mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into lstein/feat/config-migration
This commit is contained in:
Lincoln Stein
commit
59deef97c5
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README.md
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README.md
@ -3,20 +3,101 @@
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# Invoke - Professional Creative AI Tools for Visual Media
|
# Invoke - Professional Creative AI Tools for Visual Media
|
||||||
## To learn more about Invoke, or implement our Business solutions, visit [invoke.com](https://www.invoke.com/about)
|
|
||||||
|
|
||||||
|
#### To learn more about Invoke, or implement our Business solutions, visit [invoke.com]
|
||||||
|
|
||||||
|
[![discord badge]][discord link] [![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link] [![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link] [![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
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||||||
|
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||||||
[![discord badge]][discord link]
|
</div>
|
||||||
|
|
||||||
[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
|
Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
|
||||||
|
|
||||||
[![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link]
|
[Installation][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]
|
||||||
|
|
||||||
[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
|
<div align="center">
|
||||||
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||||||
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||||||
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||||||
|
</div>
|
||||||
|
|
||||||
|
## Quick Start
|
||||||
|
|
||||||
|
1. Download and unzip the installer from the bottom of the [latest release][latest release link].
|
||||||
|
2. Run the installer script.
|
||||||
|
|
||||||
|
- **Windows**: Double-click on the `install.bat` script.
|
||||||
|
- **macOS**: Open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press enter.
|
||||||
|
- **Linux**: Run `install.sh`.
|
||||||
|
|
||||||
|
3. When prompted, enter a location for the install and select your GPU type.
|
||||||
|
4. Once the install finishes, find the directory you selected during install. The default location is `C:\Users\Username\invokeai` for Windows or `~/invokeai` for Linux/macOS.
|
||||||
|
5. Run the launcher script (`invoke.bat` for Windows, `invoke.sh` for macOS and Linux) the same way you ran the installer script in step 2.
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||||||
|
6. Select option 1 to start the application. Once it starts up, open your browser and go to <http://localhost:9090>.
|
||||||
|
7. Open the model manager tab to install a starter model and then you'll be ready to generate.
|
||||||
|
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||||||
|
More detail, including hardware requirements and manual install instructions, are available in the [installation documentation][installation docs].
|
||||||
|
|
||||||
|
## Troubleshooting, FAQ and Support
|
||||||
|
|
||||||
|
Please review our [FAQ][faq] for solutions to common installation problems and other issues.
|
||||||
|
|
||||||
|
For more help, please join our [Discord][discord link].
|
||||||
|
|
||||||
|
## Features
|
||||||
|
|
||||||
|
Full details on features can be found in [our documentation][features docs].
|
||||||
|
|
||||||
|
### Web Server & UI
|
||||||
|
|
||||||
|
Invoke runs a locally hosted web server & React UI with an industry-leading user experience.
|
||||||
|
|
||||||
|
### Unified Canvas
|
||||||
|
|
||||||
|
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/out-painting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
|
||||||
|
|
||||||
|
### Workflows & Nodes
|
||||||
|
|
||||||
|
Invoke offers a fully featured workflow management solution, enabling users to combine the power of node-based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
|
||||||
|
|
||||||
|
### Board & Gallery Management
|
||||||
|
|
||||||
|
Invoke features an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow.
|
||||||
|
|
||||||
|
### Other features
|
||||||
|
|
||||||
|
- Support for both ckpt and diffusers models
|
||||||
|
- SD1.5, SD2.0, and SDXL support
|
||||||
|
- Upscaling Tools
|
||||||
|
- Embedding Manager & Support
|
||||||
|
- Model Manager & Support
|
||||||
|
- Workflow creation & management
|
||||||
|
- Node-Based Architecture
|
||||||
|
|
||||||
|
## Contributing
|
||||||
|
|
||||||
|
Anyone who wishes to contribute to this project - whether documentation, features, bug fixes, code cleanup, testing, or code reviews - is very much encouraged to do so.
|
||||||
|
|
||||||
|
Get started with contributing by reading our [contribution documentation][contributing docs], joining the [#dev-chat] or the GitHub discussion board.
|
||||||
|
|
||||||
|
We hope you enjoy using Invoke as much as we enjoy creating it, and we hope you will elect to become part of our community.
|
||||||
|
|
||||||
|
## Thanks
|
||||||
|
|
||||||
|
Invoke is a combined effort of [passionate and talented people from across the world][contributors]. We thank them for their time, hard work and effort.
|
||||||
|
|
||||||
|
Original portions of the software are Copyright © 2024 by respective contributors.
|
||||||
|
|
||||||
|
[features docs]: https://invoke-ai.github.io/InvokeAI/features/
|
||||||
|
[faq]: https://invoke-ai.github.io/InvokeAI/help/FAQ/
|
||||||
|
[contributors]: https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/
|
||||||
|
[invoke.com]: https://www.invoke.com/about
|
||||||
|
[github issues]: https://github.com/invoke-ai/InvokeAI/issues
|
||||||
|
[docs home]: https://invoke-ai.github.io/InvokeAI
|
||||||
|
[installation docs]: https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/
|
||||||
|
[#dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
|
||||||
|
[contributing docs]: https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/
|
||||||
[CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
|
[CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
|
||||||
[CI checks on main link]:https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
|
[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
|
||||||
[discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
|
[discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
|
||||||
[discord link]: https://discord.gg/ZmtBAhwWhy
|
[discord link]: https://discord.gg/ZmtBAhwWhy
|
||||||
[github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
|
[github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
|
||||||
@ -30,402 +111,6 @@
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[latest commit to main badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/main?icon=github&color=yellow&label=last%20dev%20commit&cache=900
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[latest commit to main badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/main?icon=github&color=yellow&label=last%20dev%20commit&cache=900
|
||||||
[latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
|
[latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
|
||||||
[latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
|
[latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
|
||||||
[latest release link]: https://github.com/invoke-ai/InvokeAI/releases
|
[latest release link]: https://github.com/invoke-ai/InvokeAI/releases/latest
|
||||||
[translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
|
[translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
|
||||||
[translation status link]: https://hosted.weblate.org/engage/invokeai/
|
[translation status link]: https://hosted.weblate.org/engage/invokeai/
|
||||||
|
|
||||||
</div>
|
|
||||||
|
|
||||||
InvokeAI is a leading creative engine built to empower professionals
|
|
||||||
and enthusiasts alike. Generate and create stunning visual media using
|
|
||||||
the latest AI-driven technologies. InvokeAI offers an industry leading
|
|
||||||
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/INSTALLATION/)] [<a
|
|
||||||
href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a
|
|
||||||
href="https://invoke-ai.github.io/InvokeAI/">Documentation and
|
|
||||||
Tutorials</a>]
|
|
||||||
[<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
|
|
||||||
[<a
|
|
||||||
href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion,
|
|
||||||
Ideas & Q&A</a>]
|
|
||||||
[<a
|
|
||||||
href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>]
|
|
||||||
|
|
||||||
<div align="center">
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
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|
||||||
|
|
||||||
</div>
|
|
||||||
|
|
||||||
## Table of Contents
|
|
||||||
|
|
||||||
Table of Contents 📝
|
|
||||||
|
|
||||||
**Getting Started**
|
|
||||||
1. 🏁 [Quick Start](#quick-start)
|
|
||||||
3. 🖥️ [Hardware Requirements](#hardware-requirements)
|
|
||||||
|
|
||||||
**More About Invoke**
|
|
||||||
1. 🌟 [Features](#features)
|
|
||||||
2. 📣 [Latest Changes](#latest-changes)
|
|
||||||
3. 🛠️ [Troubleshooting](#troubleshooting)
|
|
||||||
|
|
||||||
**Supporting the Project**
|
|
||||||
1. 🤝 [Contributing](#contributing)
|
|
||||||
2. 👥 [Contributors](#contributors)
|
|
||||||
3. 💕 [Support](#support)
|
|
||||||
|
|
||||||
## Quick Start
|
|
||||||
|
|
||||||
For full installation and upgrade instructions, please see:
|
|
||||||
[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.
