feat(nodes): add ResultsServiceABC & SqliteResultsService
**Doesn't actually work bc of circular imports. Can't even test it.**
- add a base class for ResultsService and SQLite implementation
- use `graph_execution_manager` `on_changed` callback to keep `results` table in sync
fix(nodes): fix results service bugs
chore(ui): regen api
fix(ui): fix type guards
feat(nodes): add `result_type` to results table, fix types
fix(nodes): do not shadow `list` builtin
feat(nodes): add results router
It doesn't work due to circular imports still
fix(nodes): Result class should use outputs classes, not fields
feat(ui): crude results router
fix(ui): send to canvas in currentimagebuttons not working
feat(nodes): add core metadata builder
feat(nodes): add design doc
feat(nodes): wip latents db stuff
feat(nodes): images_db_service and resources router
feat(nodes): wip images db & router
feat(nodes): update image related names
feat(nodes): update urlservice
feat(nodes): add high-level images service
The problem was the same seed was getting used for the seam painting pass, causing the fried look.
Same issue as if you do img2img on a txt2img with the same seed/prompt.
Thanks to @hipsterusername for teaming up to debug this. We got pretty deep into the weeds.
* added optional middleware prop and new actions needed
* accidental import
* make middleware an array
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
# Application-wide configuration service
This PR creates a new `InvokeAIAppConfig` object that reads
application-wide settings from an init file, the environment, and the
command line.
Arguments and fields are taken from the pydantic definition of the
model. Defaults can be set by creating a yaml configuration file that
has a top-level key of "InvokeAI" and subheadings for each of the
categories returned by `invokeai --help`.
The file looks like this:
[file: invokeai.yaml]
```
InvokeAI:
Paths:
root: /home/lstein/invokeai-main
conf_path: configs/models.yaml
legacy_conf_dir: configs/stable-diffusion
outdir: outputs
embedding_dir: embeddings
lora_dir: loras
autoconvert_dir: null
gfpgan_model_dir: models/gfpgan/GFPGANv1.4.pth
Models:
model: stable-diffusion-1.5
embeddings: true
Memory/Performance:
xformers_enabled: false
sequential_guidance: false
precision: float16
max_loaded_models: 4
always_use_cpu: false
free_gpu_mem: false
Features:
nsfw_checker: true
restore: true
esrgan: true
patchmatch: true
internet_available: true
log_tokenization: false
Cross-Origin Resource Sharing:
allow_origins: []
allow_credentials: true
allow_methods:
- '*'
allow_headers:
- '*'
Web Server:
host: 127.0.0.1
port: 8081
```
The default name of the configuration file is `invokeai.yaml`, located
in INVOKEAI_ROOT. You can use any OmegaConf dictionary by passing it to
the config object at initialization time:
```
omegaconf = OmegaConf.load('/tmp/init.yaml')
conf = InvokeAIAppConfig(conf=omegaconf)
```
The default name of the configuration file is `invokeai.yaml`, located
in INVOKEAI_ROOT. You can replace supersede this by providing
anyOmegaConf dictionary object initialization time:
```
omegaconf = OmegaConf.load('/tmp/init.yaml')
conf = InvokeAIAppConfig(conf=omegaconf)
```
By default, InvokeAIAppConfig will parse the contents of `sys.argv` at
initialization time. You may pass a list of strings in the optional
`argv` argument to use instead of the system argv:
```
conf = InvokeAIAppConfig(arg=['--xformers_enabled'])
```
It is also possible to set a value at initialization time. This value
has highest priority.
```
conf = InvokeAIAppConfig(xformers_enabled=True)
```
Any setting can be overwritten by setting an environment variable of
form: "INVOKEAI_<setting>", as in:
```
export INVOKEAI_port=8080
```
Order of precedence (from highest):
1) initialization options
2) command line options
3) environment variable options
4) config file options
5) pydantic defaults
Typical usage:
```
from invokeai.app.services.config import InvokeAIAppConfig
# get global configuration and print its nsfw_checker value
conf = InvokeAIAppConfig()
print(conf.nsfw_checker)
```
Finally, the configuration object is able to recreate its (modified)
yaml file, by calling its `to_yaml()` method:
```
conf = InvokeAIAppConfig(outdir='/tmp', port=8080)
print(conf.to_yaml())
```
# Legacy code removal and porting
This PR replaces Globals with the InvokeAIAppConfig system throughout,
and therefore removes the `globals.py` and `args.py` modules. It also
removes `generate` and the legacy CLI. ***The old CLI and web servers
are now gone.***
I have ported the functionality of the configuration script, the model
installer, and the merge and textual inversion scripts. The `invokeai`
command will now launch `invokeai-node-cli`, and `invokeai-web` will
launch the web server.
I have changed the continuous invocation tests to accommodate the new
command syntax in `invokeai-node-cli`. As a convenience function, you
can also pass invocations to `invokeai-node-cli` (or its alias
`invokeai`) on the command line as as standard input:
```
invokeai-node-cli "t2i --positive_prompt 'banana sushi' --seed 42"
invokeai < invocation_commands.txt
```
- Make environment variable settings case InSenSiTive:
INVOKEAI_MAX_LOADED_MODELS and InvokeAI_Max_Loaded_Models
environment variables will both set `max_loaded_models`
- Updated realesrgan to use new config system.
- Updated textual_inversion_training to use new config system.
- Discovered a race condition when InvokeAIAppConfig is created
at module load time, which makes it impossible to customize
or replace the help message produced with --help on the command
line. To fix this, moved all instances of get_invokeai_config()
from module load time to object initialization time. Makes code
cleaner, too.
- Added `--from_file` argument to `invokeai-node-cli` and changed
github action to match. CI tests will hopefully work now.
- invokeai-configure updated to work with new config system
- migrate invokeai.init to invokeai.yaml during configure
- replace legacy invokeai with invokeai-node-cli
- add ability to run an invocation directly from invokeai-node-cli command line
- update CI tests to work with new invokeai syntax
* refetch images list if error loading
* tell user to refresh instead of refetching
* unused import
* feat(ui): use `useAppToaster` to make toast
* fix(ui): clear selected/initial image on error
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
