mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into onnx-testing
This commit is contained in:
Brandon Rising
commit
ee7b36cea5
4
.github/CODEOWNERS
vendored
4
.github/CODEOWNERS
vendored
@ -6,7 +6,7 @@
|
||||
/mkdocs.yml @lstein @blessedcoolant
|
||||
|
||||
# nodes
|
||||
/invokeai/app/ @Kyle0654 @blessedcoolant
|
||||
/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising
|
||||
|
||||
# installation and configuration
|
||||
/pyproject.toml @lstein @blessedcoolant
|
||||
@ -22,7 +22,7 @@
|
||||
/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp
|
||||
|
||||
# generation, model management, postprocessing
|
||||
/invokeai/backend @damian0815 @lstein @blessedcoolant @jpphoto @gregghelt2 @StAlKeR7779
|
||||
/invokeai/backend @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising
|
||||
|
||||
# front ends
|
||||
/invokeai/frontend/CLI @lstein
|
||||
|
||||
2
.github/workflows/mkdocs-material.yml
vendored
2
.github/workflows/mkdocs-material.yml
vendored
@ -43,7 +43,7 @@ jobs:
|
||||
--verbose
|
||||
|
||||
- name: deploy to gh-pages
|
||||
if: ${{ github.ref == 'refs/heads/v2.3' }}
|
||||
if: ${{ github.ref == 'refs/heads/main' }}
|
||||
run: |
|
||||
python -m \
|
||||
mkdocs gh-deploy \
|
||||
|
||||
19
README.md
19
README.md
@ -132,8 +132,10 @@ and go to http://localhost:9090.
|
||||
|
||||
### Command-Line Installation (for developers and users familiar with Terminals)
|
||||
|
||||
You must have Python 3.9 or 3.10 installed on your machine. Earlier or later versions are
|
||||
not supported.
|
||||
You must have Python 3.9 or 3.10 installed on your machine. Earlier or
|
||||
later versions are not supported.
|
||||
Node.js also needs to be installed along with yarn (can be installed with
|
||||
the command `npm install -g yarn` 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:
|
||||
@ -197,11 +199,18 @@ not supported.
|
||||
7. Launch the web server (do it every time you run InvokeAI):
|
||||
|
||||
```terminal
|
||||
invokeai --web
|
||||
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`.
|
||||
8. Build Node.js assets
|
||||
|
||||
```terminal
|
||||
cd invokeai/frontend/web/
|
||||
yarn vite build
|
||||
```
|
||||
|
||||
9. Point your browser to http://localhost:9090 to bring up the web interface.
|
||||
10. 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`.
|
||||
|
||||
@ -617,8 +617,6 @@ sections describe what's new for InvokeAI.
|
||||
- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
|
||||
backward compatibility.
|
||||
- Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
|
||||
- Support for [inpainting](deprecated/INPAINTING.md) and
|
||||
[outpainting](features/OUTPAINTING.md)
|
||||
- img2img runs on all k\* samplers
|
||||
- Support for
|
||||
[negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 983 KiB After Width: | Height: | Size: 1.1 MiB |
@ -81,3 +81,193 @@ pytest --cov; open ./coverage/html/index.html
|
||||
<!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
|
||||
|
||||
--8<-- "invokeai/frontend/web/README.md"
|
||||
|
||||
## Developing InvokeAI in VSCode
|
||||
|
||||
VSCode offers some nice tools:
|
||||
|
||||
- python debugger
|
||||
- automatic `venv` activation
|
||||
- remote dev (e.g. run InvokeAI on a beefy linux desktop while you type in
|
||||
comfort on your macbook)
|
||||
|
||||
### Setup
|
||||
|
||||
You'll need the
|
||||
[Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
|
||||
and
|
||||
[Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
|
||||
extensions installed first.
|
||||
|
||||
It's also really handy to install the `Jupyter` extensions:
|
||||
|
||||
- [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter)
|
||||
- [Jupyter Cell Tags](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-cell-tags)
|
||||
- [Jupyter Notebook Renderers](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter-renderers)
|
||||
- [Jupyter Slide Show](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-slideshow)
|
||||
|
||||
#### InvokeAI workspace
|
||||
|
||||
Creating a VSCode workspace for working on InvokeAI is highly recommended. It
|
||||
can hold InvokeAI-specific settings and configs.
|
||||
|
||||
To make a workspace:
|
||||
|
||||
- Open the InvokeAI repo dir in VSCode
|
||||
- `File` > `Save Workspace As` > save it _outside_ the repo
|
||||
|
||||
#### Default python interpreter (i.e. automatic virtual environment activation)
|
||||
|
||||
- Use command palette to run command
|
||||
`Preferences: Open Workspace Settings (JSON)`
|
||||
- Add `python.defaultInterpreterPath` to `settings`, pointing to your `venv`'s
|
||||
python
|
||||
|
||||
Should look something like this:
|
||||
|
||||
```jsonc
|
||||
{
|
||||
// I like to have all InvokeAI-related folders in my workspace
|
||||
"folders": [
|
||||
{
|
||||
// repo root
|
||||
"path": "InvokeAI"
|
||||
},
|
||||
{
|
||||
// InvokeAI root dir, where `invokeai.yaml` lives
|
||||
"path": "/path/to/invokeai_root"
|
||||
}
|
||||
],
|
||||
"settings": {
|
||||
// Where your InvokeAI `venv`'s python executable lives
|
||||
"python.defaultInterpreterPath": "/path/to/invokeai_root/.venv/bin/python"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Now when you open the VSCode integrated terminal, or do anything that needs to
|
||||
run python, it will automatically be in your InvokeAI virtual environment.
|
||||
|
||||
Bonus: When you create a Jupyter notebook, when you run it, you'll be prompted
|
||||
for the python interpreter to run in. This will default to your `venv` python,
|
||||
and so you'll have access to the same python environment as the InvokeAI app.
|
||||
|
||||
This is _super_ handy.
|
||||
|
||||
#### Debugging configs with `launch.json`
|
||||
|
||||
Debugging configs are managed in a `launch.json` file. Like most VSCode configs,
|
||||
these can be scoped to a workspace or folder.
|
||||
|
||||
Follow the [official guide](https://code.visualstudio.com/docs/python/debugging)
|
||||
to set up your `launch.json` and try it out.
|
||||
|
||||
Now we can create the InvokeAI debugging configs:
|
||||
|
||||
```jsonc
|
||||
{
|
||||
// Use IntelliSense to learn about possible attributes.
|
||||
// Hover to view descriptions of existing attributes.
|
||||
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
{
|
||||
// Run the InvokeAI backend & serve the pre-built UI
|
||||
"name": "InvokeAI Web",
|
||||
"type": "python",
|
||||
"request": "launch",
|
||||
"program": "scripts/invokeai-web.py",
|
||||
"args": [
|
||||
// Your InvokeAI root dir (where `invokeai.yaml` lives)
|
||||
"--root",
|
||||
"/path/to/invokeai_root",
|
||||
// Access the app from anywhere on your local network
|
||||
"--host",
|
||||
"0.0.0.0"
|
||||
],
|
||||
"justMyCode": true
|
||||
},
|
||||
{
|
||||
// Run the nodes-based CLI
|
||||
"name": "InvokeAI CLI",
|
||||
"type": "python",
|
||||
"request": "launch",
|
||||
"program": "scripts/invokeai-cli.py",
|
||||
"justMyCode": true
|
||||
},
|
||||
{
|
||||
// Run tests
|
||||
"name": "InvokeAI Test",
|
||||
"type": "python",
|
||||
"request": "launch",
|
||||
"module": "pytest",
|
||||
"args": ["--capture=no"],
|
||||
"justMyCode": true
|
||||
},
|
||||
{
|
||||
// Run a single test
|
||||
"name": "InvokeAI Single Test",
|
||||
"type": "python",
|
||||
"request": "launch",
|
||||
"module": "pytest",
|
||||
"args": [
|
||||
// Change this to point to the specific test you are working on
|
||||
"tests/nodes/test_invoker.py"
|
||||
],
|
||||
"justMyCode": true
|
||||
},
|
||||
{
|
||||
// This is the default, useful to just run a single file
|
||||
"name": "Python: File",
|
||||
"type": "python",
|
||||
"request": "launch",
|
||||
"program": "${file}",
|
||||
"justMyCode": true
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
You'll see these configs in the debugging configs drop down. Running them will
|
||||
start InvokeAI with attached debugger, in the correct environment, and work just
|
||||
like the normal app.
|
||||
|
||||
Enjoy debugging InvokeAI with ease (not that we have any bugs of course).
|
||||
|
||||
#### Remote dev
|
||||
|
||||
This is very easy to set up and provides the same very smooth experience as
|
||||
local development. Environments and debugging, as set up above, just work,
|
||||
though you'd need to recreate the workspace and debugging configs on the remote.
|
||||
|
||||
Consult the
|
||||
[official guide](https://code.visualstudio.com/docs/remote/remote-overview) to
|
||||
get it set up.
|
||||
|
||||
Suggest using VSCode's included settings sync so that your remote dev host has
|
||||
all the same app settings and extensions automagically.
|
||||
|
||||
##### One remote dev gotcha
|
||||
|
||||
I've found the automatic port forwarding to be very flakey. You can disable it
|
||||
in `Preferences: Open Remote Settings (ssh: hostname)`. Search for
|
||||
`remote.autoForwardPorts` and untick the box.
|
||||
|
||||
To forward ports very reliably, use SSH on the remote dev client (e.g. your
|
||||
macbook). Here's how to forward both backend API port (`9090`) and the frontend
|
||||
live dev server port (`5173`):
|
||||
|
||||
```bash
|
||||
ssh \
|
||||
-L 9090:localhost:9090 \
|
||||
-L 5173:localhost:5173 \
|
||||
user@remote-dev-host
|
||||
```
|
||||
|
||||
The forwarding stops when you close the terminal window, so suggest to do this
|
||||
_outside_ the VSCode integrated terminal in case you need to restart VSCode for
|
||||
an extension update or something
|
||||
|
||||
Now, on your remote dev client, you can open `localhost:9090` and access the UI,
|
||||
now served from the remote dev host, just the same as if it was running on the
|
||||
client.
|
||||
|
||||
287
docs/features/CONFIGURATION.md
Normal file
287
docs/features/CONFIGURATION.md
Normal file
@ -0,0 +1,287 @@
|
||||
---
|
||||
title: Configuration
|
||||
---
|
||||
|
||||
# :material-tune-variant: InvokeAI Configuration
|
||||
|
||||
## Intro
|
||||
|
||||
InvokeAI has numerous runtime settings which can be used to adjust
|
||||
many aspects of its operations, including the location of files and
|
||||
directories, memory usage, and performance. These settings can be
|
||||
viewed and customized in several ways:
|
||||
|
||||
1. By editing settings in the `invokeai.yaml` file.
|
||||
2. By setting environment variables.
|
||||
3. On the command-line, when InvokeAI is launched.
|
||||
|
||||
In addition, the most commonly changed settings are accessible
|
||||
graphically via the `invokeai-configure` script.
|
||||
|
||||
### How the Configuration System Works
|
||||
|
||||
When InvokeAI is launched, the very first thing it needs to do is to
|
||||
find its "root" directory, which contains its configuration files,
|
||||
installed models, its database of images, and the folder(s) of
|
||||
generated images themselves. In this document, the root directory will
|
||||
be referred to as ROOT.
|
||||
|
||||
#### Finding the Root Directory
|
||||
|
||||
To find its root directory, InvokeAI uses the following recipe:
|
||||
|
||||
1. It first looks for the argument `--root <path>` on the command line
|
||||
it was launched from, and uses the indicated path if present.
|
||||
|
||||
2. Next it looks for the environment variable INVOKEAI_ROOT, and uses
|
||||
the directory path found there if present.
|
||||
|
||||
3. If neither of these are present, then InvokeAI looks for the
|
||||
folder containing the `.venv` Python virtual environment directory for
|
||||
the currently active environment. This directory is checked for files
|
||||
expected inside the InvokeAI root before it is used.
|
||||
|
||||
4. Finally, InvokeAI looks for a directory in the current user's home
|
||||
directory named `invokeai`.
|
||||
|
||||
#### Reading the InvokeAI Configuration File
|
||||
|
||||
Once the root directory has been located, InvokeAI looks for a file
|
||||
named `ROOT/invokeai.yaml`, and if present reads configuration values
|
||||
from it. The top of this file looks like this:
|
||||
|
||||
```
|
||||
InvokeAI:
|
||||
Web Server:
|
||||
host: localhost
|
||||
port: 9090
|
||||
allow_origins: []
|
||||
allow_credentials: true
|
||||
allow_methods:
|
||||
- '*'
|
||||
allow_headers:
|
||||
- '*'
|
||||
Features:
|
||||
esrgan: true
|
||||
internet_available: true
|
||||
log_tokenization: false
|
||||
nsfw_checker: false
|
||||
patchmatch: true
|
||||
restore: true
|
||||
...
|
||||
```
|
||||
|
||||
This lines in this file are used to establish default values for
|
||||
Invoke's settings. In the above fragment, the Web Server's listening
|
||||
port is set to 9090 by the `port` setting.
|
||||
|
||||
You can edit this file with a text editor such as "Notepad" (do not
|
||||
use Word or any other word processor). When editing, be careful to
|
||||
maintain the indentation, and do not add extraneous text, as syntax
|
||||
errors will prevent InvokeAI from launching. A basic guide to the
|
||||
format of YAML files can be found
|
||||
[here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
|
||||
|
||||
You can fix a broken `invokeai.yaml` by deleting it and running the
|
||||
configuration script again -- option [7] in the launcher, "Re-run the
|
||||
configure script".
|
||||
|
||||
#### Reading Environment Variables
|
||||
|
||||
Next InvokeAI looks for defined environment variables in the format
|
||||
`INVOKEAI_<setting_name>`, for example `INVOKEAI_port`. Environment
|
||||
variable values take precedence over configuration file variables. On
|
||||
a Macintosh system, for example, you could change the port that the
|
||||
web server listens on by setting the environment variable this way:
|
||||
|
||||
```
|
||||
export INVOKEAI_port=8000
|
||||
invokeai-web
|
||||
```
|
||||
|
||||
Please check out these
|
||||
[Macintosh](https://phoenixnap.com/kb/set-environment-variable-mac)
|
||||
and
|
||||
[Windows](https://phoenixnap.com/kb/windows-set-environment-variable)
|
||||
guides for setting temporary and permanent environment variables.
|
||||
|
||||
#### Reading the Command Line
|
||||
|
||||
Lastly, InvokeAI takes settings from the command line, which override
|
||||
everything else. The command-line settings have the same name as the
|
||||
corresponding configuration file settings, preceded by a `--`, for
|
||||
example `--port 8000`.
|
||||
|
||||
If you are using the launcher (`invoke.sh` or `invoke.bat`) to launch
|
||||
InvokeAI, then just pass the command-line arguments to the launcher:
|
||||
|
||||
```
|
||||
invoke.bat --port 8000 --host 0.0.0.0
|
||||
```
|
||||
|
||||
The arguments will be applied when you select the web server option
|
||||
(and the other options as well).
|
||||
|
||||
If, on the other hand, you prefer to launch InvokeAI directly from the
|
||||
command line, you would first activate the virtual environment (known
|
||||
as the "developer's console" in the launcher), and run `invokeai-web`:
|
||||
|
||||
```
|
||||
> C:\Users\Fred\invokeai\.venv\scripts\activate
|
||||
(.venv) > invokeai-web --port 8000 --host 0.0.0.0
|
||||
```
|
||||
|
||||
You can get a listing and brief instructions for each of the
|
||||
command-line options by giving the `--help` argument:
|
||||
|
||||
```
|
||||
(.venv) > invokeai-web --help
|
||||
usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials]
|
||||
[--allow_methods [ALLOW_METHODS ...]] [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan]
|
||||
[--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
|
||||
[--nsfw_checker | --no-nsfw_checker] [--patchmatch | --no-patchmatch] [--restore | --no-restore]
|
||||
[--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_cache_size MAX_CACHE_SIZE]
|
||||
[--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--precision {auto,float16,float32,autocast}]
|
||||
[--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled]
|
||||
[--tiled_decode | --no-tiled_decode] [--root ROOT] [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR]
|
||||
[--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH] [--models_dir MODELS_DIR]
|
||||
[--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
|
||||
[--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]]
|
||||
[--log_format {plain,color,syslog,legacy}] [--log_level {debug,info,warning,error,critical}]
|
||||
...
|
||||
```
|
||||
|
||||
## The Configuration Settings
|
||||
|
||||
The configuration settings are divided into several distinct
|
||||
groups in `invokeia.yaml`:
|
||||
|
||||
### Web Server
|
||||
|
||||
| Setting | Default Value | Description |
|
||||
|----------|----------------|--------------|
|
||||
| `host` | `localhost` | Name or IP address of the network interface that the web server will listen on |
|
||||
| `port` | `9090` | Network port number that the web server will listen on |
|
||||
| `allow_origins` | `[]` | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
|
||||
| `allow_credentials | `true` | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
|
||||
| `allow_methods` | `*` | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API |
|
||||
| `allow_headers` | `*` | List of HTTP headers that the web server will accept when accessing the API |
|
||||
|
||||
The documentation for InvokeAI's API can be accessed by browsing to the following URL: [http://localhost:9090/docs].
|
||||
|
||||
### Features
|
||||
|
||||
These configuration settings allow you to enable and disable various InvokeAI features:
|
||||
|
||||
| Setting | Default Value | Description |
|
||||
|----------|----------------|--------------|
|
||||
| `esrgan` | `true` | Activate the ESRGAN upscaling options|
|
||||
| `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 |
|
||||
| `nsfw_checker` | `true` | Activate the NSFW checker to blur out risque images |
|
||||
| `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
|
||||
|
||||
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 |
|
||||
|
||||
### Paths
|
||||
|
||||
These options set the paths of various directories and files used by
|
||||
InvokeAI. Relative paths are interpreted relative to INVOKEAI_ROOT, so
|
||||
if INVOKEAI_ROOT is `/home/fred/invokeai` and the path is
|
||||
`autoimport/main`, then the corresponding directory will be located at
|
||||
`/home/fred/invokeai/autoimport/main`.
|
||||
|
||||
| Setting | Default Value | Description |
|
||||
|----------|----------------|--------------|
|
||||
| `autoimport_dir` | `autoimport/main` | At startup time, read and import any main model files found in this directory |
|
||||
| `lora_dir` | `autoimport/lora` | At startup time, read and import any LoRA/LyCORIS models found in this directory |
|
||||
| `embedding_dir` | `autoimport/embedding` | At startup time, read and import any textual inversion (embedding) models found in this directory |
|
||||
| `controlnet_dir` | `autoimport/controlnet` | At startup time, read and import any ControlNet models found in this directory |
|
||||
| `conf_path` | `configs/models.yaml` | Location of the `models.yaml` model configuration file |
|
||||
| `models_dir` | `models` | Location of the directory containing models installed by InvokeAI's model manager |
|
||||
| `legacy_conf_dir` | `configs/stable-diffusion` | Location of the directory containing the .yaml configuration files for legacy checkpoint models |
|
||||
| `db_dir` | `databases` | Location of the directory containing InvokeAI's image, schema and session database |
|
||||
| `outdir` | `outputs` | Location of the directory in which the gallery of generated and uploaded images will be stored |
|
||||
| `use_memory_db` | `false` | Keep database information in memory rather than on disk; this will not preserve image gallery information across restarts |
|
||||
|
||||
Note that the autoimport directories will be searched recursively,
|
||||
allowing you to organize the models into folders and subfolders in any
|
||||
way you wish. In addition, while we have split up autoimport
|
||||
directories by the type of model they contain, this isn't
|
||||
necessary. You can combine different model types in the same folder
|
||||
and InvokeAI will figure out what they are. So you can easily use just
|
||||
one autoimport directory by commenting out the unneeded paths:
|
||||
|
||||
```
|
||||
Paths:
|
||||
autoimport_dir: autoimport
|
||||
# lora_dir: null
|
||||
# embedding_dir: null
|
||||
# controlnet_dir: null
|
||||
```
|
||||
|
||||
### Logging
|
||||
|
||||
These settings control the information, warning, and debugging
|
||||
messages printed to the console log while InvokeAI is running:
|
||||
|
||||
| Setting | Default Value | Description |
|
||||
|----------|----------------|--------------|
|
||||
| `log_handlers` | `console` | This controls where log messages are sent, and can be a list of one or more destinations. Values include `console`, `file`, `syslog` and `http`. These are described in more detail below |
|
||||
| `log_format` | `color` | This controls the formatting of the log messages. Values are `plain`, `color`, `legacy` and `syslog` |
|
||||
| `log_level` | `debug` | This filters messages according to the level of severity and can be one of `debug`, `info`, `warning`, `error` and `critical`. For example, setting to `warning` will display all messages at the warning level or higher, but won't display "debug" or "info" messages |
|
||||
|
||||
Several different log handler destinations are available, and multiple destinations are supported by providing a list:
|
||||
|
||||
```
|
||||
log_handlers:
|
||||
- console
|
||||
- syslog=localhost
|
||||
- file=/var/log/invokeai.log
|
||||
```
|
||||
|
||||
* `console` is the default. It prints log messages to the command-line window from which InvokeAI was launched.
|
||||
|
||||
* `syslog` is only available on Linux and Macintosh systems. It uses
|
||||
the operating system's "syslog" facility to write log file entries
|
||||
locally or to a remote logging machine. `syslog` offers a variety
|
||||
of configuration options:
|
||||
|
||||
```
|
||||
syslog=/dev/log` - log to the /dev/log device
|
||||
syslog=localhost` - log to the network logger running on the local machine
|
||||
syslog=localhost:512` - same as above, but using a non-standard port
|
||||
syslog=fredserver,facility=LOG_USER,socktype=SOCK_DRAM`
|
||||
- Log to LAN-connected server "fredserver" using the facility LOG_USER and datagram packets.
|
||||
```
|
||||
|
||||
* `http` can be used to log to a remote web server. The server must be
|
||||
properly configured to receive and act on log messages. The option
|
||||
accepts the URL to the web server, and a `method` argument
|
||||
indicating whether the message should be submitted using the GET or
|
||||
POST method.
|
||||
|
||||
```
|
||||
http=http://my.server/path/to/logger,method=POST
|
||||
```
|
||||
|
||||
The `log_format` option provides several alternative formats:
|
||||
|
||||
* `color` - default format providing time, date and a message, using text colors to distinguish different log severities
|
||||
* `plain` - same as above, but monochrome text only
|
||||
* `syslog` - the log level and error message only, allowing the syslog system to attach the time and date
|
||||
* `legacy` - a format similar to the one used by the legacy 2.3 InvokeAI releases.
|
||||
206
docs/features/NODES.md
Normal file
206
docs/features/NODES.md
Normal file
@ -0,0 +1,206 @@
|
||||
# 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()` |
|
||||
| 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 |
|
||||
| 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.
|
||||
|
||||
<img width="654" alt="groupsnoise" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/2e8d297e-ad55-4d27-bc93-c119dad2a2c5">
|
||||
|
||||
### 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.
|
||||
|
||||
<img width="1024" alt="groupsconditioning" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/f8f7ad8a-8d9c-418e-b5ad-1437b774b27e">
|
||||
|
||||
### 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.
|
||||
|
||||
<img width="637" alt="groupsimgvae" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/dd99969c-e0a8-4f78-9b17-3ffe179cef9a">
|
||||
|
||||
### 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.
|
||||
|
||||
<img width="922" alt="groupsrandseed" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/af55bc20-60f6-438e-aba5-3ec871443710">
|
||||
|
||||
### 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.
|
||||
|
||||
<img width="805" alt="groupscontrol" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/cc9c5de7-23a7-46c8-bbad-1f3609d999a6">
|
||||
|
||||
### 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.
|
||||
|
||||
<img width="993" alt="groupslora" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/630962b0-d914-4505-b3ea-ccae9b0269da">
|
||||
|
||||
### 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.
|
||||
|
||||
<img width="644" alt="groupsallscale" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/99314f05-dd9f-4b6d-b378-31de55346a13">
|
||||
|
||||
### 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.
|
||||
|
||||
<img width="788" alt="groupsiterate" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/4af5ca27-82c9-4018-8c5b-024d3ee0a121">
|
||||
|
||||
### 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.
|
||||
|
||||
<img width="1027" alt="groupsmultigenseeding" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/518d1b2b-fed1-416b-a052-ab06552521b3">
|
||||
|
||||
## 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
|
||||
|
||||
<img width="875" alt="nodest2i" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/17c67720-c376-4db8-94f0-5e00381a61ee">
|
||||
|
||||
- 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
|
||||
|
||||
<img width="998" alt="nodesi2i" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/3f2c95d5-cee7-4415-9b79-b46ee60a92fe">
|
||||
|
||||
- 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
|
||||
|
||||
<img width="703" alt="nodescontrol" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/b02ded86-ceb4-44a2-9910-e19ad184d471">
|
||||
|
||||
- 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
|
||||
@ -76,10 +76,10 @@ From top to bottom, these are:
|
||||
with outpainting,and modify interior portions of the image with
|
||||
inpainting, erase portions of a starting image and have the AI fill in
|
||||
the erased region from a text prompt.
|
||||
4. Workflow Management (not yet implemented) - this panel will allow you to create
|
||||
4. Node Editor - this panel allows you to create
|
||||
pipelines of common operations and combine them into workflows.
|
||||
5. Training (not yet implemented) - this panel will provide an interface to [textual
|
||||
inversion training](TEXTUAL_INVERSION.md) and fine tuning.
|
||||
5. Model Manager - this panel allows you to import and configure new
|
||||
models using URLs, local paths, or HuggingFace diffusers repo_ids.
|
||||
|
||||
The inpainting, outpainting and postprocessing tabs are currently in
|
||||
development. However, limited versions of their features can already be accessed
|
||||
|
||||
@ -37,7 +37,7 @@ guide also covers optimizing models to load quickly.
|
||||
Teach an old model new tricks. Merge 2-3 models together to create a
|
||||
new model that combines characteristics of the originals.
|
||||
|
||||
## * [Textual Inversion](TEXTUAL_INVERSION.md)
|
||||
## * [Textual Inversion](TRAINING.md)
|
||||
Personalize models by adding your own style or subjects.
|
||||
|
||||
# Other Features
|
||||
|
||||
@ -146,13 +146,15 @@ This method is recommended for those familiar with running Docker containers
|
||||
- [Installing](installation/050_INSTALLING_MODELS.md)
|
||||
- [Model Merging](features/MODEL_MERGING.md)
|
||||
- [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
|
||||
- [Textual Inversion](features/TEXTUAL_INVERSION.md)
|
||||
- [Not Safe for Work (NSFW) Checker](features/NSFW.md)
|
||||
<!-- seperator -->
|
||||
### Prompt Engineering
|
||||
- [Prompt Syntax](features/PROMPTS.md)
|
||||
- [Generating Variations](features/VARIATIONS.md)
|
||||
|
||||
### InvokeAI Configuration
|
||||
- [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
|
||||
|
||||
## :octicons-log-16: Important Changes Since Version 2.3
|
||||
|
||||
### Nodes
|
||||
|
||||
@ -354,8 +354,8 @@ experimental versions later.
|
||||
|
||||
12. **InvokeAI Options**: You can launch InvokeAI with several different command-line arguments that
|
||||
customize its behavior. For example, you can change the location of the
|
||||
image output directory, or select your favorite sampler. See the
|
||||
[Command-Line Interface](../features/CLI.md) for a full list of the options.
|
||||
image output directory or balance memory usage vs performance. See
|
||||
[Configuration](../features/CONFIGURATION.md) for a full list of the options.
|
||||
|
||||
- To set defaults that will take effect every time you launch InvokeAI,
|
||||
use a text editor (e.g. Notepad) to exit the file
|
||||
|
||||
@ -256,7 +256,7 @@ manager, please follow these steps:
|
||||
|
||||
10. Render away!
|
||||
|
||||
Browse the [features](../features/CLI.md) section to learn about all the
|
||||
Browse the [features](../features/index.md) section to learn about all the
|
||||
things you can do with InvokeAI.
|
||||
|
||||
|
||||
@ -270,7 +270,7 @@ manager, please follow these steps:
|
||||
|
||||
12. Other scripts
|
||||
|
||||
The [Textual Inversion](../features/TEXTUAL_INVERSION.md) script can be launched with the command:
|
||||
The [Textual Inversion](../features/TRAINING.md) script can be launched with the command:
|
||||
|
||||
```bash
|
||||
invokeai-ti --gui
|
||||
|
||||
@ -43,24 +43,7 @@ InvokeAI comes with support for a good set of starter models. You'll
|
||||
find them listed in the master models file
|
||||
`configs/INITIAL_MODELS.yaml` in the InvokeAI root directory. The
|
||||
subset that are currently installed are found in
|
||||
`configs/models.yaml`. As of v2.3.1, the list of starter models is:
|
||||
|
||||
|Model Name | HuggingFace Repo ID | Description | URL |
|
||||
|---------- | ---------- | ----------- | --- |
|
||||
|stable-diffusion-1.5|runwayml/stable-diffusion-v1-5|Stable Diffusion version 1.5 diffusers model (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-v1-5 |
|
||||
|sd-inpainting-1.5|runwayml/stable-diffusion-inpainting|RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-inpainting |
|
||||
|stable-diffusion-2.1|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
|
||||
|sd-inpainting-2.0|stabilityai/stable-diffusion-2-inpainting|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-inpainting |
|
||||
|analog-diffusion-1.0|wavymulder/Analog-Diffusion|An SD-1.5 model trained on diverse analog photographs (2.13 GB)|https://huggingface.co/wavymulder/Analog-Diffusion |
|
||||
|deliberate-1.0|XpucT/Deliberate|Versatile model that produces detailed images up to 768px (4.27 GB)|https://huggingface.co/XpucT/Deliberate |
|
||||
|d&d-diffusion-1.0|0xJustin/Dungeons-and-Diffusion|Dungeons & Dragons characters (2.13 GB)|https://huggingface.co/0xJustin/Dungeons-and-Diffusion |
|
||||
|dreamlike-photoreal-2.0|dreamlike-art/dreamlike-photoreal-2.0|A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)|https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0 |
|
||||
|inkpunk-1.0|Envvi/Inkpunk-Diffusion|Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)|https://huggingface.co/Envvi/Inkpunk-Diffusion |
|
||||
|openjourney-4.0|prompthero/openjourney|An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)|https://huggingface.co/prompthero/openjourney |
|
||||
|portrait-plus-1.0|wavymulder/portraitplus|An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)|https://huggingface.co/wavymulder/portraitplus |
|
||||
|seek-art-mega-1.0|coreco/seek.art_MEGA|A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)|https://huggingface.co/coreco/seek.art_MEGA |
|
||||
|trinart-2.0|naclbit/trinart_stable_diffusion_v2|An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)|https://huggingface.co/naclbit/trinart_stable_diffusion_v2 |
|
||||
|waifu-diffusion-1.4|hakurei/waifu-diffusion|An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)|https://huggingface.co/hakurei/waifu-diffusion |
|
||||
`configs/models.yaml`.
|
||||
|
||||
Note that these files are covered by an "Ethical AI" license which
|
||||
forbids certain uses. When you initially download them, you are asked
|
||||
@ -71,8 +54,7 @@ with the model terms by visiting the URLs in the table above.
|
||||
|
||||
## Community-Contributed Models
|
||||
|
||||
There are too many to list here and more are being contributed every
|
||||
day. [HuggingFace](https://huggingface.co/models?library=diffusers)
|
||||
[HuggingFace](https://huggingface.co/models?library=diffusers)
|
||||
is a great resource for diffusers models, and is also the home of a
|
||||
[fast-growing repository](https://huggingface.co/sd-concepts-library)
|
||||
of embedding (".bin") models that add subjects and/or styles to your
|
||||
@ -86,310 +68,106 @@ only `.safetensors` and `.ckpt` models, but they can be easily loaded
|
||||
into InvokeAI and/or converted into optimized `diffusers` models. Be
|
||||
aware that CIVITAI hosts many models that generate NSFW content.
|
||||
|
||||
!!! note
|
||||
|
||||
InvokeAI 2.3.x does not support directly importing and
|
||||
running Stable Diffusion version 2 checkpoint models. You may instead
|
||||
convert them into `diffusers` models using the conversion methods
|
||||
described below.
|
||||
|
||||
## Installation
|
||||
|
||||
There are multiple ways to install and manage models:
|
||||
There are two ways to install and manage models:
|
||||
|
||||
1. The `invokeai-configure` script which will download and install them for you.
|
||||
1. The `invokeai-model-install` script which will download and install
|
||||
them for you. In addition to supporting main models, you can install
|
||||
ControlNet, LoRA and Textual Inversion models.
|
||||
|
||||
2. The command-line tool (CLI) has commands that allows you to import, configure and modify
|
||||
models files.
|
||||
|
||||
3. The web interface (WebUI) has a GUI for importing and managing
|
||||
2. The web interface (WebUI) has a GUI for importing and managing
|
||||
models.
|
||||
|
||||
### Installation via `invokeai-configure`
|
||||
3. By placing models (or symbolic links to models) inside one of the
|
||||
InvokeAI root directory's `autoimport` folder.
|
||||
|
||||
From the `invoke` launcher, choose option (6) "re-run the configure
|
||||
script to download new models." This will launch the same script that
|
||||
prompted you to select models at install time. You can use this to add
|
||||
models that you skipped the first time around. It is all right to
|
||||
specify a model that was previously downloaded; the script will just
|
||||
confirm that the files are complete.
|
||||
### Installation via `invokeai-model-install`
|
||||
|
||||
### Installation via the CLI
|
||||
From the `invoke` launcher, choose option [5] "Download and install
|
||||
models." This will launch the same script that prompted you to select
|
||||
models at install time. You can use this to add models that you
|
||||
skipped the first time around. It is all right to specify a model that
|
||||
was previously downloaded; the script will just confirm that the files
|
||||
are complete.
|
||||
|
||||
You can install a new model, including any of the community-supported ones, via
|
||||
the command-line client's `!import_model` command.
|
||||
The installer has different panels for installing main models from
|
||||
HuggingFace, models from Civitai and other arbitrary web sites,
|
||||
ControlNet models, LoRA/LyCORIS models, and Textual Inversion
|
||||
embeddings. Each section has a text box in which you can enter a new
|
||||
model to install. You can refer to a model using its:
|
||||
|
||||
#### Installing individual `.ckpt` and `.safetensors` models
|
||||
1. Local path to the .ckpt, .safetensors or diffusers folder on your local machine
|
||||
2. A directory on your machine that contains multiple models
|
||||
3. A URL that points to a downloadable model
|
||||
4. A HuggingFace repo id
|
||||
|
||||
If the model is already downloaded to your local disk, use
|
||||
`!import_model /path/to/file.ckpt` to load it. For example:
|
||||
Previously-installed models are shown with checkboxes. Uncheck a box
|
||||
to unregister the model from InvokeAI. Models that are physically
|
||||
installed inside the InvokeAI root directory will be deleted and
|
||||
purged (after a confirmation warning). Models that are located outside
|
||||
the InvokeAI root directory will be unregistered but not deleted.
|
||||
|
||||
```bash
|
||||
invoke> !import_model C:/Users/fred/Downloads/martians.safetensors
|
||||
Note: The installer script uses a console-based text interface that requires
|
||||
significant amounts of horizontal and vertical space. If the display
|
||||
looks messed up, just enlarge the terminal window and/or relaunch the
|
||||
script.
|
||||
|
||||
If you wish you can script model addition and deletion, as well as
|
||||
listing installed models. Start the "developer's console" and give the
|
||||
command `invokeai-model-install --help`. This will give you a series
|
||||
of command-line parameters that will let you control model
|
||||
installation. Examples:
|
||||
|
||||
```
|
||||
# (list all controlnet models)
|
||||
invokeai-model-install --list controlnet
|
||||
|
||||
# (install the model at the indicated URL)
|
||||
invokeai-model-install --add http://civitai.com/2860
|
||||
|
||||
# (delete the named model)
|
||||
invokeai-model-install --delete sd-1/main/analog-diffusion
|
||||
```
|
||||
|
||||
!!! tip "Forward Slashes"
|
||||
On Windows systems, use forward slashes rather than backslashes
|
||||
in your file paths.
|
||||
If you do use backslashes,
|
||||
you must double them like this:
|
||||
`C:\\Users\\fred\\Downloads\\martians.safetensors`
|
||||
### Installation via the Web GUI
|
||||
|
||||
Alternatively you can directly import the file using its URL:
|
||||
To install a new model using the Web GUI, do the following:
|
||||
|
||||
```bash
|
||||
invoke> !import_model https://example.org/sd_models/martians.safetensors
|
||||
```
|
||||
1. Open the InvokeAI Model Manager (cube at the bottom of the
|
||||
left-hand panel) and navigate to *Import Models*
|
||||
|
||||
For this to work, the URL must not be password-protected. Otherwise
|
||||
you will receive a 404 error.
|
||||
2. In the field labeled *Location* type in the path to the model you
|
||||
wish to install. You may use a URL, HuggingFace repo id, or a path on
|
||||
your local disk.
|
||||
|
||||
When you import a legacy model, the CLI will first ask you what type
|
||||
of model this is. You can indicate whether it is a model based on
|
||||
Stable Diffusion 1.x (1.4 or 1.5), one based on Stable Diffusion 2.x,
|
||||
or a 1.x inpainting model. Be careful to indicate the correct model
|
||||
type, or it will not load correctly. You can correct the model type
|
||||
after the fact using the `!edit_model` command.
|
||||
3. Alternatively, the *Scan for Models* button allows you to paste in
|
||||
the path to a folder somewhere on your machine. It will be scanned for
|
||||
importable models and prompt you to add the ones of your choice.
|
||||
|
||||
The system will then ask you a few other questions about the model,
|
||||
including what size image it was trained on (usually 512x512), what
|
||||
name and description you wish to use for it, and whether you would
|
||||
like to install a custom VAE (variable autoencoder) file for the
|
||||
model. For recent models, the answer to the VAE question is usually
|
||||
"no," but it won't hurt to answer "yes".
|
||||
4. Press *Add Model* and wait for confirmation that the model
|
||||
was added.
|
||||
|
||||
After importing, the model will load. If this is successful, you will
|
||||
be asked if you want to keep the model loaded in memory to start
|
||||
generating immediately. You'll also be asked if you wish to make this
|
||||
the default model on startup. You can change this later using
|
||||
`!edit_model`.
|
||||
To delete a model, Select *Model Manager* to list all the currently
|
||||
installed models. Press the trash can icons to delete any models you
|
||||
wish to get rid of. Models whose weights are located inside the
|
||||
InvokeAI `models` directory will be purged from disk, while those
|
||||
located outside will be unregistered from InvokeAI, but not deleted.
|
||||
|
||||
#### Importing a batch of `.ckpt` and `.safetensors` models from a directory
|
||||
You can see where model weights are located by clicking on the model name.
|
||||
This will bring up an editable info panel showing the model's characteristics,
|
||||
including the `Model Location` of its files.
|
||||
|
||||
You may also point `!import_model` to a directory containing a set of
|
||||
`.ckpt` or `.safetensors` files. They will be imported _en masse_.
|
||||
### Installation via the `autoimport` function
|
||||
|
||||
!!! example
|
||||
In the InvokeAI root directory you will find a series of folders under
|
||||
`autoimport`, one each for main models, controlnets, embeddings and
|
||||
Loras. Any models that you add to these directories will be scanned
|
||||
at startup time and registered automatically.
|
||||
|
||||
```console
|
||||
invoke> !import_model C:/Users/fred/Downloads/civitai_models/
|
||||
```
|
||||
You may create symbolic links from these folders to models located
|
||||
elsewhere on disk and they will be autoimported. You can also create
|
||||
subfolders and organize them as you wish.
|
||||
|
||||
You will be given the option to import all models found in the
|
||||
directory, or select which ones to import. If there are subfolders
|
||||
within the directory, they will be searched for models to import.
|
||||
|
||||
#### Installing `diffusers` models
|
||||
|
||||
You can install a `diffusers` model from the HuggingFace site using
|
||||
`!import_model` and the HuggingFace repo_id for the model:
|
||||
|
||||
```bash
|
||||
invoke> !import_model andite/anything-v4.0
|
||||
```
|
||||
|
||||
Alternatively, you can download the model to disk and import it from
|
||||
there. The model may be distributed as a ZIP file, or as a Git
|
||||
repository:
|
||||
|
||||
```bash
|
||||
invoke> !import_model C:/Users/fred/Downloads/andite--anything-v4.0
|
||||
```
|
||||
|
||||
!!! tip "The CLI supports file path autocompletion"
|
||||
Type a bit of the path name and hit ++tab++ in order to get a choice of
|
||||
possible completions.
|
||||
|
||||
!!! tip "On Windows, you can drag model files onto the command-line"
|
||||
Once you have typed in `!import_model `, you can drag the
|
||||
model file or directory onto the command-line to insert the model path. This way, you don't need to
|
||||
type it or copy/paste. However, you will need to reverse or
|
||||
double backslashes as noted above.
|
||||
|
||||
Before installing, the CLI will ask you for a short name and
|
||||
description for the model, whether to make this the default model that
|
||||
is loaded at InvokeAI startup time, and whether to replace its
|
||||
VAE. Generally the answer to the latter question is "no".
|
||||
|
||||
### Converting legacy models into `diffusers`
|
||||
|
||||
The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
|
||||
models file into `diffusers` and install it.This will enable the model
|
||||
to load and run faster without loss of image quality.
|
||||
|
||||
The usage is identical to `!import_model`. You may point the command
|
||||
to either a downloaded model file on disk, or to a (non-password
|
||||
protected) URL:
|
||||
|
||||
```bash
|
||||
invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
|
||||
```
|
||||
|
||||
After a successful conversion, the CLI will offer you the option of
|
||||
deleting the original `.ckpt` or `.safetensors` file.
|
||||
|
||||
### Optimizing a previously-installed model
|
||||
|
||||
Lastly, if you have previously installed a `.ckpt` or `.safetensors`
|
||||
file and wish to convert it into a `diffusers` model, you can do this
|
||||
without re-downloading and converting the original file using the
|
||||
`!optimize_model` command. Simply pass the short name of an existing
|
||||
installed model:
|
||||
|
||||
```bash
|
||||
invoke> !optimize_model martians-v1.0
|
||||
```
|
||||
|
||||
The model will be converted into `diffusers` format and replace the
|
||||
previously installed version. You will again be offered the
|
||||
opportunity to delete the original `.ckpt` or `.safetensors` file.
|
||||
|
||||
### Related CLI Commands
|
||||
|
||||
There are a whole series of additional model management commands in
|
||||
the CLI that you can read about in [Command-Line
|
||||
Interface](../features/CLI.md). These include:
|
||||
|
||||
* `!models` - List all installed models
|
||||
* `!switch <model name>` - Switch to the indicated model
|
||||
* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
|
||||
* `!del_model <model name>` - Delete the indicated model
|
||||
|
||||
### Manually editing `configs/models.yaml`
|
||||
|
||||
|
||||
If you are comfortable with a text editor then you may simply edit `models.yaml`
|
||||
directly.
|
||||
|
||||
You will need to download the desired `.ckpt/.safetensors` file and
|
||||
place it somewhere on your machine's filesystem. Alternatively, for a
|
||||
`diffusers` model, record the repo_id or download the whole model
|
||||
directory. Then using a **text** editor (e.g. the Windows Notepad
|
||||
application), open the file `configs/models.yaml`, and add a new
|
||||
stanza that follows this model:
|
||||
|
||||
#### A legacy model
|
||||
|
||||
A legacy `.ckpt` or `.safetensors` entry will look like this:
|
||||
|
||||
```yaml
|
||||
arabian-nights-1.0:
|
||||
description: A great fine-tune in Arabian Nights style
|
||||
weights: ./path/to/arabian-nights-1.0.ckpt
|
||||
config: ./configs/stable-diffusion/v1-inference.yaml
|
||||
format: ckpt
|
||||
width: 512
|
||||
height: 512
|
||||
default: false
|
||||
```
|
||||
|
||||
Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
|
||||
|
||||
#### A diffusers model
|
||||
|
||||
A stanza for a `diffusers` model will look like this for a HuggingFace
|
||||
model with a repository ID:
|
||||
|
||||
```yaml
|
||||
arabian-nights-1.1:
|
||||
description: An even better fine-tune of the Arabian Nights
|
||||
repo_id: captahab/arabian-nights-1.1
|
||||
format: diffusers
|
||||
default: true
|
||||
```
|
||||
|
||||
And for a downloaded directory:
|
||||
|
||||
```yaml
|
||||
arabian-nights-1.1:
|
||||
description: An even better fine-tune of the Arabian Nights
|
||||
path: /path/to/captahab-arabian-nights-1.1
|
||||
format: diffusers
|
||||
default: true
|
||||
```
|
||||
|
||||
There is additional syntax for indicating an external VAE to use with
|
||||
this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
|
||||
|
||||
After you save the modified `models.yaml` file relaunch
|
||||
`invokeai`. The new model will now be available for your use.
|
||||
|
||||
### Installation via the WebUI
|
||||
|
||||
To access the WebUI Model Manager, click on the button that looks like
|
||||
a cube in the upper right side of the browser screen. This will bring
|
||||
up a dialogue that lists the models you have already installed, and
|
||||
allows you to load, delete or edit them:
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
To add a new model, click on **+ Add New** and select to either a
|
||||
checkpoint/safetensors model, or a diffusers model:
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
In this example, we chose **Add Diffusers**. As shown in the figure
|
||||
below, a new dialogue prompts you to enter the name to use for the
|
||||
model, its description, and either the location of the `diffusers`
|
||||
model on disk, or its Repo ID on the HuggingFace web site. If you
|
||||
choose to enter a path to disk, the system will autocomplete for you
|
||||
as you type:
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
Press **Add Model** at the bottom of the dialogue (scrolled out of
|
||||
site in the figure), and the model will be downloaded, imported, and
|
||||
registered in `models.yaml`.
|
||||
|
||||
The **Add Checkpoint/Safetensor Model** option is similar, except that
|
||||
in this case you can choose to scan an entire folder for
|
||||
checkpoint/safetensors files to import. Simply type in the path of the
|
||||
directory and press the "Search" icon. This will display the
|
||||
`.ckpt` and `.safetensors` found inside the directory and its
|
||||
subfolders, and allow you to choose which ones to import:
|
||||
|
||||
<figure markdown>
|
||||
|
||||
|
||||
|
||||
</figure>
|
||||
|
||||
## Model Management Startup Options
|
||||
|
||||
The `invoke` launcher and the `invokeai` script accept a series of
|
||||
command-line arguments that modify InvokeAI's behavior when loading
|
||||
models. These can be provided on the command line, or added to the
|
||||
InvokeAI root directory's `invokeai.init` initialization file.
|
||||
|
||||
The arguments are:
|
||||
|
||||
* `--model <model name>` -- Start up with the indicated model loaded
|
||||
* `--ckpt_convert` -- When a checkpoint/safetensors model is loaded, convert it into a `diffusers` model in memory. This does not permanently save the converted model to disk.
|
||||
* `--autoconvert <path/to/directory>` -- Scan the indicated directory path for new checkpoint/safetensors files, convert them into `diffusers` models, and import them into InvokeAI.
|
||||
|
||||
Here is an example of providing an argument on the command line using
|
||||
the `invoke.sh` launch script:
|
||||
|
||||
```bash
|
||||
invoke.sh --autoconvert /home/fred/stable-diffusion-checkpoints
|
||||
```
|
||||
|
||||
And here is what the same argument looks like in `invokeai.init`:
|
||||
|
||||
```bash
|
||||
--outdir="/home/fred/invokeai/outputs
|
||||
--no-nsfw_checker
|
||||
--autoconvert /home/fred/stable-diffusion-checkpoints
|
||||
```
|
||||
The location of the autoimport directories are controlled by settings
|
||||
in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).
|
||||
@ -24,7 +24,8 @@ read -e -p "Tag this repo with '${VERSION}' and '${LATEST_TAG}'? [n]: " input
|
||||
RESPONSE=${input:='n'}
|
||||
if [ "$RESPONSE" == 'y' ]; then
|
||||
|
||||
if ! git tag $VERSION ; then
|
||||
git push origin :refs/tags/$VERSION
|
||||
if ! git tag -fa $VERSION ; then
|
||||
echo "Existing/invalid tag"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
@ -38,7 +38,7 @@ echo https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist
|
||||
echo.
|
||||
echo See %INSTRUCTIONS% for more details.
|
||||
echo.
|
||||
echo "For the best user experience we suggest enlarging or maximizing this window now."
|
||||
echo FOR THE BEST USER EXPERIENCE WE SUGGEST MAXIMIZING THIS WINDOW NOW.