|
|
||||||
|
|
||||||
### Automatic Installer (suggested for 1st time users)
|
|
||||||
|
|
||||||
1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/latest)
|
|
||||||
|
|
||||||
2. Download the .zip file for your OS (Windows/macOS/Linux).
|
|
||||||
|
|
||||||
3. Unzip the file.
|
|
||||||
|
|
||||||
4. **Windows:** double-click on the `install.bat` script. **macOS:** Open a Terminal window, drag the file `install.sh` from Finder
|
|
||||||
into the Terminal, and press return. **Linux:** run `install.sh`.
|
|
||||||
|
|
||||||
5. You'll be asked to confirm the location of the folder in which
|
|
||||||
to install InvokeAI and its image generation model files. Pick a
|
|
||||||
location with at least 15 GB of free memory. More if you plan on
|
|
||||||
installing lots of models.
|
|
||||||
|
|
||||||
6. Wait while the installer does its thing. After installing the software,
|
|
||||||
the installer will launch a script that lets you configure InvokeAI and
|
|
||||||
select a set of starting image generation models.
|
|
||||||
|
|
||||||
7. Find the folder that InvokeAI was installed into (it is not the
|
|
||||||
same as the unpacked zip file directory!) The default location of this
|
|
||||||
folder (if you didn't change it in step 5) is `~/invokeai` on
|
|
||||||
Linux/Mac systems, and `C:\Users\YourName\invokeai` on Windows. This directory will contain launcher scripts named `invoke.sh` and `invoke.bat`.
|
|
||||||
|
|
||||||
8. On Windows systems, double-click on the `invoke.bat` file. On
|
|
||||||
macOS, open a Terminal window, drag `invoke.sh` from the folder into
|
|
||||||
the Terminal, and press return. On Linux, run `invoke.sh`
|
|
||||||
|
|
||||||
9. Press 2 to open the "browser-based UI", press enter/return, wait a
|
|
||||||
minute or two for Stable Diffusion to start up, then open your browser
|
|
||||||
and go to http://localhost:9090.
|
|
||||||
|
|
||||||
10. Type `banana sushi` in the box on the top left and click `Invoke`
|
|
||||||
|
|
||||||
### Command-Line Installation (for developers and users familiar with Terminals)
|
|
||||||
|
|
||||||
You must have Python 3.10 through 3.11 installed on your machine. Earlier or
|
|
||||||
later versions are not supported.
|
|
||||||
Node.js also needs to be installed along with `pnpm` (can be installed with
|
|
||||||
the command `npm install -g pnpm` if needed)
|
|
||||||
|
|
||||||
1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
|
|
||||||
2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
|
|
||||||
|
|
||||||
```terminal
|
|
||||||
mkdir invokeai
|
|
||||||
````
|
|
||||||
|
|
||||||
3. Create a virtual environment named `.venv` inside this directory and activate it:
|
|
||||||
|
|
||||||
```terminal
|
|
||||||
cd invokeai
|
|
||||||
python -m venv .venv --prompt InvokeAI
|
|
||||||
```
|
|
||||||
|
|
||||||
4. Activate the virtual environment (do it every time you run InvokeAI)
|
|
||||||
|
|
||||||
_For Linux/Mac users:_
|
|
||||||
|
|
||||||
```sh
|
|
||||||
source .venv/bin/activate
|
|
||||||
```
|
|
||||||
|
|
||||||
_For Windows users:_
|
|
||||||
|
|
||||||
```ps
|
|
||||||
.venv\Scripts\activate
|
|
||||||
```
|
|
||||||
|
|
||||||
5. Install the InvokeAI module and its dependencies. Choose the command suited for your platform & GPU.
|
|
||||||
|
|
||||||
_For Windows/Linux with an NVIDIA GPU:_
|
|
||||||
|
|
||||||
```terminal
|
|
||||||
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
|
|
||||||
```
|
|
||||||
|
|
||||||
_For Linux with an AMD GPU:_
|
|
||||||
|
|
||||||
```sh
|
|
||||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
|
|
||||||
```
|
|
||||||
|
|
||||||
_For non-GPU systems:_
|
|
||||||
```terminal
|
|
||||||
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
|
|
||||||
```
|
|
||||||
|
|
||||||
_For Macintoshes, either Intel or M1/M2/M3:_
|
|
||||||
|
|
||||||
```sh
|
|
||||||
pip install InvokeAI --use-pep517
|
|
||||||
```
|
|
||||||
|
|
||||||
6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
|
|
||||||
|
|
||||||
```terminal
|
|
||||||
invokeai-configure --root .
|
|
||||||
```
|
|
||||||
Don't miss the dot at the end!
|
|
||||||
|
|
||||||
7. Launch the web server (do it every time you run InvokeAI):
|
|
||||||
|
|
||||||
```terminal
|
|
||||||
invokeai-web
|
|
||||||
```
|
|
||||||
|
|
||||||
8. Point your browser to http://localhost:9090 to bring up the web interface.
|
|
||||||
|
|
||||||
9. Type `banana sushi` in the box on the top left and click `Invoke`.
|
|
||||||
|
|
||||||
Be sure to activate the virtual environment each time before re-launching InvokeAI,
|
|
||||||
using `source .venv/bin/activate` or `.venv\Scripts\activate`.
|
|
||||||
|
|
||||||
## Detailed Installation Instructions
|
|
||||||
|
|
||||||
This fork is supported across Linux, Windows and Macintosh. Linux
|
|
||||||
users can use either an Nvidia-based card (with CUDA support) or an
|
|
||||||
AMD card (using the ROCm driver). For full installation and upgrade
|
|
||||||
instructions, please see:
|
|
||||||
[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_SOURCE/)
|
|
||||||
|
|
||||||
<a name="migrating-to-3"></a>
|
|
||||||
### Migrating a v2.3 InvokeAI root directory
|
|
||||||
|
|
||||||
The InvokeAI root directory is where the InvokeAI startup file,
|
|
||||||
installed models, and generated images are stored. It is ordinarily
|
|
||||||
named `invokeai` and located in your home directory. The contents and
|
|
||||||
layout of this directory has changed between versions 2.3 and 3.0 and
|
|
||||||
cannot be used directly.
|
|
||||||
|
|
||||||
We currently recommend that you use the installer to create a new root
|
|
||||||
directory named differently from the 2.3 one, e.g. `invokeai-3` and
|
|
||||||
then use a migration script to copy your 2.3 models into the new
|
|
||||||
location. However, if you choose, you can upgrade this directory in
|
|
||||||
place. This section gives both recipes.
|
|
||||||
|
|
||||||
#### Creating a new root directory and migrating old models
|
|
||||||
|
|
||||||
This is the safer recipe because it leaves your old root directory in
|
|
||||||
place to fall back on.
|
|
||||||
|
|
||||||
1. Follow the instructions above to create and install InvokeAI in a
|
|
||||||
directory that has a different name from the 2.3 invokeai directory.
|
|
||||||
In this example, we will use "invokeai-3"
|
|
||||||
|
|
||||||
2. When you are prompted to select models to install, select a minimal
|
|
||||||
set of models, such as stable-diffusion-v1.5 only.
