.controlNets, (controlNet) => {
|
||||
if (
|
||||
controlNet.controlImage === imageDTO.image_name ||
|
||||
controlNet.processedControlImage === imageDTO.image_name
|
||||
) {
|
||||
dispatch(
|
||||
controlNetImageChanged({
|
||||
controlNetId: controlNet.controlNetId,
|
||||
controlImage: null,
|
||||
})
|
||||
);
|
||||
dispatch(
|
||||
controlNetProcessedImageChanged({
|
||||
controlNetId: controlNet.controlNetId,
|
||||
processedControlImage: null,
|
||||
})
|
||||
);
|
||||
}
|
||||
});
|
||||
|
||||
if (imagesUsage.some((i) => i.isNodesImage)) {
|
||||
dispatch(nodeEditorReset());
|
||||
}
|
||||
// reset nodes that use the deleted images
|
||||
getState().nodes.nodes.forEach((node) => {
|
||||
if (!isInvocationNode(node)) {
|
||||
return;
|
||||
}
|
||||
|
||||
forEach(node.data.inputs, (input) => {
|
||||
if (
|
||||
input.type === 'ImageField' &&
|
||||
input.value?.image_name === imageDTO.image_name
|
||||
) {
|
||||
dispatch(
|
||||
fieldImageValueChanged({
|
||||
nodeId: node.data.id,
|
||||
fieldName: input.name,
|
||||
value: undefined,
|
||||
})
|
||||
);
|
||||
}
|
||||
});
|
||||
});
|
||||
});
|
||||
} catch {
|
||||
// no-op
|
||||
}
|
||||
|
||||
@ -5,6 +5,7 @@ import { modelsApi } from 'services/api/endpoints/models';
|
||||
import { receivedOpenAPISchema } from 'services/api/thunks/schema';
|
||||
import { appSocketConnected, socketConnected } from 'services/events/actions';
|
||||
import { startAppListening } from '../..';
|
||||
import { size } from 'lodash-es';
|
||||
|
||||
export const addSocketConnectedEventListener = () => {
|
||||
startAppListening({
|
||||
@ -18,7 +19,7 @@ export const addSocketConnectedEventListener = () => {
|
||||
|
||||
const { disabledTabs } = config;
|
||||
|
||||
if (!nodes.schema && !disabledTabs.includes('nodes')) {
|
||||
if (!size(nodes.nodeTemplates) && !disabledTabs.includes('nodes')) {
|
||||
dispatch(receivedOpenAPISchema());
|
||||
}
|
||||
|
||||
|
||||
@ -8,8 +8,8 @@ import {
|
||||
import { memo, ReactNode } from 'react';
|
||||
|
||||
export interface IAIButtonProps extends ButtonProps {
|
||||
tooltip?: string;
|
||||
tooltipProps?: Omit<TooltipProps, 'children'>;
|
||||
tooltip?: TooltipProps['label'];
|
||||
tooltipProps?: Omit<TooltipProps, 'children' | 'label'>;
|
||||
isChecked?: boolean;
|
||||
children: ReactNode;
|
||||
}
|
||||
|
||||
@ -34,14 +34,10 @@ const IAICollapse = (props: IAIToggleCollapseProps) => {
|
||||
gap: 2,
|
||||
borderTopRadius: 'base',
|
||||
borderBottomRadius: isOpen ? 0 : 'base',
|
||||
bg: isOpen
|
||||
? mode('base.200', 'base.750')(colorMode)
|
||||
: mode('base.150', 'base.800')(colorMode),
|
||||
bg: mode('base.250', 'base.750')(colorMode),
|
||||
color: mode('base.900', 'base.100')(colorMode),
|
||||
_hover: {
|
||||
bg: isOpen
|
||||
? mode('base.250', 'base.700')(colorMode)
|
||||
: mode('base.200', 'base.750')(colorMode),
|
||||
bg: mode('base.300', 'base.700')(colorMode),
|
||||
},
|
||||
fontSize: 'sm',
|
||||
fontWeight: 600,
|
||||
@ -90,9 +86,10 @@ const IAICollapse = (props: IAIToggleCollapseProps) => {
|
||||
<Collapse in={isOpen} animateOpacity style={{ overflow: 'unset' }}>
|
||||
<Box
|
||||
sx={{
|
||||
p: 4,
|
||||
p: 2,
|
||||
pt: 3,
|
||||
borderBottomRadius: 'base',
|
||||
bg: 'base.100',
|
||||
bg: 'base.150',
|
||||
_dark: {
|
||||
bg: 'base.800',
|
||||
},
|
||||
|
||||
@ -100,14 +100,18 @@ const IAIDndImage = (props: IAIDndImageProps) => {
|
||||
const [isHovered, setIsHovered] = useState(false);
|
||||
const handleMouseOver = useCallback(
|
||||
(e: MouseEvent<HTMLDivElement>) => {
|
||||
if (onMouseOver) onMouseOver(e);
|
||||
if (onMouseOver) {
|
||||
onMouseOver(e);
|
||||
}
|
||||
setIsHovered(true);
|
||||
},
|
||||
[onMouseOver]
|
||||
);
|
||||
const handleMouseOut = useCallback(
|
||||
(e: MouseEvent<HTMLDivElement>) => {
|
||||
if (onMouseOut) onMouseOut(e);
|
||||
if (onMouseOut) {
|
||||
onMouseOut(e);
|
||||
}
|
||||
setIsHovered(false);
|
||||
},
|
||||
[onMouseOut]
|
||||