|
||||
pause
|
||||
|
||||
@rem ---------------------------- check Python version ---------------
|
||||
|
||||
@ -19,7 +19,7 @@ echo 8. Open the developer console
|
||||
echo 9. Update InvokeAI
|
||||
echo 10. Command-line help
|
||||
echo Q - Quit
|
||||
set /P choice="Please enter 1-10, Q: [2] "
|
||||
set /P choice="Please enter 1-10, Q: [1] "
|
||||
if not defined choice set choice=1
|
||||
IF /I "%choice%" == "1" (
|
||||
echo Starting the InvokeAI browser-based UI..
|
||||
|
||||
@ -11,6 +11,7 @@ from invokeai.app.services.board_images import (
|
||||
)
|
||||
from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
|
||||
from invokeai.app.services.boards import BoardService, BoardServiceDependencies
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
|
||||
from invokeai.app.services.images import ImageService, ImageServiceDependencies
|
||||
from invokeai.app.services.resource_name import SimpleNameService
|
||||
@ -20,7 +21,6 @@ from invokeai.version.invokeai_version import __version__
|
||||
|
||||
from ..services.default_graphs import create_system_graphs
|
||||
from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
|
||||
from ..services.restoration_services import RestorationServices
|
||||
from ..services.graph import GraphExecutionState, LibraryGraph
|
||||
from ..services.image_file_storage import DiskImageFileStorage
|
||||
from ..services.invocation_queue import MemoryInvocationQueue
|
||||
@ -57,8 +57,8 @@ class ApiDependencies:
|
||||
invoker: Invoker = None
|
||||
|
||||
@staticmethod
|
||||
def initialize(config, event_handler_id: int, logger: Logger = logger):
|
||||
logger.debug(f'InvokeAI version {__version__}')
|
||||
def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
|
||||
logger.debug(f"InvokeAI version {__version__}")
|
||||
logger.debug(f"Internet connectivity is {config.internet_available}")
|
||||
|
||||
events = FastAPIEventService(event_handler_id)
|
||||
@ -117,7 +117,7 @@ class ApiDependencies:
|
||||
)
|
||||
|
||||
services = InvocationServices(
|
||||
model_manager=ModelManagerService(config,logger),
|
||||
model_manager=ModelManagerService(config, logger),
|
||||
events=events,
|
||||
latents=latents,
|
||||
images=images,
|
||||
@ -129,7 +129,6 @@ class ApiDependencies:
|
||||
),
|
||||
graph_execution_manager=graph_execution_manager,
|
||||
processor=DefaultInvocationProcessor(),
|
||||
restoration=RestorationServices(config, logger),
|
||||
configuration=config,
|
||||
logger=logger,
|
||||
)
|
||||
|
||||
@ -19,6 +19,9 @@ from ..dependencies import ApiDependencies
|
||||
|
||||
images_router = APIRouter(prefix="/v1/images", tags=["images"])
|
||||
|
||||
# images are immutable; set a high max-age
|
||||
IMAGE_MAX_AGE = 31536000
|
||||
|
||||
|
||||
@images_router.post(
|
||||
"/",
|
||||
@ -155,12 +158,14 @@ async def get_image_full(
|
||||
if not ApiDependencies.invoker.services.images.validate_path(path):
|
||||
raise HTTPException(status_code=404)
|
||||
|
||||
return FileResponse(
|
||||
response = FileResponse(
|
||||
path,
|
||||
media_type="image/png",
|
||||
filename=image_name,
|
||||
content_disposition_type="inline",
|
||||
)
|
||||
response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
|
||||
return response
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=404)
|
||||
|
||||
@ -189,9 +194,11 @@ async def get_image_thumbnail(
|
||||
if not ApiDependencies.invoker.services.images.validate_path(path):
|
||||
raise HTTPException(status_code=404)
|
||||
|
||||
return FileResponse(
|
||||
response = FileResponse(
|
||||
path, media_type="image/webp", content_disposition_type="inline"
|
||||
)
|
||||
response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
|
||||
return response
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=404)
|
||||
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2024 Lincoln Stein
|
||||
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2023 Lincoln D. Stein
|
||||
|
||||
|
||||
import pathlib
|
||||
from typing import Literal, List, Optional, Union
|
||||
|
||||
from fastapi import Body, Path, Query, Response
|
||||
@ -12,8 +13,11 @@ from invokeai.backend import BaseModelType, ModelType
|
||||
from invokeai.backend.model_management.models import (
|
||||
OPENAPI_MODEL_CONFIGS,
|
||||
SchedulerPredictionType,
|
||||
ModelNotFoundException,
|
||||
InvalidModelException,
|
||||
)
|
||||
from invokeai.backend.model_management import MergeInterpolationMethod
|
||||
|
||||
from ..dependencies import ApiDependencies
|
||||
|
||||
models_router = APIRouter(prefix="/v1/models", tags=["models"])
|
||||
@ -22,6 +26,7 @@ UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
|
||||
class ModelsList(BaseModel):
|
||||
models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
|
||||
@ -32,11 +37,16 @@ class ModelsList(BaseModel):
|
||||
responses={200: {"model": ModelsList }},
|
||||
)
|
||||
async def list_models(
|
||||
base_model: Optional[BaseModelType] = Query(default=None, description="Base model"),
|
||||
base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
|
||||
model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
|
||||
) -> ModelsList:
|
||||
"""Gets a list of models"""
|
||||
models_raw = ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type)
|
||||
if base_models and len(base_models)>0:
|
||||
models_raw = list()
|
||||
for base_model in base_models:
|
||||
models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
|
||||
else:
|
||||
models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
|
||||
models = parse_obj_as(ModelsList, { "models": models_raw })
|
||||
return models
|
||||
|
||||
@ -44,8 +54,9 @@ async def list_models(
|
||||
"/{base_model}/{model_type}/{model_name}",
|
||||
operation_id="update_model",
|
||||
responses={200: {"description" : "The model was updated successfully"},
|
||||
400: {"description" : "Bad request"},
|
||||
404: {"description" : "The model could not be found"},
|
||||
400: {"description" : "Bad request"}
|
||||
409: {"description" : "There is already a model corresponding to the new name"},
|
||||
},
|
||||
status_code = 200,
|
||||
response_model = UpdateModelResponse,
|
||||
@ -56,33 +67,69 @@ async def update_model(
|
||||
model_name: str = Path(description="model name"),
|
||||
info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
|
||||
) -> UpdateModelResponse:
|
||||
""" Add Model """
|
||||
""" Update model contents with a new config. If the model name or base fields are changed, then the model is renamed. """
|
||||
logger = ApiDependencies.invoker.services.logger
|
||||
|
||||
|
||||
try:
|
||||
previous_info = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
)
|
||||
|
||||
# rename operation requested
|
||||
if info.model_name != model_name or info.base_model != base_model:
|
||||
ApiDependencies.invoker.services.model_manager.rename_model(
|
||||
base_model = base_model,
|
||||
model_type = model_type,
|
||||
model_name = model_name,
|
||||
new_name = info.model_name,
|
||||
new_base = info.base_model,
|
||||
)
|
||||
logger.info(f'Successfully renamed {base_model}/{model_name}=>{info.base_model}/{info.model_name}')
|
||||
# update information to support an update of attributes
|
||||
model_name = info.model_name
|
||||
base_model = info.base_model
|
||||
new_info = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
)
|
||||
if new_info.get('path') != previous_info.get('path'): # model manager moved model path during rename - don't overwrite it
|
||||
info.path = new_info.get('path')
|
||||
|
||||
ApiDependencies.invoker.services.model_manager.update_model(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
model_attributes=info.dict()
|
||||
)
|
||||
|
||||
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
)
|
||||
model_response = parse_obj_as(UpdateModelResponse, model_raw)
|
||||
except KeyError as e:
|
||||
except ModelNotFoundException as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=409, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
|
||||
return model_response
|
||||
|
||||
@models_router.post(
|
||||
"/",
|
||||
"/import",
|
||||
operation_id="import_model",
|
||||
responses= {
|
||||
201: {"description" : "The model imported successfully"},
|
||||
404: {"description" : "The model could not be found"},
|
||||
415: {"description" : "Unrecognized file/folder format"},
|
||||
424: {"description" : "The model appeared to import successfully, but could not be found in the model manager"},
|
||||
409: {"description" : "There is already a model corresponding to this path or repo_id"},
|
||||
},
|
||||
@ -94,7 +141,7 @@ async def import_model(
|
||||
prediction_type: Optional[Literal['v_prediction','epsilon','sample']] = \
|
||||
Body(description='Prediction type for SDv2 checkpoint files', default="v_prediction"),
|
||||
) -> ImportModelResponse:
|
||||
""" Add a model using its local path, repo_id, or remote URL """
|
||||
""" Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically """
|
||||
|
||||
items_to_import = {location}
|
||||
prediction_types = { x.value: x for x in SchedulerPredictionType }
|
||||
@ -109,7 +156,7 @@ async def import_model(
|
||||
|
||||
if not info:
|
||||
logger.error("Import failed")
|
||||
raise HTTPException(status_code=424)
|
||||
raise HTTPException(status_code=415)
|
||||
|
||||
logger.info(f'Successfully imported {location}, got {info}')
|
||||
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
@ -119,25 +166,66 @@ async def import_model(
|
||||
)
|
||||
return parse_obj_as(ImportModelResponse, model_raw)
|
||||
|
||||
except KeyError as e:
|
||||
except ModelNotFoundException as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except InvalidModelException as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=415)
|
||||
except ValueError as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=409, detail=str(e))
|
||||
|
||||
@models_router.post(
|
||||
"/add",
|
||||
operation_id="add_model",
|
||||
responses= {
|
||||
201: {"description" : "The model added successfully"},
|
||||
404: {"description" : "The model could not be found"},
|
||||
424: {"description" : "The model appeared to add successfully, but could not be found in the model manager"},
|
||||
409: {"description" : "There is already a model corresponding to this path or repo_id"},
|
||||
},
|
||||
status_code=201,
|
||||
response_model=ImportModelResponse
|
||||
)
|
||||
async def add_model(
|
||||
info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
|
||||
) -> ImportModelResponse:
|
||||
""" Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
|
||||
|
||||
logger = ApiDependencies.invoker.services.logger
|
||||
|
||||
try:
|
||||
ApiDependencies.invoker.services.model_manager.add_model(
|
||||
info.model_name,
|
||||
info.base_model,
|
||||
info.model_type,
|
||||
model_attributes = info.dict()
|
||||
)
|
||||
logger.info(f'Successfully added {info.model_name}')
|
||||
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
model_name=info.model_name,
|
||||
base_model=info.base_model,
|
||||
model_type=info.model_type
|
||||
)
|
||||
return parse_obj_as(ImportModelResponse, model_raw)
|
||||
except ModelNotFoundException as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=409, detail=str(e))
|
||||
|
||||
|
||||
|
||||
@models_router.delete(
|
||||
"/{base_model}/{model_type}/{model_name}",
|
||||
operation_id="del_model",
|
||||
responses={
|
||||
204: {
|
||||
"description": "Model deleted successfully"
|
||||
},
|
||||
404: {
|
||||
"description": "Model not found"
|
||||
}
|
||||
204: { "description": "Model deleted successfully" },
|
||||
404: { "description": "Model not found" }
|
||||
},
|
||||
status_code = 204,
|
||||
response_model = None,
|
||||
)
|
||||
async def delete_model(
|
||||
base_model: BaseModelType = Path(description="Base model"),
|
||||
@ -154,9 +242,9 @@ async def delete_model(
|
||||
)
|
||||
logger.info(f"Deleted model: {model_name}")
|
||||
return Response(status_code=204)
|
||||
except KeyError:
|
||||
logger.error(f"Model not found: {model_name}")
|
||||
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
|
||||
except ModelNotFoundException as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
|
||||
@models_router.put(
|
||||
"/convert/{base_model}/{model_type}/{model_name}",
|
||||
@ -173,24 +261,74 @@ async def convert_model(
|
||||
base_model: BaseModelType = Path(description="Base model"),
|
||||
model_type: ModelType = Path(description="The type of model"),
|
||||
model_name: str = Path(description="model name"),
|
||||
convert_dest_directory: Optional[str] = Query(default=None, description="Save the converted model to the designated directory"),
|
||||
) -> ConvertModelResponse:
|
||||
"""Convert a checkpoint model into a diffusers model"""
|
||||
"""Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none."""
|
||||
logger = ApiDependencies.invoker.services.logger
|
||||
try:
|
||||
logger.info(f"Converting model: {model_name}")
|
||||
dest = pathlib.Path(convert_dest_directory) if convert_dest_directory else None
|
||||
ApiDependencies.invoker.services.model_manager.convert_model(model_name,
|
||||
base_model = base_model,
|
||||
model_type = model_type
|
||||
model_type = model_type,
|
||||
convert_dest_directory = dest,
|
||||
)
|
||||
model_raw = ApiDependencies.invoker.services.model_manager.list_model(model_name,
|
||||
base_model = base_model,
|
||||
model_type = model_type)
|
||||
response = parse_obj_as(ConvertModelResponse, model_raw)
|
||||
except KeyError:
|
||||
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
|
||||
except ModelNotFoundException as e:
|
||||
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
return response
|
||||
|
||||
@models_router.get(
|
||||
"/search",
|
||||
operation_id="search_for_models",
|
||||
responses={
|
||||
200: { "description": "Directory searched successfully" },
|
||||
404: { "description": "Invalid directory path" },
|
||||
},
|
||||
status_code = 200,
|
||||
response_model = List[pathlib.Path]
|
||||
)
|
||||
async def search_for_models(
|
||||
search_path: pathlib.Path = Query(description="Directory path to search for models")
|
||||
)->List[pathlib.Path]:
|
||||
if not search_path.is_dir():
|
||||
raise HTTPException(status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory")
|
||||
return ApiDependencies.invoker.services.model_manager.search_for_models([search_path])
|
||||
|
||||
@models_router.get(
|
||||
"/ckpt_confs",
|
||||
operation_id="list_ckpt_configs",
|
||||
responses={
|
||||
200: { "description" : "paths retrieved successfully" },
|
||||
},
|
||||
status_code = 200,
|
||||
response_model = List[pathlib.Path]
|
||||
)
|
||||
async def list_ckpt_configs(
|
||||
)->List[pathlib.Path]:
|
||||
"""Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT."""
|
||||
return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()
|
||||
|
||||
|
||||
@models_router.get(
|
||||
"/sync",
|
||||
operation_id="sync_to_config",
|
||||
responses={
|
||||
201: { "description": "synchronization successful" },
|
||||
},
|
||||
status_code = 201,
|
||||
response_model = None
|
||||
)
|
||||
async def sync_to_config(
|
||||
)->None:
|
||||
"""Call after making changes to models.yaml, autoimport directories or models directory to synchronize
|
||||
in-memory data structures with disk data structures."""
|
||||
return ApiDependencies.invoker.services.model_manager.sync_to_config()
|
||||
|
||||
@models_router.put(
|
||||
"/merge/{base_model}",
|
||||
@ -210,24 +348,75 @@ async def merge_models(
|
||||
alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
|
||||
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
|
||||
force: Optional[bool] = Body(description="Force merging of models created with different versions of diffusers", default=False),
|
||||
merge_dest_directory: Optional[str] = Body(description="Save the merged model to the designated directory (with 'merged_model_name' appended)", default=None)
|
||||
) -> MergeModelResponse:
|
||||
"""Convert a checkpoint model into a diffusers model"""
|
||||
logger = ApiDependencies.invoker.services.logger
|
||||
try:
|
||||
logger.info(f"Merging models: {model_names}")
|
||||
logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
|
||||
dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
|
||||
result = ApiDependencies.invoker.services.model_manager.merge_models(model_names,
|
||||
base_model,
|
||||
merged_model_name or "+".join(model_names),
|
||||
alpha,
|
||||
interp,
|
||||
force)
|
||||
merged_model_name=merged_model_name or "+".join(model_names),
|
||||
alpha=alpha,
|
||||
interp=interp,
|
||||
force=force,
|
||||
merge_dest_directory = dest
|
||||
)
|
||||
model_raw = ApiDependencies.invoker.services.model_manager.list_model(result.name,
|
||||
base_model = base_model,
|
||||
model_type = ModelType.Main,
|
||||
)
|
||||
response = parse_obj_as(ConvertModelResponse, model_raw)
|
||||
except KeyError:
|
||||
except ModelNotFoundException:
|
||||
raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
return response
|
||||
|
||||
# The rename operation is now supported by update_model and no longer needs to be
|
||||
# a standalone route.
|
||||
# @models_router.post(
|
||||
# "/rename/{base_model}/{model_type}/{model_name}",
|
||||
# operation_id="rename_model",
|
||||
# responses= {
|
||||
# 201: {"description" : "The model was renamed successfully"},
|
||||
# 404: {"description" : "The model could not be found"},
|
||||
# 409: {"description" : "There is already a model corresponding to the new name"},
|
||||
# },
|
||||
# status_code=201,
|
||||
# response_model=ImportModelResponse
|
||||
# )
|
||||
# async def rename_model(
|
||||
# base_model: BaseModelType = Path(description="Base model"),
|
||||
# model_type: ModelType = Path(description="The type of model"),
|
||||
# model_name: str = Path(description="current model name"),
|
||||
# new_name: Optional[str] = Query(description="new model name", default=None),
|
||||
# new_base: Optional[BaseModelType] = Query(description="new model base", default=None),
|
||||
# ) -> ImportModelResponse:
|
||||
# """ Rename a model"""
|
||||
|
||||
# logger = ApiDependencies.invoker.services.logger
|
||||
|
||||
# try:
|
||||
# result = ApiDependencies.invoker.services.model_manager.rename_model(
|
||||
# base_model = base_model,
|
||||
# model_type = model_type,
|
||||
# model_name = model_name,
|
||||
# new_name = new_name,
|
||||
# new_base = new_base,
|
||||
# )
|
||||
# logger.debug(result)
|
||||
# logger.info(f'Successfully renamed {model_name}=>{new_name}')
|
||||
# model_raw = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
# model_name=new_name or model_name,
|
||||
# base_model=new_base or base_model,
|
||||
# model_type=model_type
|
||||
# )
|
||||
# return parse_obj_as(ImportModelResponse, model_raw)
|
||||
# except ModelNotFoundException as e:
|
||||
# logger.error(str(e))
|
||||
# raise HTTPException(status_code=404, detail=str(e))
|
||||
# except ValueError as e:
|
||||
# logger.error(str(e))
|
||||
# raise HTTPException(status_code=409, detail=str(e))
|
||||
|
||||
@ -39,6 +39,7 @@ from .invocations.baseinvocation import BaseInvocation
|
||||
|
||||
|
||||
import torch
|
||||
import invokeai.backend.util.hotfixes
|
||||
if torch.backends.mps.is_available():
|
||||
import invokeai.backend.util.mps_fixes
|
||||
|
||||
|
||||
@ -54,10 +54,10 @@ from .services.invocation_services import InvocationServices
|
||||
from .services.invoker import Invoker
|
||||
from .services.model_manager_service import ModelManagerService
|
||||
from .services.processor import DefaultInvocationProcessor
|
||||
from .services.restoration_services import RestorationServices
|
||||
from .services.sqlite import SqliteItemStorage
|
||||
|
||||
import torch
|
||||
import invokeai.backend.util.hotfixes
|
||||
if torch.backends.mps.is_available():
|
||||
import invokeai.backend.util.mps_fixes
|
||||
|
||||
@ -295,7 +295,6 @@ def invoke_cli():
|
||||
),
|
||||
graph_execution_manager=graph_execution_manager,
|
||||
processor=DefaultInvocationProcessor(),
|
||||
restoration=RestorationServices(config,logger=logger),
|
||||
logger=logger,
|
||||
configuration=config,
|
||||
)
|
||||
|
||||
@ -4,17 +4,12 @@ from typing import Literal
|
||||
|
||||
import numpy as np
|
||||
from pydantic import Field, validator
|
||||
from invokeai.app.models.image import ImageField
|
||||
|
||||
from invokeai.app.models.image import ImageField
|
||||
from invokeai.app.util.misc import SEED_MAX, get_random_seed
|
||||
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
InvocationConfig,
|
||||
InvocationContext,
|
||||
BaseInvocationOutput,
|
||||
UIConfig,
|
||||
)
|
||||
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
|
||||
InvocationConfig, InvocationContext, UIConfig)
|
||||
|
||||
|
||||
class IntCollectionOutput(BaseInvocationOutput):
|
||||
@ -32,7 +27,8 @@ class FloatCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["float_collection"] = "float_collection"
|
||||
|
||||
# Outputs
|
||||
collection: list[float] = Field(default=[], description="The float collection")
|
||||
collection: list[float] = Field(
|
||||
default=[], description="The float collection")
|
||||
|
||||
|
||||
class ImageCollectionOutput(BaseInvocationOutput):
|
||||
@ -41,7 +37,8 @@ class ImageCollectionOutput(BaseInvocationOutput):
|
||||
type: Literal["image_collection"] = "image_collection"
|
||||
|
||||
# Outputs
|
||||
collection: list[ImageField] = Field(default=[], description="The output images")
|
||||
collection: list[ImageField] = Field(
|
||||
default=[], description="The output images")
|
||||
|
||||
class Config:
|
||||
schema_extra = {"required": ["type", "collection"]}
|
||||
@ -57,6 +54,14 @@ class RangeInvocation(BaseInvocation):
|
||||
stop: int = Field(default=10, description="The stop of the range")
|
||||
step: int = Field(default=1, description="The step of the range")
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Range",
|
||||
"tags": ["range", "integer", "collection"]
|
||||
},
|
||||
}
|
||||
|
||||
@validator("stop")
|
||||
def stop_gt_start(cls, v, values):
|
||||
if "start" in values and v <= values["start"]:
|
||||
@ -79,10 +84,20 @@ class RangeOfSizeInvocation(BaseInvocation):
|
||||
size: int = Field(default=1, description="The number of values")
|
||||
step: int = Field(default=1, description="The step of the range")
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Sized Range",
|
||||
"tags": ["range", "integer", "size", "collection"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntCollectionOutput:
|
||||
return IntCollectionOutput(
|
||||
collection=list(range(self.start, self.start + self.size, self.step))
|
||||
)
|
||||
collection=list(
|
||||
range(
|
||||
self.start, self.start + self.size,
|
||||
self.step)))
|
||||
|
||||
|
||||
class RandomRangeInvocation(BaseInvocation):
|
||||
@ -103,11 +118,21 @@ class RandomRangeInvocation(BaseInvocation):
|
||||
default_factory=get_random_seed,
|
||||
)
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Random Range",
|
||||
"tags": ["range", "integer", "random", "collection"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntCollectionOutput:
|
||||
rng = np.random.default_rng(self.seed)
|
||||
return IntCollectionOutput(
|
||||
collection=list(rng.integers(low=self.low, high=self.high, size=self.size))
|
||||
)
|
||||
collection=list(
|
||||
rng.integers(
|
||||
low=self.low, high=self.high,
|
||||
size=self.size)))
|
||||
|
||||
|
||||
class ImageCollectionInvocation(BaseInvocation):
|
||||
@ -121,6 +146,7 @@ class ImageCollectionInvocation(BaseInvocation):
|
||||
default=[], description="The image collection to load"
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageCollectionOutput:
|
||||
return ImageCollectionOutput(collection=self.images)
|
||||
|
||||
@ -128,6 +154,7 @@ class ImageCollectionInvocation(BaseInvocation):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"type_hints": {
|
||||
"title": "Image Collection",
|
||||
"images": "image_collection",
|
||||
}
|
||||
},
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from typing import Literal, Optional, Union, List
|
||||
from typing import Literal, Optional, Union, List, Annotated
|
||||
from pydantic import BaseModel, Field
|
||||
import re
|
||||
|
||||
@ -10,7 +10,7 @@ from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
|
||||
from ...backend.model_management import BaseModelType, ModelType, SubModelType, ModelPatcher
|
||||
|
||||
import torch
|
||||
from compel import Compel
|
||||
from compel import Compel, ReturnedEmbeddingsType
|
||||
from compel.prompt_parser import (Blend, Conjunction,
|
||||
CrossAttentionControlSubstitute,
|
||||
FlattenedPrompt, Fragment)
|
||||
@ -22,6 +22,7 @@ from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
|
||||
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
|
||||
InvocationConfig, InvocationContext)
|
||||
from .model import ClipField
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
class ConditioningField(BaseModel):
|
||||
@ -31,6 +32,34 @@ class ConditioningField(BaseModel):
|
||||
class Config:
|
||||
schema_extra = {"required": ["conditioning_name"]}
|
||||
|
||||
@dataclass
|
||||
class BasicConditioningInfo:
|
||||
#type: Literal["basic_conditioning"] = "basic_conditioning"
|
||||
embeds: torch.Tensor
|
||||
extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
|
||||
# weight: float
|
||||
# mode: ConditioningAlgo
|
||||
|
||||
@dataclass
|
||||
class SDXLConditioningInfo(BasicConditioningInfo):
|
||||
#type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
|
||||
pooled_embeds: torch.Tensor
|
||||
add_time_ids: torch.Tensor
|
||||
|
||||
ConditioningInfoType = Annotated[
|
||||
Union[BasicConditioningInfo, SDXLConditioningInfo],
|
||||
Field(discriminator="type")
|
||||
]
|
||||
|
||||
@dataclass
|
||||
class ConditioningFieldData:
|
||||
conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
|
||||
#unconditioned: Optional[torch.Tensor]
|
||||
|
||||
#class ConditioningAlgo(str, Enum):
|
||||
# Compose = "compose"
|
||||
# ComposeEx = "compose_ex"
|
||||
# PerpNeg = "perp_neg"
|
||||
|
||||
class CompelOutput(BaseInvocationOutput):
|
||||
"""Compel parser output"""
|
||||
@ -65,10 +94,10 @@ class CompelInvocation(BaseInvocation):
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
tokenizer_info = context.services.model_manager.get_model(
|
||||
**self.clip.tokenizer.dict(),
|
||||
**self.clip.tokenizer.dict(), context=context,
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**self.clip.text_encoder.dict(),
|
||||
**self.clip.text_encoder.dict(), context=context,
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
@ -90,6 +119,7 @@ class CompelInvocation(BaseInvocation):
|
||||
model_name=name,
|
||||
base_model=self.clip.text_encoder.base_model,
|
||||
model_type=ModelType.TextualInversion,
|
||||
context=context,
|
||||
).context.model
|
||||
)
|
||||
except ModelNotFoundException:
|
||||
@ -108,7 +138,7 @@ class CompelInvocation(BaseInvocation):
|
||||
text_encoder=text_encoder,
|
||||
textual_inversion_manager=ti_manager,
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=True, # TODO:
|
||||
truncate_long_prompts=True,
|
||||
)
|
||||
|
||||
conjunction = Compel.parse_prompt_string(self.prompt)
|
||||
@ -120,19 +150,25 @@ class CompelInvocation(BaseInvocation):
|
||||
c, options = compel.build_conditioning_tensor_for_prompt_object(
|
||||
prompt)
|
||||
|
||||
# TODO: long prompt support
|
||||
# if not self.truncate_long_prompts:
|
||||
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
||||
tokens_count_including_eos_bos=get_max_token_count(
|
||||
tokenizer, conjunction),
|
||||
cross_attention_control_args=options.get(
|
||||
"cross_attention_control", None),)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
c = c.detach().to("cpu")
|
||||
|
||||
# TODO: hacky but works ;D maybe rename latents somehow?
|
||||
context.services.latents.save(conditioning_name, (c, ec))
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
BasicConditioningInfo(
|
||||
embeds=c,
|
||||
extra_conditioning=ec,
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
@ -140,6 +176,397 @@ class CompelInvocation(BaseInvocation):
|
||||
),
|
||||
)
|
||||
|
||||
class SDXLPromptInvocationBase:
|
||||
def run_clip_raw(self, context, clip_field, prompt, get_pooled):
|
||||
tokenizer_info = context.services.model_manager.get_model(
|
||||
**clip_field.tokenizer.dict(),
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**clip_field.text_encoder.dict(),
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
for lora in clip_field.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}))
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
#loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
|
||||
ti_list = []
|
||||
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
|
||||
name = trigger[1:-1]
|
||||
try:
|
||||
ti_list.append(
|
||||
context.services.model_manager.get_model(
|
||||
model_name=name,
|
||||
base_model=clip_field.text_encoder.base_model,
|
||||
model_type=ModelType.TextualInversion,
|
||||
).context.model
|
||||
)
|
||||
except ModelNotFoundException:
|
||||
# print(e)
|
||||
#import traceback
|
||||
#print(traceback.format_exc())
|
||||
print(f"Warn: trigger: \"{trigger}\" not found")
|
||||
|
||||
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
|
||||
text_encoder_info as text_encoder:
|
||||
|
||||
text_inputs = tokenizer(
|
||||
prompt,
|
||||
padding="max_length",
|
||||
max_length=tokenizer.model_max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
text_input_ids = text_inputs.input_ids
|
||||
prompt_embeds = text_encoder(
|
||||
text_input_ids.to(text_encoder.device),
|
||||
output_hidden_states=True,
|
||||
)
|
||||
if get_pooled:
|
||||
c_pooled = prompt_embeds[0]
|
||||
else:
|
||||
c_pooled = None
|
||||
c = prompt_embeds.hidden_states[-2]
|
||||
|
||||
del tokenizer
|
||||
del text_encoder
|
||||
del tokenizer_info
|
||||
del text_encoder_info
|
||||
|
||||
c = c.detach().to("cpu")
|
||||
if c_pooled is not None:
|
||||
c_pooled = c_pooled.detach().to("cpu")
|
||||
|
||||
return c, c_pooled, None
|
||||
|
||||
def run_clip_compel(self, context, clip_field, prompt, get_pooled):
|
||||
tokenizer_info = context.services.model_manager.get_model(
|
||||
**clip_field.tokenizer.dict(),
|
||||
)
|
||||
text_encoder_info = context.services.model_manager.get_model(
|
||||
**clip_field.text_encoder.dict(),
|
||||
)
|
||||
|
||||
def _lora_loader():
|
||||
for lora in clip_field.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}))
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
#loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
|
||||
|
||||
ti_list = []
|
||||
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
|
||||
name = trigger[1:-1]
|
||||
try:
|
||||
ti_list.append(
|
||||
context.services.model_manager.get_model(
|
||||
model_name=name,
|
||||
base_model=clip_field.text_encoder.base_model,
|
||||
model_type=ModelType.TextualInversion,
|
||||
).context.model
|
||||
)
|
||||
except ModelNotFoundException:
|
||||
# print(e)
|
||||
#import traceback
|
||||
#print(traceback.format_exc())
|
||||
print(f"Warn: trigger: \"{trigger}\" not found")
|
||||
|
||||
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
|
||||
text_encoder_info as text_encoder:
|
||||
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
text_encoder=text_encoder,
|
||||
textual_inversion_manager=ti_manager,
|
||||
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,
|
||||
)
|
||||
|
||||
conjunction = Compel.parse_prompt_string(prompt)
|
||||
|
||||
if context.services.configuration.log_tokenization:
|
||||
# TODO: better logging for and syntax
|
||||
for prompt_obj in conjunction.prompts:
|
||||
log_tokenization_for_prompt_object(prompt_obj, tokenizer)
|
||||
|
||||
# TODO: ask for optimizations? to not run text_encoder twice
|
||||
c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
|
||||
if get_pooled:
|
||||
c_pooled = compel.conditioning_provider.get_pooled_embeddings([prompt])
|
||||
else:
|
||||
c_pooled = None
|
||||
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
||||
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
|
||||
cross_attention_control_args=options.get("cross_attention_control", None),
|
||||
)
|
||||
|
||||
del tokenizer
|
||||
del text_encoder
|
||||
del tokenizer_info
|
||||
del text_encoder_info
|
||||
|
||||
c = c.detach().to("cpu")
|
||||
if c_pooled is not None:
|
||||
c_pooled = c_pooled.detach().to("cpu")
|
||||
|
||||
return c, c_pooled, ec
|
||||
|
||||
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
|
||||
type: Literal["sdxl_compel_prompt"] = "sdxl_compel_prompt"
|
||||
|
||||
prompt: str = Field(default="", description="Prompt")
|
||||
style: str = Field(default="", description="Style prompt")
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
crop_left: int = Field(0, description="")
|
||||
target_width: int = Field(1024, description="")
|
||||
target_height: int = Field(1024, description="")
|
||||
clip: ClipField = Field(None, description="Clip to use")
|
||||
clip2: ClipField = Field(None, description="Clip2 to use")
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Prompt (Compel)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False)
|
||||
if self.style.strip() == "":
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True)
|
||||
else:
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
|
||||
|
||||
original_size = (self.original_height, self.original_width)
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
target_size = (self.target_height, self.target_width)
|
||||
|
||||
add_time_ids = torch.tensor([
|
||||
original_size + crop_coords + target_size
|
||||
])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
embeds=torch.cat([c1, c2], dim=-1),
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec1,
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
|
||||
type: Literal["sdxl_refiner_compel_prompt"] = "sdxl_refiner_compel_prompt"
|
||||
|
||||
style: str = Field(default="", description="Style prompt") # TODO: ?
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
crop_left: int = Field(0, description="")
|
||||
aesthetic_score: float = Field(6.0, description="")
|
||||
clip2: ClipField = Field(None, description="Clip to use")
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Refiner Prompt (Compel)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True)
|
||||
|
||||
original_size = (self.original_height, self.original_width)
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
|
||||
add_time_ids = torch.tensor([
|
||||
original_size + crop_coords + (self.aesthetic_score,)
|
||||
])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
embeds=c2,
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec2, # or None
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Pass unmodified prompt to conditioning without compel processing."""
|
||||
|
||||
type: Literal["sdxl_raw_prompt"] = "sdxl_raw_prompt"
|
||||
|
||||
prompt: str = Field(default="", description="Prompt")
|
||||
style: str = Field(default="", description="Style prompt")
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
crop_left: int = Field(0, description="")
|
||||
target_width: int = Field(1024, description="")
|
||||
target_height: int = Field(1024, description="")
|
||||
clip: ClipField = Field(None, description="Clip to use")
|
||||
clip2: ClipField = Field(None, description="Clip2 to use")
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Prompt (Raw)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False)
|
||||
if self.style.strip() == "":
|
||||
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True)
|
||||
else:
|
||||
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True)
|
||||
|
||||
original_size = (self.original_height, self.original_width)
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
target_size = (self.target_height, self.target_width)
|
||||
|
||||
add_time_ids = torch.tensor([
|
||||
original_size + crop_coords + target_size
|
||||
])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
embeds=torch.cat([c1, c2], dim=-1),
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec1,
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
|
||||
"""Parse prompt using compel package to conditioning."""
|
||||
|
||||
type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt"
|
||||
|
||||
style: str = Field(default="", description="Style prompt") # TODO: ?
|
||||
original_width: int = Field(1024, description="")
|
||||
original_height: int = Field(1024, description="")
|
||||
crop_top: int = Field(0, description="")
|
||||
crop_left: int = Field(0, description="")
|
||||
aesthetic_score: float = Field(6.0, description="")
|
||||
clip2: ClipField = Field(None, description="Clip to use")
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Refiner Prompt (Raw)",
|
||||
"tags": ["prompt", "compel"],
|
||||
"type_hints": {
|
||||
"model": "model"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.style, True)
|
||||
|
||||
original_size = (self.original_height, self.original_width)
|
||||
crop_coords = (self.crop_top, self.crop_left)
|
||||
|
||||
add_time_ids = torch.tensor([
|
||||
original_size + crop_coords + (self.aesthetic_score,)
|
||||
])
|
||||
|
||||
conditioning_data = ConditioningFieldData(
|
||||
conditionings=[
|
||||
SDXLConditioningInfo(
|
||||
embeds=c2,
|
||||
pooled_embeds=c2_pooled,
|
||||
add_time_ids=add_time_ids,
|
||||
extra_conditioning=ec2, # or None
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
context.services.latents.save(conditioning_name, conditioning_data)
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class ClipSkipInvocationOutput(BaseInvocationOutput):
|
||||
"""Clip skip node output"""
|
||||
type: Literal["clip_skip_output"] = "clip_skip_output"
|
||||
@ -152,6 +579,14 @@ class ClipSkipInvocation(BaseInvocation):
|
||||
clip: ClipField = Field(None, description="Clip to use")
|
||||
skipped_layers: int = Field(0, description="Number of layers to skip in text_encoder")
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "CLIP Skip",
|
||||
"tags": ["clip", "skip"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
|
||||
self.clip.skipped_layers += self.skipped_layers
|
||||
return ClipSkipInvocationOutput(
|
||||
|
||||
@ -1,42 +1,25 @@
|
||||
# Invocations for ControlNet image preprocessors
|
||||
# initial implementation by Gregg Helt, 2023
|
||||
# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
|
||||
from builtins import float, bool
|
||||
from builtins import bool, float
|
||||
from typing import Dict, List, Literal, Optional, Union
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Literal, Optional, Union, List, Dict
|
||||
from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector,
|
||||
LeresDetector, LineartAnimeDetector,
|
||||
LineartDetector, MediapipeFaceDetector,
|
||||
MidasDetector, MLSDdetector, NormalBaeDetector,
|
||||
OpenposeDetector, PidiNetDetector, SamDetector,
|
||||
ZoeDetector)
|
||||
from controlnet_aux.util import HWC3, ade_palette
|
||||
from PIL import Image
|
||||
from pydantic import BaseModel, Field, validator
|
||||
|
||||
from ..models.image import ImageField, ImageCategory, ResourceOrigin
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
InvocationContext,
|
||||
InvocationConfig,
|
||||
)
|
||||
|
||||
from controlnet_aux import (
|
||||
CannyDetector,
|
||||
HEDdetector,
|
||||
LineartDetector,
|
||||
LineartAnimeDetector,
|
||||
MidasDetector,
|
||||
MLSDdetector,
|
||||
NormalBaeDetector,
|
||||
OpenposeDetector,
|
||||
PidiNetDetector,
|
||||
ContentShuffleDetector,
|
||||
ZoeDetector,
|
||||
MediapipeFaceDetector,
|
||||
SamDetector,
|
||||
LeresDetector,
|
||||
)
|
||||
|
||||
from controlnet_aux.util import HWC3, ade_palette
|
||||
|
||||
|
||||
from ...backend.model_management import BaseModelType, ModelType
|
||||
from ..models.image import ImageCategory, ImageField, ResourceOrigin
|
||||
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
|
||||
InvocationConfig, InvocationContext)
|
||||
from .image import ImageOutput, PILInvocationConfig
|
||||
|
||||
CONTROLNET_DEFAULT_MODELS = [
|
||||
@ -74,66 +57,82 @@ CONTROLNET_DEFAULT_MODELS = [
|
||||
"lllyasviel/control_v11e_sd15_ip2p",
|
||||
"lllyasviel/control_v11f1e_sd15_tile",
|
||||
|
||||
#################################################
|
||||
# thibaud sd v2.1 models (ControlNet v1.0? or v1.1?
|
||||
##################################################
|
||||
"thibaud/controlnet-sd21-openpose-diffusers",
|
||||
"thibaud/controlnet-sd21-canny-diffusers",
|
||||
"thibaud/controlnet-sd21-depth-diffusers",
|
||||
"thibaud/controlnet-sd21-scribble-diffusers",
|
||||
"thibaud/controlnet-sd21-hed-diffusers",
|
||||
"thibaud/controlnet-sd21-zoedepth-diffusers",
|
||||
"thibaud/controlnet-sd21-color-diffusers",
|
||||
"thibaud/controlnet-sd21-openposev2-diffusers",
|
||||
"thibaud/controlnet-sd21-lineart-diffusers",
|
||||
"thibaud/controlnet-sd21-normalbae-diffusers",
|
||||
"thibaud/controlnet-sd21-ade20k-diffusers",
|
||||
#################################################
|
||||
# thibaud sd v2.1 models (ControlNet v1.0? or v1.1?
|
||||
##################################################
|
||||
"thibaud/controlnet-sd21-openpose-diffusers",
|
||||
"thibaud/controlnet-sd21-canny-diffusers",
|
||||
"thibaud/controlnet-sd21-depth-diffusers",
|
||||
"thibaud/controlnet-sd21-scribble-diffusers",
|
||||
"thibaud/controlnet-sd21-hed-diffusers",
|
||||
"thibaud/controlnet-sd21-zoedepth-diffusers",
|
||||
"thibaud/controlnet-sd21-color-diffusers",
|
||||
"thibaud/controlnet-sd21-openposev2-diffusers",
|
||||
"thibaud/controlnet-sd21-lineart-diffusers",
|
||||
"thibaud/controlnet-sd21-normalbae-diffusers",
|
||||
"thibaud/controlnet-sd21-ade20k-diffusers",
|
||||
|
||||
##############################################
|
||||
# ControlNetMediaPipeface, ControlNet v1.1
|
||||
##############################################
|
||||
# ["CrucibleAI/ControlNetMediaPipeFace", "diffusion_sd15"], # SD 1.5
|
||||
# diffusion_sd15 needs to be passed to from_pretrained() as subfolder arg
|
||||
# hacked t2l to split to model & subfolder if format is "model,subfolder"
|
||||
"CrucibleAI/ControlNetMediaPipeFace,diffusion_sd15", # SD 1.5
|
||||
"CrucibleAI/ControlNetMediaPipeFace", # SD 2.1?
|
||||
##############################################
|
||||
# ControlNetMediaPipeface, ControlNet v1.1
|
||||
##############################################
|
||||
# ["CrucibleAI/ControlNetMediaPipeFace", "diffusion_sd15"], # SD 1.5
|
||||
# diffusion_sd15 needs to be passed to from_pretrained() as subfolder arg
|
||||
# hacked t2l to split to model & subfolder if format is "model,subfolder"
|
||||
"CrucibleAI/ControlNetMediaPipeFace,diffusion_sd15", # SD 1.5
|
||||
"CrucibleAI/ControlNetMediaPipeFace", # SD 2.1?