|
|
||||||
|
|
||||||
3. After installation is complete launch `invokeai.sh` (Linux/Mac) or
|
|
||||||
`invokeai.bat` and select option 8 "Open the developers console". This
|
|
||||||
will take you to the command line.
|
|
||||||
|
|
||||||
4. Issue the command `invokeai-migrate3 --from /path/to/v2.3-root --to
|
|
||||||
/path/to/invokeai-3-root`. Provide the correct `--from` and `--to`
|
|
||||||
paths for your v2.3 and v3.0 root directories respectively.
|
|
||||||
|
|
||||||
This will copy and convert your old models from 2.3 format to 3.0
|
|
||||||
format and create a new `models` directory in the 3.0 directory. The
|
|
||||||
old models directory (which contains the models selected at install
|
|
||||||
time) will be renamed `models.orig` and can be deleted once you have
|
|
||||||
confirmed that the migration was successful.
|
|
||||||
|
|
||||||
If you wish, you can pass the 2.3 root directory to both `--from` and
|
|
||||||
`--to` in order to update in place. Warning: this directory will no
|
|
||||||
longer be usable with InvokeAI 2.3.
|
|
||||||
|
|
||||||
#### Migrating in place
|
|
||||||
|
|
||||||
For the adventurous, you may do an in-place upgrade from 2.3 to 3.0
|
|
||||||
without touching the command line. ***This recipe does not work on
|
|
||||||
Windows platforms due to a bug in the Windows version of the 2.3
|
|
||||||
upgrade script.** See the next section for a Windows recipe.
|
|
||||||
|
|
||||||
##### For Mac and Linux Users:
|
|
||||||
|
|
||||||
1. Launch the InvokeAI launcher script in your current v2.3 root directory.
|
|
||||||
|
|
||||||
2. Select option [9] "Update InvokeAI" to bring up the updater dialog.
|
|
||||||
|
|
||||||
3. Select option [1] to upgrade to the latest release.
|
|
||||||
|
|
||||||
4. Once the upgrade is finished you will be returned to the launcher
|
|
||||||
menu. Select option [6] "Re-run the configure script to fix a broken
|
|
||||||
install or to complete a major upgrade".
|
|
||||||
|
|
||||||
This will run the configure script against the v2.3 directory and
|
|
||||||
update it to the 3.0 format. The following files will be replaced:
|
|
||||||
|
|
||||||
- The invokeai.init file, replaced by invokeai.yaml
|
|
||||||
- The models directory
|
|
||||||
- The configs/models.yaml model index
|
|
||||||
|
|
||||||
The original versions of these files will be saved with the suffix
|
|
||||||
".orig" appended to the end. Once you have confirmed that the upgrade
|
|
||||||
worked, you can safely remove these files. Alternatively you can
|
|
||||||
restore a working v2.3 directory by removing the new files and
|
|
||||||
restoring the ".orig" files' original names.
|
|
||||||
|
|
||||||
##### For Windows Users:
|
|
||||||
|
|
||||||
Windows Users can upgrade with the
|
|
||||||
|
|
||||||
1. Enter the 2.3 root directory you wish to upgrade
|
|
||||||
2. Launch `invoke.sh` or `invoke.bat`
|
|
||||||
3. Select the "Developer's console" option [8]
|
|
||||||
4. Type the following commands
|
|
||||||
|
|
||||||
```
|
|
||||||
pip install "invokeai @ https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v3.0.0" --use-pep517 --upgrade
|
|
||||||
invokeai-configure --root .
|
|
||||||
```
|
|
||||||
(Replace `v3.0.0` with the current release number if this document is out of date).
|
|
||||||
|
|
||||||
The first command will install and upgrade new software to run
|
|
||||||
InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
|
|
||||||
You may now launch the WebUI in the usual way, by selecting option [1]
|
|
||||||
from the launcher script
|
|
||||||
|
|
||||||
#### Migrating Images
|
|
||||||
|
|
||||||
The migration script will migrate your invokeai settings and models,
|
|
||||||
including textual inversion models, LoRAs and merges that you may have
|
|
||||||
installed previously. However it does **not** migrate the generated
|
|
||||||
images stored in your 2.3-format outputs directory. To do this, you
|
|
||||||
need to run an additional step:
|
|
||||||
|
|
||||||
1. From a working InvokeAI 3.0 root directory, start the launcher and
|
|
||||||
enter menu option [8] to open the "developer's console".
|
|
||||||
|
|
||||||
2. At the developer's console command line, type the command:
|
|
||||||
|
|
||||||
```bash
|
|
||||||
invokeai-import-images
|
|
||||||
```
|
|
||||||
|
|
||||||
3. This will lead you through the process of confirming the desired
|
|
||||||
source and destination for the imported images. The images will
|
|
||||||
appear in the gallery board of your choice, and contain the
|
|
||||||
original prompt, model name, and other parameters used to generate
|
|
||||||
the image.
|
|
||||||
|
|
||||||
(Many kudos to **techjedi** for contributing this script.)
|
|
||||||
|
|
||||||
## Hardware Requirements
|
|
||||||
|
|
||||||
InvokeAI is supported across Linux, Windows and macOS. Linux
|
|
||||||
users can use either an Nvidia-based card (with CUDA support) or an
|
|
||||||
AMD card (using the ROCm driver).
|
|
||||||
|
|
||||||
### System
|
|
||||||
|
|
||||||
You will need one of the following:
|
|
||||||
|
|
||||||
- An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB
|
|
||||||
of VRAM is highly recommended for rendering using the Stable
|
|
||||||
Diffusion XL models
|
|
||||||
- An Apple computer with an M1 chip.
|
|
||||||
- An AMD-based graphics card with 4GB or more VRAM memory (Linux
|
|
||||||
only), 6-8 GB for XL rendering.
|
|
||||||
|
|
||||||
We do not recommend the GTX 1650 or 1660 series video cards. They are
|
|
||||||
unable to run in half-precision mode and do not have sufficient VRAM
|
|
||||||
to render 512x512 images.
|
|
||||||
|
|
||||||
**Memory** - At least 12 GB Main Memory RAM.
|
|
||||||
|
|
||||||
**Disk** - At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
|
|
||||||
|
|
||||||
## Features
|
|
||||||
|
|
||||||
Feature documentation can be reviewed by navigating to [the InvokeAI Documentation page](https://invoke-ai.github.io/InvokeAI/features/)
|
|
||||||
|
|
||||||
### *Web Server & UI*
|
|
||||||
|
|
||||||
InvokeAI offers a locally hosted Web Server & React Frontend, with an industry leading user experience. The Web-based UI allows for simple and intuitive workflows, and is responsive for use on mobile devices and tablets accessing the web server.
|
|
||||||
|
|
||||||
### *Unified Canvas*
|
|
||||||
|
|
||||||
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
|
|
||||||
|
|
||||||
### *Workflows & Nodes*
|
|
||||||
|
|
||||||
InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
|
|
||||||
|
|
||||||
### *Board & Gallery Management*
|
|
||||||
|
|
||||||
Invoke AI provides an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow.
|
|
||||||
|
|
||||||
### Other features
|
|
||||||
|
|
||||||
- *Support for both ckpt and diffusers models*
|
|
||||||
- *SD 2.0, 2.1, XL support*
|
|
||||||
- *Upscaling Tools*
|
|
||||||
- *Embedding Manager & Support*
|
|
||||||
- *Model Manager & Support*
|
|
||||||
- *Workflow creation & management*
|
|
||||||
- *Node-Based Architecture*
|
|
||||||
|
|
||||||
|
|
||||||
### Latest Changes
|
|
||||||
|
|
||||||
For our latest changes, view our [Release
|
|
||||||
Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
|
|
||||||
[CHANGELOG](docs/CHANGELOG.md).