@ -122,7 +126,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
|
||||
? {}
|
||||
: {
|
||||
cursor: 'pointer',
|
||||
bg: mode('base.200', 'base.800')(colorMode),
|
||||
bg: mode('base.200', 'base.700')(colorMode),
|
||||
_hover: {
|
||||
bg: mode('base.300', 'base.650')(colorMode),
|
||||
color: mode('base.500', 'base.300')(colorMode),
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import { Box, Flex, Icon } from '@chakra-ui/react';
|
||||
import { memo } from 'react';
|
||||
import { FaExclamation } from 'react-icons/fa';
|
||||
|
||||
const IAIErrorLoadingImageFallback = () => {
|
||||
@ -39,4 +40,4 @@ const IAIErrorLoadingImageFallback = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAIErrorLoadingImageFallback;
|
||||
export default memo(IAIErrorLoadingImageFallback);
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import { Box, Skeleton } from '@chakra-ui/react';
|
||||
import { memo } from 'react';
|
||||
|
||||
const IAIFillSkeleton = () => {
|
||||
return (
|
||||
@ -27,4 +28,4 @@ const IAIFillSkeleton = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAIFillSkeleton;
|
||||
export default memo(IAIFillSkeleton);
|
||||
|
||||
@ -9,8 +9,8 @@ import { memo } from 'react';
|
||||
|
||||
export type IAIIconButtonProps = IconButtonProps & {
|
||||
role?: string;
|
||||
tooltip?: string;
|
||||
tooltipProps?: Omit<TooltipProps, 'children'>;
|
||||
tooltip?: TooltipProps['label'];
|
||||
tooltipProps?: Omit<TooltipProps, 'children' | 'label'>;
|
||||
isChecked?: boolean;
|
||||
};
|
||||
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import { Badge, Flex } from '@chakra-ui/react';
|
||||
import { memo } from 'react';
|
||||
import { ImageDTO } from 'services/api/types';
|
||||
|
||||
type ImageMetadataOverlayProps = {
|
||||
@ -26,4 +27,4 @@ const ImageMetadataOverlay = ({ imageDTO }: ImageMetadataOverlayProps) => {
|
||||
);
|
||||
};
|
||||
|
||||
export default ImageMetadataOverlay;
|
||||
export default memo(ImageMetadataOverlay);
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import { Box, Flex, Heading } from '@chakra-ui/react';
|
||||
import { memo } from 'react';
|
||||
import { useHotkeys } from 'react-hotkeys-hook';
|
||||
|
||||
type ImageUploadOverlayProps = {
|
||||
@ -87,4 +88,4 @@ const ImageUploadOverlay = (props: ImageUploadOverlayProps) => {
|
||||
</Box>
|
||||
);
|
||||
};
|
||||
export default ImageUploadOverlay;
|
||||
export default memo(ImageUploadOverlay);
|
||||
|
||||
@ -150,7 +150,9 @@ const ImageUploader = (props: ImageUploaderProps) => {
|
||||
{...getRootProps({ style: {} })}
|
||||
onKeyDown={(e: KeyboardEvent) => {
|
||||
// Bail out if user hits spacebar - do not open the uploader
|
||||
if (e.key === ' ') return;
|
||||
if (e.key === ' ') {
|
||||
return;
|
||||
}
|
||||
}}
|
||||
>
|
||||
<input {...getInputProps()} />
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import { Flex, Icon } from '@chakra-ui/react';
|
||||
import { memo } from 'react';
|
||||
import { FaImage } from 'react-icons/fa';
|
||||
|
||||
const SelectImagePlaceholder = () => {
|
||||
@ -19,4 +20,4 @@ const SelectImagePlaceholder = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default SelectImagePlaceholder;
|
||||
export default memo(SelectImagePlaceholder);
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import { Box } from '@chakra-ui/react';
|
||||
import { memo } from 'react';
|
||||
|
||||
type Props = {
|
||||
isSelected: boolean;
|
||||
@ -18,6 +19,7 @@ const SelectionOverlay = ({ isSelected, isHovered }: Props) => {
|
||||
opacity: isSelected ? 1 : 0.7,
|
||||
transitionProperty: 'common',
|
||||
transitionDuration: '0.1s',
|
||||
pointerEvents: 'none',
|
||||
shadow: isSelected
|
||||
? isHovered
|
||||
? 'hoverSelected.light'
|
||||
@ -39,4 +41,4 @@ const SelectionOverlay = ({ isSelected, isHovered }: Props) => {
|
||||
);
|
||||
};
|
||||
|
||||
export default SelectionOverlay;
|
||||
export default memo(SelectionOverlay);
|
||||
|
||||
@ -2,71 +2,108 @@ import { createSelector } from '@reduxjs/toolkit';