|
||||
]
|
||||
|
||||
CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
|
||||
CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control", "unbalanced"])]
|
||||
CONTROLNET_MODE_VALUES = Literal[tuple(
|
||||
["balanced", "more_prompt", "more_control", "unbalanced"])]
|
||||
# crop and fill options not ready yet
|
||||
# CONTROLNET_RESIZE_VALUES = Literal[tuple(["just_resize", "crop_resize", "fill_resize"])]
|
||||
|
||||
|
||||
class ControlNetModelField(BaseModel):
|
||||
"""ControlNet model field"""
|
||||
|
||||
model_name: str = Field(description="Name of the ControlNet model")
|
||||
base_model: BaseModelType = Field(description="Base model")
|
||||
|
||||
|
||||
class ControlField(BaseModel):
|
||||
image: ImageField = Field(default=None, description="The control image")
|
||||
control_model: Optional[str] = Field(default=None, description="The ControlNet model to use")
|
||||
control_model: Optional[ControlNetModelField] = Field(
|
||||
default=None, description="The ControlNet model to use")
|
||||
# control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
|
||||
control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
|
||||
begin_step_percent: float = Field(default=0, ge=0, le=1,
|
||||
description="When the ControlNet is first applied (% of total steps)")
|
||||
end_step_percent: float = Field(default=1, ge=0, le=1,
|
||||
description="When the ControlNet is last applied (% of total steps)")
|
||||
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
|
||||
control_weight: Union[float, List[float]] = Field(
|
||||
default=1, description="The weight given to the ControlNet")
|
||||
begin_step_percent: float = Field(
|
||||
default=0, ge=0, le=1,
|
||||
description="When the ControlNet is first applied (% of total steps)")
|
||||
end_step_percent: float = Field(
|
||||
default=1, ge=0, le=1,
|
||||
description="When the ControlNet is last applied (% of total steps)")
|
||||
control_mode: CONTROLNET_MODE_VALUES = Field(
|
||||
default="balanced", description="The control mode to use")
|
||||
# resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
|
||||
|
||||
@validator("control_weight")
|
||||
def abs_le_one(cls, v):
|
||||
"""validate that all abs(values) are <=1"""
|
||||
def validate_control_weight(cls, v):
|
||||
"""Validate that all control weights in the valid range"""
|
||||
if isinstance(v, list):
|
||||
for i in v:
|
||||
if abs(i) > 1:
|
||||
raise ValueError('all abs(control_weight) must be <= 1')
|
||||
if i < -1 or i > 2:
|
||||
raise ValueError(
|
||||
'Control weights must be within -1 to 2 range')
|
||||
else:
|
||||
if abs(v) > 1:
|
||||
raise ValueError('abs(control_weight) must be <= 1')
|
||||
if v < -1 or v > 2:
|
||||
raise ValueError('Control weights must be within -1 to 2 range')
|
||||
return v
|
||||
|
||||
class Config:
|
||||
schema_extra = {
|
||||
"required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"],
|
||||
"ui": {
|
||||
"type_hints": {
|
||||
"control_weight": "float",
|
||||
"control_model": "controlnet_model",
|
||||
# "control_weight": "number",
|
||||
}
|
||||
}
|
||||
@ -154,10 +153,10 @@ class ControlNetInvocation(BaseInvocation):
|
||||
type: Literal["controlnet"] = "controlnet"
|
||||
# Inputs
|
||||
image: ImageField = Field(default=None, description="The control image")
|
||||
control_model: CONTROLNET_NAME_VALUES = Field(default="lllyasviel/sd-controlnet-canny",
|
||||
control_model: ControlNetModelField = Field(default="lllyasviel/sd-controlnet-canny",
|
||||
description="control model used")
|
||||
control_weight: Union[float, List[float]] = Field(default=1.0, description="The weight given to the ControlNet")
|
||||
begin_step_percent: float = Field(default=0, ge=0, le=1,
|
||||
begin_step_percent: float = Field(default=0, ge=-1, le=2,
|
||||
description="When the ControlNet is first applied (% of total steps)")
|
||||
end_step_percent: float = Field(default=1, ge=0, le=1,
|
||||
description="When the ControlNet is last applied (% of total steps)")
|
||||
@ -167,13 +166,14 @@ class ControlNetInvocation(BaseInvocation):
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["latents"],
|
||||
"title": "ControlNet",
|
||||
"tags": ["controlnet", "latents"],
|
||||
"type_hints": {
|
||||
"model": "model",
|
||||
"control": "control",
|
||||
# "cfg_scale": "float",
|
||||
"cfg_scale": "number",
|
||||
"control_weight": "float",
|
||||
"model": "model",
|
||||
"control": "control",
|
||||
# "cfg_scale": "float",
|
||||
"cfg_scale": "number",
|
||||
"control_weight": "float",
|
||||
}
|
||||
},
|
||||
}
|
||||
@ -200,6 +200,13 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
|
||||
image: ImageField = Field(default=None, description="The image to process")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Image Processor",
|
||||
"tags": ["image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
# superclass just passes through image without processing
|
||||
@ -231,14 +238,15 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
|
||||
return ImageOutput(
|
||||
image=processed_image_field,
|
||||
# width=processed_image.width,
|
||||
width = image_dto.width,
|
||||
width=image_dto.width,
|
||||
# height=processed_image.height,
|
||||
height = image_dto.height,
|
||||
height=image_dto.height,
|
||||
# mode=processed_image.mode,
|
||||
)
|
||||
|
||||
|
||||
class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class CannyImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Canny edge detection for ControlNet"""
|
||||
# fmt: off
|
||||
type: Literal["canny_image_processor"] = "canny_image_processor"
|
||||
@ -247,13 +255,23 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfi
|
||||
high_threshold: int = Field(default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Canny Processor",
|
||||
"tags": ["controlnet", "canny", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
canny_processor = CannyDetector()
|
||||
processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
|
||||
processed_image = canny_processor(
|
||||
image, self.low_threshold, self.high_threshold)
|
||||
return processed_image
|
||||
|
||||
|
||||
class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class HedImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies HED edge detection to image"""
|
||||
# fmt: off
|
||||
type: Literal["hed_image_processor"] = "hed_image_processor"
|
||||
@ -265,6 +283,14 @@ class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig)
|
||||
scribble: bool = Field(default=False, description="Whether to use scribble mode")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Softedge(HED) Processor",
|
||||
"tags": ["controlnet", "softedge", "hed", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
hed_processor = HEDdetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = hed_processor(image,
|
||||
@ -277,7 +303,8 @@ class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig)
|
||||
return processed_image
|
||||
|
||||
|
||||
class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class LineartImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies line art processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["lineart_image_processor"] = "lineart_image_processor"
|
||||
@ -287,16 +314,25 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCon
|
||||
coarse: bool = Field(default=False, description="Whether to use coarse mode")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Lineart Processor",
|
||||
"tags": ["controlnet", "lineart", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
lineart_processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = lineart_processor(image,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution,
|
||||
coarse=self.coarse)
|
||||
lineart_processor = LineartDetector.from_pretrained(
|
||||
"lllyasviel/Annotators")
|
||||
processed_image = lineart_processor(
|
||||
image, detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution, coarse=self.coarse)
|
||||
return processed_image
|
||||
|
||||
|
||||
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class LineartAnimeImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies line art anime processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["lineart_anime_image_processor"] = "lineart_anime_image_processor"
|
||||
@ -305,8 +341,17 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocati
|
||||
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Lineart Anime Processor",
|
||||
"tags": ["controlnet", "lineart", "anime", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
processor = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
|
||||
processor = LineartAnimeDetector.from_pretrained(
|
||||
"lllyasviel/Annotators")
|
||||
processed_image = processor(image,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution,
|
||||
@ -314,7 +359,8 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocati
|
||||
return processed_image
|
||||
|
||||
|
||||
class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class OpenposeImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies Openpose processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["openpose_image_processor"] = "openpose_image_processor"
|
||||
@ -324,17 +370,26 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
|
||||
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Openpose Processor",
|
||||
"tags": ["controlnet", "openpose", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = openpose_processor(image,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution,
|
||||
hand_and_face=self.hand_and_face,
|
||||
)
|
||||
openpose_processor = OpenposeDetector.from_pretrained(
|
||||
"lllyasviel/Annotators")
|
||||
processed_image = openpose_processor(
|
||||
image, detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution,
|
||||
hand_and_face=self.hand_and_face,)
|
||||
return processed_image
|
||||
|
||||
|
||||
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class MidasDepthImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies Midas depth processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["midas_depth_image_processor"] = "midas_depth_image_processor"
|
||||
@ -345,6 +400,14 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocation
|
||||
# depth_and_normal: bool = Field(default=False, description="whether to use depth and normal mode")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Midas (Depth) Processor",
|
||||
"tags": ["controlnet", "midas", "depth", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
midas_processor = MidasDetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = midas_processor(image,
|
||||
@ -356,7 +419,8 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocation
|
||||
return processed_image
|
||||
|
||||
|
||||
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class NormalbaeImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies NormalBae processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["normalbae_image_processor"] = "normalbae_image_processor"
|
||||
@ -365,15 +429,25 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationC
|
||||
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Normal BAE Processor",
|
||||
"tags": ["controlnet", "normal", "bae", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = normalbae_processor(image,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution)
|
||||
normalbae_processor = NormalBaeDetector.from_pretrained(
|
||||
"lllyasviel/Annotators")
|
||||
processed_image = normalbae_processor(
|
||||
image, detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution)
|
||||
return processed_image
|
||||
|
||||
|
||||
class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class MlsdImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies MLSD processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["mlsd_image_processor"] = "mlsd_image_processor"
|
||||
@ -384,17 +458,25 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig
|
||||
thr_d: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_d`")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "MLSD Processor",
|
||||
"tags": ["controlnet", "mlsd", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
mlsd_processor = MLSDdetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = mlsd_processor(image,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution,
|
||||
thr_v=self.thr_v,
|
||||
thr_d=self.thr_d)
|
||||
processed_image = mlsd_processor(
|
||||
image, detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution, thr_v=self.thr_v,
|
||||
thr_d=self.thr_d)
|
||||
return processed_image
|
||||
|
||||
|
||||
class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class PidiImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies PIDI processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["pidi_image_processor"] = "pidi_image_processor"
|
||||
@ -405,17 +487,26 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig
|
||||
scribble: bool = Field(default=False, description="Whether to use scribble mode")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "PIDI Processor",
|
||||
"tags": ["controlnet", "pidi", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
pidi_processor = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = pidi_processor(image,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution,
|
||||
safe=self.safe,
|
||||
scribble=self.scribble)
|
||||
pidi_processor = PidiNetDetector.from_pretrained(
|
||||
"lllyasviel/Annotators")
|
||||
processed_image = pidi_processor(
|
||||
image, detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution, safe=self.safe,
|
||||
scribble=self.scribble)
|
||||
return processed_image
|
||||
|
||||
|
||||
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class ContentShuffleImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies content shuffle processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["content_shuffle_image_processor"] = "content_shuffle_image_processor"
|
||||
@ -427,6 +518,14 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvoca
|
||||
f: Optional[int] = Field(default=256, ge=0, description="Content shuffle `f` parameter")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Content Shuffle Processor",
|
||||
"tags": ["controlnet", "contentshuffle", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
content_shuffle_processor = ContentShuffleDetector()
|
||||
processed_image = content_shuffle_processor(image,
|
||||
@ -440,19 +539,30 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvoca
|
||||
|
||||
|
||||
# should work with controlnet_aux >= 0.0.4 and timm <= 0.6.13
|
||||
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class ZoeDepthImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies Zoe depth processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["zoe_depth_image_processor"] = "zoe_depth_image_processor"
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Zoe (Depth) Processor",
|
||||
"tags": ["controlnet", "zoe", "depth", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
|
||||
zoe_depth_processor = ZoeDetector.from_pretrained(
|
||||
"lllyasviel/Annotators")
|
||||
processed_image = zoe_depth_processor(image)
|
||||
return processed_image
|
||||
|
||||
|
||||
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class MediapipeFaceProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies mediapipe face processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["mediapipe_face_processor"] = "mediapipe_face_processor"
|
||||
@ -461,16 +571,27 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
|
||||
min_confidence: float = Field(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Mediapipe Processor",
|
||||
"tags": ["controlnet", "mediapipe", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
# MediaPipeFaceDetector throws an error if image has alpha channel
|
||||
# so convert to RGB if needed
|
||||
if image.mode == 'RGBA':
|
||||
image = image.convert('RGB')
|
||||
mediapipe_face_processor = MediapipeFaceDetector()
|
||||
processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
|
||||
processed_image = mediapipe_face_processor(
|
||||
image, max_faces=self.max_faces, min_confidence=self.min_confidence)
|
||||
return processed_image
|
||||
|
||||
class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
|
||||
class LeresImageProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies leres processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["leres_image_processor"] = "leres_image_processor"
|
||||
@ -482,18 +603,25 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfi
|
||||
image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Leres (Depth) Processor",
|
||||
"tags": ["controlnet", "leres", "depth", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
def run_processor(self, image):
|
||||
leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
|
||||
processed_image = leres_processor(image,
|
||||
thr_a=self.thr_a,
|
||||
thr_b=self.thr_b,
|
||||
boost=self.boost,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution)
|
||||
processed_image = leres_processor(
|
||||
image, thr_a=self.thr_a, thr_b=self.thr_b, boost=self.boost,
|
||||
detect_resolution=self.detect_resolution,
|
||||
image_resolution=self.image_resolution)
|
||||
return processed_image
|
||||
|
||||
|
||||
class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class TileResamplerProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
|
||||
# fmt: off
|
||||
type: Literal["tile_image_processor"] = "tile_image_processor"
|
||||
@ -502,6 +630,14 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
|
||||
down_sampling_rate: float = Field(default=1.0, ge=1.0, le=8.0, description="Down sampling rate")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Tile Resample Processor",
|
||||
"tags": ["controlnet", "tile", "resample", "image", "processor"]
|
||||
},
|
||||
}
|
||||
|
||||
# tile_resample copied from sd-webui-controlnet/scripts/processor.py
|
||||
def tile_resample(self,
|
||||
np_img: np.ndarray,
|
||||
@ -520,28 +656,33 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationCo
|
||||
def run_processor(self, img):
|
||||
np_img = np.array(img, dtype=np.uint8)
|
||||
processed_np_image = self.tile_resample(np_img,
|
||||
#res=self.tile_size,
|
||||
# res=self.tile_size,
|
||||
down_sampling_rate=self.down_sampling_rate
|
||||
)
|
||||
processed_image = Image.fromarray(processed_np_image)
|
||||
return processed_image
|
||||
|
||||
|
||||
|
||||
|
||||
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
|
||||
class SegmentAnythingProcessorInvocation(
|
||||
ImageProcessorInvocation, PILInvocationConfig):
|
||||
"""Applies segment anything processing to image"""
|
||||
# fmt: off
|
||||
type: Literal["segment_anything_processor"] = "segment_anything_processor"
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {"ui": {"title": "Segment Anything Processor", "tags": [
|
||||
"controlnet", "segment", "anything", "sam", "image", "processor"]}, }
|
||||
|
||||
def run_processor(self, image):
|
||||
# segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
|
||||
segment_anything_processor = SamDetectorReproducibleColors.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
|
||||
segment_anything_processor = SamDetectorReproducibleColors.from_pretrained(
|
||||
"ybelkada/segment-anything", subfolder="checkpoints")
|
||||
np_img = np.array(image, dtype=np.uint8)
|
||||
processed_image = segment_anything_processor(np_img)
|
||||
return processed_image
|
||||
|
||||
|
||||
class SamDetectorReproducibleColors(SamDetector):
|
||||
|
||||
# overriding SamDetector.show_anns() method to use reproducible colors for segmentation image
|
||||
@ -553,7 +694,8 @@ class SamDetectorReproducibleColors(SamDetector):
|
||||
return
|
||||
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
|
||||
h, w = anns[0]['segmentation'].shape
|
||||
final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
|
||||
final_img = Image.fromarray(
|
||||
np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
|
||||
palette = ade_palette()
|
||||
for i, ann in enumerate(sorted_anns):
|
||||
m = ann['segmentation']
|
||||
@ -561,5 +703,8 @@ class SamDetectorReproducibleColors(SamDetector):
|
||||
# doing modulo just in case number of annotated regions exceeds number of colors in palette
|
||||
ann_color = palette[i % len(palette)]
|
||||
img[:, :] = ann_color
|
||||
final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
|
||||
final_img.paste(
|
||||
Image.fromarray(img, mode="RGB"),
|
||||
(0, 0),
|
||||
Image.fromarray(np.uint8(m * 255)))
|
||||
return np.array(final_img, dtype=np.uint8)
|
||||
|
||||
@ -35,6 +35,14 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
|
||||
mask: ImageField = Field(default=None, description="The mask to use when inpainting")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "OpenCV Inpaint",
|
||||
"tags": ["opencv", "inpaint"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
mask = context.services.images.get_pil_image(self.mask.image_name)
|
||||
|
||||
@ -130,6 +130,7 @@ class InpaintInvocation(BaseInvocation):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["stable-diffusion", "image"],
|
||||
"title": "Inpaint"
|
||||
},
|
||||
}
|
||||
|
||||
@ -146,9 +147,13 @@ class InpaintInvocation(BaseInvocation):
|
||||
source_node_id=source_node_id,
|
||||
)
|
||||
|
||||
def get_conditioning(self, context):
|
||||
c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
|
||||
uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
|
||||
def get_conditioning(self, context, unet):
|
||||
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
|
||||
c = positive_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
extra_conditioning_info = positive_cond_data.conditionings[0].extra_conditioning
|
||||
|
||||
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
|
||||
uc = negative_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
|
||||
return (uc, c, extra_conditioning_info)
|
||||
|
||||
@ -157,13 +162,13 @@ class InpaintInvocation(BaseInvocation):
|
||||
def _lora_loader():
|
||||
for lora in self.unet.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}))
|
||||
**lora.dict(exclude={"weight"}), context=context,)
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
|
||||
vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
|
||||
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context,)
|
||||
vae_info = context.services.model_manager.get_model(**self.vae.vae.dict(), context=context,)
|
||||
|
||||
with vae_info as vae,\
|
||||
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
|
||||
@ -209,7 +214,6 @@ class InpaintInvocation(BaseInvocation):
|
||||
)
|
||||
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
|
||||
|
||||
conditioning = self.get_conditioning(context)
|
||||
scheduler = get_scheduler(
|
||||
context=context,
|
||||
scheduler_info=self.unet.scheduler,
|
||||
@ -217,6 +221,8 @@ class InpaintInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
with self.load_model_old_way(context, scheduler) as model:
|
||||
conditioning = self.get_conditioning(context, model.context.model.unet)
|
||||
|
||||
outputs = Inpaint(model).generate(
|
||||
conditioning=conditioning,
|
||||
scheduler=scheduler,
|
||||
|
||||
@ -5,6 +5,7 @@ from typing import Literal, Optional
|
||||
import numpy
|
||||
from PIL import Image, ImageFilter, ImageOps, ImageChops
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import Union
|
||||
|
||||
from ..models.image import ImageCategory, ImageField, ResourceOrigin
|
||||
from .baseinvocation import (
|
||||
@ -70,6 +71,15 @@ class LoadImageInvocation(BaseInvocation):
|
||||
default=None, description="The image to load"
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Load Image",
|
||||
"tags": ["image", "load"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -90,6 +100,14 @@ class ShowImageInvocation(BaseInvocation):
|
||||
default=None, description="The image to show"
|
||||
)
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Show Image",
|
||||
"tags": ["image", "show"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
if image:
|
||||
@ -118,6 +136,14 @@ class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
|
||||
height: int = Field(default=512, gt=0, description="The height of the crop rectangle")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Crop Image",
|
||||
"tags": ["image", "crop"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -156,6 +182,14 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
|
||||
y: int = Field(default=0, description="The top y coordinate at which to paste the image")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Paste Image",
|
||||
"tags": ["image", "paste"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
base_image = context.services.images.get_pil_image(self.base_image.image_name)
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
@ -206,6 +240,14 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
|
||||
invert: bool = Field(default=False, description="Whether or not to invert the mask")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Mask From Alpha",
|
||||
"tags": ["image", "mask", "alpha"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> MaskOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -240,6 +282,14 @@ class ImageMultiplyInvocation(BaseInvocation, PILInvocationConfig):
|
||||
image2: Optional[ImageField] = Field(default=None, description="The second image to multiply")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Multiply Images",
|
||||
"tags": ["image", "multiply"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image1 = context.services.images.get_pil_image(self.image1.image_name)
|
||||
image2 = context.services.images.get_pil_image(self.image2.image_name)
|
||||
@ -276,6 +326,14 @@ class ImageChannelInvocation(BaseInvocation, PILInvocationConfig):
|
||||
channel: IMAGE_CHANNELS = Field(default="A", description="The channel to get")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Image Channel",
|
||||
"tags": ["image", "channel"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -311,6 +369,14 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
|
||||
mode: IMAGE_MODES = Field(default="L", description="The mode to convert to")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Convert Image",
|
||||
"tags": ["image", "convert"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -344,6 +410,14 @@ class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
|
||||
blur_type: Literal["gaussian", "box"] = Field(default="gaussian", description="The type of blur")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Blur Image",
|
||||
"tags": ["image", "blur"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -398,11 +472,19 @@ class ImageResizeInvocation(BaseInvocation, PILInvocationConfig):
|
||||
|
||||
# Inputs
|
||||
image: Optional[ImageField] = Field(default=None, description="The image to resize")
|
||||
width: int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
|
||||
height: int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
|
||||
width: Union[int, None] = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
|
||||
height: Union[int, None] = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
|
||||
resample_mode: PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Resize Image",
|
||||
"tags": ["image", "resize"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -436,11 +518,19 @@ class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
|
||||
type: Literal["img_scale"] = "img_scale"
|
||||
|
||||
# Inputs
|
||||
image: Optional[ImageField] = Field(default=None, description="The image to scale")
|
||||
scale_factor: float = Field(gt=0, description="The factor by which to scale the image")
|
||||
image: Optional[ImageField] = Field(default=None, description="The image to scale")
|
||||
scale_factor: Optional[float] = Field(default=2.0, gt=0, description="The factor by which to scale the image")
|
||||
resample_mode: PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Scale Image",
|
||||
"tags": ["image", "scale"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -481,6 +571,14 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Image Linear Interpolation",
|
||||
"tags": ["image", "linear", "interpolation", "lerp"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -517,6 +615,14 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
|
||||
max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Image Inverse Linear Interpolation",
|
||||
"tags": ["image", "linear", "interpolation", "inverse"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
|
||||
@ -14,6 +14,7 @@ from invokeai.backend.image_util.patchmatch import PatchMatch
|
||||
from ..models.image import ColorField, ImageCategory, ImageField, ResourceOrigin
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
InvocationConfig,
|
||||
InvocationContext,
|
||||
)
|
||||
|
||||
@ -133,6 +134,14 @@ class InfillColorInvocation(BaseInvocation):
|
||||
description="The color to use to infill",
|
||||
)
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Color Infill",
|
||||
"tags": ["image", "inpaint", "color", "infill"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -173,6 +182,14 @@ class InfillTileInvocation(BaseInvocation):
|
||||
default_factory=get_random_seed,
|
||||
)
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Tile Infill",
|
||||
"tags": ["image", "inpaint", "tile", "infill"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
@ -206,6 +223,14 @@ class InfillPatchMatchInvocation(BaseInvocation):
|
||||
default=None, description="The image to infill"
|
||||
)
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Patch Match Infill",
|
||||
"tags": ["image", "inpaint", "patchmatch", "infill"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
|
||||
|
||||
from contextlib import ExitStack
|
||||
from typing import List, Literal, Optional, Union
|
||||
|
||||
import einops
|
||||
@ -11,6 +12,7 @@ from pydantic import BaseModel, Field, validator
|
||||
|
||||
from invokeai.app.invocations.metadata import CoreMetadata
|
||||
from invokeai.app.util.step_callback import stable_diffusion_step_callback
|
||||
from invokeai.backend.model_management.models.base import ModelType
|
||||
|
||||
from ...backend.model_management.lora import ModelPatcher
|
||||
from ...backend.stable_diffusion import PipelineIntermediateState
|
||||
@ -30,6 +32,13 @@ from .controlnet_image_processors import ControlField
|
||||
from .image import ImageOutput
|
||||
from .model import ModelInfo, UNetField, VaeField
|
||||
|
||||
from diffusers.models.attention_processor import (
|
||||
AttnProcessor2_0,
|
||||
LoRAAttnProcessor2_0,
|
||||
LoRAXFormersAttnProcessor,
|
||||
XFormersAttnProcessor,
|
||||
)
|
||||
|
||||
|
||||
class LatentsField(BaseModel):
|
||||
"""A latents field used for passing latents between invocations"""
|
||||
@ -72,16 +81,21 @@ def get_scheduler(
|
||||
scheduler_name: str,
|
||||
) -> Scheduler:
|
||||
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(
|
||||
scheduler_name, SCHEDULER_MAP['ddim'])
|
||||
scheduler_name, SCHEDULER_MAP['ddim']
|
||||
)
|
||||
orig_scheduler_info = context.services.model_manager.get_model(
|
||||
**scheduler_info.dict())
|
||||
**scheduler_info.dict(), context=context,
|
||||
)
|
||||
with orig_scheduler_info as orig_scheduler:
|
||||
scheduler_config = orig_scheduler.config
|
||||
|
||||
if "_backup" in scheduler_config:
|
||||
scheduler_config = scheduler_config["_backup"]
|
||||
scheduler_config = {**scheduler_config, **
|
||||
scheduler_extra_config, "_backup": scheduler_config}
|
||||
scheduler_config = {
|
||||
**scheduler_config,
|
||||
**scheduler_extra_config,
|
||||
"_backup": scheduler_config,
|
||||
}
|
||||
scheduler = scheduler_class.from_config(scheduler_config)
|
||||
|
||||
# hack copied over from generate.py
|
||||
@ -126,6 +140,7 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Text To Latents",
|
||||
"tags": ["latents"],
|
||||
"type_hints": {
|
||||
"model": "model",
|
||||
@ -138,8 +153,11 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
|
||||
# TODO: pass this an emitter method or something? or a session for dispatching?
|
||||
def dispatch_progress(
|
||||
self, context: InvocationContext, source_node_id: str,
|
||||
intermediate_state: PipelineIntermediateState) -> None:
|
||||
self,
|
||||
context: InvocationContext,
|
||||
source_node_id: str,
|
||||
intermediate_state: PipelineIntermediateState,
|
||||
) -> None:
|
||||
stable_diffusion_step_callback(
|
||||
context=context,
|
||||
intermediate_state=intermediate_state,
|
||||
@ -148,11 +166,17 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
def get_conditioning_data(
|
||||
self, context: InvocationContext, scheduler) -> ConditioningData:
|
||||
c, extra_conditioning_info = context.services.latents.get(
|
||||
self.positive_conditioning.conditioning_name)
|
||||
uc, _ = context.services.latents.get(
|
||||
self.negative_conditioning.conditioning_name)
|
||||
self,
|
||||
context: InvocationContext,
|
||||
scheduler,
|
||||
unet,
|
||||
) -> ConditioningData:
|
||||
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
|
||||
c = positive_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
extra_conditioning_info = positive_cond_data.conditionings[0].extra_conditioning
|
||||
|
||||
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
|
||||
uc = negative_cond_data.conditionings[0].embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
|
||||
conditioning_data = ConditioningData(
|
||||
unconditioned_embeddings=uc,
|
||||
@ -174,12 +198,15 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
eta=0.0, # ddim_eta
|
||||
|
||||
# for ancestral and sde schedulers
|
||||
generator=torch.Generator(device=uc.device).manual_seed(0),
|
||||
generator=torch.Generator(device=unet.device).manual_seed(0),
|
||||
)
|
||||
return conditioning_data
|
||||
|
||||
def create_pipeline(
|
||||
self, unet, scheduler) -> StableDiffusionGeneratorPipeline:
|
||||
self,
|
||||
unet,
|
||||
scheduler,
|
||||
) -> StableDiffusionGeneratorPipeline:
|
||||
# TODO:
|
||||
# configure_model_padding(
|
||||
# unet,
|
||||
@ -214,6 +241,7 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
model: StableDiffusionGeneratorPipeline,
|
||||
control_input: List[ControlField],
|
||||
latents_shape: List[int],
|
||||
exit_stack: ExitStack,
|
||||
do_classifier_free_guidance: bool = True,
|
||||
) -> List[ControlNetData]:
|
||||
|
||||
@ -239,25 +267,20 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
control_data = []
|
||||
control_models = []
|
||||
for control_info in control_list:
|
||||
# handle control models
|
||||
if ("," in control_info.control_model):
|
||||
control_model_split = control_info.control_model.split(",")
|
||||
control_name = control_model_split[0]
|
||||
control_subfolder = control_model_split[1]
|
||||
print("Using HF model subfolders")
|
||||
print(" control_name: ", control_name)
|
||||
print(" control_subfolder: ", control_subfolder)
|
||||
control_model = ControlNetModel.from_pretrained(
|
||||
control_name, subfolder=control_subfolder,
|
||||
torch_dtype=model.unet.dtype).to(
|
||||
model.device)
|
||||
else:
|
||||
control_model = ControlNetModel.from_pretrained(
|
||||
control_info.control_model, torch_dtype=model.unet.dtype).to(model.device)
|
||||
control_model = exit_stack.enter_context(
|
||||
context.services.model_manager.get_model(
|
||||
model_name=control_info.control_model.model_name,
|
||||
model_type=ModelType.ControlNet,
|
||||
base_model=control_info.control_model.base_model,
|
||||
context=context,
|
||||
)
|
||||
)
|
||||
|
||||
control_models.append(control_model)
|
||||
control_image_field = control_info.image
|
||||
input_image = context.services.images.get_pil_image(
|
||||
control_image_field.image_name)
|
||||
control_image_field.image_name
|
||||
)
|
||||
# self.image.image_type, self.image.image_name
|
||||
# FIXME: still need to test with different widths, heights, devices, dtypes
|
||||
# and add in batch_size, num_images_per_prompt?
|
||||
@ -279,7 +302,8 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
weight=control_info.control_weight,
|
||||
begin_step_percent=control_info.begin_step_percent,
|
||||
end_step_percent=control_info.end_step_percent,
|
||||
control_mode=control_info.control_mode,)
|
||||
control_mode=control_info.control_mode,
|
||||
)
|
||||
control_data.append(control_item)
|
||||
# MultiControlNetModel has been refactored out, just need list[ControlNetData]
|
||||
return control_data
|
||||
@ -290,7 +314,8 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
|
||||
# Get the source node id (we are invoking the prepared node)
|
||||
graph_execution_state = context.services.graph_execution_manager.get(
|
||||
context.graph_execution_state_id)
|
||||
context.graph_execution_state_id
|
||||
)
|
||||
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
|
||||
|
||||
def step_callback(state: PipelineIntermediateState):
|
||||
@ -299,16 +324,21 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
def _lora_loader():
|
||||
for lora in self.unet.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}))
|
||||
**lora.dict(exclude={"weight"}), context=context,
|
||||
)
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
unet_info = context.services.model_manager.get_model(
|
||||
**self.unet.unet.dict())
|
||||
with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
|
||||
**self.unet.unet.dict(), context=context,
|
||||
)
|
||||
with ExitStack() as exit_stack,\
|
||||
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
|
||||
unet_info as unet:
|
||||
|
||||
noise = noise.to(device=unet.device, dtype=unet.dtype)
|
||||
|
||||
scheduler = get_scheduler(
|
||||
context=context,
|
||||
scheduler_info=self.unet.scheduler,
|
||||
@ -316,13 +346,14 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
pipeline = self.create_pipeline(unet, scheduler)
|
||||
conditioning_data = self.get_conditioning_data(context, scheduler)
|
||||
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
|
||||
|
||||
control_data = self.prep_control_data(
|
||||
model=pipeline, context=context, control_input=self.control,
|
||||
latents_shape=noise.shape,
|
||||
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
|
||||
do_classifier_free_guidance=True,
|
||||
exit_stack=exit_stack,
|
||||
)
|
||||
|
||||
# TODO: Verify the noise is the right size
|
||||
@ -336,6 +367,7 @@ class TextToLatentsInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
|
||||
result_latents = result_latents.to("cpu")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
name = f'{context.graph_execution_state_id}__{self.id}'
|
||||
@ -359,6 +391,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Latent To Latents",
|
||||
"tags": ["latents"],
|
||||
"type_hints": {
|
||||
"model": "model",
|
||||
@ -375,7 +408,8 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
|
||||
|
||||
# Get the source node id (we are invoking the prepared node)
|
||||
graph_execution_state = context.services.graph_execution_manager.get(
|
||||
context.graph_execution_state_id)
|
||||
context.graph_execution_state_id
|
||||
)
|
||||
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
|
||||
|
||||
def step_callback(state: PipelineIntermediateState):
|
||||
@ -384,16 +418,22 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
|
||||
def _lora_loader():
|
||||
for lora in self.unet.loras:
|
||||
lora_info = context.services.model_manager.get_model(
|
||||
**lora.dict(exclude={"weight"}))
|
||||
**lora.dict(exclude={"weight"}), context=context,
|
||||
)
|
||||
yield (lora_info.context.model, lora.weight)
|
||||
del lora_info
|
||||
return
|
||||
|
||||
unet_info = context.services.model_manager.get_model(
|
||||
**self.unet.unet.dict())
|
||||
with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
|
||||
**self.unet.unet.dict(), context=context,
|
||||
)
|
||||
with ExitStack() as exit_stack,\
|
||||
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
|
||||
unet_info as unet:
|
||||
|
||||
noise = noise.to(device=unet.device, dtype=unet.dtype)
|
||||
latent = latent.to(device=unet.device, dtype=unet.dtype)
|
||||
|
||||
scheduler = get_scheduler(
|
||||
context=context,
|
||||
scheduler_info=self.unet.scheduler,
|
||||
@ -401,18 +441,20 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
|
||||
)
|
||||
|
||||
pipeline = self.create_pipeline(unet, scheduler)
|
||||
conditioning_data = self.get_conditioning_data(context, scheduler)
|
||||
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
|
||||
|
||||
control_data = self.prep_control_data(
|
||||
model=pipeline, context=context, control_input=self.control,
|
||||
latents_shape=noise.shape,
|
||||
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
|
||||
do_classifier_free_guidance=True,
|
||||
exit_stack=exit_stack,
|
||||
)
|
||||
|
||||
# TODO: Verify the noise is the right size
|
||||
initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
|
||||
latent, device=unet.device, dtype=latent.dtype)
|
||||
latent, device=unet.device, dtype=latent.dtype
|
||||
)
|
||||
|
||||
timesteps, _ = pipeline.get_img2img_timesteps(
|
||||
self.steps,
|
||||
@ -431,6 +473,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
|
||||
)
|
||||
|
||||
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
|
||||
result_latents = result_latents.to("cpu")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
name = f'{context.graph_execution_state_id}__{self.id}'
|
||||
@ -451,13 +494,14 @@ class LatentsToImageInvocation(BaseInvocation):
|
||||
tiled: bool = Field(
|
||||
default=False,
|
||||
description="Decode latents by overlaping tiles(less memory consumption)")
|
||||
fp32: bool = Field(False, description="Decode in full precision")
|
||||
metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
|
||||
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Latents To Image",
|
||||
"tags": ["latents", "image"],
|
||||
},
|
||||
}
|
||||
@ -467,10 +511,36 @@ class LatentsToImageInvocation(BaseInvocation):
|
||||
latents = context.services.latents.get(self.latents.latents_name)
|
||||
|
||||
vae_info = context.services.model_manager.get_model(
|
||||
**self.vae.vae.dict(),
|
||||
**self.vae.vae.dict(), context=context,
|
||||
)
|
||||
|
||||
with vae_info as vae:
|
||||
latents = latents.to(vae.device)
|
||||
if self.fp32:
|
||||
vae.to(dtype=torch.float32)
|
||||
|
||||
use_torch_2_0_or_xformers = isinstance(
|
||||
vae.decoder.mid_block.attentions[0].processor,
|
||||
(
|
||||
AttnProcessor2_0,
|
||||
XFormersAttnProcessor,
|
||||
LoRAXFormersAttnProcessor,
|
||||
LoRAAttnProcessor2_0,
|
||||
),
|
||||
)
|
||||
# if xformers or torch_2_0 is used attention block does not need
|
||||
# to be in float32 which can save lots of memory
|
||||
if use_torch_2_0_or_xformers:
|
||||
vae.post_quant_conv.to(latents.dtype)
|
||||
vae.decoder.conv_in.to(latents.dtype)
|
||||
vae.decoder.mid_block.to(latents.dtype)
|
||||
else:
|
||||
latents = latents.float()
|
||||
|
||||
else:
|
||||
vae.to(dtype=torch.float16)
|
||||
latents = latents.half()
|
||||
|
||||
if self.tiled or context.services.configuration.tiled_decode:
|
||||
vae.enable_tiling()
|
||||
else:
|
||||
@ -520,25 +590,38 @@ class ResizeLatentsInvocation(BaseInvocation):
|
||||
# Inputs
|
||||
latents: Optional[LatentsField] = Field(
|
||||
description="The latents to resize")
|
||||
width: int = Field(
|
||||
width: Union[int, None] = Field(default=512,
|
||||
ge=64, multiple_of=8, description="The width to resize to (px)")
|
||||
height: int = Field(
|
||||
height: Union[int, None] = Field(default=512,
|
||||
ge=64, multiple_of=8, description="The height to resize to (px)")
|
||||
mode: LATENTS_INTERPOLATION_MODE = Field(
|
||||
default="bilinear", description="The interpolation mode")
|
||||
antialias: bool = Field(
|
||||
default=False,
|
||||
description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Resize Latents",
|
||||
"tags": ["latents", "resize"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
latents = context.services.latents.get(self.latents.latents_name)
|
||||
|
||||
# TODO:
|
||||
device=choose_torch_device()
|
||||
|
||||
resized_latents = torch.nn.functional.interpolate(
|
||||
latents, size=(self.height // 8, self.width // 8),
|
||||
latents.to(device), size=(self.height // 8, self.width // 8),
|
||||
mode=self.mode, antialias=self.antialias
|
||||
if self.mode in ["bilinear", "bicubic"] else False,)
|
||||
if self.mode in ["bilinear", "bicubic"] else False,
|
||||
)
|
||||
|
||||
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
|
||||
resized_latents = resized_latents.to("cpu")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
name = f"{context.graph_execution_state_id}__{self.id}"
|
||||
@ -562,17 +645,30 @@ class ScaleLatentsInvocation(BaseInvocation):
|
||||
antialias: bool = Field(
|
||||
default=False,
|
||||
description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Scale Latents",
|
||||
"tags": ["latents", "scale"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
latents = context.services.latents.get(self.latents.latents_name)
|
||||
|
||||
# TODO:
|
||||
device=choose_torch_device()
|
||||
|
||||
# resizing
|
||||
resized_latents = torch.nn.functional.interpolate(
|
||||
latents, scale_factor=self.scale_factor, mode=self.mode,
|
||||
latents.to(device), scale_factor=self.scale_factor, mode=self.mode,
|
||||
antialias=self.antialias
|
||||
if self.mode in ["bilinear", "bicubic"] else False,)
|
||||
if self.mode in ["bilinear", "bicubic"] else False,
|
||||
)
|
||||
|
||||
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
|
||||
resized_latents = resized_latents.to("cpu")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
name = f"{context.graph_execution_state_id}__{self.id}"
|
||||
@ -592,12 +688,15 @@ class ImageToLatentsInvocation(BaseInvocation):
|
||||
tiled: bool = Field(
|
||||
default=False,
|
||||
description="Encode latents by overlaping tiles(less memory consumption)")
|
||||
fp32: bool = Field(False, description="Decode in full precision")
|
||||
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["latents", "image"],
|
||||
"title": "Image To Latents",
|
||||
"tags": ["latents", "image"]
|
||||
},
|
||||
}
|
||||
|
||||
@ -610,7 +709,7 @@ class ImageToLatentsInvocation(BaseInvocation):
|
||||
|
||||
#vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
|
||||
vae_info = context.services.model_manager.get_model(
|
||||
**self.vae.vae.dict(),
|
||||
**self.vae.vae.dict(), context=context,
|
||||
)
|
||||
|
||||
image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
|
||||
@ -618,6 +717,32 @@ class ImageToLatentsInvocation(BaseInvocation):
|
||||
image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
|
||||
|
||||
with vae_info as vae:
|
||||
orig_dtype = vae.dtype
|
||||
if self.fp32:
|
||||
vae.to(dtype=torch.float32)
|
||||
|
||||
use_torch_2_0_or_xformers = isinstance(
|
||||
vae.decoder.mid_block.attentions[0].processor,
|
||||
(
|
||||
AttnProcessor2_0,
|
||||
XFormersAttnProcessor,
|
||||
LoRAXFormersAttnProcessor,
|
||||
LoRAAttnProcessor2_0,
|
||||
),
|
||||
)
|
||||
# if xformers or torch_2_0 is used attention block does not need
|
||||
# to be in float32 which can save lots of memory
|
||||
if use_torch_2_0_or_xformers:
|
||||
vae.post_quant_conv.to(orig_dtype)
|
||||
vae.decoder.conv_in.to(orig_dtype)
|
||||
vae.decoder.mid_block.to(orig_dtype)
|
||||
#else:
|
||||
# latents = latents.float()
|
||||
|
||||
else:
|
||||
vae.to(dtype=torch.float16)
|
||||
#latents = latents.half()
|
||||
|
||||
if self.tiled:
|
||||
vae.enable_tiling()
|
||||
else:
|
||||
@ -632,8 +757,9 @@ class ImageToLatentsInvocation(BaseInvocation):
|
||||
) # FIXME: uses torch.randn. make reproducible!
|
||||
|
||||
latents = 0.18215 * latents
|
||||
latents = latents.to(dtype=orig_dtype)
|
||||
|
||||
name = f"{context.graph_execution_state_id}__{self.id}"
|
||||
# context.services.latents.set(name, latents)
|
||||
latents = latents.to("cpu")
|
||||
context.services.latents.save(name, latents)
|
||||
return build_latents_output(latents_name=name, latents=latents)
|
||||
|
||||
@ -52,6 +52,14 @@ class AddInvocation(BaseInvocation, MathInvocationConfig):
|
||||
b: int = Field(default=0, description="The second number")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Add",
|
||||
"tags": ["math", "add"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntOutput:
|
||||
return IntOutput(a=self.a + self.b)
|
||||
|
||||
@ -65,6 +73,14 @@ class SubtractInvocation(BaseInvocation, MathInvocationConfig):
|
||||
b: int = Field(default=0, description="The second number")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Subtract",
|
||||
"tags": ["math", "subtract"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntOutput:
|
||||
return IntOutput(a=self.a - self.b)
|
||||
|
||||
@ -78,6 +94,14 @@ class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
|
||||
b: int = Field(default=0, description="The second number")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Multiply",
|
||||
"tags": ["math", "multiply"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntOutput:
|
||||
return IntOutput(a=self.a * self.b)
|
||||
|
||||
@ -91,6 +115,14 @@ class DivideInvocation(BaseInvocation, MathInvocationConfig):
|
||||
b: int = Field(default=0, description="The second number")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Divide",
|
||||
"tags": ["math", "divide"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntOutput:
|
||||
return IntOutput(a=int(self.a / self.b))
|
||||
|
||||
@ -105,5 +137,14 @@ class RandomIntInvocation(BaseInvocation):
|
||||
default=np.iinfo(np.int32).max, description="The exclusive high value"
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Random Integer",
|
||||
"tags": ["math", "random", "integer"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntOutput:
|
||||
return IntOutput(a=np.random.randint(self.low, self.high))
|
||||
|
||||
@ -3,7 +3,7 @@ from typing import Literal, Optional, Union
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from invokeai.app.invocations.baseinvocation import (BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
BaseInvocationOutput, InvocationConfig,
|
||||
InvocationContext)
|
||||
from invokeai.app.invocations.controlnet_image_processors import ControlField
|
||||
from invokeai.app.invocations.model import (LoRAModelField, MainModelField,
|
||||
@ -97,6 +97,14 @@ class MetadataAccumulatorInvocation(BaseInvocation):
|
||||
description="The VAE used for decoding, if the main model's default was not used",
|
||||
)
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Metadata Accumulator",
|
||||
"tags": ["image", "metadata", "generation"]
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
|
||||
"""Collects and outputs a CoreMetadata object"""
|
||||
|
||||
@ -33,7 +33,6 @@ class ClipField(BaseModel):
|
||||
skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
|
||||
loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
|
||||
|
||||
|
||||
class VaeField(BaseModel):
|
||||
# TODO: better naming?
|
||||
vae: ModelInfo = Field(description="Info to load vae submodel")
|
||||
@ -50,7 +49,6 @@ class ModelLoaderOutput(BaseInvocationOutput):
|
||||
vae: VaeField = Field(default=None, description="Vae submodel")
|
||||
# fmt: on
|
||||
|
||||
|
||||
class MainModelField(BaseModel):
|
||||
"""Main model field"""
|
||||
|
||||
@ -65,7 +63,6 @@ class LoRAModelField(BaseModel):
|
||||
model_name: str = Field(description="Name of the LoRA model")
|
||||
base_model: BaseModelType = Field(description="Base model")
|
||||
|
||||
|
||||
class MainModelLoaderInvocation(BaseInvocation):
|
||||
"""Loads a main model, outputting its submodels."""
|
||||
|
||||
@ -158,6 +155,22 @@ class MainModelLoaderInvocation(BaseInvocation):
|
||||
loras=[],
|
||||
skipped_layers=0,
|
||||
),
|
||||
clip2=ClipField(
|
||||
tokenizer=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.Tokenizer2,
|
||||
),
|
||||
text_encoder=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.TextEncoder2,
|
||||
),
|
||||
loras=[],
|
||||
skipped_layers=0,
|
||||
),
|
||||
vae=VaeField(
|
||||
vae=ModelInfo(
|
||||
model_name=model_name,
|
||||
@ -168,7 +181,7 @@ class MainModelLoaderInvocation(BaseInvocation):
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
|
||||
class LoraLoaderOutput(BaseInvocationOutput):
|
||||
"""Model loader output"""
|
||||
|
||||
|
||||
@ -48,7 +48,7 @@ def get_noise(
|
||||
dtype=torch_dtype(device),
|
||||
device=noise_device_type,
|
||||
generator=generator,
|
||||
).to(device)
|
||||
).to("cpu")
|
||||
|
||||
return noise_tensor
|
||||
|
||||
@ -112,6 +112,7 @@ class NoiseInvocation(BaseInvocation):
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Noise",
|
||||
"tags": ["latents", "noise"],
|
||||
},
|
||||
}
|
||||
|
||||
@ -43,6 +43,14 @@ class FloatLinearRangeInvocation(BaseInvocation):
|
||||
stop: float = Field(default=10, description="The last value of the range")
|
||||
steps: int = Field(default=30, description="number of values to interpolate over (including start and stop)")
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Linear Range (Float)",
|
||||
"tags": ["math", "float", "linear", "range"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
|
||||
param_list = list(np.linspace(self.start, self.stop, self.steps))
|
||||
return FloatCollectionOutput(
|
||||
@ -113,6 +121,14 @@ class StepParamEasingInvocation(BaseInvocation):
|
||||
show_easing_plot: bool = Field(default=False, description="show easing plot")
|
||||
# fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Param Easing By Step",
|
||||
"tags": ["param", "step", "easing"]
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
|
||||
log_diagnostics = False
|
||||
|
||||
@ -1,9 +1,12 @@
|
||||
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import Field
|
||||
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
|
||||
from .math import IntOutput, FloatOutput
|
||||
|
||||
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
|
||||
InvocationConfig, InvocationContext)
|
||||
from .math import FloatOutput, IntOutput
|
||||
|
||||
# Pass-through parameter nodes - used by subgraphs
|
||||
|
||||
@ -14,6 +17,14 @@ class ParamIntInvocation(BaseInvocation):
|
||||
a: int = Field(default=0, description="The integer value")
|
||||
#fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["param", "integer"],
|
||||
"title": "Integer Parameter"
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> IntOutput:
|
||||
return IntOutput(a=self.a)
|
||||
|
||||
@ -24,5 +35,36 @@ class ParamFloatInvocation(BaseInvocation):
|
||||
param: float = Field(default=0.0, description="The float value")
|
||||
#fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["param", "float"],
|
||||
"title": "Float Parameter"
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> FloatOutput:
|
||||
return FloatOutput(param=self.param)
|
||||
|
||||
class StringOutput(BaseInvocationOutput):
|
||||
"""A string output"""
|
||||
type: Literal["string_output"] = "string_output"
|
||||
text: str = Field(default=None, description="The output string")
|
||||
|
||||
|
||||
class ParamStringInvocation(BaseInvocation):
|
||||
"""A string parameter"""
|
||||
type: Literal['param_string'] = 'param_string'
|
||||
text: str = Field(default='', description='The string value')
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["param", "string"],
|
||||
"title": "String Parameter"
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> StringOutput:
|
||||
return StringOutput(text=self.text)
|
||||
|
||||
@ -1,8 +1,10 @@
|
||||
from typing import Literal
|
||||
from os.path import exists
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic.fields import Field
|
||||
import numpy as np
|
||||
from pydantic import Field, validator
|
||||
|
||||
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
|
||||
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
|
||||
from dynamicprompts.generators import RandomPromptGenerator, CombinatorialPromptGenerator
|
||||
|
||||
class PromptOutput(BaseInvocationOutput):
|
||||
@ -46,6 +48,14 @@ class DynamicPromptInvocation(BaseInvocation):
|
||||
default=False, description="Whether to use the combinatorial generator"
|
||||
)
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Dynamic Prompt",
|
||||
"tags": ["prompt", "dynamic"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
|
||||
if self.combinatorial:
|
||||
generator = CombinatorialPromptGenerator()
|
||||
@ -55,3 +65,49 @@ class DynamicPromptInvocation(BaseInvocation):
|
||||
prompts = generator.generate(self.prompt, num_images=self.max_prompts)
|
||||
|
||||
return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))
|
||||
|
||||
|
||||
class PromptsFromFileInvocation(BaseInvocation):
|
||||
'''Loads prompts from a text file'''
|
||||
# fmt: off
|
||||
type: Literal['prompt_from_file'] = 'prompt_from_file'
|
||||
|
||||
# Inputs
|
||||
file_path: str = Field(description="Path to prompt text file")
|
||||
pre_prompt: Optional[str] = Field(description="String to prepend to each prompt")
|
||||
post_prompt: Optional[str] = Field(description="String to append to each prompt")
|
||||
start_line: int = Field(default=1, ge=1, description="Line in the file to start start from")
|
||||
max_prompts: int = Field(default=1, ge=0, description="Max lines to read from file (0=all)")
|
||||
#fmt: on
|
||||
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "Prompts From File",
|
||||
"tags": ["prompt", "file"]
|
||||
},
|
||||
}
|
||||
|
||||
@validator("file_path")
|
||||
def file_path_exists(cls, v):
|
||||
if not exists(v):
|
||||
raise ValueError(FileNotFoundError)
|
||||
return v
|
||||
|
||||
def promptsFromFile(self, file_path: str, pre_prompt: str, post_prompt: str, start_line: int, max_prompts: int):
|
||||
prompts = []
|
||||
start_line -= 1
|
||||
end_line = start_line + max_prompts
|
||||
if max_prompts <= 0:
|
||||
end_line = np.iinfo(np.int32).max
|
||||
with open(file_path) as f:
|
||||
for i, line in enumerate(f):
|
||||
if i >= start_line and i < end_line:
|
||||
prompts.append((pre_prompt or '') + line.strip() + (post_prompt or ''))
|
||||
if i >= end_line:
|
||||
break
|
||||
return prompts
|
||||
|
||||
def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
|
||||
prompts = self.promptsFromFile(self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts)
|
||||
return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))
|
||||
|
||||
@ -1,55 +0,0 @@
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
|
||||
|
||||
from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
|
||||
from .image import ImageOutput
|
||||
|
||||
|
||||
class RestoreFaceInvocation(BaseInvocation):
|
||||
"""Restores faces in an image."""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["restore_face"] = "restore_face"
|
||||
|
||||
# Inputs
|
||||
image: Optional[ImageField] = Field(description="The input image")
|
||||
strength: float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration" )
|
||||
# fmt: on
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["restoration", "image"],
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
results = context.services.restoration.upscale_and_reconstruct(
|
||||
image_list=[[image, 0]],
|
||||
upscale=None,
|
||||
strength=self.strength, # GFPGAN strength
|
||||
save_original=False,
|
||||
image_callback=None,
|
||||
)
|
||||
|
||||
# Results are image and seed, unwrap for now
|
||||
# TODO: can this return multiple results?
|
||||
image_dto = context.services.images.create(
|
||||
image=results[0][0],
|
||||
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,
|
||||
)
|
||||
662
invokeai/app/invocations/sdxl.py
Normal file
662
invokeai/app/invocations/sdxl.py
Normal file
@ -0,0 +1,662 @@
|
||||
import torch
|
||||
import inspect
|
||||
from tqdm import tqdm
|
||||
from typing import List, Literal, Optional, Union
|
||||
|
||||
from pydantic import Field, validator
|
||||
|
||||
from ...backend.model_management import ModelType, SubModelType
|
||||
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
|
||||
InvocationConfig, InvocationContext)
|
||||
|
||||
from .model import UNetField, ClipField, VaeField, MainModelField, ModelInfo
|
||||
from .compel import ConditioningField
|
||||
from .latent import LatentsField, SAMPLER_NAME_VALUES, LatentsOutput, get_scheduler, build_latents_output
|
||||
|
||||
class SDXLModelLoaderOutput(BaseInvocationOutput):
|
||||
"""SDXL base model loader output"""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["sdxl_model_loader_output"] = "sdxl_model_loader_output"
|
||||
|
||||
unet: UNetField = Field(default=None, description="UNet submodel")
|
||||
clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
|
||||
clip2: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
|
||||
vae: VaeField = Field(default=None, description="Vae submodel")
|
||||
# fmt: on
|
||||
|
||||
class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
|
||||
"""SDXL refiner model loader output"""
|
||||
# fmt: off
|
||||
type: Literal["sdxl_refiner_model_loader_output"] = "sdxl_refiner_model_loader_output"
|
||||
unet: UNetField = Field(default=None, description="UNet submodel")
|
||||
clip2: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
|
||||
vae: VaeField = Field(default=None, description="Vae submodel")
|
||||
# fmt: on
|
||||
#fmt: on
|
||||
|
||||
class SDXLModelLoaderInvocation(BaseInvocation):
|
||||
"""Loads an sdxl base model, outputting its submodels."""
|
||||
|
||||
type: Literal["sdxl_model_loader"] = "sdxl_model_loader"
|
||||
|
||||
model: MainModelField = Field(description="The model to load")
|
||||
# TODO: precision?
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Model Loader",
|
||||
"tags": ["model", "loader", "sdxl"],
|
||||
"type_hints": {"model": "model"},
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
|
||||
base_model = self.model.base_model
|
||||
model_name = self.model.model_name
|
||||
model_type = ModelType.Main
|
||||
|
||||
# TODO: not found exceptions
|
||||
if not context.services.model_manager.model_exists(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
):
|
||||
raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
|
||||
|
||||
return SDXLModelLoaderOutput(
|
||||
unet=UNetField(
|
||||
unet=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.UNet,
|
||||
),
|
||||
scheduler=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.Scheduler,
|
||||
),
|
||||
loras=[],
|
||||
),
|
||||
clip=ClipField(
|
||||
tokenizer=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.Tokenizer,
|
||||
),
|
||||
text_encoder=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.TextEncoder,
|
||||
),
|
||||
loras=[],
|
||||
skipped_layers=0,
|
||||
),
|
||||
clip2=ClipField(
|
||||
tokenizer=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.Tokenizer2,
|
||||
),
|
||||
text_encoder=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.TextEncoder2,
|
||||
),
|
||||
loras=[],
|
||||
skipped_layers=0,
|
||||
),
|
||||
vae=VaeField(
|
||||
vae=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.Vae,
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
class SDXLRefinerModelLoaderInvocation(BaseInvocation):
|
||||
"""Loads an sdxl refiner model, outputting its submodels."""