|
|
||||||
|
|
||||||
### Troubleshooting / FAQ
|
|
||||||
|
|
||||||
Please check out our **[FAQ](https://invoke-ai.github.io/InvokeAI/help/FAQ/)** to get solutions for common installation
|
|
||||||
problems and other issues. For more help, please join our [Discord][discord link]
|
|
||||||
|
|
||||||
## Contributing
|
|
||||||
|
|
||||||
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
|
|
||||||
cleanup, testing, or code reviews, is very much encouraged to do so.
|
|
||||||
|
|
||||||
Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
|
|
||||||
|
|
||||||
If you are unfamiliar with how
|
|
||||||
to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing:
|
|
||||||
[New Contributor Checklist](https://invoke-ai.github.io/InvokeAI/contributing/contribution_guides/newContributorChecklist/).
|
|
||||||
|
|
||||||
We hope you enjoy using our software as much as we enjoy creating it,
|
|
||||||
and we hope that some of those of you who are reading this will elect
|
|
||||||
to become part of our community.
|
|
||||||
|
|
||||||
Welcome to InvokeAI!
|
|
||||||
|
|
||||||
### Contributors
|
|
||||||
|
|
||||||
This fork is a combined effort of various people from across the world.
|
|
||||||
[Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for
|
|
||||||
their time, hard work and effort.
|
|
||||||
|
|
||||||
### Support
|
|
||||||
|
|
||||||
For support, please use this repository's GitHub Issues tracking service, or join the [Discord][discord link].
|
|
||||||
|
|
||||||
Original portions of the software are Copyright (c) 2023 by respective contributors.
|
|
||||||
|
|
||||||
|
|||||||
@ -51,13 +51,11 @@ The settings in this file will override the defaults. You only need
|
|||||||
to change this file if the default for a particular setting doesn't
|
to change this file if the default for a particular setting doesn't
|
||||||
work for you.
|
work for you.
|
||||||
|
|
||||||
|
You'll find an example file next to `invokeai.yaml` that shows the default values.
|
||||||
|
|
||||||
Some settings, like [Model Marketplace API Keys], require the YAML
|
Some settings, like [Model Marketplace API Keys], require the YAML
|
||||||
to be formatted correctly. Here is a [basic guide to YAML files].
|
to be formatted correctly. Here is a [basic guide to YAML files].
|
||||||
|
|
||||||
You can fix a broken `invokeai.yaml` by deleting it and running the
|
|
||||||
configuration script again -- option [6] in the launcher, "Re-run the
|
|
||||||
configure script".
|
|
||||||
|
|
||||||
#### Custom Config File Location
|
#### Custom Config File Location
|
||||||
|
|
||||||
You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.
|
You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.
|
||||||
|
|||||||
@ -35,6 +35,7 @@ from invokeai.app.invocations.model import ModelIdentifierField
|
|||||||
from invokeai.app.invocations.primitives import ImageOutput
|
from invokeai.app.invocations.primitives import ImageOutput
|
||||||
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
|
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
|
||||||
from invokeai.app.services.shared.invocation_context import InvocationContext
|
from invokeai.app.services.shared.invocation_context import InvocationContext
|
||||||
|
from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
|
||||||
from invokeai.backend.image_util.canny import get_canny_edges
|
from invokeai.backend.image_util.canny import get_canny_edges
|
||||||
from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
|
from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
|
||||||
from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
|
from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
|
||||||
@ -44,14 +45,6 @@ from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
|
|||||||
|
|
||||||
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
|
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
|
||||||
|
|
||||||
CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
|
|
||||||
CONTROLNET_RESIZE_VALUES = Literal[
|
|
||||||
"just_resize",
|
|
||||||
"crop_resize",
|
|
||||||
"fill_resize",
|
|
||||||
"just_resize_simple",
|
|
||||||
]
|
|
||||||
|
|
||||||
|
|
||||||
class ControlField(BaseModel):
|
class ControlField(BaseModel):
|
||||||
image: ImageField = Field(description="The control image")
|
image: ImageField = Field(description="The control image")
|
||||||
|
|||||||
@ -3,7 +3,6 @@ from typing import Any, Literal, Optional, Union
|
|||||||
from pydantic import BaseModel, ConfigDict, Field
|
from pydantic import BaseModel, ConfigDict, Field
|
||||||
|
|
||||||
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
|
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
|
||||||
from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
|
|
||||||
from invokeai.app.invocations.fields import (
|
from invokeai.app.invocations.fields import (
|
||||||
FieldDescriptions,
|
FieldDescriptions,
|
||||||
ImageField,
|
ImageField,
|
||||||
@ -14,6 +13,7 @@ from invokeai.app.invocations.fields import (
|
|||||||
)
|
)
|
||||||
from invokeai.app.invocations.model import ModelIdentifierField
|
from invokeai.app.invocations.model import ModelIdentifierField
|
||||||
from invokeai.app.services.shared.invocation_context import InvocationContext
|
from invokeai.app.services.shared.invocation_context import InvocationContext
|
||||||
|
from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
|
||||||
|
|
||||||
from ...version import __version__
|
from ...version import __version__
|
||||||
|
|
||||||
|
|||||||
@ -8,11 +8,11 @@ from invokeai.app.invocations.baseinvocation import (
|
|||||||
invocation,
|
invocation,
|
||||||
invocation_output,
|
invocation_output,
|
||||||
)
|
)
|
||||||
from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
|
|
||||||
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
|
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
|
||||||
from invokeai.app.invocations.model import ModelIdentifierField
|
from invokeai.app.invocations.model import ModelIdentifierField
|
||||||
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
|
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
|
||||||
from invokeai.app.services.shared.invocation_context import InvocationContext
|
from invokeai.app.services.shared.invocation_context import InvocationContext
|
||||||
|
from invokeai.app.util.controlnet_utils import CONTROLNET_RESIZE_VALUES
|
||||||
|
|
||||||
|
|
||||||
class T2IAdapterField(BaseModel):
|
class T2IAdapterField(BaseModel):
|
||||||
|
|||||||
@ -1,13 +1,21 @@
|
|||||||
from typing import Union
|
from typing import Any, Literal, Union
|
||||||
|
|
||||||
import cv2
|
import cv2
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import torch
|
import torch
|
||||||
from controlnet_aux.util import HWC3
|
|
||||||
from diffusers.utils import PIL_INTERPOLATION
|
|
||||||
from einops import rearrange
|
from einops import rearrange
|
||||||
from PIL import Image
|
from PIL import Image
|
||||||
|
|
||||||
|
from invokeai.backend.image_util.util import nms, normalize_image_channel_count
|
||||||
|
|
||||||
|
CONTROLNET_RESIZE_VALUES = Literal[
|
||||||
|
"just_resize",
|
||||||
|
"crop_resize",
|
||||||
|
"fill_resize",
|
||||||
|
"just_resize_simple",
|
||||||
|
]
|
||||||
|
CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
|
||||||
|
|
||||||
###################################################################
|
###################################################################
|
||||||
# Copy of scripts/lvminthin.py from Mikubill/sd-webui-controlnet
|
# Copy of scripts/lvminthin.py from Mikubill/sd-webui-controlnet
|
||||||
###################################################################
|
###################################################################
|
||||||
@ -68,17 +76,6 @@ def lvmin_thin(x, prunings=True):
|
|||||||
return y
|
return y
|
||||||
|
|
||||||
|
|
||||||
def nake_nms(x):
|
|
||||||
f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
|
|
||||||
f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
|
|
||||||
f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
|
|
||||||
f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
|
|
||||||
y = np.zeros_like(x)
|
|
||||||
for f in [f1, f2, f3, f4]:
|
|
||||||
np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
|
|
||||||
return y
|
|
||||||
|
|
||||||
|
|
||||||
################################################################################
|
################################################################################
|
||||||
# copied from Mikubill/sd-webui-controlnet external_code.py and modified for InvokeAI
|
# copied from Mikubill/sd-webui-controlnet external_code.py and modified for InvokeAI
|
||||||
################################################################################
|
################################################################################
|
||||||
@ -134,98 +131,122 @@ def pixel_perfect_resolution(
|
|||||||
return int(np.round(estimation))
|
return int(np.round(estimation))
|
||||||
|
|
||||||
|
|
||||||
|
def clone_contiguous(x: np.ndarray[Any, Any]) -> np.ndarray[Any, Any]:
|
||||||
|
"""Get a memory-contiguous clone of the given numpy array, as a safety measure and to improve computation efficiency."""