|
||||
import { stateSelector } from 'app/store/store';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
|
||||
// import { validateSeedWeights } from 'common/util/seedWeightPairs';
|
||||
import { isInvocationNode } from 'features/nodes/types/types';
|
||||
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
|
||||
import { forEach } from 'lodash-es';
|
||||
import { NON_REFINER_BASE_MODELS } from 'services/api/constants';
|
||||
import { modelsApi } from '../../services/api/endpoints/models';
|
||||
import { forEach, map } from 'lodash-es';
|
||||
import { getConnectedEdges } from 'reactflow';
|
||||
|
||||
const readinessSelector = createSelector(
|
||||
const selector = createSelector(
|
||||
[stateSelector, activeTabNameSelector],
|
||||
(state, activeTabName) => {
|
||||
const { generation, system } = state;
|
||||
const { initialImage } = generation;
|
||||
const { generation, system, nodes } = state;
|
||||
const { initialImage, model } = generation;
|
||||
|
||||
const { isProcessing, isConnected } = system;
|
||||
|
||||
let isReady = true;
|
||||
const reasonsWhyNotReady: string[] = [];
|
||||
const reasons: string[] = [];
|
||||
|
||||
if (activeTabName === 'img2img' && !initialImage) {
|
||||
isReady = false;
|
||||
reasonsWhyNotReady.push('No initial image selected');
|
||||
}
|
||||
|
||||
const { isSuccess: mainModelsSuccessfullyLoaded } =
|
||||
modelsApi.endpoints.getMainModels.select(NON_REFINER_BASE_MODELS)(state);
|
||||
if (!mainModelsSuccessfullyLoaded) {
|
||||
isReady = false;
|
||||
reasonsWhyNotReady.push('Models are not loaded');
|
||||
}
|
||||
|
||||
// TODO: job queue
|
||||
// Cannot generate if already processing an image
|
||||
if (isProcessing) {
|
||||
isReady = false;
|
||||
reasonsWhyNotReady.push('System Busy');
|
||||
reasons.push('System busy');
|
||||
}
|
||||
|
||||
// Cannot generate if not connected
|
||||
if (!isConnected) {
|
||||
isReady = false;
|
||||
reasonsWhyNotReady.push('System Disconnected');
|
||||
reasons.push('System disconnected');
|
||||
}
|
||||
|
||||
// // Cannot generate variations without valid seed weights
|
||||
// if (
|
||||
// shouldGenerateVariations &&
|
||||
// (!(validateSeedWeights(seedWeights) || seedWeights === '') || seed === -1)
|
||||
// ) {
|
||||
// isReady = false;
|
||||
// reasonsWhyNotReady.push('Seed-Weights badly formatted.');
|
||||
// }
|
||||
if (activeTabName === 'img2img' && !initialImage) {
|
||||
reasons.push('No initial image selected');
|
||||
}
|
||||
|
||||
forEach(state.controlNet.controlNets, (controlNet, id) => {
|
||||
if (!controlNet.model) {
|
||||
isReady = false;
|
||||
reasonsWhyNotReady.push(`ControlNet ${id} has no model selected.`);
|
||||
if (activeTabName === 'nodes' && nodes.shouldValidateGraph) {
|
||||
if (!nodes.nodes.length) {
|
||||
reasons.push('No nodes in graph');
|
||||
}
|
||||
});
|
||||
|
||||
// All good
|
||||
return { isReady, reasonsWhyNotReady };
|
||||
nodes.nodes.forEach((node) => {
|
||||
if (!isInvocationNode(node)) {
|
||||
return;
|
||||
}
|
||||
|
||||
const nodeTemplate = nodes.nodeTemplates[node.data.type];
|
||||
|
||||
if (!nodeTemplate) {
|
||||
// Node type not found
|
||||
reasons.push('Missing node template');
|
||||
return;
|
||||
}
|
||||
|
||||
const connectedEdges = getConnectedEdges([node], nodes.edges);
|
||||
|
||||
forEach(node.data.inputs, (field) => {
|
||||
const fieldTemplate = nodeTemplate.inputs[field.name];
|
||||
const hasConnection = connectedEdges.some(
|
||||
(edge) =>
|
||||
edge.target === node.id && edge.targetHandle === field.name
|
||||
);
|
||||
|
||||
if (!fieldTemplate) {
|
||||
reasons.push('Missing field template');
|
||||
return;
|
||||
}
|
||||
|
||||
if (fieldTemplate.required && !field.value && !hasConnection) {
|
||||
reasons.push(
|
||||
`${node.data.label || nodeTemplate.title} -> ${
|
||||
field.label || fieldTemplate.title
|
||||
} missing input`
|
||||
);
|
||||
return;
|
||||
}
|
||||
});
|
||||
});
|
||||
} else {
|
||||