|
||||
type: Literal["sdxl_refiner_model_loader"] = "sdxl_refiner_model_loader"
|
||||
|
||||
model: MainModelField = Field(description="The model to load")
|
||||
# TODO: precision?
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Refiner Model Loader",
|
||||
"tags": ["model", "loader", "sdxl_refiner"],
|
||||
"type_hints": {"model": "model"},
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> SDXLRefinerModelLoaderOutput:
|
||||
base_model = self.model.base_model
|
||||
model_name = self.model.model_name
|
||||
model_type = ModelType.Main
|
||||
|
||||
# TODO: not found exceptions
|
||||
if not context.services.model_manager.model_exists(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
):
|
||||
raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
|
||||
|
||||
return SDXLRefinerModelLoaderOutput(
|
||||
unet=UNetField(
|
||||
unet=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.UNet,
|
||||
),
|
||||
scheduler=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.Scheduler,
|
||||
),
|
||||
loras=[],
|
||||
),
|
||||
clip2=ClipField(
|
||||
tokenizer=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.Tokenizer2,
|
||||
),
|
||||
text_encoder=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.TextEncoder2,
|
||||
),
|
||||
loras=[],
|
||||
skipped_layers=0,
|
||||
),
|
||||
vae=VaeField(
|
||||
vae=ModelInfo(
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=SubModelType.Vae,
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
# Text to image
|
||||
class SDXLTextToLatentsInvocation(BaseInvocation):
|
||||
"""Generates latents from conditionings."""
|
||||
|
||||
type: Literal["t2l_sdxl"] = "t2l_sdxl"
|
||||
|
||||
# Inputs
|
||||
# fmt: off
|
||||
positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
|
||||
negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
|
||||
noise: Optional[LatentsField] = Field(description="The noise to use")
|
||||
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
|
||||
cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
|
||||
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
|
||||
unet: UNetField = Field(default=None, description="UNet submodel")
|
||||
denoising_end: float = Field(default=1.0, gt=0, le=1, description="")
|
||||
#control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
|
||||
#seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
|
||||
#seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
|
||||
# fmt: on
|
||||
|
||||
@validator("cfg_scale")
|
||||
def ge_one(cls, v):
|
||||
"""validate that all cfg_scale values are >= 1"""
|
||||
if isinstance(v, list):
|
||||
for i in v:
|
||||
if i < 1:
|
||||
raise ValueError('cfg_scale must be greater than 1')
|
||||
else:
|
||||
if v < 1:
|
||||
raise ValueError('cfg_scale must be greater than 1')
|
||||
return v
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Text To Latents",
|
||||
"tags": ["latents"],
|
||||
"type_hints": {
|
||||
"model": "model",
|
||||
# "cfg_scale": "float",
|
||||
"cfg_scale": "number"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
# based on
|
||||
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
latents = context.services.latents.get(self.noise.latents_name)
|
||||
|
||||
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
|
||||
prompt_embeds = positive_cond_data.conditionings[0].embeds
|
||||
pooled_prompt_embeds = positive_cond_data.conditionings[0].pooled_embeds
|
||||
add_time_ids = positive_cond_data.conditionings[0].add_time_ids
|
||||
|
||||
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
|
||||
negative_prompt_embeds = negative_cond_data.conditionings[0].embeds
|
||||
negative_pooled_prompt_embeds = negative_cond_data.conditionings[0].pooled_embeds
|
||||
add_neg_time_ids = negative_cond_data.conditionings[0].add_time_ids
|
||||
|
||||
scheduler = get_scheduler(
|
||||
context=context,
|
||||
scheduler_info=self.unet.scheduler,
|
||||
scheduler_name=self.scheduler,
|
||||
)
|
||||
|
||||
num_inference_steps = self.steps
|
||||
scheduler.set_timesteps(num_inference_steps)
|
||||
timesteps = scheduler.timesteps
|
||||
|
||||
latents = latents * scheduler.init_noise_sigma
|
||||
|
||||
|
||||
unet_info = context.services.model_manager.get_model(
|
||||
**self.unet.unet.dict()
|
||||
)
|
||||
do_classifier_free_guidance = True
|
||||
cross_attention_kwargs = None
|
||||
with unet_info as unet:
|
||||
|
||||
extra_step_kwargs = dict()
|
||||
if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
|
||||
extra_step_kwargs.update(
|
||||
eta=0.0,
|
||||
)
|
||||
if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
|
||||
extra_step_kwargs.update(
|
||||
generator=torch.Generator(device=unet.device).manual_seed(0),
|
||||
)
|
||||
|
||||
num_warmup_steps = len(timesteps) - self.steps * scheduler.order
|
||||
|
||||
# apply denoising_end
|
||||
skipped_final_steps = int(round((1 - self.denoising_end) * self.steps))
|
||||
num_inference_steps = num_inference_steps - skipped_final_steps
|
||||
timesteps = timesteps[: num_warmup_steps + scheduler.order * num_inference_steps]
|
||||
|
||||
if not context.services.configuration.sequential_guidance:
|
||||
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
||||
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
|
||||
add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
|
||||
|
||||
prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
add_text_embeds = add_text_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
|
||||
latents = latents.to(device=unet.device, dtype=unet.dtype)
|
||||
|
||||
with tqdm(total=num_inference_steps) as progress_bar:
|
||||
for i, t in enumerate(timesteps):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
|
||||
latent_model_input = scheduler.scale_model_input(latent_model_input, t)
|
||||
|
||||
# predict the noise residual
|
||||
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
|
||||
noise_pred = unet(
|
||||
latent_model_input,
|
||||
t,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
return_dict=False,
|
||||
)[0]
|
||||
|
||||
# perform guidance
|
||||
if do_classifier_free_guidance:
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
|
||||
#del noise_pred_uncond
|
||||
#del noise_pred_text
|
||||
|
||||
#if do_classifier_free_guidance and guidance_rescale > 0.0:
|
||||
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
||||
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
||||
|
||||
# call the callback, if provided
|
||||
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
|
||||
progress_bar.update()
|
||||
#if callback is not None and i % callback_steps == 0:
|
||||
# callback(i, t, latents)
|
||||
else:
|
||||
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
negative_prompt_embeds = negative_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
add_neg_time_ids = add_neg_time_ids.to(device=unet.device, dtype=unet.dtype)
|
||||
pooled_prompt_embeds = pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
|
||||
latents = latents.to(device=unet.device, dtype=unet.dtype)
|
||||
|
||||
with tqdm(total=num_inference_steps) as progress_bar:
|
||||
for i, t in enumerate(timesteps):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
#latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
|
||||
latent_model_input = scheduler.scale_model_input(latents, t)
|
||||
|
||||
#import gc
|
||||
#gc.collect()
|
||||
#torch.cuda.empty_cache()
|
||||
|
||||
# predict the noise residual
|
||||
|
||||
added_cond_kwargs = {"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_neg_time_ids}
|
||||
noise_pred_uncond = unet(
|
||||
latent_model_input,
|
||||
t,
|
||||
encoder_hidden_states=negative_prompt_embeds,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
return_dict=False,
|
||||
)[0]
|
||||
|
||||
added_cond_kwargs = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids}
|
||||
noise_pred_text = unet(
|
||||
latent_model_input,
|
||||
t,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
return_dict=False,
|
||||
)[0]
|
||||
|
||||
# perform guidance
|
||||
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
|
||||
|
||||
#del noise_pred_text
|
||||
#del noise_pred_uncond
|
||||
#import gc
|
||||
#gc.collect()
|
||||
#torch.cuda.empty_cache()
|
||||
|
||||
#if do_classifier_free_guidance and guidance_rescale > 0.0:
|
||||
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
||||
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
||||
|
||||
#del noise_pred
|
||||
#import gc
|
||||
#gc.collect()
|
||||
#torch.cuda.empty_cache()
|
||||
|
||||
# call the callback, if provided
|
||||
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
|
||||
progress_bar.update()
|
||||
#if callback is not None and i % callback_steps == 0:
|
||||
# callback(i, t, latents)
|
||||
|
||||
|
||||
|
||||
#################
|
||||
|
||||
latents = latents.to("cpu")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
name = f'{context.graph_execution_state_id}__{self.id}'
|
||||
context.services.latents.save(name, latents)
|
||||
return build_latents_output(latents_name=name, latents=latents)
|
||||
|
||||
class SDXLLatentsToLatentsInvocation(BaseInvocation):
|
||||
"""Generates latents from conditionings."""
|
||||
|
||||
type: Literal["l2l_sdxl"] = "l2l_sdxl"
|
||||
|
||||
# Inputs
|
||||
# fmt: off
|
||||
positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
|
||||
negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
|
||||
noise: Optional[LatentsField] = Field(description="The noise to use")
|
||||
steps: int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
|
||||
cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
|
||||
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
|
||||
unet: UNetField = Field(default=None, description="UNet submodel")
|
||||
latents: Optional[LatentsField] = Field(description="Initial latents")
|
||||
|
||||
denoising_start: float = Field(default=0.0, ge=0, lt=1, description="")
|
||||
denoising_end: float = Field(default=1.0, gt=0, le=1, description="")
|
||||
|
||||
#control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
|
||||
#seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
|
||||
#seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
|
||||
# fmt: on
|
||||
|
||||
@validator("cfg_scale")
|
||||
def ge_one(cls, v):
|
||||
"""validate that all cfg_scale values are >= 1"""
|
||||
if isinstance(v, list):
|
||||
for i in v:
|
||||
if i < 1:
|
||||
raise ValueError('cfg_scale must be greater than 1')
|
||||
else:
|
||||
if v < 1:
|
||||
raise ValueError('cfg_scale must be greater than 1')
|
||||
return v
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"title": "SDXL Latents to Latents",
|
||||
"tags": ["latents"],
|
||||
"type_hints": {
|
||||
"model": "model",
|
||||
# "cfg_scale": "float",
|
||||
"cfg_scale": "number"
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
# based on
|
||||
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
latents = context.services.latents.get(self.latents.latents_name)
|
||||
|
||||
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
|
||||
prompt_embeds = positive_cond_data.conditionings[0].embeds
|
||||
pooled_prompt_embeds = positive_cond_data.conditionings[0].pooled_embeds
|
||||
add_time_ids = positive_cond_data.conditionings[0].add_time_ids
|
||||
|
||||
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
|
||||
negative_prompt_embeds = negative_cond_data.conditionings[0].embeds
|
||||
negative_pooled_prompt_embeds = negative_cond_data.conditionings[0].pooled_embeds
|
||||
add_neg_time_ids = negative_cond_data.conditionings[0].add_time_ids
|
||||
|
||||
scheduler = get_scheduler(
|
||||
context=context,
|
||||
scheduler_info=self.unet.scheduler,
|
||||
scheduler_name=self.scheduler,
|
||||
)
|
||||
|
||||
# apply denoising_start
|
||||
num_inference_steps = self.steps
|
||||
scheduler.set_timesteps(num_inference_steps)
|
||||
|
||||
t_start = int(round(self.denoising_start * num_inference_steps))
|
||||
timesteps = scheduler.timesteps[t_start * scheduler.order:]
|
||||
num_inference_steps = num_inference_steps - t_start
|
||||
|
||||
# apply noise(if provided)
|
||||
if self.noise is not None:
|
||||
noise = context.services.latents.get(self.noise.latents_name)
|
||||
latents = scheduler.add_noise(latents, noise, timesteps[:1])
|
||||
del noise
|
||||
|
||||
unet_info = context.services.model_manager.get_model(
|
||||
**self.unet.unet.dict()
|
||||
)
|
||||
do_classifier_free_guidance = True
|
||||
cross_attention_kwargs = None
|
||||
with unet_info as unet:
|
||||
|
||||
# apply scheduler extra args
|
||||
extra_step_kwargs = dict()
|
||||
if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
|
||||
extra_step_kwargs.update(
|
||||
eta=0.0,
|
||||
)
|
||||
if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
|
||||
extra_step_kwargs.update(
|
||||
generator=torch.Generator(device=unet.device).manual_seed(0),
|
||||
)
|
||||
|
||||
num_warmup_steps = max(len(timesteps) - num_inference_steps * scheduler.order, 0)
|
||||
|
||||
# apply denoising_end
|
||||
skipped_final_steps = int(round((1 - self.denoising_end) * self.steps))
|
||||
num_inference_steps = num_inference_steps - skipped_final_steps
|
||||
timesteps = timesteps[: num_warmup_steps + scheduler.order * num_inference_steps]
|
||||
|
||||
if not context.services.configuration.sequential_guidance:
|
||||
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
||||
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
|
||||
add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
|
||||
|
||||
prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
add_text_embeds = add_text_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
|
||||
latents = latents.to(device=unet.device, dtype=unet.dtype)
|
||||
|
||||
with tqdm(total=num_inference_steps) as progress_bar:
|
||||
for i, t in enumerate(timesteps):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
|
||||
latent_model_input = scheduler.scale_model_input(latent_model_input, t)
|
||||
|
||||
# predict the noise residual
|
||||
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
|
||||
noise_pred = unet(
|
||||
latent_model_input,
|
||||
t,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
return_dict=False,
|
||||
)[0]
|
||||
|
||||
# perform guidance
|
||||
if do_classifier_free_guidance:
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
|
||||
#del noise_pred_uncond
|
||||
#del noise_pred_text
|
||||
|
||||
#if do_classifier_free_guidance and guidance_rescale > 0.0:
|
||||
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
||||
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
||||
|
||||
# call the callback, if provided
|
||||
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
|
||||
progress_bar.update()
|
||||
#if callback is not None and i % callback_steps == 0:
|
||||
# callback(i, t, latents)
|
||||
else:
|
||||
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
negative_prompt_embeds = negative_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
add_neg_time_ids = add_neg_time_ids.to(device=unet.device, dtype=unet.dtype)
|
||||
pooled_prompt_embeds = pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
prompt_embeds = prompt_embeds.to(device=unet.device, dtype=unet.dtype)
|
||||
add_time_ids = add_time_ids.to(device=unet.device, dtype=unet.dtype)
|
||||
latents = latents.to(device=unet.device, dtype=unet.dtype)
|
||||
|
||||
with tqdm(total=num_inference_steps) as progress_bar:
|
||||
for i, t in enumerate(timesteps):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
#latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
|
||||
latent_model_input = scheduler.scale_model_input(latents, t)
|
||||
|
||||
#import gc
|
||||
#gc.collect()
|
||||
#torch.cuda.empty_cache()
|
||||
|
||||
# predict the noise residual
|
||||
|
||||
added_cond_kwargs = {"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_time_ids}
|
||||
noise_pred_uncond = unet(
|
||||
latent_model_input,
|
||||
t,
|
||||
encoder_hidden_states=negative_prompt_embeds,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
return_dict=False,
|
||||
)[0]
|
||||
|
||||
added_cond_kwargs = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids}
|
||||
noise_pred_text = unet(
|
||||
latent_model_input,
|
||||
t,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
added_cond_kwargs=added_cond_kwargs,
|
||||
return_dict=False,
|
||||
)[0]
|
||||
|
||||
# perform guidance
|
||||
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
|
||||
|
||||
#del noise_pred_text
|
||||
#del noise_pred_uncond
|
||||
#import gc
|
||||
#gc.collect()
|
||||
#torch.cuda.empty_cache()
|
||||
|
||||
#if do_classifier_free_guidance and guidance_rescale > 0.0:
|
||||
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
||||
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
||||
|
||||
#del noise_pred
|
||||
#import gc
|
||||
#gc.collect()
|
||||
#torch.cuda.empty_cache()
|
||||
|
||||
# call the callback, if provided
|
||||
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
|
||||
progress_bar.update()
|
||||
#if callback is not None and i % callback_steps == 0:
|
||||
# callback(i, t, latents)
|
||||
|
||||
|
||||
|
||||
#################
|
||||
|
||||
latents = latents.to("cpu")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
name = f'{context.graph_execution_state_id}__{self.id}'
|
||||
context.services.latents.save(name, latents)
|
||||
return build_latents_output(latents_name=name, latents=latents)
|
||||
@ -1,48 +1,119 @@
|
||||
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
|
||||
|
||||
from typing import Literal, Optional
|
||||
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
|
||||
from pathlib import Path
|
||||
from typing import Literal, Union
|
||||
|
||||
import cv2 as cv
|
||||
import numpy as np
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||||
from PIL import Image
|
||||
from pydantic import Field
|
||||
from realesrgan import RealESRGANer
|
||||
|
||||
from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
|
||||
from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
|
||||
|
||||
from .baseinvocation import BaseInvocation, InvocationConfig, InvocationContext
|
||||
from .image import ImageOutput
|
||||
|
||||
# TODO: Populate this from disk?
|
||||
# TODO: Use model manager to load?
|
||||
ESRGAN_MODELS = Literal[
|
||||
"RealESRGAN_x4plus.pth",
|
||||
"RealESRGAN_x4plus_anime_6B.pth",
|
||||
"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
|
||||
"RealESRGAN_x2plus.pth",
|
||||
]
|
||||
|
||||
class UpscaleInvocation(BaseInvocation):
|
||||
"""Upscales an image."""
|
||||
|
||||
# fmt: off
|
||||
type: Literal["upscale"] = "upscale"
|
||||
class ESRGANInvocation(BaseInvocation):
|
||||
"""Upscales an image using RealESRGAN."""
|
||||
|
||||
# Inputs
|
||||
image: Optional[ImageField] = Field(description="The input image", default=None)
|
||||
strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
|
||||
level: Literal[2, 4] = Field(default=2, description="The upscale level")
|
||||
# fmt: on
|
||||
type: Literal["esrgan"] = "esrgan"
|
||||
image: Union[ImageField, None] = Field(default=None, description="The input image")
|
||||
model_name: ESRGAN_MODELS = Field(
|
||||
default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use"
|
||||
)
|
||||
|
||||
# Schema customisation
|
||||
class Config(InvocationConfig):
|
||||
schema_extra = {
|
||||
"ui": {
|
||||
"tags": ["upscaling", "image"],
|
||||
"title": "Upscale (RealESRGAN)",
|
||||
"tags": ["image", "upscale", "realesrgan"]
|
||||
},
|
||||
}
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
results = context.services.restoration.upscale_and_reconstruct(
|
||||
image_list=[[image, 0]],
|
||||
upscale=(self.level, self.strength),
|
||||
strength=0.0, # GFPGAN strength
|
||||
save_original=False,
|
||||
image_callback=None,
|
||||
models_path = context.services.configuration.models_path
|
||||
|
||||
rrdbnet_model = None
|
||||
netscale = None
|
||||
esrgan_model_path = None
|
||||
|
||||
if self.model_name in [
|
||||
"RealESRGAN_x4plus.pth",
|
||||
"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
|
||||
]:
|
||||
# x4 RRDBNet model
|
||||
rrdbnet_model = RRDBNet(
|
||||
num_in_ch=3,
|
||||
num_out_ch=3,
|
||||
num_feat=64,
|
||||
num_block=23,
|
||||
num_grow_ch=32,
|
||||
scale=4,
|
||||
)
|
||||
netscale = 4
|
||||
elif self.model_name in ["RealESRGAN_x4plus_anime_6B.pth"]:
|
||||
# x4 RRDBNet model, 6 blocks
|
||||
rrdbnet_model = RRDBNet(
|
||||
num_in_ch=3,
|
||||
num_out_ch=3,
|
||||
num_feat=64,
|
||||
num_block=6, # 6 blocks
|
||||
num_grow_ch=32,
|
||||
scale=4,
|
||||
)
|
||||
netscale = 4
|
||||
elif self.model_name in ["RealESRGAN_x2plus.pth"]:
|
||||
# x2 RRDBNet model
|
||||
rrdbnet_model = RRDBNet(
|
||||
num_in_ch=3,
|
||||
num_out_ch=3,
|
||||
num_feat=64,
|
||||
num_block=23,
|
||||
num_grow_ch=32,
|
||||
scale=2,
|
||||
)
|
||||
netscale = 2
|
||||
else:
|
||||
msg = f"Invalid RealESRGAN model: {self.model_name}"
|
||||
context.services.logger.error(msg)
|
||||
raise ValueError(msg)
|
||||
|
||||
esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
|
||||
|
||||
upsampler = RealESRGANer(
|
||||
scale=netscale,
|
||||
model_path=str(models_path / esrgan_model_path),
|
||||
model=rrdbnet_model,
|
||||
half=False,
|
||||
)
|
||||
|
||||
# Results are image and seed, unwrap for now
|
||||
# TODO: can this return multiple results?
|
||||
# prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
|
||||
cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
|
||||
|
||||
# We can pass an `outscale` value here, but it just resizes the image by that factor after
|
||||
# upscaling, so it's kinda pointless for our purposes. If you want something other than 4x
|
||||
# upscaling, you'll need to add a resize node after this one.
|
||||
upscaled_image, img_mode = upsampler.enhance(cv_image)
|
||||
|
||||
# back to PIL
|
||||
pil_image = Image.fromarray(
|
||||
cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)
|
||||
).convert("RGBA")
|
||||
|
||||
image_dto = context.services.images.create(
|
||||
image=results[0][0],
|
||||
image=pil_image,
|
||||
image_origin=ResourceOrigin.INTERNAL,
|
||||
image_category=ImageCategory.GENERAL,
|
||||
node_id=self.id,
|
||||
|
||||
@ -200,7 +200,7 @@ class InvokeAISettings(BaseSettings):
|
||||
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():
|
||||
if name in cls._excluded_from_yaml():
|
||||
continue
|
||||
category = field.field_info.extra.get("category") or "Uncategorized"
|
||||
value = getattr(self,name)
|
||||
@ -271,8 +271,13 @@ class InvokeAISettings(BaseSettings):
|
||||
|
||||
@classmethod
|
||||
def _excluded(self)->List[str]:
|
||||
# combination of deprecated parameters and internal ones
|
||||
return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version']
|
||||
# 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', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore']
|
||||
|
||||
class Config:
|
||||
env_file_encoding = 'utf-8'
|
||||
@ -361,7 +366,7 @@ setting environment variables INVOKEAI_<setting>.
|
||||
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
|
||||
nsfw_checker : bool = Field(default=True, description="Enable/disable the NSFW checker", category='Features')
|
||||
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
|
||||
restore : bool = Field(default=True, description="Enable/disable face restoration code", category='Features')
|
||||
restore : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED')
|
||||
|
||||
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')
|
||||
|
||||
@ -105,8 +105,6 @@ class EventServiceBase:
|
||||
def emit_model_load_started (
|
||||
self,
|
||||
graph_execution_state_id: str,
|
||||
node: dict,
|
||||
source_node_id: str,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
@ -117,8 +115,6 @@ class EventServiceBase:
|
||||
event_name="model_load_started",
|
||||
payload=dict(
|
||||
graph_execution_state_id=graph_execution_state_id,
|
||||
node=node,
|
||||
source_node_id=source_node_id,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
@ -129,8 +125,6 @@ class EventServiceBase:
|
||||
def emit_model_load_completed(
|
||||
self,
|
||||
graph_execution_state_id: str,
|
||||
node: dict,
|
||||
source_node_id: str,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
@ -142,12 +136,12 @@ class EventServiceBase:
|
||||
event_name="model_load_completed",
|
||||
payload=dict(
|
||||
graph_execution_state_id=graph_execution_state_id,
|
||||
node=node,
|
||||
source_node_id=source_node_id,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
submodel=submodel,
|
||||
model_info=model_info,
|
||||
hash=model_info.hash,
|
||||
location=model_info.location,
|
||||
precision=str(model_info.precision),
|
||||
),
|
||||
)
|
||||
|
||||
@ -10,10 +10,9 @@ if TYPE_CHECKING:
|
||||
from invokeai.app.services.model_manager_service import ModelManagerServiceBase
|
||||
from invokeai.app.services.events import EventServiceBase
|
||||
from invokeai.app.services.latent_storage import LatentsStorageBase
|
||||
from invokeai.app.services.restoration_services import RestorationServices
|
||||
from invokeai.app.services.invocation_queue import InvocationQueueABC
|
||||
from invokeai.app.services.item_storage import ItemStorageABC
|
||||
from invokeai.app.services.config import InvokeAISettings
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
|
||||
from invokeai.app.services.invoker import InvocationProcessorABC
|
||||
|
||||
@ -24,7 +23,7 @@ class InvocationServices:
|
||||
# TODO: Just forward-declared everything due to circular dependencies. Fix structure.
|
||||
board_images: "BoardImagesServiceABC"
|
||||
boards: "BoardServiceABC"
|
||||
configuration: "InvokeAISettings"
|
||||
configuration: "InvokeAIAppConfig"
|
||||
events: "EventServiceBase"
|
||||
graph_execution_manager: "ItemStorageABC"["GraphExecutionState"]
|
||||
graph_library: "ItemStorageABC"["LibraryGraph"]
|
||||
@ -34,13 +33,12 @@ class InvocationServices:
|
||||
model_manager: "ModelManagerServiceBase"
|
||||
processor: "InvocationProcessorABC"
|
||||
queue: "InvocationQueueABC"
|
||||
restoration: "RestorationServices"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
board_images: "BoardImagesServiceABC",
|
||||
boards: "BoardServiceABC",
|
||||
configuration: "InvokeAISettings",
|
||||
configuration: "InvokeAIAppConfig",
|
||||
events: "EventServiceBase",
|
||||
graph_execution_manager: "ItemStorageABC"["GraphExecutionState"],
|
||||
graph_library: "ItemStorageABC"["LibraryGraph"],
|
||||
@ -50,7 +48,6 @@ class InvocationServices:
|
||||
model_manager: "ModelManagerServiceBase",
|
||||
processor: "InvocationProcessorABC",
|
||||
queue: "InvocationQueueABC",
|
||||
restoration: "RestorationServices",
|
||||
):
|
||||
self.board_images = board_images
|
||||
self.boards = boards
|
||||
@ -65,4 +62,3 @@ class InvocationServices:
|
||||
self.model_manager = model_manager
|
||||
self.processor = processor
|
||||
self.queue = queue
|
||||
self.restoration = restoration
|
||||
|
||||
@ -18,8 +18,9 @@ from invokeai.backend.model_management import (
|
||||
SchedulerPredictionType,
|
||||
ModelMerger,
|
||||
MergeInterpolationMethod,
|
||||
ModelNotFoundException,
|
||||
)
|
||||
|
||||
from invokeai.backend.model_management.model_search import FindModels
|
||||
|
||||
import torch
|
||||
from invokeai.app.models.exceptions import CanceledException
|
||||
@ -145,7 +146,7 @@ class ModelManagerServiceBase(ABC):
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Update the named model with a dictionary of attributes. Will fail with a
|
||||
KeyErrorException if the name does not already exist.
|
||||
ModelNotFoundException if the name does not already exist.
|
||||
|
||||
On a successful update, the config will be changed in memory. Will fail
|
||||
with an assertion error if provided attributes are incorrect or
|
||||
@ -167,6 +168,27 @@ class ModelManagerServiceBase(ABC):
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def rename_model(self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
new_name: str,
|
||||
):
|
||||
"""
|
||||
Rename the indicated model.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def list_checkpoint_configs(
|
||||
self
|
||||
)->List[Path]:
|
||||
"""
|
||||
List the checkpoint config paths from ROOT/configs/stable-diffusion.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def convert_model(
|
||||
self,
|
||||
@ -220,6 +242,7 @@ class ModelManagerServiceBase(ABC):
|
||||
alpha: Optional[float] = 0.5,
|
||||
interp: Optional[MergeInterpolationMethod] = None,
|
||||
force: Optional[bool] = False,
|
||||
merge_dest_directory: Optional[Path] = None
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Merge two to three diffusrs pipeline models and save as a new model.
|
||||
@ -228,9 +251,26 @@ class ModelManagerServiceBase(ABC):
|
||||
:param merged_model_name: Name of destination merged model
|
||||
:param alpha: Alpha strength to apply to 2d and 3d model
|
||||
:param interp: Interpolation method. None (default)
|
||||
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
@abstractmethod
|
||||
def search_for_models(self, directory: Path)->List[Path]:
|
||||
"""
|
||||
Return list of all models found in the designated directory.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def sync_to_config(self):
|
||||
"""
|
||||
Re-read models.yaml, rescan the models directory, and reimport models
|
||||
in the autoimport directories. Call after making changes outside the
|
||||
model manager API.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def commit(self, conf_file: Optional[Path] = None) -> None:
|
||||
"""
|
||||
@ -299,7 +339,6 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
submodel: Optional[SubModelType] = None,
|
||||
node: Optional[BaseInvocation] = None,
|
||||
context: Optional[InvocationContext] = None,
|
||||
) -> ModelInfo:
|
||||
"""
|
||||
@ -307,11 +346,9 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
part (such as the vae) of a diffusers mode.
|
||||
"""
|
||||
|
||||
# if we are called from within a node, then we get to emit
|
||||
# load start and complete events
|
||||
if node and context:
|
||||
# we can emit model loading events if we are executing with access to the invocation context
|
||||
if context:
|
||||
self._emit_load_event(
|
||||
node=node,
|
||||
context=context,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
@ -326,9 +363,8 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
submodel,
|
||||
)
|
||||
|
||||
if node and context:
|
||||
if context:
|
||||
self._emit_load_event(
|
||||
node=node,
|
||||
context=context,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
@ -412,14 +448,14 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Update the named model with a dictionary of attributes. Will fail with a
|
||||
KeyError exception if the name does not already exist.
|
||||
ModelNotFoundException exception if the name does not already exist.
|
||||
On a successful update, the config will be changed in memory. Will fail
|
||||
with an assertion error if provided attributes are incorrect or
|
||||
the model name is missing. Call commit() to write changes to disk.
|
||||
"""
|
||||
self.logger.debug(f'update model {model_name}')
|
||||
if not self.model_exists(model_name, base_model, model_type):
|
||||
raise KeyError(f"Unknown model {model_name}")
|
||||
raise ModelNotFoundException(f"Unknown model {model_name}")
|
||||
return self.add_model(model_name, base_model, model_type, model_attributes, clobber=True)
|
||||
|
||||
def del_model(
|
||||
@ -431,16 +467,18 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
"""
|
||||
Delete the named model from configuration. If delete_files is true,
|
||||
then the underlying weight file or diffusers directory will be deleted
|
||||
as well. Call commit() to write to disk.
|
||||
as well.
|
||||
"""
|
||||
self.logger.debug(f'delete model {model_name}')
|
||||
self.mgr.del_model(model_name, base_model, model_type)
|
||||
self.mgr.commit()
|
||||
|
||||
def convert_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: Union[ModelType.Main,ModelType.Vae],
|
||||
convert_dest_directory: Optional[Path] = Field(default=None, description="Optional directory location for merged model"),
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Convert a checkpoint file into a diffusers folder, deleting the cached
|
||||
@ -449,13 +487,14 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
:param model_name: Name of the model to convert
|
||||
:param base_model: Base model type
|
||||
:param model_type: Type of model ['vae' or 'main']
|
||||
:param convert_dest_directory: Save the converted model to the designated directory (`models/etc/etc` by default)
|
||||
|
||||
This will raise a ValueError unless the model is not a checkpoint. It will
|
||||
also raise a ValueError in the event that there is a similarly-named diffusers
|
||||
directory already in place.
|
||||
"""
|
||||
self.logger.debug(f'convert model {model_name}')
|
||||
return self.mgr.convert_model(model_name, base_model, model_type)
|
||||
return self.mgr.convert_model(model_name, base_model, model_type, convert_dest_directory)
|
||||
|
||||
def commit(self, conf_file: Optional[Path]=None):
|
||||
"""
|
||||
@ -467,23 +506,19 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
|
||||
def _emit_load_event(
|
||||
self,
|
||||
node,
|
||||
context,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
submodel: SubModelType,
|
||||
submodel: Optional[SubModelType] = None,
|
||||
model_info: Optional[ModelInfo] = None,
|
||||
):
|
||||
if context.services.queue.is_canceled(context.graph_execution_state_id):
|
||||
raise CanceledException()
|
||||
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
|
||||
source_node_id = graph_execution_state.prepared_source_mapping[node.id]
|
||||
|
||||
if model_info:
|
||||
context.services.events.emit_model_load_completed(
|
||||
graph_execution_state_id=context.graph_execution_state_id,
|
||||
node=node.dict(),
|
||||
source_node_id=source_node_id,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
@ -493,8 +528,6 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
else:
|
||||
context.services.events.emit_model_load_started(
|
||||
graph_execution_state_id=context.graph_execution_state_id,
|
||||
node=node.dict(),
|
||||
source_node_id=source_node_id,
|
||||
model_name=model_name,
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
@ -536,6 +569,7 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
alpha: Optional[float] = 0.5,
|
||||
interp: Optional[MergeInterpolationMethod] = None,
|
||||
force: Optional[bool] = False,
|
||||
merge_dest_directory: Optional[Path] = Field(default=None, description="Optional directory location for merged model"),
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
Merge two to three diffusrs pipeline models and save as a new model.
|
||||
@ -544,6 +578,7 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
:param merged_model_name: Name of destination merged model
|
||||
:param alpha: Alpha strength to apply to 2d and 3d model
|
||||
:param interp: Interpolation method. None (default)
|
||||
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
|
||||
"""
|
||||
merger = ModelMerger(self.mgr)
|
||||
try:
|
||||
@ -554,7 +589,55 @@ class ModelManagerService(ModelManagerServiceBase):
|
||||
alpha = alpha,
|
||||
interp = interp,
|
||||
force = force,
|
||||
merge_dest_directory=merge_dest_directory,
|
||||
)
|
||||
except AssertionError as e:
|
||||
raise ValueError(e)
|
||||
return result
|
||||
|
||||
def search_for_models(self, directory: Path)->List[Path]:
|
||||
"""
|
||||
Return list of all models found in the designated directory.
|
||||
"""
|
||||
search = FindModels(directory,self.logger)
|
||||
return search.list_models()
|
||||
|
||||
def sync_to_config(self):
|
||||
"""
|
||||
Re-read models.yaml, rescan the models directory, and reimport models
|
||||
in the autoimport directories. Call after making changes outside the
|
||||
model manager API.
|
||||
"""
|
||||
return self.mgr.sync_to_config()
|
||||
|
||||
def list_checkpoint_configs(self)->List[Path]:
|
||||
"""
|
||||
List the checkpoint config paths from ROOT/configs/stable-diffusion.
|
||||
"""
|
||||
config = self.mgr.app_config
|
||||
conf_path = config.legacy_conf_path
|
||||
root_path = config.root_path
|
||||
return [(conf_path / x).relative_to(root_path) for x in conf_path.glob('**/*.yaml')]
|
||||
|
||||
def rename_model(self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
new_name: str = None,
|
||||
new_base: BaseModelType = None,
|
||||
):
|
||||
"""
|
||||
Rename the indicated model. Can provide a new name and/or a new base.
|
||||
:param model_name: Current name of the model
|
||||
:param base_model: Current base of the model
|
||||
:param model_type: Model type (can't be changed)
|
||||
:param new_name: New name for the model
|
||||
:param new_base: New base for the model
|
||||
"""
|
||||
self.mgr.rename_model(base_model = base_model,
|
||||
model_type = model_type,
|
||||
model_name = model_name,
|
||||
new_name = new_name,
|
||||
new_base = new_base,
|
||||
)
|
||||
|
||||
|
||||
@ -1,113 +0,0 @@
|
||||
import sys
|
||||
import traceback
|
||||
import torch
|
||||
from typing import types
|
||||
from ...backend.restoration import Restoration
|
||||
from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
|
||||
|
||||
# This should be a real base class for postprocessing functions,
|
||||
# but right now we just instantiate the existing gfpgan, esrgan
|
||||
# and codeformer functions.
|
||||
class RestorationServices:
|
||||
'''Face restoration and upscaling'''
|
||||
|
||||
def __init__(self,args,logger:types.ModuleType):
|
||||
try:
|
||||
gfpgan, codeformer, esrgan = None, None, None
|
||||
if args.restore or args.esrgan:
|
||||
restoration = Restoration()
|
||||
# TODO: redo for new model structure
|
||||
if False and args.restore:
|
||||
gfpgan, codeformer = restoration.load_face_restore_models(
|
||||
args.gfpgan_model_path
|
||||
)
|
||||
else:
|
||||
logger.info("Face restoration disabled")
|
||||
if False and args.esrgan:
|
||||
esrgan = restoration.load_esrgan(args.esrgan_bg_tile)
|
||||
else:
|
||||
logger.info("Upscaling disabled")
|
||||
else:
|
||||
logger.info("Face restoration and upscaling disabled")
|
||||
except (ModuleNotFoundError, ImportError):
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
logger.info("You may need to install the ESRGAN and/or GFPGAN modules")
|
||||
self.device = torch.device(choose_torch_device())
|
||||
self.gfpgan = gfpgan
|
||||
self.codeformer = codeformer
|
||||
self.esrgan = esrgan
|
||||
self.logger = logger
|
||||
self.logger.info('Face restoration initialized')
|
||||
|
||||
# note that this one method does gfpgan and codepath reconstruction, as well as
|
||||
# esrgan upscaling
|
||||
# TO DO: refactor into separate methods
|
||||
def upscale_and_reconstruct(
|
||||
self,
|
||||
image_list,
|
||||
facetool="gfpgan",
|
||||
upscale=None,
|
||||
upscale_denoise_str=0.75,
|
||||
strength=0.0,
|
||||
codeformer_fidelity=0.75,
|
||||
save_original=False,
|
||||
image_callback=None,
|
||||
prefix=None,
|
||||
):
|
||||
results = []
|
||||
for r in image_list:
|
||||
image, seed = r
|
||||
try:
|
||||
if strength > 0:
|
||||
if self.gfpgan is not None or self.codeformer is not None:
|
||||
if facetool == "gfpgan":
|
||||
if self.gfpgan is None:
|
||||
self.logger.info(
|
||||
"GFPGAN not found. Face restoration is disabled."
|
||||
)
|
||||
else:
|
||||
image = self.gfpgan.process(image, strength, seed)
|
||||
if facetool == "codeformer":
|
||||
if self.codeformer is None:
|
||||
self.logger.info(
|
||||
"CodeFormer not found. Face restoration is disabled."
|
||||
)
|
||||
else:
|
||||
cf_device = (
|
||||
CPU_DEVICE if self.device == MPS_DEVICE else self.device
|
||||
)
|
||||
image = self.codeformer.process(
|
||||
image=image,
|
||||
strength=strength,
|
||||
device=cf_device,
|
||||
seed=seed,
|
||||
fidelity=codeformer_fidelity,
|
||||
)
|
||||
else:
|
||||
self.logger.info("Face Restoration is disabled.")
|
||||
if upscale is not None:
|
||||
if self.esrgan is not None:
|
||||
if len(upscale) < 2:
|
||||
upscale.append(0.75)
|
||||
image = self.esrgan.process(
|
||||
image,
|
||||
upscale[1],
|
||||
seed,
|
||||
int(upscale[0]),
|
||||
denoise_str=upscale_denoise_str,
|
||||
)
|
||||
else:
|
||||
self.logger.info("ESRGAN is disabled. Image not upscaled.")
|
||||
except Exception as e:
|
||||
self.logger.info(
|
||||
f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
|
||||
)
|
||||
|
||||
if image_callback is not None:
|
||||
image_callback(image, seed, upscaled=True, use_prefix=prefix)
|
||||
else:
|
||||
r[0] = image
|
||||
|
||||
results.append([image, seed])
|
||||
|
||||
return results
|
||||
@ -30,8 +30,6 @@ from huggingface_hub import login as hf_hub_login
|
||||
from omegaconf import OmegaConf
|
||||
from tqdm import tqdm
|
||||
from transformers import (
|
||||
AutoProcessor,
|
||||
CLIPSegForImageSegmentation,
|
||||
CLIPTextModel,
|
||||
CLIPTokenizer,
|
||||
AutoFeatureExtractor,
|
||||
@ -45,7 +43,6 @@ from invokeai.app.services.config import (
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
|
||||
from invokeai.frontend.install.widgets import (
|
||||
SingleSelectColumns,
|
||||
CenteredButtonPress,
|
||||
IntTitleSlider,
|
||||
set_min_terminal_size,
|
||||
@ -72,7 +69,6 @@ transformers.logging.set_verbosity_error()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
Model_dir = "models"
|
||||
Weights_dir = "ldm/stable-diffusion-v1/"
|
||||
|
||||
Default_config_file = config.model_conf_path
|
||||
SD_Configs = config.legacy_conf_path
|
||||
@ -226,64 +222,35 @@ def download_conversion_models():
|
||||
|
||||
# ---------------------------------------------
|
||||
def download_realesrgan():
|
||||
logger.info("Installing models from RealESRGAN...")
|
||||
model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth"
|
||||
wdn_model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth"
|
||||
|
||||
model_dest = config.root_path / "models/core/upscaling/realesrgan/realesr-general-x4v3.pth"
|
||||
wdn_model_dest = config.root_path / "models/core/upscaling/realesrgan/realesr-general-wdn-x4v3.pth"
|
||||
|
||||
download_with_progress_bar(model_url, str(model_dest), "RealESRGAN")
|
||||
download_with_progress_bar(wdn_model_url, str(wdn_model_dest), "RealESRGANwdn")
|
||||
|
||||
|
||||
def download_gfpgan():
|
||||
logger.info("Installing GFPGAN models...")
|
||||
for model in (
|
||||
[
|
||||
"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",
|
||||
"./models/core/face_restoration/gfpgan/GFPGANv1.4.pth",
|
||||
],
|
||||
[
|
||||
"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth",
|
||||
"./models/core/face_restoration/gfpgan/weights/detection_Resnet50_Final.pth",
|
||||
],
|
||||
[
|
||||
"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth",
|
||||
"./models/core/face_restoration/gfpgan/weights/parsing_parsenet.pth",
|
||||
],
|
||||
):
|
||||
model_url, model_dest = model[0], config.root_path / model[1]
|
||||
download_with_progress_bar(model_url, str(model_dest), "GFPGAN weights")
|
||||
|
||||
logger.info("Installing ESRGAN Upscaling models...")
|
||||
URLs = [
|
||||
dict(
|
||||
url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
|
||||
dest = "core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
|
||||
description = "RealESRGAN_x4plus.pth",
|
||||
),
|
||||
dict(
|
||||
url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
|
||||
dest = "core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
|
||||
description = "RealESRGAN_x4plus_anime_6B.pth",
|
||||
),
|
||||
dict(
|
||||
url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
|
||||
dest= "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
|
||||
description = "ESRGAN_SRx4_DF2KOST_official.pth",
|
||||
),
|
||||
dict(
|
||||
url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
|
||||
dest= "core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
|
||||
description = "RealESRGAN_x2plus.pth",
|
||||
),
|
||||
]
|
||||
for model in URLs:
|
||||
download_with_progress_bar(model['url'], config.models_path / model['dest'], model['description'])
|
||||
|
||||
# ---------------------------------------------
|
||||
def download_codeformer():
|
||||
logger.info("Installing CodeFormer model file...")
|
||||
model_url = (
|
||||
"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
|
||||
)
|
||||
model_dest = config.root_path / "models/core/face_restoration/codeformer/codeformer.pth"
|
||||
download_with_progress_bar(model_url, str(model_dest), "CodeFormer")
|
||||
|
||||
|
||||
# ---------------------------------------------
|
||||
def download_clipseg():
|
||||
logger.info("Installing clipseg model for text-based masking...")
|
||||
CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
|
||||
try:
|
||||
hf_download_from_pretrained(AutoProcessor, CLIPSEG_MODEL, config.root_path / 'models/core/misc/clipseg')
|
||||
hf_download_from_pretrained(CLIPSegForImageSegmentation, CLIPSEG_MODEL, config.root_path / 'models/core/misc/clipseg')
|
||||
except Exception:
|
||||
logger.info("Error installing clipseg model:")
|
||||
logger.info(traceback.format_exc())
|
||||
|
||||
|
||||
def download_support_models():
|
||||
download_realesrgan()
|
||||
download_gfpgan()
|
||||
download_codeformer()
|
||||
download_clipseg()
|
||||
download_conversion_models()
|
||||
|
||||
# -------------------------------------
|
||||
@ -666,7 +633,7 @@ def run_console_ui(
|
||||
|
||||
# The third argument is needed in the Windows 11 environment to
|
||||
# launch a console window running this program.
|
||||
set_min_terminal_size(MIN_COLS, MIN_LINES,'invokeai-configure')
|
||||
set_min_terminal_size(MIN_COLS, MIN_LINES)
|
||||
|
||||
# the install-models application spawns a subprocess to install
|
||||
# models, and will crash unless this is set before running.
|
||||
@ -743,7 +710,7 @@ def migrate_if_needed(opt: Namespace, root: Path)->bool:
|
||||
old_init_file = root / 'invokeai.init'
|
||||
new_init_file = root / 'invokeai.yaml'
|
||||
old_hub = root / 'models/hub'
|
||||
migration_needed = old_init_file.exists() and not new_init_file.exists() or old_hub.exists()
|
||||
migration_needed = (old_init_file.exists() and not new_init_file.exists()) and old_hub.exists()
|
||||
|
||||
if migration_needed:
|
||||
if opt.yes_to_all or \
|
||||
@ -858,9 +825,9 @@ def main():
|
||||
download_support_models()
|
||||
|
||||
if opt.skip_sd_weights:
|
||||
logger.info("\n** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
|
||||
logger.warning("SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST")
|
||||
elif models_to_download:
|
||||
logger.info("\n** DOWNLOADING DIFFUSION WEIGHTS **")
|
||||
logger.info("DOWNLOADING DIFFUSION WEIGHTS")
|
||||
process_and_execute(opt, models_to_download)
|
||||
|
||||
postscript(errors=errors)
|
||||
|
||||
@ -593,9 +593,12 @@ script, which will perform a full upgrade in place."""