|
||||||
|
return np.ascontiguousarray(x).copy()
|
||||||
|
|
||||||
|
|
||||||
|
def np_img_to_torch(np_img: np.ndarray[Any, Any], device: torch.device) -> torch.Tensor:
|
||||||
|
"""Convert a numpy image to a PyTorch tensor. The image is normalized to 0-1, rearranged to BCHW format and sent to
|
||||||
|
the specified device."""
|
||||||
|
|
||||||
|
torch_img = torch.from_numpy(np_img)
|
||||||
|
normalized = torch_img.float() / 255.0
|
||||||
|
bchw = rearrange(normalized, "h w c -> 1 c h w")
|
||||||
|
on_device = bchw.to(device)
|
||||||
|
return on_device.clone()
|
||||||
|
|
||||||
|
|
||||||
|
def heuristic_resize(np_img: np.ndarray[Any, Any], size: tuple[int, int]) -> np.ndarray[Any, Any]:
|
||||||
|
"""Resizes an image using a heuristic to choose the best resizing strategy.
|
||||||
|
|
||||||
|
- If the image appears to be an edge map, special handling will be applied to ensure the edges are not distorted.
|
||||||
|
- Single-pixel edge maps use NMS and thinning to keep the edges as single-pixel lines.
|
||||||
|
- Low-color-count images are resized with nearest-neighbor to preserve color information (for e.g. segmentation maps).
|
||||||
|
- The alpha channel is handled separately to ensure it is resized correctly.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
np_img (np.ndarray): The input image.
|
||||||
|
size (tuple[int, int]): The target size for the image.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
np.ndarray: The resized image.
|
||||||
|
|
||||||
|
Adapted from https://github.com/Mikubill/sd-webui-controlnet.
|
||||||
|
"""
|
||||||
|
|
||||||
|
# Return early if the image is already at the requested size
|
||||||
|
if np_img.shape[0] == size[1] and np_img.shape[1] == size[0]:
|
||||||
|
return np_img
|
||||||
|
|
||||||
|
# If the image has an alpha channel, separate it for special handling later.
|
||||||
|
inpaint_mask = None
|
||||||
|
if np_img.ndim == 3 and np_img.shape[2] == 4:
|
||||||
|
inpaint_mask = np_img[:, :, 3]
|
||||||
|
np_img = np_img[:, :, 0:3]
|
||||||
|
|
||||||
|
new_size_is_smaller = (size[0] * size[1]) < (np_img.shape[0] * np_img.shape[1])
|
||||||
|
new_size_is_bigger = (size[0] * size[1]) > (np_img.shape[0] * np_img.shape[1])
|
||||||
|
unique_color_count = np.unique(np_img.reshape(-1, np_img.shape[2]), axis=0).shape[0]
|
||||||
|
is_one_pixel_edge = False
|
||||||
|
is_binary = False
|
||||||
|
|
||||||
|
if unique_color_count == 2:
|
||||||
|
# If the image has only two colors, it is likely binary. Check if the image has one-pixel edges.
|
||||||
|
is_binary = np.min(np_img) < 16 and np.max(np_img) > 240
|
||||||
|
if is_binary:
|
||||||
|
eroded = cv2.erode(np_img, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
|
||||||
|
dilated = cv2.dilate(eroded, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
|
||||||
|
one_pixel_edge_count = np.where(dilated < np_img)[0].shape[0]
|
||||||
|
all_edge_count = np.where(np_img > 127)[0].shape[0]
|
||||||
|
is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
|
||||||
|
|
||||||
|
if 2 < unique_color_count < 200:
|
||||||
|
# With a low color count, we assume this is a map where exact colors are important. Near-neighbor preserves
|
||||||
|
# the colors as needed.
|
||||||
|
interpolation = cv2.INTER_NEAREST
|
||||||
|
elif new_size_is_smaller:
|
||||||
|
# This works best for downscaling
|
||||||
|
interpolation = cv2.INTER_AREA
|
||||||
|
else:
|
||||||
|
# Fall back for other cases
|
||||||
|
interpolation = cv2.INTER_CUBIC # Must be CUBIC because we now use nms. NEVER CHANGE THIS
|
||||||
|
|
||||||
|
# This may be further transformed depending on the binary nature of the image.
|
||||||
|
resized = cv2.resize(np_img, size, interpolation=interpolation)
|
||||||
|
|
||||||
|
if inpaint_mask is not None:
|
||||||
|
# Resize the inpaint mask to match the resized image using the same interpolation method.
|
||||||
|
inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
|
||||||
|
|
||||||
|
# If the image is binary, we will perform some additional processing to ensure the edges are preserved.
|
||||||
|
if is_binary:
|
||||||
|
resized = np.mean(resized.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
|
||||||
|
if is_one_pixel_edge:
|
||||||
|
# Use NMS and thinning to keep the edges as single-pixel lines.
|
||||||
|
resized = nms(resized)
|
||||||
|
_, resized = cv2.threshold(resized, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||||
|
resized = lvmin_thin(resized, prunings=new_size_is_bigger)
|
||||||
|
else:
|
||||||
|
_, resized = cv2.threshold(resized, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||||
|
resized = np.stack([resized] * 3, axis=2)
|
||||||
|
|
||||||
|
# Restore the alpha channel if it was present.
|
||||||
|
if inpaint_mask is not None:
|
||||||
|
inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
|
||||||
|
inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
|
||||||
|
resized = np.concatenate([resized, inpaint_mask], axis=2)
|
||||||
|
|
||||||
|
return resized
|
||||||
|
|
||||||
|
|
||||||
###########################################################################
|
###########################################################################
|
||||||
# Copied from detectmap_proc method in scripts/detectmap_proc.py in Mikubill/sd-webui-controlnet
|
# Copied from detectmap_proc method in scripts/detectmap_proc.py in Mikubill/sd-webui-controlnet
|
||||||
# modified for InvokeAI
|
# modified for InvokeAI
|
||||||
###########################################################################
|
###########################################################################
|
||||||
# def detectmap_proc(detected_map, module, resize_mode, h, w):
|
def np_img_resize(
|
||||||
def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device: torch.device = torch.device("cpu")):
|
np_img: np.ndarray,
|
||||||
# if 'inpaint' in module:
|
resize_mode: CONTROLNET_RESIZE_VALUES,
|
||||||
# np_img = np_img.astype(np.float32)
|
h: int,
|
||||||
# else:
|
w: int,
|
||||||
# np_img = HWC3(np_img)
|
device: torch.device = torch.device("cpu"),
|
||||||
np_img = HWC3(np_img)
|
) -> tuple[torch.Tensor, np.ndarray[Any, Any]]:
|
||||||
|
np_img = normalize_image_channel_count(np_img)
|
||||||
|
|
||||||
def safe_numpy(x):