if (!model) {
|
||||
reasons.push('No model selected');
|
||||
}
|
||||
|
||||
if (state.controlNet.isEnabled) {
|
||||
map(state.controlNet.controlNets).forEach((controlNet, i) => {
|
||||
if (!controlNet.isEnabled) {
|
||||
return;
|
||||
}
|
||||
if (!controlNet.model) {
|
||||
reasons.push(`ControlNet ${i + 1} has no model selected.`);
|
||||
}
|
||||
|
||||
if (
|
||||
!controlNet.controlImage ||
|
||||
(!controlNet.processedControlImage &&
|
||||
controlNet.processorType !== 'none')
|
||||
) {
|
||||
reasons.push(`ControlNet ${i + 1} has no control image`);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return { isReady: !reasons.length, isProcessing, reasons };
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
export const useIsReadyToInvoke = () => {
|
||||
const { isReady } = useAppSelector(readinessSelector);
|
||||
return isReady;
|
||||
const { isReady, isProcessing, reasons } = useAppSelector(selector);
|
||||
return { isReady, isProcessing, reasons };
|
||||
};
|
||||
|
||||
@ -11,8 +11,14 @@ export default function useResolution():
|
||||
const tabletResolutions = ['md', 'lg'];
|
||||
const desktopResolutions = ['xl', '2xl'];
|
||||
|
||||
if (mobileResolutions.includes(breakpointValue)) return 'mobile';
|
||||
if (tabletResolutions.includes(breakpointValue)) return 'tablet';
|
||||
if (desktopResolutions.includes(breakpointValue)) return 'desktop';
|
||||
if (mobileResolutions.includes(breakpointValue)) {
|
||||
return 'mobile';
|
||||
}
|
||||
if (tabletResolutions.includes(breakpointValue)) {
|
||||
return 'tablet';
|
||||
}
|
||||
if (desktopResolutions.includes(breakpointValue)) {
|
||||
return 'desktop';
|
||||
}
|
||||
return 'unknown';
|
||||
}
|
||||
|
||||
@ -0,0 +1,2 @@
|
||||
export const colorTokenToCssVar = (colorToken: string) =>
|
||||
`var(--invokeai-colors-${colorToken.split('.').join('-')}`;
|
||||
@ -6,7 +6,11 @@ export const dateComparator = (a: string, b: string) => {
|
||||
const dateB = new Date(b);
|
||||
|
||||
// sort in ascending order
|
||||
if (dateA > dateB) return 1;
|
||||
if (dateA < dateB) return -1;
|
||||
if (dateA > dateB) {
|
||||
return 1;
|
||||
}
|
||||
if (dateA < dateB) {
|
||||
return -1;
|
||||
}
|
||||
return 0;
|
||||
};
|
||||
|
||||
@ -5,7 +5,9 @@ type Base64AndCaption = {
|
||||
|
||||
const openBase64ImageInTab = (images: Base64AndCaption[]) => {
|
||||
const w = window.open('');
|
||||
if (!w) return;
|
||||
if (!w) {
|
||||
return;
|
||||
}
|
||||
|
||||
images.forEach((i) => {
|
||||
const image = new Image();
|
||||
|
||||
@ -5,6 +5,7 @@ import { clearCanvasHistory } from 'features/canvas/store/canvasSlice';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { FaTrash } from 'react-icons/fa';
|
||||
import { isStagingSelector } from '../store/canvasSelectors';
|
||||
import { memo } from 'react';
|
||||
|
||||
const ClearCanvasHistoryButtonModal = () => {
|
||||
const isStaging = useAppSelector(isStagingSelector);
|
||||
@ -28,4 +29,4 @@ const ClearCanvasHistoryButtonModal = () => {
|
||||
</IAIAlertDialog>
|
||||
);
|
||||
};
|
||||
export default ClearCanvasHistoryButtonModal;
|
||||
export default memo(ClearCanvasHistoryButtonModal);
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
import { Box, chakra, Flex } from '@chakra-ui/react';
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
|
||||
import {
|
||||
canvasSelector,
|
||||
@ -9,7 +9,7 @@ import {
|
||||
import Konva from 'konva';
|
||||
import { KonvaEventObject } from 'konva/lib/Node';
|
||||
import { Vector2d } from 'konva/lib/types';
|
||||
import { useCallback, useRef } from 'react';
|
||||
import { memo, useCallback, useEffect, useRef } from 'react';
|
||||
import { Layer, Stage } from 'react-konva';
|
||||
import useCanvasDragMove from '../hooks/useCanvasDragMove';
|
||||
import useCanvasHotkeys from '../hooks/useCanvasHotkeys';
|
||||
@ -18,6 +18,7 @@ import useCanvasMouseMove from '../hooks/useCanvasMouseMove';
|
||||
import useCanvasMouseOut from '../hooks/useCanvasMouseOut';
|