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
config.parse_args(['--root',str(dest_root)])
|
||||
|
||||
# TODO: revisit
|
||||
# assert (dest_root / 'models').is_dir(), f"{dest_root} does not contain a 'models' subdirectory"
|
||||
# assert (dest_root / 'invokeai.yaml').exists(), f"{dest_root} does not contain an InvokeAI init file."
|
||||
# TODO: revisit - don't rely on invokeai.yaml to exist yet!
|
||||
dest_is_setup = (dest_root / 'models/core').exists() and (dest_root / 'databases').exists()
|
||||
if not dest_is_setup:
|
||||
import invokeai.frontend.install.invokeai_configure
|
||||
from invokeai.backend.install.invokeai_configure import initialize_rootdir
|
||||
initialize_rootdir(dest_root, True)
|
||||
|
||||
do_migrate(src_root,dest_root)
|
||||
|
||||
|
||||
@ -10,7 +10,7 @@ from tempfile import TemporaryDirectory
|
||||
from typing import List, Dict, Callable, Union, Set
|
||||
|
||||
import requests
|
||||
from diffusers import StableDiffusionPipeline
|
||||
from diffusers import DiffusionPipeline
|
||||
from diffusers import logging as dlogging
|
||||
from huggingface_hub import hf_hub_url, HfFolder, HfApi
|
||||
from omegaconf import OmegaConf
|
||||
@ -71,8 +71,6 @@ class ModelInstallList:
|
||||
class InstallSelections():
|
||||
install_models: List[str]= field(default_factory=list)
|
||||
remove_models: List[str]=field(default_factory=list)
|
||||
# scan_directory: Path = None
|
||||
# autoscan_on_startup: bool=False
|
||||
|
||||
@dataclass
|
||||
class ModelLoadInfo():
|
||||
@ -119,6 +117,7 @@ class ModelInstall(object):
|
||||
|
||||
# supplement with entries in models.yaml
|
||||
installed_models = self.mgr.list_models()
|
||||
|
||||
for md in installed_models:
|
||||
base = md['base_model']
|
||||
model_type = md['model_type']
|
||||
@ -136,6 +135,12 @@ class ModelInstall(object):
|
||||
)
|
||||
return {x : model_dict[x] for x in sorted(model_dict.keys(),key=lambda y: model_dict[y].name.lower())}
|
||||
|
||||
def list_models(self, model_type):
|
||||
installed = self.mgr.list_models(model_type=model_type)
|
||||
print(f'Installed models of type `{model_type}`:')
|
||||
for i in installed:
|
||||
print(f"{i['model_name']}\t{i['base_model']}\t{i['path']}")
|
||||
|
||||
def starter_models(self)->Set[str]:
|
||||
models = set()
|
||||
for key, value in self.datasets.items():
|
||||
@ -207,7 +212,7 @@ class ModelInstall(object):
|
||||
{'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
|
||||
]
|
||||
):
|
||||
models_installed.update(self._install_path(path))
|
||||
models_installed.update({str(model_path_id_or_url): self._install_path(path)})
|
||||
|
||||
# recursive scan
|
||||
elif path.is_dir():
|
||||
@ -305,6 +310,8 @@ class ModelInstall(object):
|
||||
if key := self.reverse_paths.get(path_name):
|
||||
(name, base, mtype) = ModelManager.parse_key(key)
|
||||
return name
|
||||
elif location.is_dir():
|
||||
return location.name
|
||||
else:
|
||||
return location.stem
|
||||
|
||||
@ -360,7 +367,7 @@ class ModelInstall(object):
|
||||
model = None
|
||||
for revision in revisions:
|
||||
try:
|
||||
model = StableDiffusionPipeline.from_pretrained(repo_id,revision=revision,safety_checker=None)
|
||||
model = DiffusionPipeline.from_pretrained(repo_id,revision=revision,safety_checker=None)
|
||||
except: # most errors are due to fp16 not being present. Fix this to catch other errors
|
||||
pass
|
||||
if model:
|
||||
|
||||
@ -3,7 +3,7 @@ Initialization file for invokeai.backend.model_management
|
||||
"""
|
||||
from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
|
||||
from .model_cache import ModelCache
|
||||
from .models import BaseModelType, ModelType, SubModelType, ModelVariantType
|
||||
from .lora import ModelPatcher, ONNXModelPatcher
|
||||
from .models import BaseModelType, ModelType, SubModelType, ModelVariantType, ModelNotFoundException
|
||||
from .model_merge import ModelMerger, MergeInterpolationMethod
|
||||
|
||||
|
||||
@ -104,7 +104,8 @@ class ModelCache(object):
|
||||
:param sha_chunksize: Chunksize to use when calculating sha256 model hash
|
||||
'''
|
||||
self.model_infos: Dict[str, ModelBase] = dict()
|
||||
self.lazy_offloading = lazy_offloading
|
||||
# allow lazy offloading only when vram cache enabled
|
||||
self.lazy_offloading = lazy_offloading and max_vram_cache_size > 0
|
||||
self.precision: torch.dtype=precision
|
||||
self.max_cache_size: float=max_cache_size
|
||||
self.max_vram_cache_size: float=max_vram_cache_size
|
||||
@ -327,6 +328,25 @@ class ModelCache(object):
|
||||
|
||||
refs = sys.getrefcount(cache_entry.model)
|
||||
|
||||
# manualy clear local variable references of just finished function calls
|
||||
# for some reason python don't want to collect it even by gc.collect() immidiately
|
||||
if refs > 2:
|
||||
while True:
|
||||
cleared = False
|
||||
for referrer in gc.get_referrers(cache_entry.model):
|
||||
if type(referrer).__name__ == "frame":
|
||||
# RuntimeError: cannot clear an executing frame
|
||||
with suppress(RuntimeError):
|
||||
referrer.clear()
|
||||
cleared = True
|
||||
#break
|
||||
|
||||
# repeat if referrers changes(due to frame clear), else exit loop
|
||||
if cleared:
|
||||
gc.collect()
|
||||
else:
|
||||
break
|
||||
|
||||
device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
|
||||
self.logger.debug(f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}, refs: {refs}")
|
||||
|
||||
@ -362,6 +382,9 @@ class ModelCache(object):
|
||||
self.logger.debug(f'GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB')
|
||||
vram_in_use += mem.vram_used # note vram_used is negative
|
||||
self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def _local_model_hash(self, model_path: Union[str, Path]) -> str:
|
||||
sha = hashlib.sha256()
|
||||
|
||||
@ -106,16 +106,16 @@ providing information about a model defined in models.yaml. For example:
|
||||
|
||||
>>> models = mgr.list_models()
|
||||
>>> json.dumps(models[0])
|
||||
{"path": "/home/lstein/invokeai-main/models/sd-1/controlnet/canny",
|
||||
"model_format": "diffusers",
|
||||
"name": "canny",
|
||||
"base_model": "sd-1",
|
||||
{"path": "/home/lstein/invokeai-main/models/sd-1/controlnet/canny",
|
||||
"model_format": "diffusers",
|
||||
"name": "canny",
|
||||
"base_model": "sd-1",
|
||||
"type": "controlnet"
|
||||
}
|
||||
|
||||
You can filter by model type and base model as shown here:
|
||||
|
||||
|
||||
|
||||
controlnets = mgr.list_models(model_type=ModelType.ControlNet,
|
||||
base_model=BaseModelType.StableDiffusion1)
|
||||
for c in controlnets:
|
||||
@ -140,14 +140,14 @@ Layout of the `models` directory:
|
||||
|
||||
models
|
||||
├── sd-1
|
||||
│ ├── controlnet
|
||||
│ ├── lora
|
||||
│ ├── main
|
||||
│ └── embedding
|
||||
│ ├── controlnet
|
||||
│ ├── lora
|
||||
│ ├── main
|
||||
│ └── embedding
|
||||
├── sd-2
|
||||
│ ├── controlnet
|
||||
│ ├── lora
|
||||
│ ├── main
|
||||
│ ├── controlnet
|
||||
│ ├── lora
|
||||
│ ├── main
|
||||
│ └── embedding
|
||||
└── core
|
||||
├── face_reconstruction
|
||||
@ -195,7 +195,7 @@ name, base model, type and a dict of model attributes. See
|
||||
`invokeai/backend/model_management/models` for the attributes required
|
||||
by each model type.
|
||||
|
||||
A model can be deleted using `del_model()`, providing the same
|
||||
A model can be deleted using `del_model()`, providing the same
|
||||
identifying information as `get_model()`
|
||||
|
||||
The `heuristic_import()` method will take a set of strings
|
||||
@ -247,6 +247,7 @@ import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.backend.util import CUDA_DEVICE, Chdir
|
||||
from .model_cache import ModelCache, ModelLocker
|
||||
from .model_search import ModelSearch
|
||||
from .models import (
|
||||
BaseModelType, ModelType, SubModelType,
|
||||
ModelError, SchedulerPredictionType, MODEL_CLASSES,
|
||||
@ -303,7 +304,7 @@ class ModelManager(object):
|
||||
logger: types.ModuleType = logger,
|
||||
):
|
||||
"""
|
||||
Initialize with the path to the models.yaml config file.
|
||||
Initialize with the path to the models.yaml config file.
|
||||
Optional parameters are the torch device type, precision, max_models,
|
||||
and sequential_offload boolean. Note that the default device
|
||||
type and precision are set up for a CUDA system running at half precision.
|
||||
@ -323,15 +324,6 @@ class ModelManager(object):
|
||||
# TODO: metadata not found
|
||||
# TODO: version check
|
||||
|
||||
self.models = dict()
|
||||
for model_key, model_config in config.items():
|
||||
model_name, base_model, model_type = self.parse_key(model_key)
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
# alias for config file
|
||||
model_config["model_format"] = model_config.pop("format")
|
||||
self.models[model_key] = model_class.create_config(**model_config)
|
||||
|
||||
# check config version number and update on disk/RAM if necessary
|
||||
self.app_config = InvokeAIAppConfig.get_config()
|
||||
self.logger = logger
|
||||
self.cache = ModelCache(
|
||||
@ -342,11 +334,41 @@ class ModelManager(object):
|
||||
sequential_offload = sequential_offload,
|
||||
logger = logger,
|
||||
)
|
||||
|
||||
self._read_models(config)
|
||||
|
||||
def _read_models(self, config: Optional[DictConfig] = None):
|
||||
if not config:
|
||||
if self.config_path:
|
||||
config = OmegaConf.load(self.config_path)
|
||||
else:
|
||||
return
|
||||
|
||||
self.models = dict()
|
||||
for model_key, model_config in config.items():
|
||||
if model_key.startswith('_'):
|
||||
continue
|
||||
model_name, base_model, model_type = self.parse_key(model_key)
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
# alias for config file
|
||||
model_config["model_format"] = model_config.pop("format")
|
||||
self.models[model_key] = model_class.create_config(**model_config)
|
||||
|
||||
# check config version number and update on disk/RAM if necessary
|
||||
self.cache_keys = dict()
|
||||
|
||||
# add controlnet, lora and textual_inversion models from disk
|
||||
self.scan_models_directory()
|
||||
|
||||
def sync_to_config(self):
|
||||
"""
|
||||
Call this when `models.yaml` has been changed externally.
|
||||
This will reinitialize internal data structures
|
||||
"""
|
||||
# Reread models directory; note that this will reinitialize the cache,
|
||||
# causing otherwise unreferenced models to be removed from memory
|
||||
self._read_models()
|
||||
|
||||
def model_exists(
|
||||
self,
|
||||
model_name: str,
|
||||
@ -409,7 +431,7 @@ class ModelManager(object):
|
||||
:param model_name: symbolic name of the model in models.yaml
|
||||
:param model_type: ModelType enum indicating the type of model to return
|
||||
:param base_model: BaseModelType enum indicating the base model used by this model
|
||||
:param submode_typel: an ModelType enum indicating the portion of
|
||||
:param submode_typel: an ModelType enum indicating the portion of
|
||||
the model to retrieve (e.g. ModelType.Vae)
|
||||
"""
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
@ -434,7 +456,7 @@ class ModelManager(object):
|
||||
raise ModelNotFoundException(f"Model not found - {model_key}")
|
||||
|
||||
# vae/movq override
|
||||
# TODO:
|
||||
# TODO:
|
||||
if submodel_type is not None and hasattr(model_config, submodel_type):
|
||||
override_path = getattr(model_config, submodel_type)
|
||||
if override_path:
|
||||
@ -467,7 +489,7 @@ class ModelManager(object):
|
||||
self.cache_keys[model_key].add(model_context.key)
|
||||
|
||||
model_hash = "<NO_HASH>" # TODO:
|
||||
|
||||
|
||||
return ModelInfo(
|
||||
context = model_context,
|
||||
name = model_name,
|
||||
@ -496,7 +518,7 @@ class ModelManager(object):
|
||||
|
||||
def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
|
||||
"""
|
||||
Return a list of (str, BaseModelType, ModelType) corresponding to all models
|
||||
Return a list of (str, BaseModelType, ModelType) corresponding to all models
|
||||
known to the configuration.
|
||||
"""
|
||||
return [(self.parse_key(x)) for x in self.models.keys()]
|
||||
@ -527,7 +549,10 @@ class ModelManager(object):
|
||||
model_keys = [self.create_key(model_name, base_model, model_type)] if model_name else sorted(self.models, key=str.casefold)
|
||||
models = []
|
||||
for model_key in model_keys:
|
||||
model_config = self.models[model_key]
|
||||
model_config = self.models.get(model_key)
|
||||
if not model_config:
|
||||
self.logger.error(f'Unknown model {model_name}')
|
||||
raise ModelNotFoundException(f'Unknown model {model_name}')
|
||||
|
||||
cur_model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
|
||||
if base_model is not None and cur_base_model != base_model:
|
||||
@ -543,6 +568,9 @@ class ModelManager(object):
|
||||
model_type=cur_model_type,
|
||||
)
|
||||
|
||||
# expose paths as absolute to help web UI
|
||||
if path := model_dict.get('path'):
|
||||
model_dict['path'] = str(self.app_config.root_path / path)
|
||||
models.append(model_dict)
|
||||
|
||||
return models
|
||||
@ -571,7 +599,7 @@ class ModelManager(object):
|
||||
model_cfg = self.models.pop(model_key, None)
|
||||
|
||||
if model_cfg is None:
|
||||
raise KeyError(f"Unknown model {model_key}")
|
||||
raise ModelNotFoundException(f"Unknown model {model_key}")
|
||||
|
||||
# note: it not garantie to release memory(model can has other references)
|
||||
cache_ids = self.cache_keys.pop(model_key, [])
|
||||
@ -589,6 +617,7 @@ class ModelManager(object):
|
||||
rmtree(str(model_path))
|
||||
else:
|
||||
model_path.unlink()
|
||||
self.commit()
|
||||
|
||||
# LS: tested
|
||||
def add_model(
|
||||
@ -609,6 +638,10 @@ class ModelManager(object):
|
||||
The returned dict has the same format as the dict returned by
|
||||
model_info().
|
||||
"""
|
||||
# relativize paths as they go in - this makes it easier to move the root directory around
|
||||
if path := model_attributes.get('path'):
|
||||
if Path(path).is_relative_to(self.app_config.root_path):
|
||||
model_attributes['path'] = str(Path(path).relative_to(self.app_config.root_path))
|
||||
|
||||
model_class = MODEL_CLASSES[base_model][model_type]
|
||||
model_config = model_class.create_config(**model_attributes)
|
||||
@ -645,11 +678,61 @@ class ModelManager(object):
|
||||
config = model_config,
|
||||
)
|
||||
|
||||
def rename_model(
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: ModelType,
|
||||
new_name: str = None,
|
||||
new_base: BaseModelType = None,
|
||||
):
|
||||
'''
|
||||
Rename or rebase a model.
|
||||
'''
|
||||
if new_name is None and new_base is None:
|
||||
self.logger.error("rename_model() called with neither a new_name nor a new_base. {model_name} unchanged.")
|
||||
return
|
||||
|
||||
model_key = self.create_key(model_name, base_model, model_type)
|
||||
model_cfg = self.models.get(model_key, None)
|
||||
if not model_cfg:
|
||||
raise ModelNotFoundException(f"Unknown model: {model_key}")
|
||||
|
||||
old_path = self.app_config.root_path / model_cfg.path
|
||||
new_name = new_name or model_name
|
||||
new_base = new_base or base_model
|
||||
new_key = self.create_key(new_name, new_base, model_type)
|
||||
if new_key in self.models:
|
||||
raise ValueError(f'Attempt to overwrite existing model definition "{new_key}"')
|
||||
|
||||
# if this is a model file/directory that we manage ourselves, we need to move it
|
||||
if old_path.is_relative_to(self.app_config.models_path):
|
||||
new_path = self.app_config.root_path / 'models' / BaseModelType(new_base).value / ModelType(model_type).value / new_name
|
||||
move(old_path, new_path)
|
||||
model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
|
||||
|
||||
# clean up caches
|
||||
old_model_cache = self._get_model_cache_path(old_path)
|
||||
if old_model_cache.exists():
|
||||
if old_model_cache.is_dir():
|
||||
rmtree(str(old_model_cache))
|
||||
else:
|
||||
old_model_cache.unlink()
|
||||
|
||||
cache_ids = self.cache_keys.pop(model_key, [])
|
||||
for cache_id in cache_ids:
|
||||
self.cache.uncache_model(cache_id)
|
||||
|
||||
self.models.pop(model_key, None) # delete
|
||||
self.models[new_key] = model_cfg
|
||||
self.commit()
|
||||
|
||||
def convert_model (
|
||||
self,
|
||||
model_name: str,
|
||||
base_model: BaseModelType,
|
||||
model_type: Union[ModelType.Main,ModelType.Vae],
|
||||
dest_directory: Optional[Path]=None,
|
||||
) -> AddModelResult:
|
||||
'''
|
||||
Convert a checkpoint file into a diffusers folder, deleting the cached
|
||||
@ -676,14 +759,14 @@ class ModelManager(object):
|
||||
)
|
||||
checkpoint_path = self.app_config.root_path / info["path"]
|
||||
old_diffusers_path = self.app_config.models_path / model.location
|
||||
new_diffusers_path = self.app_config.models_path / base_model.value / model_type.value / model_name
|
||||
new_diffusers_path = (dest_directory or self.app_config.models_path / base_model.value / model_type.value) / model_name
|
||||
if new_diffusers_path.exists():
|
||||
raise ValueError(f"A diffusers model already exists at {new_diffusers_path}")
|
||||
|
||||
try:
|
||||
move(old_diffusers_path,new_diffusers_path)
|
||||
info["model_format"] = "diffusers"
|
||||
info["path"] = str(new_diffusers_path.relative_to(self.app_config.root_path))
|
||||
info["path"] = str(new_diffusers_path) if dest_directory else str(new_diffusers_path.relative_to(self.app_config.root_path))
|
||||
info.pop('config')
|
||||
|
||||
result = self.add_model(model_name, base_model, model_type,
|
||||
@ -693,12 +776,12 @@ class ModelManager(object):
|
||||
# something went wrong, so don't leave dangling diffusers model in directory or it will cause a duplicate model error!
|
||||
rmtree(new_diffusers_path)
|
||||
raise
|
||||
|
||||
|
||||
if checkpoint_path.exists() and checkpoint_path.is_relative_to(self.app_config.models_path):
|
||||
checkpoint_path.unlink()
|
||||
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def search_models(self, search_folder):
|
||||
self.logger.info(f"Finding Models In: {search_folder}")
|
||||
models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
|
||||
@ -741,10 +824,14 @@ class ModelManager(object):
|
||||
assert config_file_path is not None,'no config file path to write to'
|
||||
config_file_path = self.app_config.root_path / config_file_path
|
||||
tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
|
||||
with open(tmpfile, "w", encoding="utf-8") as outfile:
|
||||
outfile.write(self.preamble())
|
||||
outfile.write(yaml_str)
|
||||
os.replace(tmpfile, config_file_path)
|
||||
try:
|
||||
with open(tmpfile, "w", encoding="utf-8") as outfile:
|
||||
outfile.write(self.preamble())
|
||||
outfile.write(yaml_str)
|
||||
os.replace(tmpfile, config_file_path)
|
||||
except OSError as err:
|
||||
self.logger.warning(f"Could not modify the config file at {config_file_path}")
|
||||
self.logger.warning(err)
|
||||
|
||||
def preamble(self) -> str:
|
||||
"""
|
||||
@ -823,6 +910,7 @@ class ModelManager(object):
|
||||
if (new_models_found or imported_models) and self.config_path:
|
||||
self.commit()
|
||||
|
||||
|
||||
def autoimport(self)->Dict[str, AddModelResult]:
|
||||
'''
|
||||
Scan the autoimport directory (if defined) and import new models, delete defunct models.
|
||||
@ -830,63 +918,41 @@ class ModelManager(object):
|
||||
# avoid circular import
|
||||
from invokeai.backend.install.model_install_backend import ModelInstall
|
||||
from invokeai.frontend.install.model_install import ask_user_for_prediction_type
|
||||
|
||||
|
||||
class ScanAndImport(ModelSearch):
|
||||
def __init__(self, directories, logger, ignore: Set[Path], installer: ModelInstall):
|
||||
super().__init__(directories, logger)
|
||||
self.installer = installer
|
||||
self.ignore = ignore
|
||||
|
||||
def on_search_started(self):
|
||||
self.new_models_found = dict()
|
||||
|
||||
def on_model_found(self, model: Path):
|
||||
if model not in self.ignore:
|
||||
self.new_models_found.update(self.installer.heuristic_import(model))
|
||||
|
||||
def on_search_completed(self):
|
||||
self.logger.info(f'Scanned {self._items_scanned} files and directories, imported {len(self.new_models_found)} models')
|
||||
|
||||
def models_found(self):
|
||||
return self.new_models_found
|
||||
|
||||
|
||||
installer = ModelInstall(config = self.app_config,
|
||||
model_manager = self,
|
||||
prediction_type_helper = ask_user_for_prediction_type,
|
||||
)
|
||||
|
||||
scanned_dirs = set()
|
||||
|
||||
config = self.app_config
|
||||
known_paths = {(self.app_config.root_path / x['path']) for x in self.list_models()}
|
||||
|
||||
for autodir in [config.autoimport_dir,
|
||||
config.lora_dir,
|
||||
config.embedding_dir,
|
||||
config.controlnet_dir]:
|
||||
if autodir is None:
|
||||
continue
|
||||
|
||||
self.logger.info(f'Scanning {autodir} for models to import')
|
||||
installed = dict()
|
||||
|
||||
autodir = self.app_config.root_path / autodir
|
||||
if not autodir.exists():
|
||||
continue
|
||||
|
||||
items_scanned = 0
|
||||
new_models_found = dict()
|
||||
|
||||
for root, dirs, files in os.walk(autodir):
|
||||
items_scanned += len(dirs) + len(files)
|
||||
for d in dirs:
|
||||
path = Path(root) / d
|
||||
if path in known_paths or path.parent in scanned_dirs:
|
||||
scanned_dirs.add(path)
|
||||
continue
|
||||
if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
|
||||
try:
|
||||
new_models_found.update(installer.heuristic_import(path))
|
||||
scanned_dirs.add(path)
|
||||
except ValueError as e:
|
||||
self.logger.warning(str(e))
|
||||
|
||||
for f in files:
|
||||
path = Path(root) / f
|
||||
if path in known_paths or path.parent in scanned_dirs:
|
||||
continue
|
||||
if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
|
||||
try:
|
||||
import_result = installer.heuristic_import(path)
|
||||
new_models_found.update(import_result)
|
||||
except ValueError as e:
|
||||
self.logger.warning(str(e))
|
||||
|
||||
self.logger.info(f'Scanned {items_scanned} files and directories, imported {len(new_models_found)} models')
|
||||
installed.update(new_models_found)
|
||||
|
||||
return installed
|
||||
known_paths = {config.root_path / x['path'] for x in self.list_models()}
|
||||
directories = {config.root_path / x for x in [config.autoimport_dir,
|
||||
config.lora_dir,
|
||||
config.embedding_dir,
|
||||
config.controlnet_dir]
|
||||
}
|
||||
scanner = ScanAndImport(directories, self.logger, ignore=known_paths, installer=installer)
|
||||
scanner.search()
|
||||
return scanner.models_found()
|
||||
|
||||
def heuristic_import(self,
|
||||
items_to_import: Set[str],
|
||||
@ -909,18 +975,18 @@ class ModelManager(object):
|
||||
that model.
|
||||
|
||||
May return the following exceptions:
|
||||
- KeyError - one or more of the items to import is not a valid path, repo_id or URL
|
||||
- ModelNotFoundException - one or more of the items to import is not a valid path, repo_id or URL
|
||||
- ValueError - a corresponding model already exists
|
||||
'''
|
||||
# avoid circular import here
|
||||
from invokeai.backend.install.model_install_backend import ModelInstall
|
||||
successfully_installed = dict()
|
||||
|
||||
|
||||
installer = ModelInstall(config = self.app_config,
|
||||
prediction_type_helper = prediction_type_helper,
|
||||
model_manager = self)
|
||||
for thing in items_to_import:
|
||||
installed = installer.heuristic_import(thing)
|
||||
successfully_installed.update(installed)
|
||||
self.commit()
|
||||
self.commit()
|
||||
return successfully_installed
|
||||
|
||||
@ -11,7 +11,7 @@ from enum import Enum
|
||||
from pathlib import Path
|
||||
from diffusers import DiffusionPipeline
|
||||
from diffusers import logging as dlogging
|
||||
from typing import List, Union
|
||||
from typing import List, Union, Optional
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
@ -74,6 +74,7 @@ class ModelMerger(object):
|
||||
alpha: float = 0.5,
|
||||
interp: MergeInterpolationMethod = None,
|
||||
force: bool = False,
|
||||
merge_dest_directory: Optional[Path] = None,
|
||||
**kwargs,
|
||||
) -> AddModelResult:
|
||||
"""
|
||||
@ -85,7 +86,7 @@ class ModelMerger(object):
|
||||
:param interp: The interpolation method to use for the merging. Supports "weighted_average", "sigmoid", "inv_sigmoid", "add_difference" and None.
|
||||
Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_difference" is supported. Add_difference is A+(B-C).
|
||||
:param force: Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
|
||||
|
||||
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
|
||||
**kwargs - the default DiffusionPipeline.get_config_dict kwargs:
|
||||
cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
|
||||
"""
|
||||
@ -111,7 +112,7 @@ class ModelMerger(object):
|
||||
merged_pipe = self.merge_diffusion_models(
|
||||
model_paths, alpha, merge_method, force, **kwargs
|
||||
)
|
||||
dump_path = config.models_path / base_model.value / ModelType.Main.value
|
||||
dump_path = Path(merge_dest_directory) if merge_dest_directory else config.models_path / base_model.value / ModelType.Main.value
|
||||
dump_path.mkdir(parents=True, exist_ok=True)
|
||||
dump_path = dump_path / merged_model_name
|
||||
|
||||
|
||||
@ -12,6 +12,7 @@ from picklescan.scanner import scan_file_path
|
||||
from .models import (
|
||||
BaseModelType, ModelType, ModelVariantType,
|
||||
SchedulerPredictionType, SilenceWarnings,
|
||||
InvalidModelException
|
||||
)
|
||||
from .models.base import read_checkpoint_meta
|
||||
|
||||
@ -38,6 +39,8 @@ class ModelProbe(object):
|
||||
|
||||
CLASS2TYPE = {
|
||||
'StableDiffusionPipeline' : ModelType.Main,
|
||||
'StableDiffusionXLPipeline' : ModelType.Main,
|
||||
'StableDiffusionXLImg2ImgPipeline' : ModelType.Main,
|
||||
'AutoencoderKL' : ModelType.Vae,
|
||||
'ControlNetModel' : ModelType.ControlNet,
|
||||
}
|
||||
@ -59,7 +62,7 @@ class ModelProbe(object):
|
||||
elif isinstance(model,(dict,ModelMixin,ConfigMixin)):
|
||||
return cls.probe(model_path=None, model=model, prediction_type_helper=prediction_type_helper)
|
||||
else:
|
||||
raise ValueError("model parameter {model} is neither a Path, nor a model")
|
||||
raise InvalidModelException("model parameter {model} is neither a Path, nor a model")
|
||||
|
||||
@classmethod
|
||||
def probe(cls,
|
||||
@ -99,9 +102,10 @@ class ModelProbe(object):
|
||||
upcast_attention = (base_type==BaseModelType.StableDiffusion2 \
|
||||
and prediction_type==SchedulerPredictionType.VPrediction),
|
||||
format = format,
|
||||
image_size = 768 if (base_type==BaseModelType.StableDiffusion2 \
|
||||
and prediction_type==SchedulerPredictionType.VPrediction \
|
||||
) else 512,
|
||||
image_size = 1024 if (base_type in {BaseModelType.StableDiffusionXL,BaseModelType.StableDiffusionXLRefiner}) else \
|
||||
768 if (base_type==BaseModelType.StableDiffusion2 \
|
||||
and prediction_type==SchedulerPredictionType.VPrediction ) else \
|
||||
512
|
||||
)
|
||||
except Exception:
|
||||
raise
|
||||
@ -138,7 +142,7 @@ class ModelProbe(object):
|
||||
if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
|
||||
return ModelType.TextualInversion
|
||||
|
||||
raise ValueError(f"Unable to determine model type for {model_path}")
|
||||
raise InvalidModelException(f"Unable to determine model type for {model_path}")
|
||||
|
||||
@classmethod
|
||||
def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin)->ModelType:
|
||||
@ -168,7 +172,7 @@ class ModelProbe(object):
|
||||
return type
|
||||
|
||||
# give up
|
||||
raise ValueError(f"Unable to determine model type for {folder_path}")
|
||||
raise InvalidModelException(f"Unable to determine model type for {folder_path}")
|
||||
|
||||
@classmethod
|
||||
def _scan_and_load_checkpoint(cls,model_path: Path)->dict:
|
||||
@ -237,7 +241,7 @@ class CheckpointProbeBase(ProbeBase):
|
||||
elif in_channels == 4:
|
||||
return ModelVariantType.Normal
|
||||
else:
|
||||
raise ValueError(f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}")
|
||||
raise InvalidModelException(f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}")
|
||||
|
||||
class PipelineCheckpointProbe(CheckpointProbeBase):
|
||||
def get_base_type(self)->BaseModelType:
|
||||
@ -248,7 +252,10 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
|
||||
return BaseModelType.StableDiffusion1
|
||||
if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
|
||||
return BaseModelType.StableDiffusion2
|
||||
raise ValueError("Cannot determine base type")
|
||||
# TODO: Verify that this is correct! Need an XL checkpoint file for this.
|
||||
if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
|
||||
return BaseModelType.StableDiffusionXL
|
||||
raise InvalidModelException("Cannot determine base type")
|
||||
|
||||
def get_scheduler_prediction_type(self)->SchedulerPredictionType:
|
||||
type = self.get_base_type()
|
||||
@ -329,7 +336,7 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
|
||||
return BaseModelType.StableDiffusion2
|
||||
elif self.checkpoint_path and self.helper:
|
||||
return self.helper(self.checkpoint_path)
|
||||
raise ValueError("Unable to determine base type for {self.checkpoint_path}")
|
||||
raise InvalidModelException("Unable to determine base type for {self.checkpoint_path}")
|
||||
|
||||
########################################################
|
||||
# classes for probing folders
|
||||
@ -360,8 +367,12 @@ class PipelineFolderProbe(FolderProbeBase):
|
||||
return BaseModelType.StableDiffusion1
|
||||
elif unet_conf['cross_attention_dim'] == 1024:
|
||||
return BaseModelType.StableDiffusion2
|
||||
elif unet_conf['cross_attention_dim'] == 1280:
|
||||
return BaseModelType.StableDiffusionXLRefiner
|
||||
elif unet_conf['cross_attention_dim'] == 2048:
|
||||
return BaseModelType.StableDiffusionXL
|
||||
else:
|
||||
raise ValueError(f'Unknown base model for {self.folder_path}')
|
||||
raise InvalidModelException(f'Unknown base model for {self.folder_path}')
|
||||
|
||||
def get_scheduler_prediction_type(self)->SchedulerPredictionType:
|
||||
if self.model:
|
||||
@ -418,7 +429,7 @@ class ControlNetFolderProbe(FolderProbeBase):
|
||||
def get_base_type(self)->BaseModelType:
|
||||
config_file = self.folder_path / 'config.json'
|
||||
if not config_file.exists():
|
||||
raise ValueError(f"Cannot determine base type for {self.folder_path}")
|
||||
raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
|
||||
with open(config_file,'r') as file:
|
||||
config = json.load(file)
|
||||
# no obvious way to distinguish between sd2-base and sd2-768
|
||||
@ -435,7 +446,7 @@ class LoRAFolderProbe(FolderProbeBase):
|
||||
model_file = base_file
|
||||
break
|
||||
if not model_file:
|
||||
raise ValueError('Unknown LoRA format encountered')
|
||||
raise InvalidModelException('Unknown LoRA format encountered')
|
||||
return LoRACheckpointProbe(model_file,None).get_base_type()
|
||||
|
||||
############## register probe classes ######
|
||||
|
||||
103
invokeai/backend/model_management/model_search.py
Normal file
103
invokeai/backend/model_management/model_search.py
Normal file
@ -0,0 +1,103 @@
|
||||
# Copyright 2023, Lincoln D. Stein and the InvokeAI Team
|
||||
"""
|
||||
Abstract base class for recursive directory search for models.
|
||||
"""
|
||||
|
||||
import os
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import List, Set, types
|
||||
from pathlib import Path
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
class ModelSearch(ABC):
|
||||
def __init__(self, directories: List[Path], logger: types.ModuleType=logger):
|
||||
"""
|
||||
Initialize a recursive model directory search.
|
||||
:param directories: List of directory Paths to recurse through
|
||||
:param logger: Logger to use
|
||||
"""
|
||||
self.directories = directories
|
||||
self.logger = logger
|
||||
self._items_scanned = 0
|
||||
self._models_found = 0
|
||||
self._scanned_dirs = set()
|
||||
self._scanned_paths = set()
|
||||
self._pruned_paths = set()
|
||||
|
||||
@abstractmethod
|
||||
def on_search_started(self):
|
||||
"""
|
||||
Called before the scan starts.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def on_model_found(self, model: Path):
|
||||
"""
|
||||
Process a found model. Raise an exception if something goes wrong.
|
||||
:param model: Model to process - could be a directory or checkpoint.
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def on_search_completed(self):
|
||||
"""
|
||||
Perform some activity when the scan is completed. May use instance
|
||||
variables, items_scanned and models_found
|
||||
"""
|
||||
pass
|
||||
|
||||
def search(self):
|
||||
self.on_search_started()
|
||||
for dir in self.directories:
|
||||
self.walk_directory(dir)
|
||||
self.on_search_completed()
|
||||
|
||||
def walk_directory(self, path: Path):
|
||||
for root, dirs, files in os.walk(path):
|
||||
if str(Path(root).name).startswith('.'):
|
||||
self._pruned_paths.add(root)
|
||||
if any([Path(root).is_relative_to(x) for x in self._pruned_paths]):
|
||||
continue
|
||||
|
||||
self._items_scanned += len(dirs) + len(files)
|
||||
for d in dirs:
|
||||
path = Path(root) / d
|
||||
if path in self._scanned_paths or path.parent in self._scanned_dirs:
|
||||
self._scanned_dirs.add(path)
|
||||
continue
|
||||
if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
|
||||
try:
|
||||
self.on_model_found(path)
|
||||
self._models_found += 1
|
||||
self._scanned_dirs.add(path)
|
||||
except Exception as e:
|
||||
self.logger.warning(str(e))
|
||||
|
||||
for f in files:
|
||||
path = Path(root) / f
|
||||
if path.parent in self._scanned_dirs:
|
||||
continue
|
||||
if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
|
||||
try:
|
||||
self.on_model_found(path)
|
||||
self._models_found += 1
|
||||
except Exception as e:
|
||||
self.logger.warning(str(e))
|
||||
|
||||
class FindModels(ModelSearch):
|
||||
def on_search_started(self):
|
||||
self.models_found: Set[Path] = set()
|
||||
|
||||
def on_model_found(self,model: Path):
|
||||
self.models_found.add(model)
|
||||
|
||||
def on_search_completed(self):
|
||||
pass
|
||||
|
||||
def list_models(self) -> List[Path]:
|
||||
self.search()
|
||||
return self.models_found
|
||||
|
||||
|
||||
@ -4,6 +4,7 @@ from pydantic import BaseModel
|
||||
from typing import Literal, get_origin
|
||||
from .base import BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase, ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings, ModelNotFoundException, InvalidModelException
|
||||
from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
|
||||
from .sdxl import StableDiffusionXLModel
|
||||
from .vae import VaeModel
|
||||
from .lora import LoRAModel
|
||||
from .controlnet import ControlNetModel # TODO:
|
||||
@ -28,6 +29,22 @@ MODEL_CLASSES = {
|
||||
ModelType.ControlNet: ControlNetModel,
|
||||
ModelType.TextualInversion: TextualInversionModel,
|
||||
},
|
||||
BaseModelType.StableDiffusionXL: {
|
||||
ModelType.Main: StableDiffusionXLModel,
|
||||
ModelType.Vae: VaeModel,
|
||||
# will not work until support written
|
||||
ModelType.Lora: LoRAModel,
|
||||
ModelType.ControlNet: ControlNetModel,
|
||||
ModelType.TextualInversion: TextualInversionModel,
|
||||
},
|
||||
BaseModelType.StableDiffusionXLRefiner: {
|
||||
ModelType.Main: StableDiffusionXLModel,
|
||||
ModelType.Vae: VaeModel,
|
||||
# will not work until support written
|
||||
ModelType.Lora: LoRAModel,
|
||||
ModelType.ControlNet: ControlNetModel,
|
||||
ModelType.TextualInversion: TextualInversionModel,
|
||||
},
|
||||
#BaseModelType.Kandinsky2_1: {
|
||||
# ModelType.Main: Kandinsky2_1Model,
|
||||
# ModelType.MoVQ: MoVQModel,
|
||||
@ -52,7 +69,9 @@ for base_model, models in MODEL_CLASSES.items():
|
||||
model_configs.discard(None)
|
||||
MODEL_CONFIGS.extend(model_configs)
|
||||
|
||||
for cfg in model_configs:
|
||||
# LS: sort to get the checkpoint configs first, which makes
|
||||
# for a better template in the Swagger docs
|
||||
for cfg in sorted(model_configs, key=lambda x: str(x)):
|
||||
model_name, cfg_name = cfg.__qualname__.split('.')[-2:]
|
||||
openapi_cfg_name = model_name + cfg_name
|
||||
if openapi_cfg_name in vars():
|
||||
|
||||
@ -30,6 +30,8 @@ class ModelNotFoundException(Exception):
|
||||
class BaseModelType(str, Enum):
|
||||
StableDiffusion1 = "sd-1"
|
||||
StableDiffusion2 = "sd-2"
|
||||
StableDiffusionXL = "sdxl"
|
||||
StableDiffusionXLRefiner = "sdxl-refiner"
|
||||
#Kandinsky2_1 = "kandinsky-2.1"
|
||||
|
||||
class ModelType(str, Enum):
|
||||
@ -43,7 +45,9 @@ class ModelType(str, Enum):
|
||||
class SubModelType(str, Enum):
|
||||
UNet = "unet"
|
||||
TextEncoder = "text_encoder"
|
||||
TextEncoder2 = "text_encoder_2"
|
||||
Tokenizer = "tokenizer"
|
||||
Tokenizer2 = "tokenizer_2"
|
||||
Vae = "vae"
|
||||
VaeDecoder = "vae_decoder"
|
||||
VaeEncoder = "vae_encoder"
|
||||
@ -68,7 +72,6 @@ class ModelConfigBase(BaseModel):
|
||||
path: str # or Path
|
||||
description: Optional[str] = Field(None)
|
||||
model_format: Optional[str] = Field(None)
|
||||
# do not save to config
|
||||
error: Optional[ModelError] = Field(None)
|
||||
|
||||
class Config:
|
||||
|
||||
@ -1,8 +1,7 @@
|
||||
import os
|
||||
import torch
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Optional, Union, Literal
|
||||
from typing import Optional
|
||||
from .base import (
|
||||
ModelBase,
|
||||
ModelConfigBase,
|
||||
@ -14,6 +13,7 @@ from .base import (
|
||||
calc_model_size_by_data,
|
||||
classproperty,
|
||||
InvalidModelException,
|
||||
ModelNotFoundException,
|
||||
)
|
||||
|
||||
class ControlNetModelFormat(str, Enum):
|
||||
@ -60,10 +60,20 @@ class ControlNetModel(ModelBase):
|
||||
if child_type is not None:
|
||||
raise Exception("There is no child models in controlnet model")
|
||||
|
||||
model = self.model_class.from_pretrained(
|
||||
self.model_path,
|
||||
torch_dtype=torch_dtype,
|
||||
)
|
||||
model = None
|
||||
for variant in ['fp16',None]:
|
||||
try:
|
||||
model = self.model_class.from_pretrained(
|
||||
self.model_path,
|
||||
torch_dtype=torch_dtype,
|
||||
variant=variant,
|
||||
)
|
||||
break
|
||||
except:
|
||||
pass
|
||||
if not model:
|
||||
raise ModelNotFoundException()
|
||||
|
||||
# calc more accurate size
|
||||
self.model_size = calc_model_size_by_data(model)
|
||||
return model
|
||||
|
||||
114
invokeai/backend/model_management/models/sdxl.py
Normal file
114
invokeai/backend/model_management/models/sdxl.py
Normal file
@ -0,0 +1,114 @@
|
||||
import os
|
||||
import json
|
||||
from enum import Enum
|
||||
from pydantic import Field
|
||||
from typing import Literal, Optional
|
||||
from .base import (
|
||||
ModelConfigBase,
|
||||
BaseModelType,
|
||||
ModelType,
|
||||
ModelVariantType,
|
||||
DiffusersModel,
|
||||
read_checkpoint_meta,
|
||||
classproperty,
|
||||
)
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
class StableDiffusionXLModelFormat(str, Enum):
|
||||
Checkpoint = "checkpoint"
|
||||
Diffusers = "diffusers"
|
||||
|
||||
class StableDiffusionXLModel(DiffusersModel):
|
||||
|
||||
# TODO: check that configs overwriten properly
|
||||
class DiffusersConfig(ModelConfigBase):
|
||||
model_format: Literal[StableDiffusionXLModelFormat.Diffusers]
|
||||
vae: Optional[str] = Field(None)
|
||||
variant: ModelVariantType
|
||||
|
||||
class CheckpointConfig(ModelConfigBase):
|
||||
model_format: Literal[StableDiffusionXLModelFormat.Checkpoint]
|
||||
vae: Optional[str] = Field(None)
|
||||
config: str
|
||||
variant: ModelVariantType
|
||||
|
||||
def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
|
||||
assert base_model in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner}
|
||||
assert model_type == ModelType.Main
|
||||
super().__init__(
|
||||
model_path=model_path,
|
||||
base_model=BaseModelType.StableDiffusionXL,
|
||||
model_type=ModelType.Main,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def probe_config(cls, path: str, **kwargs):
|
||||
model_format = cls.detect_format(path)
|
||||
ckpt_config_path = kwargs.get("config", None)
|
||||
if model_format == StableDiffusionXLModelFormat.Checkpoint:
|
||||
if ckpt_config_path:
|
||||
ckpt_config = OmegaConf.load(ckpt_config_path)
|
||||
ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"]
|
||||
|
||||
else:
|
||||
checkpoint = read_checkpoint_meta(path)
|
||||
checkpoint = checkpoint.get('state_dict', checkpoint)
|
||||
in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
|
||||
|
||||
elif model_format == StableDiffusionXLModelFormat.Diffusers:
|
||||
unet_config_path = os.path.join(path, "unet", "config.json")
|
||||
if os.path.exists(unet_config_path):
|
||||
with open(unet_config_path, "r") as f:
|
||||
unet_config = json.loads(f.read())
|
||||
in_channels = unet_config['in_channels']
|
||||
|
||||
else:
|
||||
raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
|
||||
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown stable diffusion 2.* format: {model_format}")
|
||||
|
||||
if in_channels == 9:
|
||||
variant = ModelVariantType.Inpaint
|
||||
elif in_channels == 5:
|
||||
variant = ModelVariantType.Depth
|
||||
elif in_channels == 4:
|
||||
variant = ModelVariantType.Normal
|
||||
else:
|
||||
raise Exception("Unkown stable diffusion 2.* model format")
|
||||
|
||||
if ckpt_config_path is None:
|
||||
# TO DO: implement picking
|
||||
pass
|
||||
|
||||
return cls.create_config(
|
||||
path=path,
|
||||
model_format=model_format,
|
||||
|
||||
config=ckpt_config_path,
|
||||
variant=variant,
|
||||
)
|
||||
|
||||
@classproperty
|
||||
def save_to_config(cls) -> bool:
|
||||
return True
|
||||
|
||||
@classmethod
|
||||
def detect_format(cls, model_path: str):
|
||||
if os.path.isdir(model_path):
|
||||
return StableDiffusionXLModelFormat.Diffusers
|
||||
else:
|
||||
return StableDiffusionXLModelFormat.Checkpoint
|
||||
|
||||
@classmethod
|
||||
def convert_if_required(
|
||||
cls,
|
||||
model_path: str,
|
||||
output_path: str,
|
||||
config: ModelConfigBase,
|
||||
base_model: BaseModelType,
|
||||
) -> str:
|
||||
if isinstance(config, cls.CheckpointConfig):
|
||||
raise NotImplementedError('conversion of SDXL checkpoint models to diffusers format is not yet supported')
|
||||
else:
|
||||
return model_path
|
||||
@ -5,14 +5,11 @@ from pydantic import Field
|
||||
from pathlib import Path
|
||||
from typing import Literal, Optional, Union
|
||||
from .base import (
|
||||
ModelBase,
|
||||
ModelConfigBase,
|
||||
BaseModelType,
|
||||
ModelType,
|
||||
SubModelType,
|
||||
ModelVariantType,
|
||||
DiffusersModel,
|
||||
SchedulerPredictionType,
|
||||
SilenceWarnings,
|
||||
read_checkpoint_meta,
|
||||
classproperty,
|
||||
@ -37,8 +34,7 @@ class StableDiffusion1Model(DiffusersModel):
|
||||
vae: Optional[str] = Field(None)
|
||||
config: str
|
||||
variant: ModelVariantType
|
||||
|
||||
|
||||
|
||||
def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
|
||||
assert base_model == BaseModelType.StableDiffusion1
|
||||
assert model_type == ModelType.Main
|
||||
@ -249,6 +245,12 @@ def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
|
||||
ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512)
|
||||
ModelVariantType.Inpaint: "v2-inpainting-inference.yaml",
|
||||
ModelVariantType.Depth: "v2-midas-inference.yaml",
|
||||
},
|
||||
# note that these .yaml files don't yet exist!
|
||||
BaseModelType.StableDiffusionXL: {
|
||||
ModelVariantType.Normal: "xl-inference-v.yaml",
|
||||
ModelVariantType.Inpaint: "xl-inpainting-inference.yaml",
|
||||
ModelVariantType.Depth: "xl-midas-inference.yaml",
|
||||
}
|
||||
}
|
||||
|
||||
@ -264,6 +266,7 @@ def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
|
||||
|
||||
|
||||
# TODO: rework
|
||||
# Note that convert_ckpt_to_diffuses does not currently support conversion of SDXL models
|
||||
def _convert_ckpt_and_cache(
|
||||
version: BaseModelType,
|
||||
model_config: Union[StableDiffusion1Model.CheckpointConfig, StableDiffusion2Model.CheckpointConfig],
|
||||
|
||||
@ -68,7 +68,7 @@ class TextualInversionModel(ModelBase):
|
||||
return None # diffusers-ti
|
||||
|
||||
if os.path.isfile(path):
|
||||
if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
|
||||
if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "bin"]]):
|
||||
return None
|
||||
|
||||
raise InvalidModelException(f"Not a valid model: {path}")
|
||||
|
||||
@ -16,6 +16,7 @@ from .base import (
|
||||
calc_model_size_by_data,
|
||||
classproperty,
|
||||
InvalidModelException,
|
||||
ModelNotFoundException,
|
||||
)
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from diffusers.utils import is_safetensors_available
|
||||
|
||||
@ -1,4 +0,0 @@
|
||||
"""
|
||||
Initialization file for the invokeai.backend.restoration package
|
||||
"""
|
||||
from .base import Restoration
|
||||
@ -1,45 +0,0 @@
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
class Restoration:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def load_face_restore_models(
|
||||
self, gfpgan_model_path="./models/core/face_restoration/gfpgan/GFPGANv1.4.pth"
|
||||
):
|
||||
# Load GFPGAN
|
||||
gfpgan = self.load_gfpgan(gfpgan_model_path)
|
||||
if gfpgan.gfpgan_model_exists:
|
||||
logger.info("GFPGAN Initialized")
|
||||
else:
|
||||
logger.info("GFPGAN Disabled")
|
||||
gfpgan = None
|
||||
|
||||
# Load CodeFormer
|
||||
codeformer = self.load_codeformer()
|
||||
if codeformer.codeformer_model_exists:
|
||||
logger.info("CodeFormer Initialized")
|
||||
else:
|
||||
logger.info("CodeFormer Disabled")
|
||||
codeformer = None
|
||||
|
||||
return gfpgan, codeformer
|
||||
|
||||
# Face Restore Models
|
||||
def load_gfpgan(self, gfpgan_model_path):
|
||||
from .gfpgan import GFPGAN
|
||||
|
||||
return GFPGAN(gfpgan_model_path)
|
||||
|
||||
def load_codeformer(self):
|
||||
from .codeformer import CodeFormerRestoration
|
||||
|
||||
return CodeFormerRestoration()
|
||||
|
||||
# Upscale Models
|
||||
def load_esrgan(self, esrgan_bg_tile=400):
|
||||
from .realesrgan import ESRGAN
|
||||
|
||||
esrgan = ESRGAN(esrgan_bg_tile)
|
||||
logger.info("ESRGAN Initialized")
|
||||
return esrgan
|
||||
@ -1,120 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
pretrained_model_url = (
|
||||
"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
|
||||
)
|
||||
|
||||
|
||||
class CodeFormerRestoration:
|
||||
def __init__(
|
||||
self, codeformer_dir="./models/core/face_restoration/codeformer", codeformer_model_path="codeformer.pth"
|
||||
) -> None:
|
||||
|
||||
self.globals = InvokeAIAppConfig.get_config()
|
||||
codeformer_dir = self.globals.root_dir / codeformer_dir
|
||||
self.model_path = codeformer_dir / codeformer_model_path
|
||||
self.codeformer_model_exists = self.model_path.exists()
|
||||
|
||||
if not self.codeformer_model_exists:
|
||||
logger.error(f"NOT FOUND: CodeFormer model not found at {self.model_path}")
|
||||
sys.path.append(os.path.abspath(codeformer_dir))
|
||||
|
||||
def process(self, image, strength, device, seed=None, fidelity=0.75):
|
||||
if seed is not None:
|
||||
logger.info(f"CodeFormer - Restoring Faces for image seed:{seed}")
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
from basicsr.utils import img2tensor, tensor2img
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
|
||||
from PIL import Image
|
||||
from torchvision.transforms.functional import normalize
|
||||
|
||||
from .codeformer_arch import CodeFormer
|
||||
|
||||
cf_class = CodeFormer
|
||||
|
||||
cf = cf_class(
|
||||
dim_embd=512,
|
||||
codebook_size=1024,
|
||||
n_head=8,
|
||||
n_layers=9,
|
||||
connect_list=["32", "64", "128", "256"],
|
||||
).to(device)
|
||||
|
||||
# note that this file should already be downloaded and cached at
|
||||
# this point
|
||||
checkpoint_path = load_file_from_url(
|
||||
url=pretrained_model_url,
|
||||
model_dir=os.path.abspath(os.path.dirname(self.model_path)),
|
||||
progress=True,
|
||||
)
|
||||
checkpoint = torch.load(checkpoint_path)["params_ema"]
|
||||
cf.load_state_dict(checkpoint)
|
||||
cf.eval()
|
||||
|
||||
image = image.convert("RGB")
|
||||
# Codeformer expects a BGR np array; make array and flip channels
|
||||
bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
|
||||
|
||||
face_helper = FaceRestoreHelper(
|
||||
upscale_factor=1,
|
||||
use_parse=True,
|
||||
device=device,
|
||||
model_rootpath = self.globals.model_path / 'core/face_restoration/gfpgan/weights'
|
||||
)
|
||||
face_helper.clean_all()
|
||||
face_helper.read_image(bgr_image_array)
|
||||
face_helper.get_face_landmarks_5(resize=640, eye_dist_threshold=5)
|
||||
face_helper.align_warp_face()
|
||||
|
||||
for idx, cropped_face in enumerate(face_helper.cropped_faces):
|
||||
cropped_face_t = img2tensor(
|
||||
cropped_face / 255.0, bgr2rgb=True, float32=True
|
||||
)
|
||||
normalize(
|
||||
cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True
|
||||
)
|
||||
cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
|
||||
|
||||
try:
|
||||
with torch.no_grad():
|
||||
output = cf(cropped_face_t, w=fidelity, adain=True)[0]
|
||||
restored_face = tensor2img(
|
||||
output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)
|
||||
)
|
||||
del output
|
||||
torch.cuda.empty_cache()
|
||||
except RuntimeError as error:
|
||||
logger.error(f"Failed inference for CodeFormer: {error}.")