|
|
||||||
# A very safe method to make sure that Apple/Mac works
|
|
||||||
y = x
|
|
||||||
|
|
||||||
# below is very boring but do not change these. If you change these Apple or Mac may fail.
|
|
||||||
y = y.copy()
|
|
||||||
y = np.ascontiguousarray(y)
|
|
||||||
y = y.copy()
|
|
||||||
return y
|
|
||||||
|
|
||||||
def get_pytorch_control(x):
|
|
||||||
# A very safe method to make sure that Apple/Mac works
|
|
||||||
y = x
|
|
||||||
|
|
||||||
# below is very boring but do not change these. If you change these Apple or Mac may fail.
|
|
||||||
y = torch.from_numpy(y)
|
|
||||||
y = y.float() / 255.0
|
|
||||||
y = rearrange(y, "h w c -> 1 c h w")
|
|
||||||
y = y.clone()
|
|
||||||
# y = y.to(devices.get_device_for("controlnet"))
|
|
||||||
y = y.to(device)
|
|
||||||
y = y.clone()
|
|
||||||
return y
|
|
||||||
|
|
||||||
def high_quality_resize(x: np.ndarray, size):
|
|
||||||
# Written by lvmin
|
|
||||||
# Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
|
|
||||||
inpaint_mask = None
|
|
||||||
if x.ndim == 3 and x.shape[2] == 4:
|
|
||||||
inpaint_mask = x[:, :, 3]
|
|
||||||
x = x[:, :, 0:3]
|
|
||||||
|
|
||||||
new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1])
|
|
||||||
new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1])
|
|
||||||
unique_color_count = np.unique(x.reshape(-1, x.shape[2]), axis=0).shape[0]
|
|
||||||
is_one_pixel_edge = False
|
|
||||||
is_binary = False
|
|
||||||
if unique_color_count == 2:
|
|
||||||
is_binary = np.min(x) < 16 and np.max(x) > 240
|
|
||||||
if is_binary:
|
|
||||||
xc = x
|
|
||||||
xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
|
|
||||||
xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
|
|
||||||
one_pixel_edge_count = np.where(xc < x)[0].shape[0]
|
|
||||||
all_edge_count = np.where(x > 127)[0].shape[0]
|
|
||||||
is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
|
|
||||||
|
|
||||||
if 2 < unique_color_count < 200:
|
|
||||||
interpolation = cv2.INTER_NEAREST
|
|
||||||
elif new_size_is_smaller:
|
|
||||||
interpolation = cv2.INTER_AREA
|
|
||||||
else:
|
|
||||||
interpolation = cv2.INTER_CUBIC # Must be CUBIC because we now use nms. NEVER CHANGE THIS
|
|
||||||
|
|
||||||
y = cv2.resize(x, size, interpolation=interpolation)
|
|
||||||
if inpaint_mask is not None:
|
|
||||||
inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
|
|
||||||
|
|
||||||
if is_binary:
|
|
||||||
y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
|
|
||||||
if is_one_pixel_edge:
|
|
||||||
y = nake_nms(y)
|
|
||||||
_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
|
||||||
y = lvmin_thin(y, prunings=new_size_is_bigger)
|
|
||||||
else:
|
|
||||||
_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
|
||||||
y = np.stack([y] * 3, axis=2)
|
|
||||||
|
|
||||||
if inpaint_mask is not None:
|
|
||||||
inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
|
|
||||||
inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
|
|
||||||
y = np.concatenate([y, inpaint_mask], axis=2)
|
|
||||||
|
|
||||||
return y
|
|
||||||
|
|
||||||
# if resize_mode == external_code.ResizeMode.RESIZE:
|
|
||||||
if resize_mode == "just_resize": # RESIZE
|
if resize_mode == "just_resize": # RESIZE
|
||||||
np_img = high_quality_resize(np_img, (w, h))
|
np_img = heuristic_resize(np_img, (w, h))
|
||||||
np_img = safe_numpy(np_img)
|
np_img = clone_contiguous(np_img)
|
||||||
return get_pytorch_control(np_img), np_img
|
return np_img_to_torch(np_img, device), np_img
|
||||||
|
|
||||||
old_h, old_w, _ = np_img.shape
|
old_h, old_w, _ = np_img.shape
|
||||||
old_w = float(old_w)
|
old_w = float(old_w)
|
||||||
@ -236,7 +257,6 @@ def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device:
|
|||||||
def safeint(x: Union[int, float]) -> int:
|
def safeint(x: Union[int, float]) -> int:
|
||||||
return int(np.round(x))
|
return int(np.round(x))
|
||||||
|
|
||||||
# if resize_mode == external_code.ResizeMode.OUTER_FIT:
|
|
||||||
if resize_mode == "fill_resize": # OUTER_FIT
|
if resize_mode == "fill_resize": # OUTER_FIT
|
||||||
k = min(k0, k1)
|
k = min(k0, k1)
|
||||||
borders = np.concatenate([np_img[0, :, :], np_img[-1, :, :], np_img[:, 0, :], np_img[:, -1, :]], axis=0)
|
borders = np.concatenate([np_img[0, :, :], np_img[-1, :, :], np_img[:, 0, :], np_img[:, -1, :]], axis=0)
|
||||||
@ -245,23 +265,23 @@ def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device:
|
|||||||
# Inpaint hijack
|
# Inpaint hijack
|
||||||
high_quality_border_color[3] = 255
|
high_quality_border_color[3] = 255
|
||||||
high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
|
high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
|
||||||
np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
|
np_img = heuristic_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
|
||||||
new_h, new_w, _ = np_img.shape
|
new_h, new_w, _ = np_img.shape
|
||||||
pad_h = max(0, (h - new_h) // 2)
|
pad_h = max(0, (h - new_h) // 2)
|
||||||
pad_w = max(0, (w - new_w) // 2)
|
pad_w = max(0, (w - new_w) // 2)
|
||||||
high_quality_background[pad_h : pad_h + new_h, pad_w : pad_w + new_w] = np_img
|
high_quality_background[pad_h : pad_h + new_h, pad_w : pad_w + new_w] = np_img
|
||||||
np_img = high_quality_background
|
np_img = high_quality_background
|
||||||
np_img = safe_numpy(np_img)
|
np_img = clone_contiguous(np_img)
|
||||||
return get_pytorch_control(np_img), np_img
|
return np_img_to_torch(np_img, device), np_img
|
||||||
else: # resize_mode == "crop_resize" (INNER_FIT)
|
else: # resize_mode == "crop_resize" (INNER_FIT)
|
||||||
k = max(k0, k1)
|
k = max(k0, k1)
|
||||||
np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
|
np_img = heuristic_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
|
||||||
new_h, new_w, _ = np_img.shape
|
new_h, new_w, _ = np_img.shape
|
||||||
pad_h = max(0, (new_h - h) // 2)
|
pad_h = max(0, (new_h - h) // 2)
|
||||||
pad_w = max(0, (new_w - w) // 2)
|
pad_w = max(0, (new_w - w) // 2)
|
||||||
np_img = np_img[pad_h : pad_h + h, pad_w : pad_w + w]
|
np_img = np_img[pad_h : pad_h + h, pad_w : pad_w + w]
|
||||||
np_img = safe_numpy(np_img)
|
np_img = clone_contiguous(np_img)
|
||||||
return get_pytorch_control(np_img), np_img
|
return np_img_to_torch(np_img, device), np_img
|
||||||
|
|
||||||
|
|
||||||
def prepare_control_image(
|
def prepare_control_image(
|
||||||
@ -269,12 +289,12 @@ def prepare_control_image(
|
|||||||
width: int,
|
width: int,
|
||||||
height: int,
|
height: int,
|
||||||
num_channels: int = 3,
|
num_channels: int = 3,
|
||||||
device="cuda",
|
device: str = "cuda",
|
||||||
dtype=torch.float16,
|
dtype: torch.dtype = torch.float16,
|
||||||
do_classifier_free_guidance=True,
|
control_mode: CONTROLNET_MODE_VALUES = "balanced",
|
||||||
control_mode="balanced",
|
resize_mode: CONTROLNET_RESIZE_VALUES = "just_resize_simple",
|
||||||
resize_mode="just_resize_simple",
|
do_classifier_free_guidance: bool = True,
|
||||||
):
|
) -> torch.Tensor:
|
||||||
"""Pre-process images for ControlNets or T2I-Adapters.