||||
import useCanvasMouseUp from '../hooks/useCanvasMouseUp';
|
||||
import useCanvasWheel from '../hooks/useCanvasZoom';
|
||||
import { canvasResized } from '../store/canvasSlice';
|
||||
import {
|
||||
setCanvasBaseLayer,
|
||||
setCanvasStage,
|
||||
@ -106,7 +107,8 @@ const IAICanvas = () => {
|
||||
shouldAntialias,
|
||||
} = useAppSelector(selector);
|
||||
useCanvasHotkeys();
|
||||
|
||||
const dispatch = useAppDispatch();
|
||||
const containerRef = useRef<HTMLDivElement>(null);
|
||||
const stageRef = useRef<Konva.Stage | null>(null);
|
||||
const canvasBaseLayerRef = useRef<Konva.Layer | null>(null);
|
||||
|
||||
@ -137,8 +139,30 @@ const IAICanvas = () => {
|
||||
const { handleDragStart, handleDragMove, handleDragEnd } =
|
||||
useCanvasDragMove();
|
||||
|
||||
useEffect(() => {
|
||||
if (!containerRef.current) {
|
||||
return;
|
||||
}
|
||||
const resizeObserver = new ResizeObserver((entries) => {
|
||||
for (const entry of entries) {
|
||||
if (entry.contentBoxSize) {
|
||||
const { width, height } = entry.contentRect;
|
||||
dispatch(canvasResized({ width, height }));
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
resizeObserver.observe(containerRef.current);
|
||||
|
||||
return () => {
|
||||
resizeObserver.disconnect();
|
||||
};
|
||||
}, [dispatch]);
|
||||
|
||||
return (
|
||||
<Flex
|
||||
id="canvas-container"
|
||||
ref={containerRef}
|
||||
sx={{
|
||||
position: 'relative',
|
||||
height: '100%',
|
||||
@ -146,13 +170,18 @@ const IAICanvas = () => {
|
||||
borderRadius: 'base',
|
||||
}}
|
||||
>
|
||||
<Box sx={{ position: 'relative' }}>
|
||||
<Box
|
||||
sx={{
|
||||
position: 'absolute',
|
||||
// top: 0,
|
||||
// insetInlineStart: 0,
|
||||
}}
|
||||
>
|
||||
<ChakraStage
|
||||
tabIndex={-1}
|
||||
ref={canvasStageRefCallback}
|
||||
sx={{
|
||||
outline: 'none',
|
||||
// boxShadow: '0px 0px 0px 1px var(--border-color-light)',
|
||||
overflow: 'hidden',
|
||||
cursor: stageCursor ? stageCursor : undefined,
|
||||
canvas: {
|
||||
@ -213,11 +242,11 @@ const IAICanvas = () => {
|
||||
/>
|
||||
</Layer>
|
||||
</ChakraStage>
|
||||
<IAICanvasStatusText />
|
||||
<IAICanvasStagingAreaToolbar />
|
||||
</Box>
|
||||
<IAICanvasStatusText />
|
||||
<IAICanvasStagingAreaToolbar />
|
||||
</Flex>
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvas;
|
||||
export default memo(IAICanvas);
|
||||
|
||||
@ -4,6 +4,7 @@ import { isEqual } from 'lodash-es';
|
||||
|
||||
import { Group, Rect } from 'react-konva';
|
||||
import { canvasSelector } from '../store/canvasSelectors';
|
||||
import { memo } from 'react';
|
||||
|
||||
const selector = createSelector(
|
||||
canvasSelector,
|
||||
@ -67,4 +68,4 @@ const IAICanvasBoundingBoxOverlay = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasBoundingBoxOverlay;
|
||||
export default memo(IAICanvasBoundingBoxOverlay);
|
||||
|
||||
@ -6,7 +6,7 @@ import { useAppSelector } from 'app/store/storeHooks';
|
||||
import { canvasSelector } from 'features/canvas/store/canvasSelectors';
|
||||
import { isEqual, range } from 'lodash-es';
|
||||
|
||||
import { ReactNode, useCallback, useLayoutEffect, useState } from 'react';
|
||||
import { ReactNode, memo, useCallback, useLayoutEffect, useState } from 'react';
|
||||
import { Group, Line as KonvaLine } from 'react-konva';
|
||||
|
||||
const selector = createSelector(
|
||||
@ -117,4 +117,4 @@ const IAICanvasGrid = () => {
|
||||
return <Group>{gridLines}</Group>;
|
||||
};
|
||||
|
||||
export default IAICanvasGrid;
|
||||
export default memo(IAICanvasGrid);
|
||||
|
||||
@ -4,6 +4,7 @@ import { useGetImageDTOQuery } from 'services/api/endpoints/images';
|
||||
import useImage from 'use-image';
|
||||
import { CanvasImage } from '../store/canvasTypes';
|
||||
import { $authToken } from 'services/api/client';
|
||||
import { memo } from 'react';
|
||||
|
||||
type IAICanvasImageProps = {
|
||||
canvasImage: CanvasImage;
|
||||
@ -25,4 +26,4 @@ const IAICanvasImage = (props: IAICanvasImageProps) => {