|
||||
restored_face = cropped_face
|
||||
|
||||
restored_face = restored_face.astype("uint8")
|
||||
face_helper.add_restored_face(restored_face)
|
||||
|
||||
face_helper.get_inverse_affine(None)
|
||||
|
||||
restored_img = face_helper.paste_faces_to_input_image()
|
||||
|
||||
# Flip the channels back to RGB
|
||||
res = Image.fromarray(restored_img[..., ::-1])
|
||||
|
||||
if strength < 1.0:
|
||||
# Resize the image to the new image if the sizes have changed
|
||||
if restored_img.size != image.size:
|
||||
image = image.resize(res.size)
|
||||
res = Image.blend(image, res, strength)
|
||||
|
||||
cf = None
|
||||
|
||||
return res
|
||||
@ -1,325 +0,0 @@
|
||||
import math
|
||||
from typing import List, Optional
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from basicsr.utils import get_root_logger
|
||||
from basicsr.utils.registry import ARCH_REGISTRY
|
||||
from torch import Tensor, nn
|
||||
|
||||
from .vqgan_arch import *
|
||||
|
||||
|
||||
def calc_mean_std(feat, eps=1e-5):
|
||||
"""Calculate mean and std for adaptive_instance_normalization.
|
||||
|
||||
Args:
|
||||
feat (Tensor): 4D tensor.
|
||||
eps (float): A small value added to the variance to avoid
|
||||
divide-by-zero. Default: 1e-5.
|
||||
"""
|
||||
size = feat.size()
|
||||
assert len(size) == 4, "The input feature should be 4D tensor."
|
||||
b, c = size[:2]
|
||||
feat_var = feat.view(b, c, -1).var(dim=2) + eps
|
||||
feat_std = feat_var.sqrt().view(b, c, 1, 1)
|
||||
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
|
||||
return feat_mean, feat_std
|
||||
|
||||
|
||||
def adaptive_instance_normalization(content_feat, style_feat):
|
||||
"""Adaptive instance normalization.
|
||||
|
||||
Adjust the reference features to have the similar color and illuminations
|
||||
as those in the degradate features.
|
||||
|
||||
Args:
|
||||
content_feat (Tensor): The reference feature.
|
||||
style_feat (Tensor): The degradate features.
|
||||
"""
|
||||
size = content_feat.size()
|
||||
style_mean, style_std = calc_mean_std(style_feat)
|
||||
content_mean, content_std = calc_mean_std(content_feat)
|
||||
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(
|
||||
size
|
||||
)
|
||||
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
|
||||
|
||||
|
||||
class PositionEmbeddingSine(nn.Module):
|
||||
"""
|
||||
This is a more standard version of the position embedding, very similar to the one
|
||||
used by the Attention is all you need paper, generalized to work on images.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
|
||||
):
|
||||
super().__init__()
|
||||
self.num_pos_feats = num_pos_feats
|
||||
self.temperature = temperature
|
||||
self.normalize = normalize
|
||||
if scale is not None and normalize is False:
|
||||
raise ValueError("normalize should be True if scale is passed")
|
||||
if scale is None:
|
||||
scale = 2 * math.pi
|
||||
self.scale = scale
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
if mask is None:
|
||||
mask = torch.zeros(
|
||||
(x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool
|
||||
)
|
||||
not_mask = ~mask
|
||||
y_embed = not_mask.cumsum(1, dtype=torch.float32)
|
||||
x_embed = not_mask.cumsum(2, dtype=torch.float32)
|
||||
if self.normalize:
|
||||
eps = 1e-6
|
||||
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
|
||||
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
|
||||
|
||||
dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
|
||||
dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
|
||||
|
||||
pos_x = x_embed[:, :, :, None] / dim_t
|
||||
pos_y = y_embed[:, :, :, None] / dim_t
|
||||
pos_x = torch.stack(
|
||||
(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos_y = torch.stack(
|
||||
(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
|
||||
).flatten(3)
|
||||
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
|
||||
return pos
|
||||
|
||||
|
||||
def _get_activation_fn(activation):
|
||||
"""Return an activation function given a string"""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
|
||||
|
||||
|
||||
class TransformerSALayer(nn.Module):
|
||||
def __init__(
|
||||
self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"
|
||||
):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
|
||||
# Implementation of Feedforward model - MLP
|
||||
self.linear1 = nn.Linear(embed_dim, dim_mlp)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_mlp, embed_dim)
|
||||
|
||||
self.norm1 = nn.LayerNorm(embed_dim)
|
||||
self.norm2 = nn.LayerNorm(embed_dim)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
|
||||
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
|
||||
return tensor if pos is None else tensor + pos
|
||||
|
||||
def forward(
|
||||
self,
|
||||
tgt,
|
||||
tgt_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
query_pos: Optional[Tensor] = None,
|
||||
):
|
||||
# self attention
|
||||
tgt2 = self.norm1(tgt)
|
||||
q = k = self.with_pos_embed(tgt2, query_pos)
|
||||
tgt2 = self.self_attn(
|
||||
q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
|
||||
)[0]
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
|
||||
# ffn
|
||||
tgt2 = self.norm2(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
return tgt
|
||||
|
||||
|
||||
class Fuse_sft_block(nn.Module):
|
||||
def __init__(self, in_ch, out_ch):
|
||||
super().__init__()
|
||||
self.encode_enc = ResBlock(2 * in_ch, out_ch)
|
||||
|
||||
self.scale = nn.Sequential(
|
||||
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
|
||||
)
|
||||
|
||||
self.shift = nn.Sequential(
|
||||
nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
|
||||
)
|
||||
|
||||
def forward(self, enc_feat, dec_feat, w=1):
|
||||
enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
|
||||
scale = self.scale(enc_feat)
|
||||
shift = self.shift(enc_feat)
|
||||
residual = w * (dec_feat * scale + shift)
|
||||
out = dec_feat + residual
|
||||
return out
|
||||
|
||||
|
||||
@ARCH_REGISTRY.register()
|
||||
class CodeFormer(VQAutoEncoder):
|
||||
def __init__(
|
||||
self,
|
||||
dim_embd=512,
|
||||
n_head=8,
|
||||
n_layers=9,
|
||||
codebook_size=1024,
|
||||
latent_size=256,
|
||||
connect_list=["32", "64", "128", "256"],
|
||||
fix_modules=["quantize", "generator"],
|
||||
):
|
||||
super(CodeFormer, self).__init__(
|
||||
512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size
|
||||
)
|
||||
|
||||
if fix_modules is not None:
|
||||
for module in fix_modules:
|
||||
for param in getattr(self, module).parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
self.connect_list = connect_list
|
||||
self.n_layers = n_layers
|
||||
self.dim_embd = dim_embd
|
||||
self.dim_mlp = dim_embd * 2
|
||||
|
||||
self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
|
||||
self.feat_emb = nn.Linear(256, self.dim_embd)
|
||||
|
||||
# transformer
|
||||
self.ft_layers = nn.Sequential(
|
||||
*[
|
||||
TransformerSALayer(
|
||||
embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0
|
||||
)
|
||||
for _ in range(self.n_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# logits_predict head
|
||||
self.idx_pred_layer = nn.Sequential(
|
||||
nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)
|
||||
)
|
||||
|
||||
self.channels = {
|
||||
"16": 512,
|
||||
"32": 256,
|
||||
"64": 256,
|
||||
"128": 128,
|
||||
"256": 128,
|
||||
"512": 64,
|
||||
}
|
||||
|
||||
# after second residual block for > 16, before attn layer for ==16
|
||||
self.fuse_encoder_block = {
|
||||
"512": 2,
|
||||
"256": 5,
|
||||
"128": 8,
|
||||
"64": 11,
|
||||
"32": 14,
|
||||
"16": 18,
|
||||
}
|
||||
# after first residual block for > 16, before attn layer for ==16
|
||||
self.fuse_generator_block = {
|
||||
"16": 6,
|
||||
"32": 9,
|
||||
"64": 12,
|
||||
"128": 15,
|
||||
"256": 18,
|
||||
"512": 21,
|
||||
}
|
||||
|
||||
# fuse_convs_dict
|
||||
self.fuse_convs_dict = nn.ModuleDict()
|
||||
for f_size in self.connect_list:
|
||||
in_ch = self.channels[f_size]
|
||||
self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
|
||||
|
||||
def _init_weights(self, module):
|
||||
if isinstance(module, (nn.Linear, nn.Embedding)):
|
||||
module.weight.data.normal_(mean=0.0, std=0.02)
|
||||
if isinstance(module, nn.Linear) and module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.bias.data.zero_()
|
||||
module.weight.data.fill_(1.0)
|
||||
|
||||
def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
|
||||
# ################### Encoder #####################
|
||||
enc_feat_dict = {}
|
||||
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
|
||||
for i, block in enumerate(self.encoder.blocks):
|
||||
x = block(x)
|
||||
if i in out_list:
|
||||
enc_feat_dict[str(x.shape[-1])] = x.clone()
|
||||
|
||||
lq_feat = x
|
||||
# ################# Transformer ###################
|
||||
# quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
|
||||
pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1)
|
||||
# BCHW -> BC(HW) -> (HW)BC
|
||||
feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1))
|
||||
query_emb = feat_emb
|
||||
# Transformer encoder
|
||||
for layer in self.ft_layers:
|
||||
query_emb = layer(query_emb, query_pos=pos_emb)
|
||||
|
||||
# output logits
|
||||
logits = self.idx_pred_layer(query_emb) # (hw)bn
|
||||
logits = logits.permute(1, 0, 2) # (hw)bn -> b(hw)n
|
||||
|
||||
if code_only: # for training stage II
|
||||
# logits doesn't need softmax before cross_entropy loss
|
||||
return logits, lq_feat
|
||||
|
||||
# ################# Quantization ###################
|
||||
# if self.training:
|
||||
# quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
|
||||
# # b(hw)c -> bc(hw) -> bchw
|
||||
# quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
|
||||
# ------------
|
||||
soft_one_hot = F.softmax(logits, dim=2)
|
||||
_, top_idx = torch.topk(soft_one_hot, 1, dim=2)
|
||||
quant_feat = self.quantize.get_codebook_feat(
|
||||
top_idx, shape=[x.shape[0], 16, 16, 256]
|
||||
)
|
||||
# preserve gradients
|
||||
# quant_feat = lq_feat + (quant_feat - lq_feat).detach()
|
||||
|
||||
if detach_16:
|
||||
quant_feat = quant_feat.detach() # for training stage III
|
||||
if adain:
|
||||
quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
|
||||
|
||||
# ################## Generator ####################
|
||||
x = quant_feat
|
||||
fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
|
||||
|
||||
for i, block in enumerate(self.generator.blocks):
|
||||
x = block(x)
|
||||
if i in fuse_list: # fuse after i-th block
|
||||
f_size = str(x.shape[-1])
|
||||
if w > 0:
|
||||
x = self.fuse_convs_dict[f_size](
|
||||
enc_feat_dict[f_size].detach(), x, w
|
||||
)
|
||||
out = x
|
||||
# logits doesn't need softmax before cross_entropy loss
|
||||
return out, logits, lq_feat
|
||||
@ -1,84 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
class GFPGAN:
|
||||
def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
|
||||
self.globals = InvokeAIAppConfig.get_config()
|
||||
if not os.path.isabs(gfpgan_model_path):
|
||||
gfpgan_model_path = self.globals.root_dir / gfpgan_model_path
|
||||
self.model_path = gfpgan_model_path
|
||||
self.gfpgan_model_exists = os.path.isfile(self.model_path)
|
||||
|
||||
if not self.gfpgan_model_exists:
|
||||
logger.error(f"NOT FOUND: GFPGAN model not found at {self.model_path}")
|
||||
return None
|
||||
|
||||
def model_exists(self):
|
||||
return os.path.isfile(self.model_path)
|
||||
|
||||
def process(self, image, strength: float, seed: str = None):
|
||||
if seed is not None:
|
||||
logger.info(f"GFPGAN - Restoring Faces for image seed:{seed}")
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
cwd = os.getcwd()
|
||||
os.chdir(self.globals.root_dir / 'models')
|
||||
try:
|
||||
from gfpgan import GFPGANer
|
||||
|
||||
self.gfpgan = GFPGANer(
|
||||
model_path=self.model_path,
|
||||
upscale=1,
|
||||
arch="clean",
|
||||
channel_multiplier=2,
|
||||
bg_upsampler=None,
|
||||
)
|
||||
except Exception:
|
||||
import traceback
|
||||
|
||||
logger.error("Error loading GFPGAN:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
os.chdir(cwd)
|
||||
|
||||
if self.gfpgan is None:
|
||||
logger.warning("WARNING: GFPGAN not initialized.")
|
||||
logger.warning(
|
||||
f"Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
|
||||
)
|
||||
|
||||
image = image.convert("RGB")
|
||||
|
||||
# GFPGAN expects a BGR np array; make array and flip channels
|
||||
bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
|
||||
|
||||
_, _, restored_img = self.gfpgan.enhance(
|
||||
bgr_image_array,
|
||||
has_aligned=False,
|
||||
only_center_face=False,
|
||||
paste_back=True,
|
||||
)
|
||||
|
||||
# Flip the channels back to RGB
|
||||
res = Image.fromarray(restored_img[..., ::-1])
|
||||
|
||||
if strength < 1.0:
|
||||
# Resize the image to the new image if the sizes have changed
|
||||
if restored_img.size != image.size:
|
||||
image = image.resize(res.size)
|
||||
res = Image.blend(image, res, strength)
|
||||
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
self.gfpgan = None
|
||||
|
||||
return res
|
||||
@ -1,118 +0,0 @@
|
||||
import math
|
||||
|
||||
from PIL import Image
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
class Outcrop(object):
|
||||
def __init__(
|
||||
self,
|
||||
image,
|
||||
generate, # current generate object
|
||||
):
|
||||
self.image = image
|
||||
self.generate = generate
|
||||
|
||||
def process(
|
||||
self,
|
||||
extents: dict,
|
||||
opt, # current options
|
||||
orig_opt, # ones originally used to generate the image
|
||||
image_callback=None,
|
||||
prefix=None,
|
||||
):
|
||||
# grow and mask the image
|
||||
extended_image = self._extend_all(extents)
|
||||
|
||||
# switch samplers temporarily
|
||||
curr_sampler = self.generate.sampler
|
||||
self.generate.sampler_name = opt.sampler_name
|
||||
self.generate._set_scheduler()
|
||||
|
||||
def wrapped_callback(img, seed, **kwargs):
|
||||
preferred_seed = (
|
||||
orig_opt.seed
|
||||
if orig_opt.seed is not None and orig_opt.seed >= 0
|
||||
else seed
|
||||
)
|
||||
image_callback(img, preferred_seed, use_prefix=prefix, **kwargs)
|
||||
|
||||
result = self.generate.prompt2image(
|
||||
opt.prompt,
|
||||
seed=opt.seed or orig_opt.seed,
|
||||
sampler=self.generate.sampler,
|
||||
steps=opt.steps,
|
||||
cfg_scale=opt.cfg_scale,
|
||||
ddim_eta=self.generate.ddim_eta,
|
||||
width=extended_image.width,
|
||||
height=extended_image.height,
|
||||
init_img=extended_image,
|
||||
strength=0.90,
|
||||
image_callback=wrapped_callback if image_callback else None,
|
||||
seam_size=opt.seam_size or 96,
|
||||
seam_blur=opt.seam_blur or 16,
|
||||
seam_strength=opt.seam_strength or 0.7,
|
||||
seam_steps=20,
|
||||
tile_size=32,
|
||||
color_match=True,
|
||||
force_outpaint=True, # this just stops the warning about erased regions
|
||||
)
|
||||
|
||||
# swap sampler back
|
||||
self.generate.sampler = curr_sampler
|
||||
return result
|
||||
|
||||
def _extend_all(
|
||||
self,
|
||||
extents: dict,
|
||||
) -> Image:
|
||||
"""
|
||||
Extend the image in direction ('top','bottom','left','right') by
|
||||
the indicated value. The image canvas is extended, and the empty
|
||||
rectangular section will be filled with a blurred copy of the
|
||||
adjacent image.
|
||||
"""
|
||||
image = self.image
|
||||
for direction in extents:
|
||||
assert direction in [
|
||||
"top",
|
||||
"left",
|
||||
"bottom",
|
||||
"right",
|
||||
], 'Direction must be one of "top", "left", "bottom", "right"'
|
||||
pixels = extents[direction]
|
||||
# round pixels up to the nearest 64
|
||||
pixels = math.ceil(pixels / 64) * 64
|
||||
logger.info(f"extending image {direction}ward by {pixels} pixels")
|
||||
image = self._rotate(image, direction)
|
||||
image = self._extend(image, pixels)
|
||||
image = self._rotate(image, direction, reverse=True)
|
||||
return image
|
||||
|
||||
def _rotate(self, image: Image, direction: str, reverse=False) -> Image:
|
||||
"""
|
||||
Rotates image so that the area to extend is always at the top top.
|
||||
Simplifies logic later. The reverse argument, if true, will undo the
|
||||
previous transpose.
|
||||
"""
|
||||
transposes = {
|
||||
"right": ["ROTATE_90", "ROTATE_270"],
|
||||
"bottom": ["ROTATE_180", "ROTATE_180"],
|
||||
"left": ["ROTATE_270", "ROTATE_90"],
|
||||
}
|
||||
if direction not in transposes:
|
||||
return image
|
||||
transpose = transposes[direction][1 if reverse else 0]
|
||||
return image.transpose(Image.Transpose.__dict__[transpose])
|
||||
|
||||
def _extend(self, image: Image, pixels: int) -> Image:
|
||||
extended_img = Image.new("RGBA", (image.width, image.height + pixels))
|
||||
|
||||
extended_img.paste((0, 0, 0), [0, 0, image.width, image.height + pixels])
|
||||
extended_img.paste(image, box=(0, pixels))
|
||||
|
||||
# now make the top part transparent to use as a mask
|
||||
alpha = extended_img.getchannel("A")
|
||||
alpha.paste(0, (0, 0, extended_img.width, pixels))
|
||||
extended_img.putalpha(alpha)
|
||||
|
||||
return extended_img
|
||||
@ -1,102 +0,0 @@
|
||||
import math
|
||||
import warnings
|
||||
|
||||
from PIL import Image, ImageFilter
|
||||
|
||||
|
||||
class Outpaint(object):
|
||||
def __init__(self, image, generate):
|
||||
self.image = image
|
||||
self.generate = generate
|
||||
|
||||
def process(self, opt, old_opt, image_callback=None, prefix=None):
|
||||
image = self._create_outpaint_image(self.image, opt.out_direction)
|
||||
|
||||
seed = old_opt.seed
|
||||
prompt = old_opt.prompt
|
||||
|
||||
def wrapped_callback(img, seed, **kwargs):
|
||||
image_callback(img, seed, use_prefix=prefix, **kwargs)
|
||||
|
||||
return self.generate.prompt2image(
|
||||
prompt,
|
||||
seed=seed,
|
||||
sampler=self.generate.sampler,
|
||||
steps=opt.steps,
|
||||
cfg_scale=opt.cfg_scale,
|
||||
ddim_eta=self.generate.ddim_eta,
|
||||
width=opt.width,
|
||||
height=opt.height,
|
||||
init_img=image,
|
||||
strength=0.83,
|
||||
image_callback=wrapped_callback,
|
||||
prefix=prefix,
|
||||
)
|
||||
|
||||
def _create_outpaint_image(self, image, direction_args):
|
||||
assert len(direction_args) in [
|
||||
1,
|
||||
2,
|
||||
], "Direction (-D) must have exactly one or two arguments."
|
||||
|
||||
if len(direction_args) == 1:
|
||||
direction = direction_args[0]
|
||||
pixels = None
|
||||
elif len(direction_args) == 2:
|
||||
direction = direction_args[0]
|
||||
pixels = int(direction_args[1])
|
||||
|
||||
assert direction in [
|
||||
"top",
|
||||
"left",
|
||||
"bottom",
|
||||
"right",
|
||||
], 'Direction (-D) must be one of "top", "left", "bottom", "right"'
|
||||
|
||||
image = image.convert("RGBA")
|
||||
# we always extend top, but rotate to extend along the requested side
|
||||
if direction == "left":
|
||||
image = image.transpose(Image.Transpose.ROTATE_270)
|
||||
elif direction == "bottom":
|
||||
image = image.transpose(Image.Transpose.ROTATE_180)
|
||||
elif direction == "right":
|
||||
image = image.transpose(Image.Transpose.ROTATE_90)
|
||||
|
||||
pixels = image.height // 2 if pixels is None else int(pixels)
|
||||
assert (
|
||||
0 < pixels < image.height
|
||||
), "Direction (-D) pixels length must be in the range 0 - image.size"
|
||||
|
||||
# the top part of the image is taken from the source image mirrored
|
||||
# coordinates (0,0) are the upper left corner of an image
|
||||
top = image.transpose(Image.Transpose.FLIP_TOP_BOTTOM).convert("RGBA")
|
||||
top = top.crop((0, top.height - pixels, top.width, top.height))
|
||||
|
||||
# setting all alpha of the top part to 0
|
||||
alpha = top.getchannel("A")
|
||||
alpha.paste(0, (0, 0, top.width, top.height))
|
||||
top.putalpha(alpha)
|
||||
|
||||
# taking the bottom from the original image
|
||||
bottom = image.crop((0, 0, image.width, image.height - pixels))
|
||||
|
||||
new_img = image.copy()
|
||||
new_img.paste(top, (0, 0))
|
||||
new_img.paste(bottom, (0, pixels))
|
||||
|
||||
# create a 10% dither in the middle
|
||||
dither = min(image.height // 10, pixels)
|
||||
for x in range(0, image.width, 2):
|
||||
for y in range(pixels - dither, pixels + dither):
|
||||
(r, g, b, a) = new_img.getpixel((x, y))
|
||||
new_img.putpixel((x, y), (r, g, b, 0))
|
||||
|
||||
# let's rotate back again
|
||||
if direction == "left":
|
||||
new_img = new_img.transpose(Image.Transpose.ROTATE_90)
|
||||
elif direction == "bottom":
|
||||
new_img = new_img.transpose(Image.Transpose.ROTATE_180)
|
||||
elif direction == "right":
|
||||
new_img = new_img.transpose(Image.Transpose.ROTATE_270)
|
||||
|
||||
return new_img
|
||||
@ -1,104 +0,0 @@
|
||||
import warnings
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
from PIL.Image import Image as ImageType
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
class ESRGAN:
|
||||
def __init__(self, bg_tile_size=400) -> None:
|
||||
self.bg_tile_size = bg_tile_size
|
||||
|
||||
def load_esrgan_bg_upsampler(self, denoise_str):
|
||||
if not torch.cuda.is_available(): # CPU or MPS on M1
|
||||
use_half_precision = False
|
||||
else:
|
||||
use_half_precision = True
|
||||
|
||||
from realesrgan import RealESRGANer
|
||||
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
|
||||
|
||||
model = SRVGGNetCompact(
|
||||
num_in_ch=3,
|
||||
num_out_ch=3,
|
||||
num_feat=64,
|
||||
num_conv=32,
|
||||
upscale=4,
|
||||
act_type="prelu",
|
||||
)
|
||||
model_path = config.models_path / "core/upscaling/realesrgan/realesr-general-x4v3.pth"
|
||||
wdn_model_path = config.models_path / "core/upscaling/realesrgan/realesr-general-wdn-x4v3.pth"
|
||||
scale = 4
|
||||
|
||||
bg_upsampler = RealESRGANer(
|
||||
scale=scale,
|
||||
model_path=[model_path, wdn_model_path],
|
||||
model=model,
|
||||
tile=self.bg_tile_size,
|
||||
dni_weight=[denoise_str, 1 - denoise_str],
|
||||
tile_pad=10,
|
||||
pre_pad=0,
|
||||
half=use_half_precision,
|
||||
)
|
||||
|
||||
return bg_upsampler
|
||||
|
||||
def process(
|
||||
self,
|
||||
image: ImageType,
|
||||
strength: float,
|
||||
seed: str = None,
|
||||
upsampler_scale: int = 2,
|
||||
denoise_str: float = 0.75,
|
||||
):
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
||||
warnings.filterwarnings("ignore", category=UserWarning)
|
||||
|
||||
try:
|
||||
upsampler = self.load_esrgan_bg_upsampler(denoise_str)
|
||||
except Exception:
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
logger.error("Error loading Real-ESRGAN:")
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
if upsampler_scale == 0:
|
||||
logger.warning("Real-ESRGAN: Invalid scaling option. Image not upscaled.")
|
||||
return image
|
||||
|
||||
if seed is not None:
|
||||
logger.info(
|
||||
f"Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
|
||||
)
|
||||
# ESRGAN outputs images with partial transparency if given RGBA images; convert to RGB
|
||||
image = image.convert("RGB")
|
||||
|
||||
# REALSRGAN expects a BGR np array; make array and flip channels
|
||||
bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
|
||||
|
||||
output, _ = upsampler.enhance(
|
||||
bgr_image_array,
|
||||
outscale=upsampler_scale,
|
||||
alpha_upsampler="realesrgan",
|
||||
)
|
||||
|
||||
# Flip the channels back to RGB
|
||||
res = Image.fromarray(output[..., ::-1])
|
||||
|
||||
if strength < 1.0:
|
||||
# Resize the image to the new image if the sizes have changed
|
||||
if output.size != image.size:
|
||||
image = image.resize(res.size)
|
||||
res = Image.blend(image, res, strength)
|
||||
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
upsampler = None
|
||||
|
||||
return res
|
||||
@ -1,514 +0,0 @@
|
||||
"""
|
||||
VQGAN code, adapted from the original created by the Unleashing Transformers authors:
|
||||
https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
|
||||
|
||||
"""
|
||||
import copy
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from basicsr.utils import get_root_logger
|
||||
from basicsr.utils.registry import ARCH_REGISTRY
|
||||
|
||||
|
||||
def normalize(in_channels):
|
||||
return torch.nn.GroupNorm(
|
||||
num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
|
||||
)
|
||||
|
||||
|
||||
@torch.jit.script
|
||||
def swish(x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
# Define VQVAE classes
|
||||
class VectorQuantizer(nn.Module):
|
||||
def __init__(self, codebook_size, emb_dim, beta):
|
||||
super(VectorQuantizer, self).__init__()
|
||||
self.codebook_size = codebook_size # number of embeddings
|
||||
self.emb_dim = emb_dim # dimension of embedding
|
||||
self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
|
||||
self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
|
||||
self.embedding.weight.data.uniform_(
|
||||
-1.0 / self.codebook_size, 1.0 / self.codebook_size
|
||||
)
|
||||
|
||||
def forward(self, z):
|
||||
# reshape z -> (batch, height, width, channel) and flatten
|
||||
z = z.permute(0, 2, 3, 1).contiguous()
|
||||
z_flattened = z.view(-1, self.emb_dim)
|
||||
|
||||
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
|
||||
d = (
|
||||
(z_flattened**2).sum(dim=1, keepdim=True)
|
||||
+ (self.embedding.weight**2).sum(1)
|
||||
- 2 * torch.matmul(z_flattened, self.embedding.weight.t())
|
||||
)
|
||||
|
||||
mean_distance = torch.mean(d)
|
||||
# find closest encodings
|
||||
# min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
|
||||
min_encoding_scores, min_encoding_indices = torch.topk(
|
||||
d, 1, dim=1, largest=False
|
||||
)
|
||||
# [0-1], higher score, higher confidence
|
||||
min_encoding_scores = torch.exp(-min_encoding_scores / 10)
|
||||
|
||||
min_encodings = torch.zeros(
|
||||
min_encoding_indices.shape[0], self.codebook_size
|
||||
).to(z)
|
||||
min_encodings.scatter_(1, min_encoding_indices, 1)
|
||||
|
||||
# get quantized latent vectors
|
||||
z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
|
||||
# compute loss for embedding
|
||||
loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
|
||||
(z_q - z.detach()) ** 2
|
||||
)
|
||||
# preserve gradients
|
||||
z_q = z + (z_q - z).detach()
|
||||
|
||||
# perplexity
|
||||
e_mean = torch.mean(min_encodings, dim=0)
|
||||
perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
|
||||
# reshape back to match original input shape
|
||||
z_q = z_q.permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
return (
|
||||
z_q,
|
||||
loss,
|
||||
{
|
||||
"perplexity": perplexity,
|
||||
"min_encodings": min_encodings,
|
||||
"min_encoding_indices": min_encoding_indices,
|
||||
"min_encoding_scores": min_encoding_scores,
|
||||
"mean_distance": mean_distance,
|
||||
},
|
||||
)
|
||||
|
||||
def get_codebook_feat(self, indices, shape):
|
||||
# input indices: batch*token_num -> (batch*token_num)*1
|
||||
# shape: batch, height, width, channel
|
||||
indices = indices.view(-1, 1)
|
||||
min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
|
||||
min_encodings.scatter_(1, indices, 1)
|
||||
# get quantized latent vectors
|
||||
z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
|
||||
|
||||
if shape is not None: # reshape back to match original input shape
|
||||
z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
return z_q
|
||||
|
||||
|
||||
class GumbelQuantizer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
codebook_size,
|
||||
emb_dim,
|
||||
num_hiddens,
|
||||
straight_through=False,
|
||||
kl_weight=5e-4,
|
||||
temp_init=1.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.codebook_size = codebook_size # number of embeddings
|
||||
self.emb_dim = emb_dim # dimension of embedding
|
||||
self.straight_through = straight_through
|
||||
self.temperature = temp_init
|
||||
self.kl_weight = kl_weight
|
||||
self.proj = nn.Conv2d(
|
||||
num_hiddens, codebook_size, 1
|
||||
) # projects last encoder layer to quantized logits
|
||||
self.embed = nn.Embedding(codebook_size, emb_dim)
|
||||
|
||||
def forward(self, z):
|
||||
hard = self.straight_through if self.training else True
|
||||
|
||||
logits = self.proj(z)
|
||||
|
||||
soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
|
||||
|
||||
z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
|
||||
|
||||
# + kl divergence to the prior loss
|
||||
qy = F.softmax(logits, dim=1)
|
||||
diff = (
|
||||
self.kl_weight
|
||||
* torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
|
||||
)
|
||||
min_encoding_indices = soft_one_hot.argmax(dim=1)
|
||||
|
||||
return z_q, diff, {"min_encoding_indices": min_encoding_indices}
|
||||
|
||||
|
||||
class Downsample(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.conv = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=3, stride=2, padding=0
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
pad = (0, 1, 0, 1)
|
||||
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=3, stride=1, padding=1
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.interpolate(x, scale_factor=2.0, mode="nearest")
|
||||
x = self.conv(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class ResBlock(nn.Module):
|
||||
def __init__(self, in_channels, out_channels=None):
|
||||
super(ResBlock, self).__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = in_channels if out_channels is None else out_channels
|
||||
self.norm1 = normalize(in_channels)
|
||||
self.conv1 = nn.Conv2d(
|
||||
in_channels, out_channels, kernel_size=3, stride=1, padding=1
|
||||
)
|
||||
self.norm2 = normalize(out_channels)
|
||||
self.conv2 = nn.Conv2d(
|
||||
out_channels, out_channels, kernel_size=3, stride=1, padding=1
|
||||
)
|
||||
if self.in_channels != self.out_channels:
|
||||
self.conv_out = nn.Conv2d(
|
||||
in_channels, out_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
|
||||
def forward(self, x_in):
|
||||
x = x_in
|
||||
x = self.norm1(x)
|
||||
x = swish(x)
|
||||
x = self.conv1(x)
|
||||
x = self.norm2(x)
|
||||
x = swish(x)
|
||||
x = self.conv2(x)
|
||||
if self.in_channels != self.out_channels:
|
||||
x_in = self.conv_out(x_in)
|
||||
|
||||
return x + x_in
|
||||
|
||||
|
||||
class AttnBlock(nn.Module):
|
||||
def __init__(self, in_channels):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = normalize(in_channels)
|
||||
self.q = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
self.k = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
self.v = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
self.proj_out = torch.nn.Conv2d(
|
||||
in_channels, in_channels, kernel_size=1, stride=1, padding=0
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q = self.q(h_)
|
||||
k = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b, c, h, w = q.shape
|
||||
q = q.reshape(b, c, h * w)
|
||||
q = q.permute(0, 2, 1)
|
||||
k = k.reshape(b, c, h * w)
|
||||
w_ = torch.bmm(q, k)
|
||||
w_ = w_ * (int(c) ** (-0.5))
|
||||
w_ = F.softmax(w_, dim=2)
|
||||
|
||||
# attend to values
|
||||
v = v.reshape(b, c, h * w)
|
||||
w_ = w_.permute(0, 2, 1)
|
||||
h_ = torch.bmm(v, w_)
|
||||
h_ = h_.reshape(b, c, h, w)
|
||||
|
||||
h_ = self.proj_out(h_)
|
||||
|
||||
return x + h_
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
nf,
|
||||
emb_dim,
|
||||
ch_mult,
|
||||
num_res_blocks,
|
||||
resolution,
|
||||
attn_resolutions,
|
||||
):
|
||||
super().__init__()
|
||||
self.nf = nf
|
||||
self.num_resolutions = len(ch_mult)
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.resolution = resolution
|
||||
self.attn_resolutions = attn_resolutions
|
||||
|
||||
curr_res = self.resolution
|
||||
in_ch_mult = (1,) + tuple(ch_mult)
|
||||
|
||||
blocks = []
|
||||
# initial convultion
|
||||
blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
|
||||
|
||||
# residual and downsampling blocks, with attention on smaller res (16x16)
|
||||
for i in range(self.num_resolutions):
|
||||
block_in_ch = nf * in_ch_mult[i]
|
||||
block_out_ch = nf * ch_mult[i]
|
||||
for _ in range(self.num_res_blocks):
|
||||
blocks.append(ResBlock(block_in_ch, block_out_ch))
|
||||
block_in_ch = block_out_ch
|
||||
if curr_res in attn_resolutions:
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
|
||||
if i != self.num_resolutions - 1:
|
||||
blocks.append(Downsample(block_in_ch))
|
||||
curr_res = curr_res // 2
|
||||
|
||||
# non-local attention block
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
|
||||
# normalise and convert to latent size
|
||||
blocks.append(normalize(block_in_ch))
|
||||
blocks.append(
|
||||
nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)
|
||||
)
|
||||
self.blocks = nn.ModuleList(blocks)
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Generator(nn.Module):
|
||||
def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
|
||||
super().__init__()
|
||||
self.nf = nf
|
||||
self.ch_mult = ch_mult
|
||||
self.num_resolutions = len(self.ch_mult)
|
||||
self.num_res_blocks = res_blocks
|
||||
self.resolution = img_size
|
||||
self.attn_resolutions = attn_resolutions
|
||||
self.in_channels = emb_dim
|
||||
self.out_channels = 3
|
||||
block_in_ch = self.nf * self.ch_mult[-1]
|
||||
curr_res = self.resolution // 2 ** (self.num_resolutions - 1)
|
||||
|
||||
blocks = []
|
||||
# initial conv
|
||||
blocks.append(
|
||||
nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)
|
||||
)
|
||||
|
||||
# non-local attention block
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
blocks.append(ResBlock(block_in_ch, block_in_ch))
|
||||
|
||||
for i in reversed(range(self.num_resolutions)):
|
||||
block_out_ch = self.nf * self.ch_mult[i]
|
||||
|
||||
for _ in range(self.num_res_blocks):
|
||||
blocks.append(ResBlock(block_in_ch, block_out_ch))
|
||||
block_in_ch = block_out_ch
|
||||
|
||||
if curr_res in self.attn_resolutions:
|
||||
blocks.append(AttnBlock(block_in_ch))
|
||||
|
||||
if i != 0:
|
||||
blocks.append(Upsample(block_in_ch))
|
||||
curr_res = curr_res * 2
|
||||
|
||||
blocks.append(normalize(block_in_ch))
|
||||
blocks.append(
|
||||
nn.Conv2d(
|
||||
block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1
|
||||
)
|
||||
)
|
||||
|
||||
self.blocks = nn.ModuleList(blocks)
|
||||
|
||||
def forward(self, x):
|
||||
for block in self.blocks:
|
||||
x = block(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
@ARCH_REGISTRY.register()
|
||||
class VQAutoEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
img_size,
|
||||
nf,
|
||||
ch_mult,
|
||||
quantizer="nearest",
|
||||
res_blocks=2,
|
||||
attn_resolutions=[16],
|
||||
codebook_size=1024,
|
||||
emb_dim=256,
|
||||
beta=0.25,
|
||||
gumbel_straight_through=False,
|
||||
gumbel_kl_weight=1e-8,
|
||||
model_path=None,
|
||||
):
|
||||
super().__init__()
|
||||
logger = get_root_logger()
|
||||
self.in_channels = 3
|
||||
self.nf = nf
|
||||
self.n_blocks = res_blocks
|
||||
self.codebook_size = codebook_size
|
||||
self.embed_dim = emb_dim
|
||||
self.ch_mult = ch_mult
|
||||
self.resolution = img_size
|
||||
self.attn_resolutions = attn_resolutions
|
||||
self.quantizer_type = quantizer
|
||||
self.encoder = Encoder(
|
||||
self.in_channels,
|
||||
self.nf,
|
||||
self.embed_dim,
|
||||
self.ch_mult,
|
||||
self.n_blocks,
|
||||
self.resolution,
|
||||
self.attn_resolutions,
|
||||
)
|
||||
if self.quantizer_type == "nearest":
|
||||
self.beta = beta # 0.25
|
||||
self.quantize = VectorQuantizer(
|
||||
self.codebook_size, self.embed_dim, self.beta
|
||||
)
|
||||
elif self.quantizer_type == "gumbel":
|
||||
self.gumbel_num_hiddens = emb_dim
|
||||
self.straight_through = gumbel_straight_through
|
||||
self.kl_weight = gumbel_kl_weight
|
||||
self.quantize = GumbelQuantizer(
|
||||
self.codebook_size,
|
||||
self.embed_dim,
|
||||
self.gumbel_num_hiddens,
|
||||
self.straight_through,
|
||||
self.kl_weight,
|
||||
)
|
||||
self.generator = Generator(
|
||||
self.nf,
|
||||
self.embed_dim,
|
||||
self.ch_mult,
|
||||
self.n_blocks,
|
||||
self.resolution,
|
||||
self.attn_resolutions,
|
||||
)
|
||||
|
||||
if model_path is not None:
|
||||
chkpt = torch.load(model_path, map_location="cpu")
|
||||
if "params_ema" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu")["params_ema"]
|
||||
)
|
||||
logger.info(f"vqgan is loaded from: {model_path} [params_ema]")
|
||||
elif "params" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu")["params"]
|
||||
)
|
||||
logger.info(f"vqgan is loaded from: {model_path} [params]")
|
||||
else:
|
||||
raise ValueError(f"Wrong params!")
|
||||
|
||||
def forward(self, x):
|
||||
x = self.encoder(x)
|
||||
quant, codebook_loss, quant_stats = self.quantize(x)
|
||||
x = self.generator(quant)
|
||||
return x, codebook_loss, quant_stats
|
||||
|
||||
|
||||
# patch based discriminator
|
||||
@ARCH_REGISTRY.register()
|
||||
class VQGANDiscriminator(nn.Module):
|
||||
def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
|
||||
super().__init__()
|
||||
|
||||
layers = [
|
||||
nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
]
|
||||
ndf_mult = 1
|
||||
ndf_mult_prev = 1
|
||||
for n in range(1, n_layers): # gradually increase the number of filters
|
||||
ndf_mult_prev = ndf_mult
|
||||
ndf_mult = min(2**n, 8)
|
||||
layers += [
|
||||
nn.Conv2d(
|
||||
ndf * ndf_mult_prev,
|
||||
ndf * ndf_mult,
|
||||
kernel_size=4,
|
||||
stride=2,
|
||||
padding=1,
|
||||
bias=False,
|
||||
),
|
||||
nn.BatchNorm2d(ndf * ndf_mult),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
]
|
||||
|
||||
ndf_mult_prev = ndf_mult
|
||||
ndf_mult = min(2**n_layers, 8)
|
||||
|
||||
layers += [
|
||||
nn.Conv2d(
|
||||
ndf * ndf_mult_prev,
|
||||
ndf * ndf_mult,
|
||||
kernel_size=4,
|
||||
stride=1,
|
||||
padding=1,
|
||||
bias=False,
|
||||
),
|
||||
nn.BatchNorm2d(ndf * ndf_mult),
|
||||
nn.LeakyReLU(0.2, True),
|
||||
]
|
||||
|
||||
layers += [
|
||||
nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)
|
||||
] # output 1 channel prediction map
|
||||
self.main = nn.Sequential(*layers)
|
||||
|
||||
if model_path is not None:
|
||||
chkpt = torch.load(model_path, map_location="cpu")
|
||||
if "params_d" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu")["params_d"]
|
||||
)
|
||||
elif "params" in chkpt:
|
||||
self.load_state_dict(
|
||||
torch.load(model_path, map_location="cpu")["params"]
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Wrong params!")
|
||||
|
||||
def forward(self, x):
|
||||
return self.main(x)
|
||||
@ -221,7 +221,7 @@ class ControlNetData:
|
||||
control_mode: str = Field(default="balanced")
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
@dataclass
|
||||
class ConditioningData:
|
||||
unconditioned_embeddings: torch.Tensor
|
||||
text_embeddings: torch.Tensor
|
||||
@ -422,7 +422,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
noise: torch.Tensor,
|
||||
callback: Callable[[PipelineIntermediateState], None] = None,
|
||||
run_id=None,
|
||||
**kwargs,
|
||||
) -> InvokeAIStableDiffusionPipelineOutput:
|
||||
r"""
|
||||
Function invoked when calling the pipeline for generation.