|
"""Pre-process images for ControlNets or T2I-Adapters.
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
@ -292,26 +312,15 @@ def prepare_control_image(
|
|||||||
resize_mode (str, optional): Defaults to "just_resize_simple".
|
resize_mode (str, optional): Defaults to "just_resize_simple".
|
||||||
|
|
||||||
Raises:
|
Raises:
|
||||||
NotImplementedError: If resize_mode == "crop_resize_simple".
|
|
||||||
NotImplementedError: If resize_mode == "fill_resize_simple".
|
|
||||||
ValueError: If `resize_mode` is not recognized.
|
ValueError: If `resize_mode` is not recognized.
|
||||||
ValueError: If `num_channels` is out of range.
|
ValueError: If `num_channels` is out of range.
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
torch.Tensor: The pre-processed input tensor.
|
torch.Tensor: The pre-processed input tensor.
|
||||||
"""
|
"""
|
||||||
if (
|
|
||||||
resize_mode == "just_resize_simple"
|
|
||||||
or resize_mode == "crop_resize_simple"
|
|
||||||
or resize_mode == "fill_resize_simple"
|
|
||||||
):
|
|
||||||
image = image.convert("RGB")
|
|
||||||
if resize_mode == "just_resize_simple":
|
if resize_mode == "just_resize_simple":
|
||||||
image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
|
image = image.convert("RGB")
|
||||||
elif resize_mode == "crop_resize_simple":
|
image = image.resize((width, height), resample=Image.LANCZOS)
|
||||||
raise NotImplementedError(f"prepare_control_image is not implemented for resize_mode='{resize_mode}'.")
|
|
||||||
elif resize_mode == "fill_resize_simple":
|
|
||||||
raise NotImplementedError(f"prepare_control_image is not implemented for resize_mode='{resize_mode}'.")
|
|
||||||
nimage = np.array(image)
|
nimage = np.array(image)
|
||||||
nimage = nimage[None, :]
|
nimage = nimage[None, :]
|
||||||
nimage = np.concatenate([nimage], axis=0)
|
nimage = np.concatenate([nimage], axis=0)
|
||||||
@ -328,8 +337,7 @@ def prepare_control_image(
|
|||||||
resize_mode=resize_mode,
|
resize_mode=resize_mode,
|
||||||
h=height,
|
h=height,
|
||||||
w=width,
|
w=width,
|
||||||
# device=torch.device('cpu')
|
device=torch.device(device),
|
||||||
device=device,
|
|
||||||
)
|
)
|
||||||
else:
|
else:
|
||||||
raise ValueError(f"Unsupported resize_mode: '{resize_mode}'.")
|
raise ValueError(f"Unsupported resize_mode: '{resize_mode}'.")
|
||||||
|
|||||||
@ -8,7 +8,7 @@ from huggingface_hub import hf_hub_download
|
|||||||
from PIL import Image
|
from PIL import Image
|
||||||
|
|
||||||
from invokeai.backend.image_util.util import (
|
from invokeai.backend.image_util.util import (
|
||||||
non_maximum_suppression,
|
nms,
|
||||||
normalize_image_channel_count,
|
normalize_image_channel_count,
|
||||||
np_to_pil,
|
np_to_pil,
|
||||||
pil_to_np,
|
pil_to_np,
|
||||||
@ -134,7 +134,7 @@ class HEDProcessor:
|
|||||||
detected_map = cv2.resize(detected_map, (width, height), interpolation=cv2.INTER_LINEAR)
|
detected_map = cv2.resize(detected_map, (width, height), interpolation=cv2.INTER_LINEAR)
|
||||||
|
|
||||||
if scribble:
|
if scribble:
|
||||||
detected_map = non_maximum_suppression(detected_map, 127, 3.0)
|
detected_map = nms(detected_map, 127, 3.0)
|
||||||
detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
|
detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
|
||||||
detected_map[detected_map > 4] = 255
|
detected_map[detected_map > 4] = 255
|
||||||
detected_map[detected_map < 255] = 0
|
detected_map[detected_map < 255] = 0
|
||||||
|
|||||||
@ -1,4 +1,5 @@
|
|||||||
from math import ceil, floor, sqrt
|
from math import ceil, floor, sqrt
|
||||||
|
from typing import Optional
|
||||||
|
|
||||||
import cv2
|
import cv2
|
||||||
import numpy as np
|
import numpy as np
|
||||||
@ -153,10 +154,13 @@ def resize_image_to_resolution(input_image: np.ndarray, resolution: int) -> np.n
|
|||||||
return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_AREA)
|
return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_AREA)
|
||||||
|
|
||||||
|
|
||||||
def non_maximum_suppression(image: np.ndarray, threshold: int, sigma: float):
|
def nms(np_img: np.ndarray, threshold: Optional[int] = None, sigma: Optional[float] = None) -> np.ndarray:
|
||||||
"""
|
"""
|
||||||
Apply non-maximum suppression to an image.
|
Apply non-maximum suppression to an image.
|
||||||
|
|
||||||
|
If both threshold and sigma are provided, the image will blurred before the suppression and thresholded afterwards,
|
||||||
|
resulting in a binary output image.
|
||||||
|
|
||||||
This function is adapted from https://github.com/lllyasviel/ControlNet.
|
This function is adapted from https://github.com/lllyasviel/ControlNet.