|
||||
return <Image x={x} y={y} image={image} listening={false} />;
|
||||
};
|
||||
|
||||
export default IAICanvasImage;
|
||||
export default memo(IAICanvasImage);
|
||||
|
||||
@ -4,7 +4,7 @@ import { systemSelector } from 'features/system/store/systemSelectors';
|
||||
import { ImageConfig } from 'konva/lib/shapes/Image';
|
||||
import { isEqual } from 'lodash-es';
|
||||
|
||||
import { useEffect, useState } from 'react';
|
||||
import { memo, useEffect, useState } from 'react';
|
||||
import { Image as KonvaImage } from 'react-konva';
|
||||
import { canvasSelector } from '../store/canvasSelectors';
|
||||
|
||||
@ -66,4 +66,4 @@ const IAICanvasIntermediateImage = (props: Props) => {
|
||||
) : null;
|
||||
};
|
||||
|
||||
export default IAICanvasIntermediateImage;
|
||||
export default memo(IAICanvasIntermediateImage);
|
||||
|
||||
@ -7,7 +7,7 @@ import { Rect } from 'react-konva';
|
||||
import { rgbaColorToString } from 'features/canvas/util/colorToString';
|
||||
import Konva from 'konva';
|
||||
import { isNumber } from 'lodash-es';
|
||||
import { useCallback, useEffect, useRef, useState } from 'react';
|
||||
import { memo, useCallback, useEffect, useRef, useState } from 'react';
|
||||
|
||||
export const canvasMaskCompositerSelector = createSelector(
|
||||
canvasSelector,
|
||||
@ -125,7 +125,9 @@ const IAICanvasMaskCompositer = (props: IAICanvasMaskCompositerProps) => {
|
||||
}, [offset]);
|
||||
|
||||
useEffect(() => {
|
||||
if (fillPatternImage) return;
|
||||
if (fillPatternImage) {
|
||||
return;
|
||||
}
|
||||
const image = new Image();
|
||||
|
||||
image.onload = () => {
|
||||
@ -135,7 +137,9 @@ const IAICanvasMaskCompositer = (props: IAICanvasMaskCompositerProps) => {
|
||||
}, [fillPatternImage, maskColorString]);
|
||||
|
||||
useEffect(() => {
|
||||
if (!fillPatternImage) return;
|
||||
if (!fillPatternImage) {
|
||||
return;
|
||||
}
|
||||
fillPatternImage.src = getColoredSVG(maskColorString);
|
||||
}, [fillPatternImage, maskColorString]);
|
||||
|
||||
@ -151,8 +155,9 @@ const IAICanvasMaskCompositer = (props: IAICanvasMaskCompositerProps) => {
|
||||
!isNumber(stageScale) ||
|
||||
!isNumber(stageDimensions.width) ||
|
||||
!isNumber(stageDimensions.height)
|
||||
)
|
||||
) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return (
|
||||
<Rect
|
||||
@ -172,4 +177,4 @@ const IAICanvasMaskCompositer = (props: IAICanvasMaskCompositerProps) => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasMaskCompositer;
|
||||
export default memo(IAICanvasMaskCompositer);
|
||||
|
||||
@ -6,6 +6,7 @@ import { isEqual } from 'lodash-es';
|
||||
|
||||
import { Group, Line } from 'react-konva';
|
||||
import { isCanvasMaskLine } from '../store/canvasTypes';
|
||||
import { memo } from 'react';
|
||||
|
||||
export const canvasLinesSelector = createSelector(
|
||||
[canvasSelector],
|
||||
@ -52,4 +53,4 @@ const IAICanvasLines = (props: InpaintingCanvasLinesProps) => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasLines;
|
||||
export default memo(IAICanvasLines);
|
||||
|
||||
@ -12,6 +12,7 @@ import {
|
||||
isCanvasFillRect,
|
||||
} from '../store/canvasTypes';
|
||||
import IAICanvasImage from './IAICanvasImage';
|
||||
import { memo } from 'react';
|
||||
|
||||
const selector = createSelector(
|
||||
[canvasSelector],
|
||||
@ -33,7 +34,9 @@ const selector = createSelector(
|
||||
const IAICanvasObjectRenderer = () => {
|
||||
const { objects } = useAppSelector(selector);
|
||||
|
||||
if (!objects) return null;
|
||||
if (!objects) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return (
|
||||
<Group name="outpainting-objects" listening={false}>
|
||||
@ -101,4 +104,4 @@ const IAICanvasObjectRenderer = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasObjectRenderer;
|
||||
export default memo(IAICanvasObjectRenderer);
|
||||
|
||||
@ -1,89 +0,0 @@
|
||||
import { Flex, Spinner } from '@chakra-ui/react';
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import {
|
||||