|
||||
@ -443,7 +442,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
noise=noise,
|
||||
run_id=run_id,
|
||||
callback=callback,
|
||||
**kwargs,
|
||||
)
|
||||
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
|
||||
torch.cuda.empty_cache()
|
||||
@ -469,7 +467,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
run_id=None,
|
||||
callback: Callable[[PipelineIntermediateState], None] = None,
|
||||
control_data: List[ControlNetData] = None,
|
||||
**kwargs,
|
||||
) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
|
||||
if self.scheduler.config.get("cpu_only", False):
|
||||
scheduler_device = torch.device('cpu')
|
||||
@ -487,11 +484,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
timesteps,
|
||||
conditioning_data,
|
||||
noise=noise,
|
||||
additional_guidance=additional_guidance,
|
||||
run_id=run_id,
|
||||
callback=callback,
|
||||
additional_guidance=additional_guidance,
|
||||
control_data=control_data,
|
||||
**kwargs,
|
||||
|
||||
callback=callback,
|
||||
)
|
||||
return result.latents, result.attention_map_saver
|
||||
|
||||
@ -505,7 +502,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
run_id: str = None,
|
||||
additional_guidance: List[Callable] = None,
|
||||
control_data: List[ControlNetData] = None,
|
||||
**kwargs,
|
||||
):
|
||||
self._adjust_memory_efficient_attention(latents)
|
||||
if run_id is None:
|
||||
@ -546,7 +542,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
total_step_count=len(timesteps),
|
||||
additional_guidance=additional_guidance,
|
||||
control_data=control_data,
|
||||
**kwargs,
|
||||
)
|
||||
latents = step_output.prev_sample
|
||||
|
||||
@ -588,7 +583,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
total_step_count: int,
|
||||
additional_guidance: List[Callable] = None,
|
||||
control_data: List[ControlNetData] = None,
|
||||
**kwargs,
|
||||
):
|
||||
# invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
|
||||
timestep = t[0]
|
||||
@ -632,9 +626,12 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
|
||||
if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned
|
||||
encoder_hidden_states = conditioning_data.text_embeddings
|
||||
encoder_attention_mask = None
|
||||
else:
|
||||
encoder_hidden_states = torch.cat([conditioning_data.unconditioned_embeddings,
|
||||
conditioning_data.text_embeddings])
|
||||
encoder_hidden_states, encoder_attention_mask = self.invokeai_diffuser._concat_conditionings_for_batch(
|
||||
conditioning_data.unconditioned_embeddings,
|
||||
conditioning_data.text_embeddings,
|
||||
)
|
||||
if isinstance(control_datum.weight, list):
|
||||
# if controlnet has multiple weights, use the weight for the current step
|
||||
controlnet_weight = control_datum.weight[step_index]
|
||||
@ -649,6 +646,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
controlnet_cond=control_datum.image_tensor,
|
||||
conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel
|
||||
return_dict=False,
|
||||
)
|
||||
|
||||
@ -237,15 +237,53 @@ class InvokeAIDiffuserComponent:
|
||||
)
|
||||
return latents
|
||||
|
||||
def _concat_conditionings_for_batch(self, unconditioning, conditioning):
|
||||
def _pad_conditioning(cond, target_len, encoder_attention_mask):
|
||||
conditioning_attention_mask = torch.ones((cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype)
|
||||
|
||||
if cond.shape[1] < max_len:
|
||||
conditioning_attention_mask = torch.cat([
|
||||
conditioning_attention_mask,
|
||||
torch.zeros((cond.shape[0], max_len - cond.shape[1]), device=cond.device, dtype=cond.dtype),
|
||||
], dim=1)
|
||||
|
||||
cond = torch.cat([
|
||||
cond,
|
||||
torch.zeros((cond.shape[0], max_len - cond.shape[1], cond.shape[2]), device=cond.device, dtype=cond.dtype),
|
||||
], dim=1)
|
||||
|
||||
if encoder_attention_mask is None:
|
||||
encoder_attention_mask = conditioning_attention_mask
|
||||
else:
|
||||
encoder_attention_mask = torch.cat([
|
||||
encoder_attention_mask,
|
||||
conditioning_attention_mask,
|
||||
])
|
||||
|
||||
return cond, encoder_attention_mask
|
||||
|
||||
encoder_attention_mask = None
|
||||
if unconditioning.shape[1] != conditioning.shape[1]:
|
||||
max_len = max(unconditioning.shape[1], conditioning.shape[1])
|
||||
unconditioning, encoder_attention_mask = _pad_conditioning(unconditioning, max_len, encoder_attention_mask)
|
||||
conditioning, encoder_attention_mask = _pad_conditioning(conditioning, max_len, encoder_attention_mask)
|
||||
|
||||
return torch.cat([unconditioning, conditioning]), encoder_attention_mask
|
||||
|
||||
# methods below are called from do_diffusion_step and should be considered private to this class.
|
||||
|
||||
def _apply_standard_conditioning(self, x, sigma, unconditioning, conditioning, **kwargs):
|
||||
# fast batched path
|
||||
x_twice = torch.cat([x] * 2)
|
||||
sigma_twice = torch.cat([sigma] * 2)
|
||||
both_conditionings = torch.cat([unconditioning, conditioning])
|
||||
|
||||
both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
|
||||
unconditioning, conditioning
|
||||
)
|
||||
both_results = self.model_forward_callback(
|
||||
x_twice, sigma_twice, both_conditionings, **kwargs,
|
||||
x_twice, sigma_twice, both_conditionings,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
**kwargs,
|
||||
)
|
||||
unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
|
||||
return unconditioned_next_x, conditioned_next_x
|
||||
@ -259,8 +297,32 @@ class InvokeAIDiffuserComponent:
|
||||
**kwargs,
|
||||
):
|
||||
# low-memory sequential path
|
||||
unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning, **kwargs)
|
||||
conditioned_next_x = self.model_forward_callback(x, sigma, conditioning, **kwargs)
|
||||
uncond_down_block, cond_down_block = None, None
|
||||
down_block_additional_residuals = kwargs.pop("down_block_additional_residuals", None)
|
||||
if down_block_additional_residuals is not None:
|
||||
uncond_down_block, cond_down_block = [], []
|
||||
for down_block in down_block_additional_residuals:
|
||||
_uncond_down, _cond_down = down_block.chunk(2)
|
||||
uncond_down_block.append(_uncond_down)
|
||||
cond_down_block.append(_cond_down)
|
||||
|
||||
uncond_mid_block, cond_mid_block = None, None
|
||||
mid_block_additional_residual = kwargs.pop("mid_block_additional_residual", None)
|
||||
if mid_block_additional_residual is not None:
|
||||
uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
|
||||
|
||||
unconditioned_next_x = self.model_forward_callback(
|
||||
x, sigma, unconditioning,
|
||||
down_block_additional_residuals=uncond_down_block,
|
||||
mid_block_additional_residual=uncond_mid_block,
|
||||
**kwargs,
|
||||
)
|
||||
conditioned_next_x = self.model_forward_callback(
|
||||
x, sigma, conditioning,
|
||||
down_block_additional_residuals=cond_down_block,
|
||||
mid_block_additional_residual=cond_mid_block,
|
||||
**kwargs,
|
||||
)
|
||||
return unconditioned_next_x, conditioned_next_x
|
||||
|
||||
# TODO: looks unused
|
||||
@ -294,6 +356,20 @@ class InvokeAIDiffuserComponent:
|
||||
):
|
||||
context: Context = self.cross_attention_control_context
|
||||
|
||||
uncond_down_block, cond_down_block = None, None
|
||||
down_block_additional_residuals = kwargs.pop("down_block_additional_residuals", None)
|
||||
if down_block_additional_residuals is not None:
|
||||
uncond_down_block, cond_down_block = [], []
|
||||
for down_block in down_block_additional_residuals:
|
||||
_uncond_down, _cond_down = down_block.chunk(2)
|
||||
uncond_down_block.append(_uncond_down)
|
||||
cond_down_block.append(_cond_down)
|
||||
|
||||
uncond_mid_block, cond_mid_block = None, None
|
||||
mid_block_additional_residual = kwargs.pop("mid_block_additional_residual", None)
|
||||
if mid_block_additional_residual is not None:
|
||||
uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
|
||||
|
||||
cross_attn_processor_context = SwapCrossAttnContext(
|
||||
modified_text_embeddings=context.arguments.edited_conditioning,
|
||||
index_map=context.cross_attention_index_map,
|
||||
@ -306,6 +382,8 @@ class InvokeAIDiffuserComponent:
|
||||
sigma,
|
||||
unconditioning,
|
||||
{"swap_cross_attn_context": cross_attn_processor_context},
|
||||
down_block_additional_residuals=uncond_down_block,
|
||||
mid_block_additional_residual=uncond_mid_block,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@ -318,6 +396,8 @@ class InvokeAIDiffuserComponent:
|
||||
sigma,
|
||||
conditioning,
|
||||
{"swap_cross_attn_context": cross_attn_processor_context},
|
||||
down_block_additional_residuals=cond_down_block,
|
||||
mid_block_additional_residual=cond_mid_block,
|
||||
**kwargs,
|
||||
)
|
||||
return unconditioned_next_x, conditioned_next_x
|
||||
|
||||
@ -24,7 +24,7 @@ import torch.utils.checkpoint
|
||||
import transformers
|
||||
from accelerate import Accelerator
|
||||
from accelerate.logging import get_logger
|
||||
from accelerate.utils import set_seed
|
||||
from accelerate.utils import set_seed, ProjectConfiguration
|
||||
from diffusers import (
|
||||
AutoencoderKL,
|
||||
DDPMScheduler,
|
||||
@ -35,7 +35,6 @@ from diffusers.optimization import get_scheduler
|
||||
from diffusers.utils import check_min_version
|
||||
from diffusers.utils.import_utils import is_xformers_available
|
||||
from huggingface_hub import HfFolder, Repository, whoami
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
|
||||
from packaging import version
|
||||
@ -47,6 +46,8 @@ from transformers import CLIPTextModel, CLIPTokenizer
|
||||
|
||||
# invokeai stuff
|
||||
from invokeai.app.services.config import InvokeAIAppConfig,PagingArgumentParser
|
||||
from invokeai.app.services.model_manager_service import ModelManagerService
|
||||
from invokeai.backend.model_management.models import SubModelType
|
||||
|
||||
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
|
||||
PIL_INTERPOLATION = {
|
||||
@ -132,7 +133,7 @@ def parse_args():
|
||||
model_group.add_argument(
|
||||
"--model",
|
||||
type=str,
|
||||
default="stable-diffusion-1.5",
|
||||
default="sd-1/main/stable-diffusion-v1-5",
|
||||
help="Name of the diffusers model to train against, as defined in configs/models.yaml.",
|
||||
)
|
||||
model_group.add_argument(
|
||||
@ -565,7 +566,6 @@ def do_textual_inversion_training(
|
||||
checkpointing_steps: int = 500,
|
||||
resume_from_checkpoint: Path = None,
|
||||
enable_xformers_memory_efficient_attention: bool = False,
|
||||
root_dir: Path = None,
|
||||
hub_model_id: str = None,
|
||||
**kwargs,
|
||||
):
|
||||
@ -584,13 +584,17 @@ def do_textual_inversion_training(
|
||||
|
||||
logging_dir = output_dir / logging_dir
|
||||
|
||||
accelerator_config = ProjectConfiguration()
|
||||
accelerator_config.logging_dir = logging_dir
|
||||
accelerator = Accelerator(
|
||||
gradient_accumulation_steps=gradient_accumulation_steps,
|
||||
mixed_precision=mixed_precision,
|
||||
log_with=report_to,
|
||||
logging_dir=logging_dir,
|
||||
project_config=accelerator_config,
|
||||
)
|
||||
|
||||
model_manager = ModelManagerService(config,logger)
|
||||
|
||||
# Make one log on every process with the configuration for debugging.
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
@ -628,46 +632,46 @@ def do_textual_inversion_training(
|
||||
elif output_dir is not None:
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
models_conf = OmegaConf.load(config.model_conf_path)
|
||||
model_conf = models_conf.get(model, None)
|
||||
assert model_conf is not None, f"Unknown model: {model}"
|
||||
known_models = model_manager.model_names()
|
||||
model_name = model.split('/')[-1]
|
||||
model_meta = next((mm for mm in known_models if mm[0].endswith(model_name)), None)
|
||||
assert model_meta is not None, f"Unknown model: {model}"
|
||||
model_info = model_manager.model_info(*model_meta)
|
||||
assert (
|
||||
model_conf.get("format", "diffusers") == "diffusers"
|
||||
model_info['model_format'] == "diffusers"
|
||||
), "This script only works with models of type 'diffusers'"
|
||||
pretrained_model_name_or_path = model_conf.get("repo_id", None) or Path(
|
||||
model_conf.get("path")
|
||||
)
|
||||
assert (
|
||||
pretrained_model_name_or_path
|
||||
), f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
|
||||
pipeline_args = dict(cache_dir=config.cache_dir)
|
||||
tokenizer_info = model_manager.get_model(*model_meta, submodel=SubModelType.Tokenizer)
|
||||
noise_scheduler_info = model_manager.get_model(*model_meta, submodel=SubModelType.Scheduler)
|
||||
text_encoder_info = model_manager.get_model(*model_meta, submodel=SubModelType.TextEncoder)
|
||||
vae_info = model_manager.get_model(*model_meta, submodel=SubModelType.Vae)
|
||||
unet_info = model_manager.get_model(*model_meta, submodel=SubModelType.UNet)
|
||||
|
||||
# Load tokenizer
|
||||
pipeline_args = dict(local_files_only=True)
|
||||
if tokenizer_name:
|
||||
tokenizer = CLIPTokenizer.from_pretrained(tokenizer_name, **pipeline_args)
|
||||
else:
|
||||
tokenizer = CLIPTokenizer.from_pretrained(
|
||||
pretrained_model_name_or_path, subfolder="tokenizer", **pipeline_args
|
||||
tokenizer_info.location, subfolder='tokenizer', **pipeline_args
|
||||
)
|
||||
|
||||
# Load scheduler and models
|
||||
noise_scheduler = DDPMScheduler.from_pretrained(
|
||||
pretrained_model_name_or_path, subfolder="scheduler", **pipeline_args
|
||||
noise_scheduler_info.location, subfolder="scheduler", **pipeline_args
|
||||
)
|
||||
text_encoder = CLIPTextModel.from_pretrained(
|
||||
pretrained_model_name_or_path,
|
||||
text_encoder_info.location,
|
||||
subfolder="text_encoder",
|
||||
revision=revision,
|
||||
**pipeline_args,
|
||||
)
|
||||
vae = AutoencoderKL.from_pretrained(
|
||||
pretrained_model_name_or_path,
|
||||
vae_info.location,
|
||||
subfolder="vae",
|
||||
revision=revision,
|
||||
**pipeline_args,
|
||||
)
|
||||
unet = UNet2DConditionModel.from_pretrained(
|
||||
pretrained_model_name_or_path,
|
||||
unet_info.location,
|
||||
subfolder="unet",
|
||||
revision=revision,
|
||||
**pipeline_args,
|
||||
@ -989,7 +993,7 @@ def do_textual_inversion_training(
|
||||
save_full_model = not only_save_embeds
|
||||
if save_full_model:
|
||||
pipeline = StableDiffusionPipeline.from_pretrained(
|
||||
pretrained_model_name_or_path,
|
||||
unet_info.location,
|
||||
text_encoder=accelerator.unwrap_model(text_encoder),
|
||||
vae=vae,
|
||||
unet=unet,
|
||||
|
||||
634
invokeai/backend/util/hotfixes.py
Normal file
634
invokeai/backend/util/hotfixes.py
Normal file
@ -0,0 +1,634 @@
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
||||
from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
|
||||
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
|
||||
from diffusers.models.modeling_utils import ModelMixin
|
||||
from diffusers.models.unet_2d_blocks import (
|
||||
CrossAttnDownBlock2D,
|
||||
DownBlock2D,
|
||||
UNetMidBlock2DCrossAttn,
|
||||
get_down_block,
|
||||
)
|
||||
from diffusers.models.unet_2d_condition import UNet2DConditionModel
|
||||
|
||||
import diffusers
|
||||
from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
|
||||
|
||||
# Modified ControlNetModel with encoder_attention_mask argument added
|
||||
|
||||
class ControlNetModel(ModelMixin, ConfigMixin):
|
||||
"""
|
||||
A ControlNet model.
|
||||
|
||||
Args:
|
||||
in_channels (`int`, defaults to 4):
|
||||
The number of channels in the input sample.
|
||||
flip_sin_to_cos (`bool`, defaults to `True`):
|
||||
Whether to flip the sin to cos in the time embedding.
|
||||
freq_shift (`int`, defaults to 0):
|
||||
The frequency shift to apply to the time embedding.
|
||||
down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
|
||||
The tuple of downsample blocks to use.
|
||||
only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
|
||||
block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
|
||||
The tuple of output channels for each block.
|
||||
layers_per_block (`int`, defaults to 2):
|
||||
The number of layers per block.
|
||||
downsample_padding (`int`, defaults to 1):
|
||||
The padding to use for the downsampling convolution.
|
||||
mid_block_scale_factor (`float`, defaults to 1):
|
||||
The scale factor to use for the mid block.
|
||||
act_fn (`str`, defaults to "silu"):
|
||||
The activation function to use.
|
||||
norm_num_groups (`int`, *optional*, defaults to 32):
|
||||
The number of groups to use for the normalization. If None, normalization and activation layers is skipped
|
||||
in post-processing.
|
||||
norm_eps (`float`, defaults to 1e-5):
|
||||
The epsilon to use for the normalization.
|
||||
cross_attention_dim (`int`, defaults to 1280):
|
||||
The dimension of the cross attention features.
|
||||
attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
|
||||
The dimension of the attention heads.
|
||||
use_linear_projection (`bool`, defaults to `False`):
|
||||
class_embed_type (`str`, *optional*, defaults to `None`):
|
||||
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
|
||||
`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
|
||||
num_class_embeds (`int`, *optional*, defaults to 0):
|
||||
Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
|
||||
class conditioning with `class_embed_type` equal to `None`.
|
||||
upcast_attention (`bool`, defaults to `False`):
|
||||
resnet_time_scale_shift (`str`, defaults to `"default"`):
|
||||
Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
|
||||
projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
|
||||
The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
|
||||
`class_embed_type="projection"`.
|
||||
controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
|
||||
The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
|
||||
conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
|
||||
The tuple of output channel for each block in the `conditioning_embedding` layer.
|
||||
global_pool_conditions (`bool`, defaults to `False`):
|
||||
"""
|
||||
|
||||
_supports_gradient_checkpointing = True
|
||||
|
||||
@register_to_config
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int = 4,
|
||||
conditioning_channels: int = 3,
|
||||
flip_sin_to_cos: bool = True,
|
||||
freq_shift: int = 0,
|
||||
down_block_types: Tuple[str] = (
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"CrossAttnDownBlock2D",
|
||||
"DownBlock2D",
|
||||
),
|
||||
only_cross_attention: Union[bool, Tuple[bool]] = False,
|
||||
block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
|
||||
layers_per_block: int = 2,
|
||||
downsample_padding: int = 1,
|
||||
mid_block_scale_factor: float = 1,
|
||||
act_fn: str = "silu",
|
||||
norm_num_groups: Optional[int] = 32,
|
||||
norm_eps: float = 1e-5,
|
||||
cross_attention_dim: int = 1280,
|
||||
attention_head_dim: Union[int, Tuple[int]] = 8,
|
||||
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
|
||||
use_linear_projection: bool = False,
|
||||
class_embed_type: Optional[str] = None,
|
||||
num_class_embeds: Optional[int] = None,
|
||||
upcast_attention: bool = False,
|
||||
resnet_time_scale_shift: str = "default",
|
||||
projection_class_embeddings_input_dim: Optional[int] = None,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
|
||||
global_pool_conditions: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
# If `num_attention_heads` is not defined (which is the case for most models)
|
||||
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
|
||||
# The reason for this behavior is to correct for incorrectly named variables that were introduced
|
||||
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
|
||||
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
|
||||
# which is why we correct for the naming here.
|
||||
num_attention_heads = num_attention_heads or attention_head_dim
|
||||
|
||||
# Check inputs
|
||||
if len(block_out_channels) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
|
||||
raise ValueError(
|
||||
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
|
||||
)
|
||||
|
||||
# input
|
||||
conv_in_kernel = 3
|
||||
conv_in_padding = (conv_in_kernel - 1) // 2
|
||||
self.conv_in = nn.Conv2d(
|
||||
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
|
||||
)
|
||||
|
||||
# time
|
||||
time_embed_dim = block_out_channels[0] * 4
|
||||
|
||||
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
|
||||
timestep_input_dim = block_out_channels[0]
|
||||
|
||||
self.time_embedding = TimestepEmbedding(
|
||||
timestep_input_dim,
|
||||
time_embed_dim,
|
||||
act_fn=act_fn,
|
||||
)
|
||||
|
||||
# class embedding
|
||||
if class_embed_type is None and num_class_embeds is not None:
|
||||
self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
|
||||
elif class_embed_type == "timestep":
|
||||
self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
|
||||
elif class_embed_type == "identity":
|
||||
self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
|
||||
elif class_embed_type == "projection":
|
||||
if projection_class_embeddings_input_dim is None:
|
||||
raise ValueError(
|
||||
"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
|
||||
)
|
||||
# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
|
||||
# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
|
||||
# 2. it projects from an arbitrary input dimension.
|
||||
#
|
||||
# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
|
||||
# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
|
||||
# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
|
||||
self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
|
||||
else:
|
||||
self.class_embedding = None
|
||||
|
||||
# control net conditioning embedding
|
||||
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
|
||||
conditioning_embedding_channels=block_out_channels[0],
|
||||
block_out_channels=conditioning_embedding_out_channels,
|
||||
conditioning_channels=conditioning_channels,
|
||||
)
|
||||
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
self.controlnet_down_blocks = nn.ModuleList([])
|
||||
|
||||
if isinstance(only_cross_attention, bool):
|
||||
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
||||
|
||||
if isinstance(attention_head_dim, int):
|
||||
attention_head_dim = (attention_head_dim,) * len(down_block_types)
|
||||
|
||||
if isinstance(num_attention_heads, int):
|
||||
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
||||
|
||||
# down
|
||||
output_channel = block_out_channels[0]
|
||||
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
for i, down_block_type in enumerate(down_block_types):
|
||||
input_channel = output_channel
|
||||
output_channel = block_out_channels[i]
|
||||
is_final_block = i == len(block_out_channels) - 1
|
||||
|
||||
down_block = get_down_block(
|
||||
down_block_type,
|
||||
num_layers=layers_per_block,
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
add_downsample=not is_final_block,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
resnet_groups=norm_num_groups,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=num_attention_heads[i],
|
||||
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
|
||||
downsample_padding=downsample_padding,
|
||||
use_linear_projection=use_linear_projection,
|
||||
only_cross_attention=only_cross_attention[i],
|
||||
upcast_attention=upcast_attention,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
)
|
||||
self.down_blocks.append(down_block)
|
||||
|
||||
for _ in range(layers_per_block):
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
if not is_final_block:
|
||||
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_down_blocks.append(controlnet_block)
|
||||
|
||||
# mid
|
||||
mid_block_channel = block_out_channels[-1]
|
||||
|
||||
controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
|
||||
controlnet_block = zero_module(controlnet_block)
|
||||
self.controlnet_mid_block = controlnet_block
|
||||
|
||||
self.mid_block = UNetMidBlock2DCrossAttn(
|
||||
in_channels=mid_block_channel,
|
||||
temb_channels=time_embed_dim,
|
||||
resnet_eps=norm_eps,
|
||||
resnet_act_fn=act_fn,
|
||||
output_scale_factor=mid_block_scale_factor,
|
||||
resnet_time_scale_shift=resnet_time_scale_shift,
|
||||
cross_attention_dim=cross_attention_dim,
|
||||
num_attention_heads=num_attention_heads[-1],
|
||||
resnet_groups=norm_num_groups,
|
||||
use_linear_projection=use_linear_projection,
|
||||
upcast_attention=upcast_attention,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_unet(
|
||||
cls,
|
||||
unet: UNet2DConditionModel,
|
||||
controlnet_conditioning_channel_order: str = "rgb",
|
||||
conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
|
||||
load_weights_from_unet: bool = True,
|
||||
):
|
||||
r"""
|
||||
Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`].
|
||||
|
||||
Parameters:
|
||||
unet (`UNet2DConditionModel`):
|
||||
The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied
|
||||
where applicable.
|
||||
"""
|
||||
controlnet = cls(
|
||||
in_channels=unet.config.in_channels,
|
||||
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
||||
freq_shift=unet.config.freq_shift,
|
||||
down_block_types=unet.config.down_block_types,
|
||||
only_cross_attention=unet.config.only_cross_attention,
|
||||
block_out_channels=unet.config.block_out_channels,
|
||||
layers_per_block=unet.config.layers_per_block,
|
||||
downsample_padding=unet.config.downsample_padding,
|
||||
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
||||
act_fn=unet.config.act_fn,
|
||||
norm_num_groups=unet.config.norm_num_groups,
|
||||
norm_eps=unet.config.norm_eps,
|
||||
cross_attention_dim=unet.config.cross_attention_dim,
|
||||
attention_head_dim=unet.config.attention_head_dim,
|
||||
num_attention_heads=unet.config.num_attention_heads,
|
||||
use_linear_projection=unet.config.use_linear_projection,
|
||||
class_embed_type=unet.config.class_embed_type,
|
||||
num_class_embeds=unet.config.num_class_embeds,
|
||||
upcast_attention=unet.config.upcast_attention,
|
||||
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
||||
projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
|
||||
controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
|
||||
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
||||
)
|
||||
|
||||
if load_weights_from_unet:
|
||||
controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
|
||||
controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
|
||||
controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
|
||||
|
||||
if controlnet.class_embedding:
|
||||
controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
|
||||
|
||||
controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
|
||||
controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
|
||||
|
||||
return controlnet
|
||||
|
||||
@property
|
||||
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
|
||||
def attn_processors(self) -> Dict[str, AttentionProcessor]:
|
||||
r"""
|
||||
Returns:
|
||||
`dict` of attention processors: A dictionary containing all attention processors used in the model with
|
||||
indexed by its weight name.
|
||||
"""
|
||||
# set recursively
|
||||
processors = {}
|
||||
|
||||
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
|
||||
if hasattr(module, "set_processor"):
|
||||
processors[f"{name}.processor"] = module.processor
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
|
||||
|
||||
return processors
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_add_processors(name, module, processors)
|
||||
|
||||
return processors
|
||||
|
||||
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
||||
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
|
||||
r"""
|
||||
Sets the attention processor to use to compute attention.
|
||||
|
||||
Parameters:
|
||||
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
|
||||
The instantiated processor class or a dictionary of processor classes that will be set as the processor
|
||||
for **all** `Attention` layers.
|
||||
|
||||
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
|
||||
processor. This is strongly recommended when setting trainable attention processors.
|
||||
|
||||
"""
|
||||
count = len(self.attn_processors.keys())
|
||||
|
||||
if isinstance(processor, dict) and len(processor) != count:
|
||||
raise ValueError(
|
||||
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
|
||||
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
|
||||
)
|
||||
|
||||
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
|
||||
if hasattr(module, "set_processor"):
|
||||
if not isinstance(processor, dict):
|
||||
module.set_processor(processor)
|
||||
else:
|
||||
module.set_processor(processor.pop(f"{name}.processor"))
|
||||
|
||||
for sub_name, child in module.named_children():
|
||||
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
|
||||
|
||||
for name, module in self.named_children():
|
||||
fn_recursive_attn_processor(name, module, processor)
|
||||
|
||||
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
|
||||
def set_default_attn_processor(self):
|
||||
"""
|
||||
Disables custom attention processors and sets the default attention implementation.
|
||||
"""
|
||||
self.set_attn_processor(AttnProcessor())
|
||||
|
||||
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
||||
def set_attention_slice(self, slice_size):
|
||||
r"""
|
||||
Enable sliced attention computation.
|
||||
|
||||
When this option is enabled, the attention module splits the input tensor in slices to compute attention in
|
||||
several steps. This is useful for saving some memory in exchange for a small decrease in speed.
|
||||
|
||||
Args:
|
||||
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
|
||||
When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
|
||||
`"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
|
||||
provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
|
||||
must be a multiple of `slice_size`.
|
||||
"""
|
||||
sliceable_head_dims = []
|
||||
|
||||
def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
sliceable_head_dims.append(module.sliceable_head_dim)
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_retrieve_sliceable_dims(child)
|
||||
|
||||
# retrieve number of attention layers
|
||||
for module in self.children():
|
||||
fn_recursive_retrieve_sliceable_dims(module)
|
||||
|
||||
num_sliceable_layers = len(sliceable_head_dims)
|
||||
|
||||
if slice_size == "auto":
|
||||
# half the attention head size is usually a good trade-off between
|
||||
# speed and memory
|
||||
slice_size = [dim // 2 for dim in sliceable_head_dims]
|
||||
elif slice_size == "max":
|
||||
# make smallest slice possible
|
||||
slice_size = num_sliceable_layers * [1]
|
||||
|
||||
slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
|
||||
|
||||
if len(slice_size) != len(sliceable_head_dims):
|
||||
raise ValueError(
|
||||
f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
|
||||
f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
|
||||
)
|
||||
|
||||
for i in range(len(slice_size)):
|
||||
size = slice_size[i]
|
||||
dim = sliceable_head_dims[i]
|
||||
if size is not None and size > dim:
|
||||
raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
|
||||
|
||||
# Recursively walk through all the children.
|
||||
# Any children which exposes the set_attention_slice method
|
||||
# gets the message
|
||||
def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
|
||||
if hasattr(module, "set_attention_slice"):
|
||||
module.set_attention_slice(slice_size.pop())
|
||||
|
||||
for child in module.children():
|
||||
fn_recursive_set_attention_slice(child, slice_size)
|
||||
|
||||
reversed_slice_size = list(reversed(slice_size))
|
||||
for module in self.children():
|
||||
fn_recursive_set_attention_slice(module, reversed_slice_size)
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
def forward(
|
||||
self,
|
||||
sample: torch.FloatTensor,
|
||||
timestep: Union[torch.Tensor, float, int],
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
controlnet_cond: torch.FloatTensor,
|
||||
conditioning_scale: float = 1.0,
|
||||
class_labels: Optional[torch.Tensor] = None,
|
||||
timestep_cond: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
||||
encoder_attention_mask: Optional[torch.Tensor] = None,
|
||||
guess_mode: bool = False,
|
||||
return_dict: bool = True,
|
||||
) -> Union[ControlNetOutput, Tuple]:
|
||||
"""
|
||||
The [`ControlNetModel`] forward method.
|
||||
|
||||
Args:
|
||||
sample (`torch.FloatTensor`):
|
||||
The noisy input tensor.
|
||||
timestep (`Union[torch.Tensor, float, int]`):
|
||||
The number of timesteps to denoise an input.
|
||||
encoder_hidden_states (`torch.Tensor`):
|
||||
The encoder hidden states.
|
||||
controlnet_cond (`torch.FloatTensor`):
|
||||
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
||||
conditioning_scale (`float`, defaults to `1.0`):
|
||||
The scale factor for ControlNet outputs.
|
||||
class_labels (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
|
||||
timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
|
||||
cross_attention_kwargs(`dict[str]`, *optional*, defaults to `None`):
|
||||
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
|
||||
encoder_attention_mask (`torch.Tensor`):
|
||||
A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
|
||||
`True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
|
||||
which adds large negative values to the attention scores corresponding to "discard" tokens.
|
||||
guess_mode (`bool`, defaults to `False`):
|
||||
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
|
||||
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
||||
return_dict (`bool`, defaults to `True`):
|
||||
Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.
|
||||
|
||||
Returns:
|
||||
[`~models.controlnet.ControlNetOutput`] **or** `tuple`:
|
||||
If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
|
||||
returned where the first element is the sample tensor.
|
||||
"""
|
||||
# check channel order
|
||||
channel_order = self.config.controlnet_conditioning_channel_order
|
||||
|
||||
if channel_order == "rgb":
|
||||
# in rgb order by default
|
||||
...
|
||||
elif channel_order == "bgr":
|
||||
controlnet_cond = torch.flip(controlnet_cond, dims=[1])
|
||||
else:
|
||||
raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
|
||||
|
||||
# prepare attention_mask
|
||||
if attention_mask is not None:
|
||||
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
|
||||
# convert encoder_attention_mask to a bias the same way we do for attention_mask
|
||||
if encoder_attention_mask is not None:
|
||||
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
|
||||
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
|
||||
|
||||
# 1. time
|
||||
timesteps = timestep
|
||||
if not torch.is_tensor(timesteps):
|
||||
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
|
||||
# This would be a good case for the `match` statement (Python 3.10+)
|
||||
is_mps = sample.device.type == "mps"
|
||||
if isinstance(timestep, float):
|
||||
dtype = torch.float32 if is_mps else torch.float64
|
||||
else:
|
||||
dtype = torch.int32 if is_mps else torch.int64
|
||||
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
||||
elif len(timesteps.shape) == 0:
|
||||
timesteps = timesteps[None].to(sample.device)
|
||||
|
||||
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
||||
timesteps = timesteps.expand(sample.shape[0])
|
||||
|
||||
t_emb = self.time_proj(timesteps)
|
||||
|
||||
# timesteps does not contain any weights and will always return f32 tensors
|
||||
# but time_embedding might actually be running in fp16. so we need to cast here.
|
||||
# there might be better ways to encapsulate this.
|
||||
t_emb = t_emb.to(dtype=sample.dtype)
|
||||
|
||||
emb = self.time_embedding(t_emb, timestep_cond)
|
||||
|
||||
if self.class_embedding is not None:
|
||||
if class_labels is None:
|
||||
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
||||
|
||||
if self.config.class_embed_type == "timestep":
|
||||
class_labels = self.time_proj(class_labels)
|
||||
|
||||
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
||||
emb = emb + class_emb
|
||||
|
||||
# 2. pre-process
|
||||
sample = self.conv_in(sample)
|
||||
|
||||
controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
|
||||
|
||||
sample = sample + controlnet_cond
|
||||
|
||||
# 3. down
|
||||
down_block_res_samples = (sample,)
|
||||
for downsample_block in self.down_blocks:
|
||||
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
|
||||
sample, res_samples = downsample_block(
|
||||
hidden_states=sample,
|
||||
temb=emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
)
|
||||
else:
|
||||
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
||||
|
||||
down_block_res_samples += res_samples
|
||||
|
||||
# 4. mid
|
||||
if self.mid_block is not None:
|
||||
sample = self.mid_block(
|
||||
sample,
|
||||
emb,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
cross_attention_kwargs=cross_attention_kwargs,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
)
|
||||
|
||||
# 5. Control net blocks
|
||||
|
||||
controlnet_down_block_res_samples = ()
|
||||
|
||||
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
|
||||
down_block_res_sample = controlnet_block(down_block_res_sample)
|
||||
controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
|
||||
|
||||
down_block_res_samples = controlnet_down_block_res_samples
|
||||
|
||||
mid_block_res_sample = self.controlnet_mid_block(sample)
|
||||
|
||||
# 6. scaling
|
||||
if guess_mode and not self.config.global_pool_conditions:
|
||||
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
|
||||
|
||||
scales = scales * conditioning_scale
|
||||
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
|
||||
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
|
||||
else:
|
||||
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
|
||||
mid_block_res_sample = mid_block_res_sample * conditioning_scale
|
||||
|
||||
if self.config.global_pool_conditions:
|
||||
down_block_res_samples = [
|
||||
torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
|
||||
]
|
||||
mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)
|
||||
|
||||
if not return_dict:
|
||||
return (down_block_res_samples, mid_block_res_sample)
|
||||
|
||||
return ControlNetOutput(
|
||||
down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
|
||||
)
|
||||
|
||||
diffusers.ControlNetModel = ControlNetModel
|
||||
diffusers.models.controlnet.ControlNetModel = ControlNetModel
|
||||
@ -16,6 +16,14 @@ sd-2/main/stable-diffusion-2-inpainting:
|
||||
description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
|
||||
repo_id: stabilityai/stable-diffusion-2-inpainting
|
||||
recommended: False
|
||||
sdxl/main/stable-diffusion-xl-base-0-9:
|
||||
description: Stable Diffusion XL base model (12 GB; access token required)
|
||||
repo_id: stabilityai/stable-diffusion-xl-base-0.9
|
||||
recommended: False
|
||||
sdxl-refiner/main/stable-diffusion-xl-refiner-0-9:
|
||||
description: Stable Diffusion XL refiner model (12 GB; access token required)
|
||||
repo_id: stabilityai/stable-diffusion-xl-refiner-0.9
|
||||
recommended: False
|
||||
sd-1/main/Analog-Diffusion:
|
||||
description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
|
||||
repo_id: wavymulder/Analog-Diffusion
|
||||
@ -58,22 +66,29 @@ sd-1/main/waifu-diffusion:
|
||||
recommended: False
|
||||
sd-1/controlnet/canny:
|
||||
repo_id: lllyasviel/control_v11p_sd15_canny
|
||||
recommended: True
|
||||
sd-1/controlnet/inpaint:
|
||||
repo_id: lllyasviel/control_v11p_sd15_inpaint
|
||||
sd-1/controlnet/mlsd:
|
||||
repo_id: lllyasviel/control_v11p_sd15_mlsd
|
||||
sd-1/controlnet/depth:
|
||||
repo_id: lllyasviel/control_v11f1p_sd15_depth
|
||||
recommended: True
|
||||
sd-1/controlnet/normal_bae:
|
||||
repo_id: lllyasviel/control_v11p_sd15_normalbae
|
||||
sd-1/controlnet/seg:
|
||||
repo_id: lllyasviel/control_v11p_sd15_seg
|
||||
sd-1/controlnet/lineart:
|
||||
repo_id: lllyasviel/control_v11p_sd15_lineart
|
||||
recommended: True
|
||||
sd-1/controlnet/lineart_anime:
|
||||
repo_id: lllyasviel/control_v11p_sd15s2_lineart_anime
|
||||
sd-1/controlnet/openpose:
|
||||
repo_id: lllyasviel/control_v11p_sd15_openpose
|
||||
recommended: True
|
||||
sd-1/controlnet/scribble:
|
||||
repo_id: lllyasviel/control_v11p_sd15_scribble
|
||||
recommended: False
|
||||
sd-1/controlnet/softedge:
|
||||
repo_id: lllyasviel/control_v11p_sd15_softedge
|
||||
sd-1/controlnet/shuffle:
|
||||
@ -84,9 +99,11 @@ sd-1/controlnet/ip2p:
|
||||
repo_id: lllyasviel/control_v11e_sd15_ip2p
|
||||
sd-1/embedding/EasyNegative:
|
||||
path: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
|
||||
recommended: True
|
||||
sd-1/embedding/ahx-beta-453407d:
|
||||
repo_id: sd-concepts-library/ahx-beta-453407d
|
||||
sd-1/lora/LowRA:
|
||||
path: https://civitai.com/api/download/models/63006
|
||||
recommended: True
|
||||
sd-1/lora/Ink scenery:
|
||||
path: https://civitai.com/api/download/models/83390
|
||||
|
||||
@ -256,6 +256,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
widgets = dict()
|
||||
model_list = [x for x in self.all_models if self.all_models[x].model_type==model_type and not x in exclude]
|
||||
model_labels = [self.model_labels[x] for x in model_list]
|
||||
|
||||
show_recommended = len(self.installed_models)==0
|
||||
if len(model_list) > 0:
|
||||
max_width = max([len(x) for x in model_labels])
|
||||
columns = window_width // (max_width+8) # 8 characters for "[x] " and padding
|
||||
@ -280,7 +282,8 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
|
||||
value=[
|
||||
model_list.index(x)
|
||||
for x in model_list
|
||||
if self.all_models[x].installed
|
||||
if (show_recommended and self.all_models[x].recommended) \
|
||||
or self.all_models[x].installed
|
||||
],
|
||||
max_height=len(model_list)//columns + 1,
|
||||
relx=4,
|
||||
@ -672,7 +675,9 @@ def select_and_download_models(opt: Namespace):
|
||||
# pass
|
||||
|
||||
installer = ModelInstall(config, prediction_type_helper=helper)
|
||||
if opt.add or opt.delete:
|
||||
if opt.list_models:
|
||||
installer.list_models(opt.list_models)
|
||||
elif opt.add or opt.delete:
|
||||
selections = InstallSelections(
|
||||
install_models = opt.add or [],
|
||||
remove_models = opt.delete or []
|
||||
@ -696,7 +701,7 @@ def select_and_download_models(opt: Namespace):
|
||||
|
||||
# the third argument is needed in the Windows 11 environment in
|
||||
# order to launch and resize a console window running this program
|
||||
set_min_terminal_size(MIN_COLS, MIN_LINES,'invokeai-model-install')
|
||||
set_min_terminal_size(MIN_COLS, MIN_LINES)
|
||||
installApp = AddModelApplication(opt)
|
||||
try:
|
||||
installApp.run()
|
||||
@ -745,7 +750,7 @@ def main():
|
||||
)
|
||||
parser.add_argument(
|
||||
"--list-models",
|
||||
choices=["diffusers","loras","controlnets","tis"],
|
||||
choices=[x.value for x in ModelType],
|
||||
help="list installed models",
|
||||
)
|
||||
parser.add_argument(
|
||||
@ -773,7 +778,7 @@ def main():
|
||||
config.parse_args(invoke_args)
|
||||
logger = InvokeAILogger().getLogger(config=config)
|
||||
|
||||
if not (config.conf_path / 'models.yaml').exists():
|
||||
if not config.model_conf_path.exists():
|
||||
logger.info(
|
||||
"Your InvokeAI root directory is not set up. Calling invokeai-configure."
|
||||
)
|
||||
|
||||
@ -17,28 +17,20 @@ from shutil import get_terminal_size
|
||||
from curses import BUTTON2_CLICKED,BUTTON3_CLICKED
|
||||
|
||||
# minimum size for UIs
|
||||
MIN_COLS = 130
|
||||
MIN_COLS = 136
|
||||
MIN_LINES = 45
|
||||
|
||||
# -------------------------------------
|
||||
def set_terminal_size(columns: int, lines: int, launch_command: str=None):
|
||||
def set_terminal_size(columns: int, lines: int):
|
||||
ts = get_terminal_size()
|
||||
width = max(columns,ts.columns)
|
||||
height = max(lines,ts.lines)
|
||||
|
||||
OS = platform.uname().system
|
||||
if OS == "Windows":
|
||||
# The new Windows Terminal doesn't resize, so we relaunch in a CMD window.
|
||||
# Would prefer to use execvpe() here, but somehow it is not working properly
|
||||
# in the Windows 10 environment.
|
||||
if 'IA_RELAUNCHED' not in os.environ:
|
||||
args=['conhost']
|
||||
args.extend([launch_command] if launch_command else [sys.argv[0]])
|
||||
args.extend(sys.argv[1:])
|
||||
os.environ['IA_RELAUNCHED'] = 'True'
|
||||
os.execvp('conhost',args)
|
||||
else:
|
||||
_set_terminal_size_powershell(width,height)
|
||||
pass
|
||||
# not working reliably - ask user to adjust the window
|
||||
#_set_terminal_size_powershell(width,height)
|
||||
elif OS in ["Darwin", "Linux"]:
|
||||
_set_terminal_size_unix(width,height)
|
||||
|
||||
@ -84,20 +76,14 @@ def _set_terminal_size_unix(width: int, height: int):
|
||||
sys.stdout.write("\x1b[8;{height};{width}t".format(height=height, width=width))
|
||||
sys.stdout.flush()
|
||||
|
||||
def set_min_terminal_size(min_cols: int, min_lines: int, launch_command: str=None):
|
||||
def set_min_terminal_size(min_cols: int, min_lines: int):
|
||||
# make sure there's enough room for the ui
|
||||
term_cols, term_lines = get_terminal_size()
|
||||
if term_cols >= min_cols and term_lines >= min_lines:
|
||||
return
|
||||
cols = max(term_cols, min_cols)
|
||||
lines = max(term_lines, min_lines)
|
||||
set_terminal_size(cols, lines, launch_command)
|
||||
|
||||
# did it work?
|
||||
term_cols, term_lines = get_terminal_size()
|
||||
if term_cols < cols or term_lines < lines:
|
||||
print(f'This window is too small for optimal display. For best results please enlarge it.')
|
||||
input('After resizing, press any key to continue...')