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
@ -166,23 +170,36 @@ def non_maximum_suppression(image: np.ndarray, threshold: int, sigma: float):
|
|||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
The image after non-maximum suppression.
|
The image after non-maximum suppression.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If only one of threshold and sigma provided.
|
||||||
"""
|
"""
|
||||||
|
|
||||||
image = cv2.GaussianBlur(image.astype(np.float32), (0, 0), sigma)
|
# Raise a value error if only one of threshold and sigma is provided
|
||||||
|
if (threshold is None) != (sigma is None):
|
||||||
|
raise ValueError("Both threshold and sigma must be provided if one is provided.")
|
||||||
|
|
||||||
|
if sigma is not None and threshold is not None:
|
||||||
|
# Blurring the image can help to thin out features
|
||||||
|
np_img = cv2.GaussianBlur(np_img.astype(np.float32), (0, 0), sigma)
|
||||||
|
|
||||||
filter_1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
|
filter_1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
|
||||||
filter_2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
|
filter_2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
|
||||||
filter_3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
|
filter_3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
|
||||||
filter_4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
|
filter_4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
|
||||||
|
|
||||||
y = np.zeros_like(image)
|
nms_img = np.zeros_like(np_img)
|
||||||
|
|
||||||
for f in [filter_1, filter_2, filter_3, filter_4]:
|
for f in [filter_1, filter_2, filter_3, filter_4]:
|
||||||
np.putmask(y, cv2.dilate(image, kernel=f) == image, image)
|
np.putmask(nms_img, cv2.dilate(np_img, kernel=f) == np_img, np_img)
|
||||||
|
|
||||||
z = np.zeros_like(y, dtype=np.uint8)
|
if sigma is not None and threshold is not None:
|
||||||
z[y > threshold] = 255
|
# We blurred - now threshold to get a binary image
|
||||||
return z
|
thresholded = np.zeros_like(nms_img, dtype=np.uint8)
|
||||||
|
thresholded[nms_img > threshold] = 255
|
||||||
|
return thresholded
|
||||||
|
|
||||||
|
return nms_img
|
||||||
|
|
||||||
|
|
||||||
def safe_step(x: np.ndarray, step: int = 2) -> np.ndarray:
|
def safe_step(x: np.ndarray, step: int = 2) -> np.ndarray:
|
||||||
|
|||||||
@ -51,6 +51,7 @@ LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[Sched
|
|||||||
},
|
},
|
||||||
BaseModelType.StableDiffusionXL: {
|
BaseModelType.StableDiffusionXL: {
|
||||||
ModelVariantType.Normal: "sd_xl_base.yaml",
|
ModelVariantType.Normal: "sd_xl_base.yaml",
|
||||||
|
ModelVariantType.Inpaint: "sd_xl_inpaint.yaml",
|
||||||
},
|
},
|
||||||
BaseModelType.StableDiffusionXLRefiner: {
|
BaseModelType.StableDiffusionXLRefiner: {
|
||||||
ModelVariantType.Normal: "sd_xl_refiner.yaml",
|
ModelVariantType.Normal: "sd_xl_refiner.yaml",
|
||||||
|
|||||||
98
invokeai/configs/stable-diffusion/sd_xl_inpaint.yaml
Normal file
98
invokeai/configs/stable-diffusion/sd_xl_inpaint.yaml
Normal file
@ -0,0 +1,98 @@
|
|||||||
|
model:
|
||||||
|
target: sgm.models.diffusion.DiffusionEngine
|
||||||
|
params:
|
||||||
|
scale_factor: 0.13025
|
||||||
|
disable_first_stage_autocast: True
|
||||||
|
|
||||||
|
denoiser_config:
|
||||||
|
target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
|
||||||
|
params:
|
||||||
|
num_idx: 1000
|
||||||
|
|
||||||
|
weighting_config:
|
||||||
|
target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
|
||||||
|
scaling_config:
|
||||||
|
target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
|
||||||
|
discretization_config:
|
||||||
|
target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
|
||||||
|
|
||||||
|
network_config:
|
||||||
|
target: sgm.modules.diffusionmodules.openaimodel.UNetModel
|
||||||
|
params:
|
||||||
|
adm_in_channels: 2816
|
||||||
|
num_classes: sequential
|
||||||
|
use_checkpoint: True
|
||||||
|
in_channels: 9
|
||||||
|
out_channels: 4
|
||||||
|
model_channels: 320
|
||||||
|
attention_resolutions: [4, 2]
|
||||||
|
num_res_blocks: 2
|
||||||
|
channel_mult: [1, 2, 4]
|
||||||
|
num_head_channels: 64
|
||||||
|
use_spatial_transformer: True
|
||||||
|
use_linear_in_transformer: True
|
||||||
|
transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
|
||||||
|
context_dim: 2048
|
||||||
|
spatial_transformer_attn_type: softmax-xformers
|
||||||
|
legacy: False
|
||||||
|
|
||||||
|
conditioner_config:
|
||||||
|
target: sgm.modules.GeneralConditioner
|
||||||
|
params:
|
||||||
|
emb_models:
|
||||||
|
# crossattn cond
|
||||||
|
- is_trainable: False
|
||||||
|
input_key: txt
|
||||||
|
target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
|
||||||
|
params:
|
||||||
|
layer: hidden
|
||||||
|
layer_idx: 11
|
||||||
|
# crossattn and vector cond
|
||||||
|
- is_trainable: False
|
||||||
|
input_key: txt
|
||||||
|
target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
|
||||||
|
params:
|
||||||
|
arch: ViT-bigG-14
|
||||||
|
version: laion2b_s39b_b160k
|
||||||
|
freeze: True
|
||||||
|
layer: penultimate
|
||||||
|
always_return_pooled: True
|
||||||
|
legacy: False
|
||||||
|
# vector cond
|
||||||
|
- is_trainable: False
|
||||||
|
input_key: original_size_as_tuple
|
||||||
|
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
|
||||||
|
params:
|
||||||
|
outdim: 256 # multiplied by two
|
||||||
|
# vector cond
|
||||||
|
- is_trainable: False
|
||||||
|
input_key: crop_coords_top_left
|
||||||
|
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
|
||||||
|
params:
|
||||||
|
outdim: 256 # multiplied by two
|
||||||
|
# vector cond
|
||||||
|
- is_trainable: False
|
||||||
|
input_key: target_size_as_tuple
|
||||||
|
target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
|
||||||
|
params:
|
||||||
|
outdim: 256 # multiplied by two
|
||||||
|
|
||||||
|
first_stage_config:
|
||||||
|
target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
|
||||||
|
params:
|
||||||
|
embed_dim: 4
|
||||||
|
monitor: val/rec_loss
|
||||||
|
ddconfig:
|
||||||
|
attn_type: vanilla-xformers
|
||||||
|
double_z: true
|
||||||
|
z_channels: 4
|
||||||
|
resolution: 256
|
||||||
|
in_channels: 3
|
||||||
|
out_ch: 3
|
||||||
|
ch: 128
|
||||||
|
ch_mult: [1, 2, 4, 4]
|
||||||
|
num_res_blocks: 2
|
||||||
|
attn_resolutions: []
|
||||||
|
dropout: 0.0
|
||||||
|
lossconfig:
|
||||||
|
target: torch.nn.Identity
|
||||||
@ -1 +1 @@
|
|||||||
__version__ = "4.2.0a2"
|
__version__ = "4.2.0a3"
|
||||||
|
|||||||
@ -3,6 +3,7 @@ import pytest
|
|||||||
from PIL import Image
|
from PIL import Image
|
||||||
|
|
||||||
from invokeai.app.util.controlnet_utils import prepare_control_image
|
from invokeai.app.util.controlnet_utils import prepare_control_image
|
||||||
|
from invokeai.backend.image_util.util import nms
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.parametrize("num_channels", [1, 2, 3])
|
@pytest.mark.parametrize("num_channels", [1, 2, 3])
|
||||||
@ -40,3 +41,10 @@ def test_prepare_control_image_num_channels_too_large(num_channels):
|
|||||||
device="cpu",
|
device="cpu",
|
||||||
do_classifier_free_guidance=False,
|
do_classifier_free_guidance=False,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize("threshold,sigma", [(None, 1.0), (1, None)])
|
||||||
|
def test_nms_invalid_options(threshold: None | int, sigma: None | float):
|
||||||
|
"""Test that an exception is raised in nms(...) if only one of the `threshold` or `sigma` parameters are provided."""
|
||||||
|
with pytest.raises(ValueError):
|
||||||
|
nms(np.zeros((256, 256, 3), dtype=np.uint8), threshold, sigma)
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user