canvasSelector,
|
||||
initialCanvasImageSelector,
|
||||
} from 'features/canvas/store/canvasSelectors';
|
||||
import {
|
||||
resizeAndScaleCanvas,
|
||||
resizeCanvas,
|
||||
setCanvasContainerDimensions,
|
||||
setDoesCanvasNeedScaling,
|
||||
} from 'features/canvas/store/canvasSlice';
|
||||
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
|
||||
import { useLayoutEffect, useRef } from 'react';
|
||||
|
||||
const canvasResizerSelector = createSelector(
|
||||
canvasSelector,
|
||||
initialCanvasImageSelector,
|
||||
activeTabNameSelector,
|
||||
(canvas, initialCanvasImage, activeTabName) => {
|
||||
const { doesCanvasNeedScaling, isCanvasInitialized } = canvas;
|
||||
return {
|
||||
doesCanvasNeedScaling,
|
||||
activeTabName,
|
||||
initialCanvasImage,
|
||||
isCanvasInitialized,
|
||||
};
|
||||
}
|
||||
);
|
||||
|
||||
const IAICanvasResizer = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
const {
|
||||
doesCanvasNeedScaling,
|
||||
activeTabName,
|
||||
initialCanvasImage,
|
||||
isCanvasInitialized,
|
||||
} = useAppSelector(canvasResizerSelector);
|
||||
|
||||
const ref = useRef<HTMLDivElement>(null);
|
||||
|
||||
useLayoutEffect(() => {
|
||||
window.setTimeout(() => {
|
||||
if (!ref.current) return;
|
||||
|
||||
const { clientWidth, clientHeight } = ref.current;
|
||||
|
||||
dispatch(
|
||||
setCanvasContainerDimensions({
|
||||
width: clientWidth,
|
||||
height: clientHeight,
|
||||
})
|
||||
);
|
||||
|
||||
if (!isCanvasInitialized) {
|
||||
dispatch(resizeAndScaleCanvas());
|
||||
} else {
|
||||
dispatch(resizeCanvas());
|
||||
}
|
||||
|
||||
dispatch(setDoesCanvasNeedScaling(false));
|
||||
}, 0);
|
||||
}, [
|
||||
dispatch,
|
||||
initialCanvasImage,
|
||||
doesCanvasNeedScaling,
|
||||
activeTabName,
|
||||
isCanvasInitialized,
|
||||
]);
|
||||
|
||||
return (
|
||||
<Flex
|
||||
ref={ref}
|
||||
sx={{
|
||||
flexDirection: 'column',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
gap: 4,
|
||||
width: '100%',
|
||||
height: '100%',
|
||||
}}
|
||||
>
|
||||
<Spinner thickness="2px" size="xl" />
|
||||
</Flex>
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasResizer;
|
||||
@ -6,6 +6,7 @@ import { isEqual } from 'lodash-es';
|
||||
|
||||
import { Group, Rect } from 'react-konva';
|
||||
import IAICanvasImage from './IAICanvasImage';
|
||||
import { memo } from 'react';
|
||||
|
||||
const selector = createSelector(
|
||||
[canvasSelector],
|
||||
@ -88,4 +89,4 @@ const IAICanvasStagingArea = (props: Props) => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasStagingArea;
|
||||
export default memo(IAICanvasStagingArea);
|
||||
|
||||
@ -13,7 +13,7 @@ import {
|
||||
} from 'features/canvas/store/canvasSlice';
|
||||
import { isEqual } from 'lodash-es';
|
||||
|
||||
import { useCallback } from 'react';
|
||||
import { memo, useCallback } from 'react';
|
||||
import { useHotkeys } from 'react-hotkeys-hook';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import {
|
||||
@ -129,7 +129,9 @@ const IAICanvasStagingAreaToolbar = () => {
|
||||
currentStagingAreaImage?.imageName ?? skipToken
|
||||
);
|
||||
|
||||
if (!currentStagingAreaImage) return null;
|
||||
if (!currentStagingAreaImage) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return (
|
||||
<Flex
|
||||
@ -138,11 +140,10 @@ const IAICanvasStagingAreaToolbar = () => {
|
||||
w="100%"
|
||||
align="center"
|
||||
justify="center"
|
||||
filter="drop-shadow(0 0.5rem 1rem rgba(0,0,0))"
|
||||
onMouseOver={handleMouseOver}
|
||||
onMouseOut={handleMouseOut}
|
||||
>
|
||||
<ButtonGroup isAttached>
|
||||
<ButtonGroup isAttached borderRadius="base" shadow="dark-lg">
|
||||
<IAIIconButton
|
||||
tooltip={`${t('unifiedCanvas.previous')} (Left)`}
|
||||
aria-label={`${t('unifiedCanvas.previous')} (Left)`}
|
||||
@ -207,4 +208,4 @@ const IAICanvasStagingAreaToolbar = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasStagingAreaToolbar;
|
||||
export default memo(IAICanvasStagingAreaToolbar);
|
||||
|
||||