|
||||
set_terminal_size(cols, lines)
|
||||
|
||||
class IntSlider(npyscreen.Slider):
|
||||
def translate_value(self):
|
||||
|
||||
@ -36,6 +36,7 @@ module.exports = {
|
||||
],
|
||||
'prettier/prettier': ['error', { endOfLine: 'auto' }],
|
||||
'@typescript-eslint/ban-ts-comment': 'warn',
|
||||
'@typescript-eslint/no-explicit-any': 'warn',
|
||||
'@typescript-eslint/no-empty-interface': [
|
||||
'error',
|
||||
{
|
||||
|
||||
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302
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vendored
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302
invokeai/frontend/web/dist/assets/ThemeLocaleProvider-529591b4.js
vendored
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9
invokeai/frontend/web/dist/assets/ThemeLocaleProvider-5b992bc7.css
vendored
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9
invokeai/frontend/web/dist/assets/ThemeLocaleProvider-5b992bc7.css
vendored
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125
invokeai/frontend/web/dist/assets/index-adc79457.js
vendored
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125
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vendored
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2
invokeai/frontend/web/dist/index.html
vendored
2
invokeai/frontend/web/dist/index.html
vendored
@ -12,7 +12,7 @@
|
||||
margin: 0;
|
||||
}
|
||||
</style>
|
||||
<script type="module" crossorigin src="./assets/index-63d3e2f4.js"></script>
|
||||
<script type="module" crossorigin src="./assets/index-adc79457.js"></script>
|
||||
</head>
|
||||
|
||||
<body dir="ltr">
|
||||
|
||||
33
invokeai/frontend/web/dist/locales/en.json
vendored
33
invokeai/frontend/web/dist/locales/en.json
vendored
@ -119,7 +119,7 @@
|
||||
"pinGallery": "Pin Gallery",
|
||||
"allImagesLoaded": "All Images Loaded",
|
||||
"loadMore": "Load More",
|
||||
"noImagesInGallery": "No Images In Gallery",
|
||||
"noImagesInGallery": "No Images to Display",
|
||||
"deleteImage": "Delete Image",
|
||||
"deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
|
||||
"deleteImagePermanent": "Deleted images cannot be restored.",
|
||||
@ -343,6 +343,7 @@
|
||||
"safetensorModels": "SafeTensors",
|
||||
"modelAdded": "Model Added",
|
||||
"modelUpdated": "Model Updated",
|
||||
"modelUpdateFailed": "Model Update Failed",
|
||||
"modelEntryDeleted": "Model Entry Deleted",
|
||||
"cannotUseSpaces": "Cannot Use Spaces",
|
||||
"addNew": "Add New",
|
||||
@ -397,8 +398,10 @@
|
||||
"delete": "Delete",
|
||||
"deleteModel": "Delete Model",
|
||||
"deleteConfig": "Delete Config",
|
||||
"deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?",
|
||||
"deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to.",
|
||||
"deleteMsg1": "Are you sure you want to delete this model from InvokeAI?",
|
||||
"modelDeleted": "Model Deleted",
|
||||
"modelDeleteFailed": "Failed to delete model",
|
||||
"deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
|
||||
"formMessageDiffusersModelLocation": "Diffusers Model Location",
|
||||
"formMessageDiffusersModelLocationDesc": "Please enter at least one.",
|
||||
"formMessageDiffusersVAELocation": "VAE Location",
|
||||
@ -407,11 +410,13 @@
|
||||
"convertToDiffusers": "Convert To Diffusers",
|
||||
"convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
|
||||
"convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
|
||||
"convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
|
||||
"convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
|
||||
"convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
|
||||
"convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 4GB-7GB in size.",
|
||||
"convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
|
||||
"convertToDiffusersHelpText6": "Do you wish to convert this model?",
|
||||
"convertToDiffusersSaveLocation": "Save Location",
|
||||
"noCustomLocationProvided": "No Custom Location Provided",
|
||||
"convertingModelBegin": "Converting Model. Please wait.",
|
||||
"v1": "v1",
|
||||
"v2_base": "v2 (512px)",
|
||||
"v2_768": "v2 (768px)",
|
||||
@ -420,12 +425,14 @@
|
||||
"pathToCustomConfig": "Path To Custom Config",
|
||||
"statusConverting": "Converting",
|
||||
"modelConverted": "Model Converted",
|
||||
"modelConversionFailed": "Model Conversion Failed",
|
||||
"sameFolder": "Same folder",
|
||||
"invokeRoot": "InvokeAI folder",
|
||||
"custom": "Custom",
|
||||
"customSaveLocation": "Custom Save Location",
|
||||
"merge": "Merge",
|
||||
"modelsMerged": "Models Merged",
|
||||
"modelsMergeFailed": "Model Merge Failed",
|
||||
"mergeModels": "Merge Models",
|
||||
"modelOne": "Model 1",
|
||||
"modelTwo": "Model 2",
|
||||
@ -446,7 +453,9 @@
|
||||
"weightedSum": "Weighted Sum",
|
||||
"none": "none",
|
||||
"addDifference": "Add Difference",
|
||||
"pickModelType": "Pick Model Type"
|
||||
"pickModelType": "Pick Model Type",
|
||||
"selectModel": "Select Model",
|
||||
"importModels": "Import Models"
|
||||
},
|
||||
"parameters": {
|
||||
"general": "General",
|
||||
@ -568,6 +577,7 @@
|
||||
"uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
|
||||
"downloadImageStarted": "Image Download Started",
|
||||
"imageCopied": "Image Copied",
|
||||
"problemCopyingImage": "Unable to Copy Image",
|
||||
"imageLinkCopied": "Image Link Copied",
|
||||
"problemCopyingImageLink": "Unable to Copy Image Link",
|
||||
"imageNotLoaded": "No Image Loaded",
|
||||
@ -684,6 +694,15 @@
|
||||
"reloadSchema": "Reload Schema",
|
||||
"saveNodes": "Save Nodes",
|
||||
"loadNodes": "Load Nodes",
|
||||
"clearNodes": "Clear Nodes"
|
||||
"clearNodes": "Clear Nodes",
|
||||
"zoomInNodes": "Zoom In",
|
||||
"zoomOutNodes": "Zoom Out",
|
||||
"fitViewportNodes": "Fit View",
|
||||
"hideGraphNodes": "Hide Graph Overlay",
|
||||
"showGraphNodes": "Show Graph Overlay",
|
||||
"hideLegendNodes": "Hide Field Type Legend",
|
||||
"showLegendNodes": "Show Field Type Legend",
|
||||
"hideMinimapnodes": "Hide MiniMap",
|
||||
"showMinimapnodes": "Show MiniMap"
|
||||
}
|
||||
}
|
||||
|
||||
@ -119,7 +119,7 @@
|
||||
"pinGallery": "Pin Gallery",
|
||||
"allImagesLoaded": "All Images Loaded",
|
||||
"loadMore": "Load More",
|
||||
"noImagesInGallery": "No Images In Gallery",
|
||||
"noImagesInGallery": "No Images to Display",
|
||||
"deleteImage": "Delete Image",
|
||||
"deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
|
||||
"deleteImagePermanent": "Deleted images cannot be restored.",
|
||||
@ -343,6 +343,7 @@
|
||||
"safetensorModels": "SafeTensors",
|
||||
"modelAdded": "Model Added",
|
||||
"modelUpdated": "Model Updated",
|
||||
"modelUpdateFailed": "Model Update Failed",
|
||||
"modelEntryDeleted": "Model Entry Deleted",
|
||||
"cannotUseSpaces": "Cannot Use Spaces",
|
||||
"addNew": "Add New",
|
||||
@ -397,8 +398,10 @@
|
||||
"delete": "Delete",
|
||||
"deleteModel": "Delete Model",
|
||||
"deleteConfig": "Delete Config",
|
||||
"deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?",
|
||||
"deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to.",
|
||||
"deleteMsg1": "Are you sure you want to delete this model from InvokeAI?",
|
||||
"modelDeleted": "Model Deleted",
|
||||
"modelDeleteFailed": "Failed to delete model",
|
||||
"deleteMsg2": "This WILL delete the model from disk if it is in the InvokeAI root folder. If you are using a custom location, then the model WILL NOT be deleted from disk.",
|
||||
"formMessageDiffusersModelLocation": "Diffusers Model Location",
|
||||
"formMessageDiffusersModelLocationDesc": "Please enter at least one.",
|
||||
"formMessageDiffusersVAELocation": "VAE Location",
|
||||
@ -407,11 +410,13 @@
|
||||
"convertToDiffusers": "Convert To Diffusers",
|
||||
"convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
|
||||
"convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
|
||||
"convertToDiffusersHelpText3": "Your checkpoint file on the disk will NOT be deleted or modified in anyway. You can add your checkpoint to the Model Manager again if you want to.",
|
||||
"convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
|
||||
"convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
|
||||
"convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 4GB-7GB in size.",
|
||||
"convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
|
||||
"convertToDiffusersHelpText6": "Do you wish to convert this model?",
|
||||
"convertToDiffusersSaveLocation": "Save Location",
|
||||
"noCustomLocationProvided": "No Custom Location Provided",
|
||||
"convertingModelBegin": "Converting Model. Please wait.",
|
||||
"v1": "v1",
|
||||
"v2_base": "v2 (512px)",
|
||||
"v2_768": "v2 (768px)",
|
||||
@ -420,12 +425,14 @@
|
||||
"pathToCustomConfig": "Path To Custom Config",
|
||||
"statusConverting": "Converting",
|
||||
"modelConverted": "Model Converted",
|
||||
"modelConversionFailed": "Model Conversion Failed",
|
||||
"sameFolder": "Same folder",
|
||||
"invokeRoot": "InvokeAI folder",
|
||||
"custom": "Custom",
|
||||
"customSaveLocation": "Custom Save Location",
|
||||
"merge": "Merge",
|
||||
"modelsMerged": "Models Merged",
|
||||
"modelsMergeFailed": "Model Merge Failed",
|
||||
"mergeModels": "Merge Models",
|
||||
"modelOne": "Model 1",
|
||||
"modelTwo": "Model 2",
|
||||
@ -446,7 +453,9 @@
|
||||
"weightedSum": "Weighted Sum",
|
||||
"none": "none",
|
||||
"addDifference": "Add Difference",
|
||||
"pickModelType": "Pick Model Type"
|
||||
"pickModelType": "Pick Model Type",
|
||||
"selectModel": "Select Model",
|
||||
"importModels": "Import Models"
|
||||
},
|
||||
"parameters": {
|
||||
"general": "General",
|
||||
@ -568,6 +577,7 @@
|
||||
"uploadFailedInvalidUploadDesc": "Must be single PNG or JPEG image",
|
||||
"downloadImageStarted": "Image Download Started",
|
||||
"imageCopied": "Image Copied",
|
||||
"problemCopyingImage": "Unable to Copy Image",
|
||||
"imageLinkCopied": "Image Link Copied",
|
||||
"problemCopyingImageLink": "Unable to Copy Image Link",
|
||||
"imageNotLoaded": "No Image Loaded",
|
||||
@ -684,6 +694,15 @@
|
||||
"reloadSchema": "Reload Schema",
|
||||
"saveNodes": "Save Nodes",
|
||||
"loadNodes": "Load Nodes",
|
||||
"clearNodes": "Clear Nodes"
|
||||
"clearNodes": "Clear Nodes",
|
||||
"zoomInNodes": "Zoom In",
|
||||
"zoomOutNodes": "Zoom Out",
|
||||
"fitViewportNodes": "Fit View",
|
||||
"hideGraphNodes": "Hide Graph Overlay",
|
||||
"showGraphNodes": "Show Graph Overlay",
|
||||
"hideLegendNodes": "Hide Field Type Legend",
|
||||
"showLegendNodes": "Show Field Type Legend",
|
||||
"hideMinimapnodes": "Hide MiniMap",
|
||||
"showMinimapnodes": "Show MiniMap"
|
||||
}
|
||||
}
|
||||
|
||||
@ -6,9 +6,7 @@ import { PartialAppConfig } from 'app/types/invokeai';
|
||||
import ImageUploader from 'common/components/ImageUploader';
|
||||
import GalleryDrawer from 'features/gallery/components/GalleryPanel';
|
||||
import DeleteImageModal from 'features/imageDeletion/components/DeleteImageModal';
|
||||
import Lightbox from 'features/lightbox/components/Lightbox';
|
||||
import SiteHeader from 'features/system/components/SiteHeader';
|
||||
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
|
||||
import { configChanged } from 'features/system/store/configSlice';
|
||||
import { languageSelector } from 'features/system/store/systemSelectors';
|
||||
import FloatingGalleryButton from 'features/ui/components/FloatingGalleryButton';
|
||||
@ -34,8 +32,6 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
|
||||
|
||||
const log = useLogger();
|
||||
|
||||
const isLightboxEnabled = useFeatureStatus('lightbox').isFeatureEnabled;
|
||||
|
||||
const dispatch = useAppDispatch();
|
||||
|
||||
useEffect(() => {
|
||||
@ -54,7 +50,6 @@ const App = ({ config = DEFAULT_CONFIG, headerComponent }: Props) => {
|
||||
return (
|
||||
<>
|
||||
<Grid w="100vw" h="100vh" position="relative" overflow="hidden">
|
||||
{isLightboxEnabled && <Lightbox />}
|
||||
<ImageUploader>
|
||||
<Grid
|
||||
sx={{
|
||||
|
||||
@ -1,7 +1,9 @@
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { RootState } from 'app/store/store';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { requestCanvasRescale } from 'features/canvas/store/thunks/requestCanvasScale';
|
||||
import { shiftKeyPressed } from 'features/ui/store/hotkeysSlice';
|
||||
import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
|
||||
import {
|
||||
setActiveTab,
|
||||
toggleGalleryPanel,
|
||||
@ -14,10 +16,11 @@ import React, { memo } from 'react';
|
||||
import { isHotkeyPressed, useHotkeys } from 'react-hotkeys-hook';
|
||||
|
||||
const globalHotkeysSelector = createSelector(
|
||||
(state: RootState) => state.hotkeys,
|
||||
(hotkeys) => {
|
||||
[(state: RootState) => state.hotkeys, (state: RootState) => state.ui],
|
||||
(hotkeys, ui) => {
|
||||
const { shift } = hotkeys;
|
||||
return { shift };
|
||||
const { shouldPinParametersPanel, shouldPinGallery } = ui;
|
||||
return { shift, shouldPinGallery, shouldPinParametersPanel };
|
||||
},
|
||||
{
|
||||
memoizeOptions: {
|
||||
@ -34,7 +37,10 @@ const globalHotkeysSelector = createSelector(
|
||||
*/
|
||||
const GlobalHotkeys: React.FC = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
const { shift } = useAppSelector(globalHotkeysSelector);
|
||||
const { shift, shouldPinParametersPanel, shouldPinGallery } = useAppSelector(
|
||||
globalHotkeysSelector
|
||||
);
|
||||
const activeTabName = useAppSelector(activeTabNameSelector);
|
||||
|
||||
useHotkeys(
|
||||
'*',
|
||||
@ -51,18 +57,30 @@ const GlobalHotkeys: React.FC = () => {
|
||||
|
||||
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', () => {
|
||||
|
||||
@ -1,8 +1,4 @@
|
||||
import { Box, ChakraProps, Flex, Heading, Image } from '@chakra-ui/react';
|
||||
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 { memo } from 'react';
|
||||
import { TypesafeDraggableData } from './typesafeDnd';
|
||||
|
||||
@ -32,24 +28,7 @@ const STYLES: ChakraProps['sx'] = {
|
||||
},
|
||||
};
|
||||
|
||||
const selector = createSelector(
|
||||
stateSelector,
|
||||
(state) => {
|
||||
const gallerySelectionCount = state.gallery.selection.length;
|
||||
const batchSelectionCount = state.batch.selection.length;
|
||||
|
||||
return {
|
||||
gallerySelectionCount,
|
||||
batchSelectionCount,
|
||||
};
|
||||
},
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
const DragPreview = (props: OverlayDragImageProps) => {
|
||||
const { gallerySelectionCount, batchSelectionCount } =
|
||||
useAppSelector(selector);
|
||||
|
||||
if (!props.dragData) {
|
||||
return;
|
||||
}
|
||||
@ -82,7 +61,7 @@ const DragPreview = (props: OverlayDragImageProps) => {
|
||||
);
|
||||
}
|
||||
|
||||
if (props.dragData.payloadType === 'BATCH_SELECTION') {
|
||||
if (props.dragData.payloadType === 'IMAGE_NAMES') {
|
||||
return (
|
||||
<Flex
|
||||
sx={{
|
||||
@ -95,26 +74,7 @@ const DragPreview = (props: OverlayDragImageProps) => {
|
||||
...STYLES,
|
||||
}}
|
||||
>
|
||||
<Heading>{batchSelectionCount}</Heading>
|
||||
<Heading size="sm">Images</Heading>
|
||||
</Flex>
|
||||
);
|
||||
}
|
||||
|
||||
if (props.dragData.payloadType === 'GALLERY_SELECTION') {
|
||||
return (
|
||||
<Flex
|
||||
sx={{
|
||||
cursor: 'none',
|
||||
userSelect: 'none',
|
||||
position: 'relative',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
flexDir: 'column',
|
||||
...STYLES,
|
||||
}}
|
||||
>
|
||||
<Heading>{gallerySelectionCount}</Heading>
|
||||
<Heading>{props.dragData.payload.image_names.length}</Heading>
|
||||
<Heading size="sm">Images</Heading>
|
||||
</Flex>
|
||||
);
|
||||
|
||||
@ -6,18 +6,18 @@ import {
|
||||
useSensor,
|
||||
useSensors,
|
||||
} from '@dnd-kit/core';
|
||||
import { snapCenterToCursor } from '@dnd-kit/modifiers';
|
||||
import { dndDropped } from 'app/store/middleware/listenerMiddleware/listeners/imageDropped';
|
||||
import { useAppDispatch } from 'app/store/storeHooks';
|
||||
import { AnimatePresence, motion } from 'framer-motion';
|
||||
import { PropsWithChildren, memo, useCallback, useState } from 'react';
|
||||
import DragPreview from './DragPreview';
|
||||
import { snapCenterToCursor } from '@dnd-kit/modifiers';
|
||||
import { AnimatePresence, motion } from 'framer-motion';
|
||||
import {
|
||||
DndContext,
|
||||
DragEndEvent,
|
||||
DragStartEvent,
|
||||
TypesafeDraggableData,
|
||||
} from './typesafeDnd';
|
||||
import { useAppDispatch } from 'app/store/storeHooks';
|
||||
import { imageDropped } from 'app/store/middleware/listenerMiddleware/listeners/imageDropped';
|
||||
|
||||
type ImageDndContextProps = PropsWithChildren;
|
||||
|
||||
@ -42,18 +42,18 @@ const ImageDndContext = (props: ImageDndContextProps) => {
|
||||
if (!activeData || !overData) {
|
||||
return;
|
||||
}
|
||||
dispatch(imageDropped({ overData, activeData }));
|
||||
dispatch(dndDropped({ overData, activeData }));
|
||||
setActiveDragData(null);
|
||||
},
|
||||
[dispatch]
|
||||
);
|
||||
|
||||
const mouseSensor = useSensor(MouseSensor, {
|
||||
activationConstraint: { delay: 150, tolerance: 5 },
|
||||
activationConstraint: { distance: 10 },
|
||||
});
|
||||
|
||||
const touchSensor = useSensor(TouchSensor, {
|
||||
activationConstraint: { delay: 150, tolerance: 5 },
|
||||
activationConstraint: { distance: 10 },
|
||||
});
|
||||
|
||||
// TODO: Use KeyboardSensor - needs composition of multiple collisionDetection algos
|
||||
|
||||
@ -77,18 +77,14 @@ export type ImageDraggableData = BaseDragData & {
|
||||
payload: { imageDTO: ImageDTO };
|
||||
};
|
||||
|
||||
export type GallerySelectionDraggableData = BaseDragData & {
|
||||
payloadType: 'GALLERY_SELECTION';
|
||||
};
|
||||
|
||||
export type BatchSelectionDraggableData = BaseDragData & {
|
||||
payloadType: 'BATCH_SELECTION';
|
||||
export type ImageNamesDraggableData = BaseDragData & {
|
||||
payloadType: 'IMAGE_NAMES';
|
||||
payload: { image_names: string[] };
|
||||
};
|
||||
|
||||
export type TypesafeDraggableData =
|
||||
| ImageDraggableData
|
||||
| GallerySelectionDraggableData
|
||||
| BatchSelectionDraggableData;
|
||||
| ImageNamesDraggableData;
|
||||
|
||||
interface UseDroppableTypesafeArguments
|
||||
extends Omit<UseDroppableArguments, 'data'> {
|
||||
@ -159,13 +155,11 @@ export const isValidDrop = (
|
||||
case 'SET_NODES_IMAGE':
|
||||
return payloadType === 'IMAGE_DTO';
|
||||
case 'SET_MULTI_NODES_IMAGE':
|
||||
return payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION';
|
||||
return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
|
||||
case 'ADD_TO_BATCH':
|
||||
return payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION';
|
||||
return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
|
||||
case 'MOVE_BOARD':
|
||||
return (
|
||||
payloadType === 'IMAGE_DTO' || 'GALLERY_SELECTION' || 'BATCH_SELECTION'
|
||||
);
|
||||
return payloadType === 'IMAGE_DTO' || 'IMAGE_NAMES';
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
|
||||
@ -9,9 +9,9 @@ import { theme as invokeAITheme } from 'theme/theme';
|
||||
|
||||
import '@fontsource-variable/inter';
|
||||
import { MantineProvider } from '@mantine/core';
|
||||
import { mantineTheme } from 'mantine-theme/theme';
|
||||
import 'overlayscrollbars/overlayscrollbars.css';
|
||||
import 'theme/css/overlayscrollbars.css';
|
||||
import { useMantineTheme } from 'mantine-theme/theme';
|
||||
|
||||
type ThemeLocaleProviderProps = {
|
||||
children: ReactNode;
|
||||
@ -35,8 +35,10 @@ function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
|
||||
document.body.dir = direction;
|
||||
}, [direction]);
|
||||
|
||||
const mantineTheme = useMantineTheme();
|
||||
|
||||
return (
|
||||
<MantineProvider withGlobalStyles theme={mantineTheme}>
|
||||
<MantineProvider theme={mantineTheme}>
|
||||
<ChakraProvider theme={theme} colorModeManager={manager}>
|
||||
{children}
|
||||
</ChakraProvider>
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
import { SchedulerParam } from 'features/parameters/store/parameterZodSchemas';
|
||||
|
||||
// zod needs the array to be `as const` to infer the type correctly
|
||||
|
||||
import { SchedulerParam } from 'features/parameters/types/parameterSchemas';
|
||||
|
||||
// this is the source of the `SchedulerParam` type, which is generated by zod
|
||||
export const SCHEDULER_NAMES_AS_CONST = [
|
||||
'euler',
|
||||
@ -58,15 +59,8 @@ export const SCHEDULER_LABEL_MAP: Record<SchedulerParam, string> = {
|
||||
|
||||
export type Scheduler = (typeof SCHEDULER_NAMES)[number];
|
||||
|
||||
// Valid upscaling levels
|
||||
export const UPSCALING_LEVELS: Array<{ label: string; value: string }> = [
|
||||
{ label: '2x', value: '2' },
|
||||
{ label: '4x', value: '4' },
|
||||
];
|
||||
export const NUMPY_RAND_MIN = 0;
|
||||
|
||||
export const NUMPY_RAND_MAX = 2147483647;
|
||||
|
||||
export const FACETOOL_TYPES = ['gfpgan', 'codeformer'] as const;
|
||||
|
||||
export const NODE_MIN_WIDTH = 250;
|
||||
|
||||
@ -1,67 +0,0 @@
|
||||
// import { createAction } from '@reduxjs/toolkit';
|
||||
// import * as InvokeAI from 'app/types/invokeai';
|
||||
// import { GalleryCategory } from 'features/gallery/store/gallerySlice';
|
||||
// import { InvokeTabName } from 'features/ui/store/tabMap';
|
||||
|
||||
// /**
|
||||
// * We can't use redux-toolkit's createSlice() to make these actions,
|
||||
// * because they have no associated reducer. They only exist to dispatch
|
||||
// * requests to the server via socketio. These actions will be handled
|
||||
// * by the middleware.
|
||||
// */
|
||||
|
||||
// export const generateImage = createAction<InvokeTabName>(
|
||||
// 'socketio/generateImage'
|
||||
// );
|
||||
// export const runESRGAN = createAction<InvokeAI._Image>('socketio/runESRGAN');
|
||||
// export const runFacetool = createAction<InvokeAI._Image>(
|
||||
// 'socketio/runFacetool'
|
||||
// );
|
||||
// export const deleteImage = createAction<InvokeAI._Image>(
|
||||
// 'socketio/deleteImage'
|
||||
// );
|
||||
// export const requestImages = createAction<GalleryCategory>(
|
||||
// 'socketio/requestImages'
|
||||
// );
|
||||
// export const requestNewImages = createAction<GalleryCategory>(
|
||||
// 'socketio/requestNewImages'
|
||||
// );
|
||||
// export const cancelProcessing = createAction<undefined>(
|
||||
// 'socketio/cancelProcessing'
|
||||
// );
|
||||
|
||||
// export const requestSystemConfig = createAction<undefined>(
|
||||
// 'socketio/requestSystemConfig'
|
||||
// );
|
||||
|
||||
// export const searchForModels = createAction<string>('socketio/searchForModels');
|
||||
|
||||
// export const addNewModel = createAction<
|
||||
// InvokeAI.InvokeModelConfigProps | InvokeAI.InvokeDiffusersModelConfigProps
|
||||
// >('socketio/addNewModel');
|
||||
|
||||
// export const deleteModel = createAction<string>('socketio/deleteModel');
|
||||
|
||||
// export const convertToDiffusers =
|
||||
// createAction<InvokeAI.InvokeModelConversionProps>(
|
||||
// 'socketio/convertToDiffusers'
|
||||
// );
|
||||
|
||||
// export const mergeDiffusersModels =
|
||||
// createAction<InvokeAI.InvokeModelMergingProps>(
|
||||
// 'socketio/mergeDiffusersModels'
|
||||
// );
|
||||
|
||||
// export const requestModelChange = createAction<string>(
|
||||
// 'socketio/requestModelChange'
|
||||
// );
|
||||
|
||||
// export const saveStagingAreaImageToGallery = createAction<string>(
|
||||
// 'socketio/saveStagingAreaImageToGallery'
|
||||
// );
|
||||
|
||||
// export const emptyTempFolder = createAction<undefined>(
|
||||
// 'socketio/requestEmptyTempFolder'
|
||||
// );
|
||||
|
||||
export default {};
|
||||
@ -1,209 +0,0 @@
|
||||
import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
|
||||
import * as InvokeAI from 'app/types/invokeai';
|
||||
import type { RootState } from 'app/store/store';
|
||||
import {
|
||||
frontendToBackendParameters,
|
||||
FrontendToBackendParametersConfig,
|
||||
} from 'common/util/parameterTranslation';
|
||||
import dateFormat from 'dateformat';
|
||||
import {
|
||||
GalleryCategory,
|
||||
GalleryState,
|
||||
removeImage,
|
||||
} from 'features/gallery/store/gallerySlice';
|
||||
import {
|
||||
generationRequested,
|
||||
modelChangeRequested,
|
||||
modelConvertRequested,
|
||||
modelMergingRequested,
|
||||
setIsProcessing,
|
||||
} from 'features/system/store/systemSlice';
|
||||
import { InvokeTabName } from 'features/ui/store/tabMap';
|
||||
import { Socket } from 'socket.io-client';
|
||||
|
||||
/**
|
||||
* Returns an object containing all functions which use `socketio.emit()`.
|
||||
* i.e. those which make server requests.
|
||||
*/
|
||||
const makeSocketIOEmitters = (
|
||||
store: MiddlewareAPI<Dispatch<AnyAction>, RootState>,
|
||||
socketio: Socket
|
||||
) => {
|
||||
// We need to dispatch actions to redux and get pieces of state from the store.
|
||||
const { dispatch, getState } = store;
|
||||
|
||||
return {
|
||||
emitGenerateImage: (generationMode: InvokeTabName) => {
|
||||
dispatch(setIsProcessing(true));
|
||||
|
||||
const state: RootState = getState();
|
||||
|
||||
const {
|
||||
generation: generationState,
|
||||
postprocessing: postprocessingState,
|
||||
system: systemState,
|
||||
canvas: canvasState,
|
||||
} = state;
|
||||
|
||||
const frontendToBackendParametersConfig: FrontendToBackendParametersConfig =
|
||||
{
|
||||
generationMode,
|
||||
generationState,
|
||||
postprocessingState,
|
||||
canvasState,
|
||||
systemState,
|
||||
};
|
||||
|
||||
dispatch(generationRequested());
|
||||
|
||||
const { generationParameters, esrganParameters, facetoolParameters } =
|
||||
frontendToBackendParameters(frontendToBackendParametersConfig);
|
||||
|
||||
socketio.emit(
|
||||
'generateImage',
|
||||
generationParameters,
|
||||
esrganParameters,
|
||||
facetoolParameters
|
||||
);
|
||||
|
||||
// we need to truncate the init_mask base64 else it takes up the whole log
|
||||
// TODO: handle maintaining masks for reproducibility in future
|
||||
if (generationParameters.init_mask) {
|
||||
generationParameters.init_mask = generationParameters.init_mask
|
||||
.substr(0, 64)
|
||||
.concat('...');
|
||||
}
|
||||
if (generationParameters.init_img) {
|
||||
generationParameters.init_img = generationParameters.init_img
|
||||
.substr(0, 64)
|
||||
.concat('...');
|
||||
}
|
||||
|
||||
dispatch(
|
||||
addLogEntry({
|
||||
timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
message: `Image generation requested: ${JSON.stringify({
|
||||
...generationParameters,
|
||||
...esrganParameters,
|
||||
...facetoolParameters,
|
||||
})}`,
|
||||
})
|
||||
);
|
||||
},
|
||||
emitRunESRGAN: (imageToProcess: InvokeAI._Image) => {
|
||||
dispatch(setIsProcessing(true));
|
||||
|
||||
const {
|
||||
postprocessing: {
|
||||
upscalingLevel,
|
||||
upscalingDenoising,
|
||||
upscalingStrength,
|
||||
},
|
||||
} = getState();
|
||||
|
||||
const esrganParameters = {
|
||||
upscale: [upscalingLevel, upscalingDenoising, upscalingStrength],
|
||||
};
|
||||
socketio.emit('runPostprocessing', imageToProcess, {
|
||||
type: 'esrgan',
|
||||
...esrganParameters,
|
||||
});
|
||||
dispatch(
|
||||
addLogEntry({
|
||||
timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
message: `ESRGAN upscale requested: ${JSON.stringify({
|
||||
file: imageToProcess.url,
|
||||
...esrganParameters,
|
||||
})}`,
|
||||
})
|
||||
);
|
||||
},
|
||||
emitRunFacetool: (imageToProcess: InvokeAI._Image) => {
|
||||
dispatch(setIsProcessing(true));
|
||||
|
||||
const {
|
||||
postprocessing: { facetoolType, facetoolStrength, codeformerFidelity },
|
||||
} = getState();
|
||||
|
||||
const facetoolParameters: Record<string, unknown> = {
|
||||
facetool_strength: facetoolStrength,
|
||||
};
|
||||
|
||||
if (facetoolType === 'codeformer') {
|
||||
facetoolParameters.codeformer_fidelity = codeformerFidelity;
|
||||
}
|
||||
|
||||
socketio.emit('runPostprocessing', imageToProcess, {
|
||||
type: facetoolType,
|
||||
...facetoolParameters,
|
||||
});
|
||||
dispatch(
|
||||
addLogEntry({
|
||||
timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
message: `Face restoration (${facetoolType}) requested: ${JSON.stringify(
|
||||
{
|
||||
file: imageToProcess.url,
|
||||
...facetoolParameters,
|
||||
}
|
||||
)}`,
|
||||
})
|
||||
);
|
||||
},
|
||||
emitDeleteImage: (imageToDelete: InvokeAI._Image) => {
|
||||
const { url, uuid, category, thumbnail } = imageToDelete;
|
||||
dispatch(removeImage(imageToDelete));
|
||||
socketio.emit('deleteImage', url, thumbnail, uuid, category);
|
||||
},
|
||||
emitRequestImages: (category: GalleryCategory) => {
|
||||
const gallery: GalleryState = getState().gallery;
|
||||
const { earliest_mtime } = gallery.categories[category];
|
||||
socketio.emit('requestImages', category, earliest_mtime);
|
||||
},
|
||||
emitRequestNewImages: (category: GalleryCategory) => {
|
||||
const gallery: GalleryState = getState().gallery;
|
||||
const { latest_mtime } = gallery.categories[category];
|
||||
socketio.emit('requestLatestImages', category, latest_mtime);
|
||||
},
|
||||
emitCancelProcessing: () => {
|
||||
socketio.emit('cancel');
|
||||
},
|
||||
emitRequestSystemConfig: () => {
|
||||
socketio.emit('requestSystemConfig');
|
||||
},
|
||||
emitSearchForModels: (modelFolder: string) => {
|
||||
socketio.emit('searchForModels', modelFolder);
|
||||
},
|
||||
emitAddNewModel: (modelConfig: InvokeAI.InvokeModelConfigProps) => {
|
||||
socketio.emit('addNewModel', modelConfig);
|
||||
},
|
||||
emitDeleteModel: (modelName: string) => {
|
||||
socketio.emit('deleteModel', modelName);
|
||||
},
|
||||
emitConvertToDiffusers: (
|
||||
modelToConvert: InvokeAI.InvokeModelConversionProps
|
||||
) => {
|
||||
dispatch(modelConvertRequested());
|
||||
socketio.emit('convertToDiffusers', modelToConvert);
|
||||
},
|
||||
emitMergeDiffusersModels: (
|
||||
modelMergeInfo: InvokeAI.InvokeModelMergingProps
|
||||
) => {
|
||||
dispatch(modelMergingRequested());
|
||||
socketio.emit('mergeDiffusersModels', modelMergeInfo);
|
||||
},
|
||||
emitRequestModelChange: (modelName: string) => {
|
||||
dispatch(modelChangeRequested());
|
||||
socketio.emit('requestModelChange', modelName);
|
||||
},
|
||||
emitSaveStagingAreaImageToGallery: (url: string) => {
|
||||
socketio.emit('requestSaveStagingAreaImageToGallery', url);
|
||||
},
|
||||
emitRequestEmptyTempFolder: () => {
|
||||
socketio.emit('requestEmptyTempFolder');
|
||||
},
|
||||
};
|
||||
};
|
||||
|
||||
export default makeSocketIOEmitters;
|
||||
|
||||
export default {};
|
||||
@ -1,502 +0,0 @@
|
||||
// import { AnyAction, Dispatch, MiddlewareAPI } from '@reduxjs/toolkit';
|
||||
// import dateFormat from 'dateformat';
|
||||
// import i18n from 'i18n';
|
||||
// import { v4 as uuidv4 } from 'uuid';
|
||||
|
||||
// import * as InvokeAI from 'app/types/invokeai';
|
||||
|
||||
// import {
|
||||
// addToast,
|
||||
// errorOccurred,
|
||||
// processingCanceled,
|
||||
// setCurrentStatus,
|
||||
// setFoundModels,
|
||||
// setIsCancelable,
|
||||
// setIsConnected,
|
||||
// setIsProcessing,
|
||||
// setModelList,
|
||||
// setSearchFolder,
|
||||
// setSystemConfig,
|
||||
// setSystemStatus,
|
||||
// } from 'features/system/store/systemSlice';
|
||||
|
||||
// import {
|
||||
// addGalleryImages,
|
||||
// addImage,
|
||||
// clearIntermediateImage,
|
||||
// GalleryState,
|
||||
// removeImage,
|
||||
// setIntermediateImage,
|
||||
// } from 'features/gallery/store/gallerySlice';
|
||||
|
||||
// import type { RootState } from 'app/store/store';
|
||||
// import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
|
||||
// import {
|
||||
// clearInitialImage,
|
||||
// initialImageSelected,
|
||||
// setInfillMethod,
|
||||
// // setInitialImage,
|
||||
// setMaskPath,
|
||||
// } from 'features/parameters/store/generationSlice';
|
||||
// import { tabMap } from 'features/ui/store/tabMap';
|
||||
// import {
|
||||
// requestImages,
|
||||
// requestNewImages,
|
||||
// requestSystemConfig,
|
||||
// } from './actions';
|
||||
|
||||
// /**
|
||||
// * Returns an object containing listener callbacks for socketio events.
|
||||
// * TODO: This file is large, but simple. Should it be split up further?
|
||||
// */
|
||||
// const makeSocketIOListeners = (
|
||||
// store: MiddlewareAPI<Dispatch<AnyAction>, RootState>
|
||||
// ) => {
|
||||
// const { dispatch, getState } = store;
|
||||
|
||||
// return {
|
||||
// /**
|
||||
// * Callback to run when we receive a 'connect' event.
|
||||
// */
|
||||
// onConnect: () => {
|
||||
// try {
|
||||
// dispatch(setIsConnected(true));
|
||||
// dispatch(setCurrentStatus(i18n.t('common.statusConnected')));
|
||||
// dispatch(requestSystemConfig());
|
||||
// const gallery: GalleryState = getState().gallery;
|
||||
|
||||
// if (gallery.categories.result.latest_mtime) {
|
||||
// dispatch(requestNewImages('result'));
|
||||
// } else {
|
||||
// dispatch(requestImages('result'));
|
||||
// }
|
||||
|
||||
// if (gallery.categories.user.latest_mtime) {
|
||||
// dispatch(requestNewImages('user'));
|
||||
// } else {
|
||||
// dispatch(requestImages('user'));
|
||||
// }
|
||||
// } catch (e) {
|
||||
// console.error(e);
|
||||
// }
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive a 'disconnect' event.
|
||||
// */
|
||||
// onDisconnect: () => {
|
||||
// try {
|
||||
// dispatch(setIsConnected(false));
|
||||
// dispatch(setCurrentStatus(i18n.t('common.statusDisconnected')));
|
||||
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Disconnected from server`,
|
||||
// level: 'warning',
|
||||
// })
|
||||
// );
|
||||
// } catch (e) {
|
||||
// console.error(e);
|
||||
// }
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive a 'generationResult' event.
|
||||
// */
|
||||
// onGenerationResult: (data: InvokeAI.ImageResultResponse) => {
|
||||
// try {
|
||||
// const state = getState();
|
||||
// const { activeTab } = state.ui;
|
||||
// const { shouldLoopback } = state.postprocessing;
|
||||
// const { boundingBox: _, generationMode, ...rest } = data;
|
||||
|
||||
// const newImage = {
|
||||
// uuid: uuidv4(),
|
||||
// ...rest,
|
||||
// };
|
||||
|
||||
// if (['txt2img', 'img2img'].includes(generationMode)) {
|
||||
// dispatch(
|
||||
// addImage({
|
||||
// category: 'result',
|
||||
// image: { ...newImage, category: 'result' },
|
||||
// })
|
||||
// );
|
||||
// }
|
||||
|
||||
// if (generationMode === 'unifiedCanvas' && data.boundingBox) {
|
||||
// const { boundingBox } = data;
|
||||
// dispatch(
|
||||
// addImageToStagingArea({
|
||||
// image: { ...newImage, category: 'temp' },
|
||||
// boundingBox,
|
||||
// })
|
||||
// );
|
||||
|
||||
// if (state.canvas.shouldAutoSave) {
|
||||
// dispatch(
|
||||
// addImage({
|
||||
// image: { ...newImage, category: 'result' },
|
||||
// category: 'result',
|
||||
// })
|
||||
// );
|
||||
// }
|
||||
// }
|
||||
|
||||
// // TODO: fix
|
||||
// // if (shouldLoopback) {
|
||||
// // const activeTabName = tabMap[activeTab];
|
||||
// // switch (activeTabName) {
|
||||
// // case 'img2img': {
|
||||
// // dispatch(initialImageSelected(newImage.uuid));
|
||||
// // // dispatch(setInitialImage(newImage));
|
||||
// // break;
|
||||
// // }
|
||||
// // }
|
||||
// // }
|
||||
|
||||
// dispatch(clearIntermediateImage());
|
||||
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Image generated: ${data.url}`,
|
||||
// })
|
||||
// );
|
||||
// } catch (e) {
|
||||
// console.error(e);
|
||||
// }
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive a 'intermediateResult' event.
|
||||
// */
|
||||
// onIntermediateResult: (data: InvokeAI.ImageResultResponse) => {
|
||||
// try {
|
||||
// dispatch(
|
||||
// setIntermediateImage({
|
||||
// uuid: uuidv4(),
|
||||
// ...data,
|
||||
// category: 'result',
|
||||
// })
|
||||
// );
|
||||
// if (!data.isBase64) {
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Intermediate image generated: ${data.url}`,
|
||||
// })
|
||||
// );
|
||||
// }
|
||||
// } catch (e) {
|
||||
// console.error(e);
|
||||
// }
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive an 'esrganResult' event.
|
||||
// */
|
||||
// onPostprocessingResult: (data: InvokeAI.ImageResultResponse) => {
|
||||
// try {
|
||||
// dispatch(
|
||||
// addImage({
|
||||
// category: 'result',
|
||||
// image: {
|
||||
// uuid: uuidv4(),
|
||||
// ...data,
|
||||
// category: 'result',
|
||||
// },
|
||||
// })
|
||||
// );
|
||||
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Postprocessed: ${data.url}`,
|
||||
// })
|
||||
// );
|
||||
// } catch (e) {
|
||||
// console.error(e);
|
||||
// }
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive a 'progressUpdate' event.
|
||||
// * TODO: Add additional progress phases
|
||||
// */
|
||||
// onProgressUpdate: (data: InvokeAI.SystemStatus) => {
|
||||
// try {
|
||||
// dispatch(setIsProcessing(true));
|
||||
// dispatch(setSystemStatus(data));
|
||||
// } catch (e) {
|
||||
// console.error(e);
|
||||
// }
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive a 'progressUpdate' event.
|
||||
// */
|
||||
// onError: (data: InvokeAI.ErrorResponse) => {
|
||||
// const { message, additionalData } = data;
|
||||
|
||||
// if (additionalData) {
|
||||
// // TODO: handle more data than short message
|
||||
// }
|
||||
|
||||
// try {
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Server error: ${message}`,
|
||||
// level: 'error',
|
||||
// })
|
||||
// );
|
||||
// dispatch(errorOccurred());
|
||||
// dispatch(clearIntermediateImage());
|
||||
// } catch (e) {
|
||||
// console.error(e);
|
||||
// }
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive a 'galleryImages' event.
|
||||
// */
|
||||
// onGalleryImages: (data: InvokeAI.GalleryImagesResponse) => {
|
||||
// const { images, areMoreImagesAvailable, category } = data;
|
||||
|
||||
// /**
|
||||
// * the logic here ideally would be in the reducer but we have a side effect:
|
||||
// * generating a uuid. so the logic needs to be here, outside redux.
|
||||
// */
|
||||
|
||||
// // Generate a UUID for each image
|
||||
// const preparedImages = images.map((image): InvokeAI._Image => {
|
||||
// return {
|
||||
// uuid: uuidv4(),
|
||||
// ...image,
|
||||
// };
|
||||
// });
|
||||
|
||||
// dispatch(
|
||||
// addGalleryImages({
|
||||
// images: preparedImages,
|
||||
// areMoreImagesAvailable,
|
||||
// category,
|
||||
// })
|
||||
// );
|
||||
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Loaded ${images.length} images`,
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive a 'processingCanceled' event.
|
||||
// */
|
||||
// onProcessingCanceled: () => {
|
||||
// dispatch(processingCanceled());
|
||||
|
||||
// const { intermediateImage } = getState().gallery;
|
||||
|
||||
// if (intermediateImage) {
|
||||
// if (!intermediateImage.isBase64) {
|
||||
// dispatch(
|
||||
// addImage({
|
||||
// category: 'result',
|
||||
// image: intermediateImage,
|
||||
// })
|
||||
// );
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Intermediate image saved: ${intermediateImage.url}`,
|
||||
// })
|
||||
// );
|
||||
// }
|
||||
// dispatch(clearIntermediateImage());
|
||||
// }
|
||||
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Processing canceled`,
|
||||
// level: 'warning',
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// /**
|
||||
// * Callback to run when we receive a 'imageDeleted' event.
|
||||
// */
|
||||
// onImageDeleted: (data: InvokeAI.ImageDeletedResponse) => {
|
||||
// const { url } = data;
|
||||
|
||||
// // remove image from gallery
|
||||
// dispatch(removeImage(data));
|
||||
|
||||
// // remove references to image in options
|
||||
// const {
|
||||
// generation: { initialImage, maskPath },
|
||||
// } = getState();
|
||||
|
||||
// if (
|
||||
// initialImage === url ||
|
||||
// (initialImage as InvokeAI._Image)?.url === url
|
||||
// ) {
|
||||
// dispatch(clearInitialImage());
|
||||
// }
|
||||
|
||||
// if (maskPath === url) {
|
||||
// dispatch(setMaskPath(''));
|
||||
// }
|
||||
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Image deleted: ${url}`,
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// onSystemConfig: (data: InvokeAI.SystemConfig) => {
|
||||
// dispatch(setSystemConfig(data));
|
||||
// if (!data.infill_methods.includes('patchmatch')) {
|
||||
// dispatch(setInfillMethod(data.infill_methods[0]));
|
||||
// }
|
||||
// },
|
||||
// onFoundModels: (data: InvokeAI.FoundModelResponse) => {
|
||||
// const { search_folder, found_models } = data;
|
||||
// dispatch(setSearchFolder(search_folder));
|
||||
// dispatch(setFoundModels(found_models));
|
||||
// },
|
||||
// onNewModelAdded: (data: InvokeAI.ModelAddedResponse) => {
|
||||
// const { new_model_name, model_list, update } = data;
|
||||
// dispatch(setModelList(model_list));
|
||||
// dispatch(setIsProcessing(false));
|
||||
// dispatch(setCurrentStatus(i18n.t('modelManager.modelAdded')));
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Model Added: ${new_model_name}`,
|
||||
// level: 'info',
|
||||
// })
|
||||
// );
|
||||
// dispatch(
|
||||
// addToast({
|
||||
// title: !update
|
||||
// ? `${i18n.t('modelManager.modelAdded')}: ${new_model_name}`
|
||||
// : `${i18n.t('modelManager.modelUpdated')}: ${new_model_name}`,
|
||||
// status: 'success',
|
||||
// duration: 2500,
|
||||
// isClosable: true,
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// onModelDeleted: (data: InvokeAI.ModelDeletedResponse) => {
|
||||
// const { deleted_model_name, model_list } = data;
|
||||
// dispatch(setModelList(model_list));
|
||||
// dispatch(setIsProcessing(false));
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `${i18n.t(
|
||||
// 'modelManager.modelAdded'
|
||||
// )}: ${deleted_model_name}`,
|
||||
// level: 'info',
|
||||
// })
|
||||
// );
|
||||
// dispatch(
|
||||
// addToast({
|
||||
// title: `${i18n.t(
|
||||
// 'modelManager.modelEntryDeleted'
|
||||
// )}: ${deleted_model_name}`,
|
||||
// status: 'success',
|
||||
// duration: 2500,
|
||||
// isClosable: true,
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// onModelConverted: (data: InvokeAI.ModelConvertedResponse) => {
|
||||
// const { converted_model_name, model_list } = data;
|
||||
// dispatch(setModelList(model_list));
|
||||
// dispatch(setCurrentStatus(i18n.t('common.statusModelConverted')));
|
||||
// dispatch(setIsProcessing(false));
|
||||
// dispatch(setIsCancelable(true));
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Model converted: ${converted_model_name}`,
|
||||
// level: 'info',
|
||||
// })
|
||||
// );
|
||||
// dispatch(
|
||||
// addToast({
|
||||
// title: `${i18n.t(
|
||||
// 'modelManager.modelConverted'
|
||||
// )}: ${converted_model_name}`,
|
||||
// status: 'success',
|
||||
// duration: 2500,
|
||||
// isClosable: true,
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// onModelsMerged: (data: InvokeAI.ModelsMergedResponse) => {
|
||||
// const { merged_models, merged_model_name, model_list } = data;
|
||||
// dispatch(setModelList(model_list));
|
||||
// dispatch(setCurrentStatus(i18n.t('common.statusMergedModels')));
|
||||
// dispatch(setIsProcessing(false));
|
||||
// dispatch(setIsCancelable(true));
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Models merged: ${merged_models}`,
|
||||
// level: 'info',
|
||||
// })
|
||||
// );
|
||||
// dispatch(
|
||||
// addToast({
|
||||
// title: `${i18n.t('modelManager.modelsMerged')}: ${merged_model_name}`,
|
||||
// status: 'success',
|
||||
// duration: 2500,
|
||||
// isClosable: true,
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// onModelChanged: (data: InvokeAI.ModelChangeResponse) => {
|
||||
// const { model_name, model_list } = data;
|
||||
// dispatch(setModelList(model_list));
|
||||
// dispatch(setCurrentStatus(i18n.t('common.statusModelChanged')));
|
||||
// dispatch(setIsProcessing(false));
|
||||
// dispatch(setIsCancelable(true));
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Model changed: ${model_name}`,
|
||||
// level: 'info',
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// onModelChangeFailed: (data: InvokeAI.ModelChangeResponse) => {
|
||||
// const { model_name, model_list } = data;
|
||||
// dispatch(setModelList(model_list));
|
||||
// dispatch(setIsProcessing(false));
|
||||
// dispatch(setIsCancelable(true));
|
||||
// dispatch(errorOccurred());
|
||||
// dispatch(
|
||||
// addLogEntry({
|
||||
// timestamp: dateFormat(new Date(), 'isoDateTime'),
|
||||
// message: `Model change failed: ${model_name}`,
|
||||
// level: 'error',
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// onTempFolderEmptied: () => {
|
||||
// dispatch(
|
||||
// addToast({
|
||||
// title: i18n.t('toast.tempFoldersEmptied'),
|
||||
// status: 'success',
|
||||
// duration: 2500,
|
||||
// isClosable: true,
|
||||
// })
|
||||
// );
|
||||
// },
|
||||
// };
|
||||
// };
|
||||
|
||||
// export default makeSocketIOListeners;
|
||||
|
||||
export default {};
|
||||
@ -1,248 +0,0 @@
|
||||
// import { Middleware } from '@reduxjs/toolkit';
|
||||
// import { io } from 'socket.io-client';
|
||||
|
||||
// import makeSocketIOEmitters from './emitters';
|
||||
// import makeSocketIOListeners from './listeners';
|
||||
|
||||
// import * as InvokeAI from 'app/types/invokeai';
|
||||
|
||||
// /**
|
||||
// * Creates a socketio middleware to handle communication with server.
|
||||
// *
|
||||
// * Special `socketio/actionName` actions are created in actions.ts and
|
||||
// * exported for use by the application, which treats them like any old
|
||||
// * action, using `dispatch` to dispatch them.
|
||||
// *
|
||||
// * These actions are intercepted here, where `socketio.emit()` calls are
|
||||
// * made on their behalf - see `emitters.ts`. The emitter functions
|
||||
// * are the outbound communication to the server.
|
||||
// *
|
||||
// * Listeners are also established here - see `listeners.ts`. The listener
|
||||
// * functions receive communication from the server and usually dispatch
|
||||
// * some new action to handle whatever data was sent from the server.
|
||||
// */
|
||||
// export const socketioMiddleware = () => {
|
||||
// const { origin } = new URL(window.location.href);
|
||||
|
||||
// const socketio = io(origin, {
|
||||
// timeout: 60000,
|
||||
// path: `${window.location.pathname}socket.io`,
|
||||
// });
|
||||
|
||||
// socketio.disconnect();
|
||||
|
||||
// let areListenersSet = false;
|
||||
|
||||
// const middleware: Middleware = (store) => (next) => (action) => {
|
||||
// const {
|
||||
// onConnect,
|
||||
// onDisconnect,
|
||||
// onError,
|
||||
// onPostprocessingResult,
|
||||
// onGenerationResult,
|
||||
// onIntermediateResult,
|
||||
// onProgressUpdate,
|
||||
// onGalleryImages,
|
||||
// onProcessingCanceled,
|
||||
// onImageDeleted,
|
||||
// onSystemConfig,
|
||||
// onModelChanged,
|
||||
// onFoundModels,
|
||||
// onNewModelAdded,
|
||||
// onModelDeleted,
|
||||
// onModelConverted,
|
||||
// onModelsMerged,
|
||||
// onModelChangeFailed,
|
||||
// onTempFolderEmptied,
|
||||
// } = makeSocketIOListeners(store);
|
||||
|
||||
// const {
|
||||
// emitGenerateImage,
|
||||
// emitRunESRGAN,
|
||||
// emitRunFacetool,
|
||||
// emitDeleteImage,
|
||||
// emitRequestImages,
|
||||
// emitRequestNewImages,
|
||||
// emitCancelProcessing,
|
||||
// emitRequestSystemConfig,
|
||||
// emitSearchForModels,
|
||||
// emitAddNewModel,
|
||||
// emitDeleteModel,
|
||||
// emitConvertToDiffusers,
|
||||
// emitMergeDiffusersModels,
|
||||
// emitRequestModelChange,
|
||||
// emitSaveStagingAreaImageToGallery,
|
||||
// emitRequestEmptyTempFolder,
|
||||
// } = makeSocketIOEmitters(store, socketio);
|
||||
|
||||
// /**
|
||||
// * If this is the first time the middleware has been called (e.g. during store setup),
|
||||
// * initialize all our socket.io listeners.
|
||||
// */
|
||||
// if (!areListenersSet) {
|
||||
// socketio.on('connect', () => onConnect());
|
||||
|
||||
// socketio.on('disconnect', () => onDisconnect());
|
||||
|
||||
// socketio.on('error', (data: InvokeAI.ErrorResponse) => onError(data));
|
||||
|
||||
// socketio.on('generationResult', (data: InvokeAI.ImageResultResponse) =>
|
||||
// onGenerationResult(data)
|
||||
// );
|
||||
|
||||
// socketio.on(
|
||||
// 'postprocessingResult',
|
||||
// (data: InvokeAI.ImageResultResponse) => onPostprocessingResult(data)
|
||||
// );
|
||||
|
||||
// socketio.on('intermediateResult', (data: InvokeAI.ImageResultResponse) =>
|
||||
// onIntermediateResult(data)
|
||||
// );
|
||||
|
||||
// socketio.on('progressUpdate', (data: InvokeAI.SystemStatus) =>
|
||||
// onProgressUpdate(data)
|
||||
// );
|
||||
|
||||
// socketio.on('galleryImages', (data: InvokeAI.GalleryImagesResponse) =>
|
||||
// onGalleryImages(data)
|
||||
// );
|
||||
|
||||
// socketio.on('processingCanceled', () => {
|
||||
// onProcessingCanceled();
|
||||
// });
|
||||
|
||||
// socketio.on('imageDeleted', (data: InvokeAI.ImageDeletedResponse) => {
|
||||
// onImageDeleted(data);
|
||||
// });
|
||||
|
||||
// socketio.on('systemConfig', (data: InvokeAI.SystemConfig) => {
|
||||
// onSystemConfig(data);
|
||||
// });
|
||||
|
||||
// socketio.on('foundModels', (data: InvokeAI.FoundModelResponse) => {
|
||||
// onFoundModels(data);
|
||||
// });
|
||||
|
||||
// socketio.on('newModelAdded', (data: InvokeAI.ModelAddedResponse) => {
|
||||
// onNewModelAdded(data);
|
||||
// });
|
||||
|
||||
// socketio.on('modelDeleted', (data: InvokeAI.ModelDeletedResponse) => {
|
||||
// onModelDeleted(data);
|
||||
// });
|
||||
|
||||
// socketio.on('modelConverted', (data: InvokeAI.ModelConvertedResponse) => {
|
||||
// onModelConverted(data);
|
||||
// });
|
||||
|
||||
// socketio.on('modelsMerged', (data: InvokeAI.ModelsMergedResponse) => {
|
||||
// onModelsMerged(data);
|
||||
// });
|
||||
|
||||
// socketio.on('modelChanged', (data: InvokeAI.ModelChangeResponse) => {
|
||||
// onModelChanged(data);
|
||||
// });
|
||||
|
||||
// socketio.on('modelChangeFailed', (data: InvokeAI.ModelChangeResponse) => {
|
||||
// onModelChangeFailed(data);
|
||||
// });
|
||||
|
||||
// socketio.on('tempFolderEmptied', () => {
|
||||
// onTempFolderEmptied();
|
||||
// });
|
||||
|
||||
// areListenersSet = true;
|
||||
// }
|
||||
|
||||
// /**
|
||||
// * Handle redux actions caught by middleware.
|
||||
// */
|
||||
// switch (action.type) {
|
||||
// case 'socketio/generateImage': {
|
||||
// emitGenerateImage(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/runESRGAN': {
|
||||
// emitRunESRGAN(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/runFacetool': {
|
||||
// emitRunFacetool(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/deleteImage': {
|
||||
// emitDeleteImage(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/requestImages': {
|
||||
// emitRequestImages(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/requestNewImages': {
|
||||
// emitRequestNewImages(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/cancelProcessing': {
|
||||
// emitCancelProcessing();
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/requestSystemConfig': {
|
||||
// emitRequestSystemConfig();
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/searchForModels': {
|
||||
// emitSearchForModels(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/addNewModel': {
|
||||
// emitAddNewModel(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/deleteModel': {
|
||||
// emitDeleteModel(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/convertToDiffusers': {
|
||||
// emitConvertToDiffusers(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/mergeDiffusersModels': {
|
||||
// emitMergeDiffusersModels(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/requestModelChange': {
|
||||
// emitRequestModelChange(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/saveStagingAreaImageToGallery': {
|
||||
// emitSaveStagingAreaImageToGallery(action.payload);
|
||||
// break;
|
||||
// }
|
||||
|
||||
// case 'socketio/requestEmptyTempFolder': {
|
||||
// emitRequestEmptyTempFolder();
|
||||
// break;
|
||||
// }
|
||||
// }
|
||||
|
||||
// next(action);
|
||||
// };
|
||||
|
||||
// return middleware;
|
||||
// };
|
||||
|
||||
export default {};
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user