@ -7,6 +7,7 @@ import { isEqual } from 'lodash-es';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import roundToHundreth from '../util/roundToHundreth';
|
||||
import IAICanvasStatusTextCursorPos from './IAICanvasStatusText/IAICanvasStatusTextCursorPos';
|
||||
import { memo } from 'react';
|
||||
|
||||
const warningColor = 'var(--invokeai-colors-warning-500)';
|
||||
|
||||
@ -162,4 +163,4 @@ const IAICanvasStatusText = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasStatusText;
|
||||
export default memo(IAICanvasStatusText);
|
||||
|
||||
@ -10,6 +10,7 @@ import {
|
||||
COLOR_PICKER_SIZE,
|
||||
COLOR_PICKER_STROKE_RADIUS,
|
||||
} from '../util/constants';
|
||||
import { memo } from 'react';
|
||||
|
||||
const canvasBrushPreviewSelector = createSelector(
|
||||
canvasSelector,
|
||||
@ -134,7 +135,9 @@ const IAICanvasToolPreview = (props: GroupConfig) => {
|
||||
clip,
|
||||
} = useAppSelector(canvasBrushPreviewSelector);
|
||||
|
||||
if (!shouldDrawBrushPreview) return null;
|
||||
if (!shouldDrawBrushPreview) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return (
|
||||
<Group listening={false} {...clip} {...rest}>
|
||||
@ -206,4 +209,4 @@ const IAICanvasToolPreview = (props: GroupConfig) => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasToolPreview;
|
||||
export default memo(IAICanvasToolPreview);
|
||||
|
||||
@ -19,7 +19,7 @@ import { KonvaEventObject } from 'konva/lib/Node';
|
||||
import { Vector2d } from 'konva/lib/types';
|
||||
import { isEqual } from 'lodash-es';
|
||||
|
||||
import { useCallback, useEffect, useRef, useState } from 'react';
|
||||
import { memo, useCallback, useEffect, useRef, useState } from 'react';
|
||||
import { useHotkeys } from 'react-hotkeys-hook';
|
||||
import { Group, Rect, Transformer } from 'react-konva';
|
||||
|
||||
@ -85,7 +85,9 @@ const IAICanvasBoundingBox = (props: IAICanvasBoundingBoxPreviewProps) => {
|
||||
useState(false);
|
||||
|
||||
useEffect(() => {
|
||||
if (!transformerRef.current || !shapeRef.current) return;
|
||||
if (!transformerRef.current || !shapeRef.current) {
|
||||
return;
|
||||
}
|
||||
transformerRef.current.nodes([shapeRef.current]);
|
||||
transformerRef.current.getLayer()?.batchDraw();
|
||||
}, []);
|
||||
@ -133,7 +135,9 @@ const IAICanvasBoundingBox = (props: IAICanvasBoundingBoxPreviewProps) => {
|
||||
* not its width and height. We need to un-scale the width and height before
|
||||
* setting the values.
|
||||
*/
|
||||
if (!shapeRef.current) return;
|
||||
if (!shapeRef.current) {
|
||||
return;
|
||||
}
|
||||
|
||||
const rect = shapeRef.current;
|
||||
|
||||
@ -313,4 +317,4 @@ const IAICanvasBoundingBox = (props: IAICanvasBoundingBoxPreviewProps) => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasBoundingBox;
|
||||
export default memo(IAICanvasBoundingBox);
|
||||
|
||||
@ -20,6 +20,7 @@ import {
|
||||
} from 'features/canvas/store/canvasSlice';
|
||||
import { rgbaColorToString } from 'features/canvas/util/colorToString';
|
||||
import { isEqual } from 'lodash-es';
|
||||
import { memo } from 'react';
|
||||
|
||||
import { useHotkeys } from 'react-hotkeys-hook';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
@ -150,4 +151,4 @@ const IAICanvasMaskOptions = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasMaskOptions;
|
||||
export default memo(IAICanvasMaskOptions);
|
||||
|
||||
@ -18,7 +18,7 @@ import {
|
||||
} from 'features/canvas/store/canvasSlice';
|
||||
import { isEqual } from 'lodash-es';
|
||||
|
||||
import { ChangeEvent } from 'react';
|
||||
import { ChangeEvent, memo } from 'react';
|
||||
import { useHotkeys } from 'react-hotkeys-hook';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { FaWrench } from 'react-icons/fa';
|
||||
@ -163,4 +163,4 @@ const IAICanvasSettingsButtonPopover = () => {
|
||||
);
|
||||
};
|
||||
|
||||
export default IAICanvasSettingsButtonPopover;
|
||||
export default memo(IAICanvasSettingsButtonPopover);
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user