## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [x] Yes
- [ ] No, because:
## Description
[fix(nodes,ui): optional
metadata](78b8cfede3)
- Make all metadata items optional. This will reduce errors related to
metadata not being provided when we update the backend but old queue
items still exist
- Fix a bug in t2i adapter metadata handling where it checked for ip
adapter metadata instaed of t2i adapter metadata
- Fix some metadata fields that were not using `InputField`
- Make all metadata items optional. This will reduce errors related to metadata not being provided when we update the backend but old queue items still exist
- Fix a bug in t2i adapter metadata handling where it checked for ip adapter metadata instaed of t2i adapter metadata
- Fix some metadata fields that were not using `InputField`
Currently translated at 91.4% (1112 of 1216 strings)
translationBot(ui): update translation (Italian)
Currently translated at 90.4% (1100 of 1216 strings)
translationBot(ui): update translation (Italian)
Currently translated at 90.4% (1100 of 1216 strings)
Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
`mallinfo2` is not available on `glibc` < 2.33.
On these systems, we successfully load the library but get an `AttributeError` on attempting to access `mallinfo2`.
I'm not sure if the old `mallinfo` will work, and not sure how to install it safely to test, so for now we just handle the `AttributeError`.
This means the enhanced memory snapshot logic will be skipped for these systems, which isn't a big deal.
## What type of PR is this? (check all applicable)
- [X] Optimization
-
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
This PR changes the pypi-release workflow so that it will upload to PyPi
whenever a release is initiated from the `main` branch or another branch
beginning with `release/`. Previous support for v2.3 branches has been
removed.
* added HrfScale type with initial value
* working
* working
* working
* working
* working
* added addHrfToGraph
* continueing to implement this
* working on this
* comments
* working
* made hrf into its own collapse
* working on adding strength slider
* working
* working
* refactoring
* working
* change of this working: 0
* removed onnx support since apparently its not used
* working
* made scale integer
* trying out psycicpebbles idea
* working
* working on this
* working
* added toggle
* comments
* self review
* fixing things
* remove 'any' type
* fixing typing
* changed initial strength value to 3 (large values cause issues)
* set denoising start to be 1 - strength to resemble image to image
* set initial value
* added image to image
* pr1
* pr2
* updating to resolution finding
* working
* working
* working
* working
* working
* working
* working
* working
* working
* use memo
* connect rescale hw to noise
* working
* fixed min bug
* nit
* hides elements conditionally
* style
* feat(ui): add config for HRF, disable if feature disabled or ONNX model in use
* fix(ui): use `useCallback` for HRF toggle
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
* #4665 hides value of the corresponding metadata item by click on arrow
* #4787 return recall button back:)
* #4787 optional hide of metadata item, truncation and scrolling
* remove unused import
* #4787 recall parameters as separate tab in panel
* #4787 remove debug code
* fix(ui): undo changes to dist/locales/en.json
This file is autogenerated by our translation system and shouldn't be modified directly
* feat(ui): use scrollbar-enabled component for parameter recall tab
* fix(ui): revert unnecessary changes to DataViewer component
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
v3.3.0 was accidentally released with more changes than intended. This workflows change will allow us release to pypi from a separate branch rather than main.
## What type of PR is this? (check all applicable)
v3.3.0 release
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
The `invokeai-configure` TUI's slider for the RAM cache was not picking
up the current settings in `invokeai.yaml`, leading users to think their
change hadn't taken effect. This is fixed in this PR.
## Related Tickets & Documents
First described here:
https://discord.com/channels/1020123559063990373/1161919551441735711/1162058518417907743
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [X] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
A regression in 3.2.0 causes a seemingly nonsensical multiple choice
menu to appear when importing an SD-1 checkpoint model from the
autoimport directory. The menu asks the user to identify which type of
SD-2 model they are trying to import, which makes no sense.
In fact, the menu is popping up because there are now both "epsilon" and
"vprediction" SchedulerPredictionTypes for SD-1 as well as SD-2 models,
and the prober can't determine which prediction type to use. This PR
does two things:
1) rewords the menu as shown below
2) defaults to the most likely choice -- epsilon for v1 models and
vprediction for v2s
Here is the revised multiple-choice menu:
```
Please select the scheduler prediction type of the checkpoint named v1-5-pruned-emaonly.safetensors:
[1] "epsilon" - most v1.5 models and v2 models trained on 512 pixel images
[2] "vprediction" - v2 models trained on 768 pixel images and a few v1.5 models
[3] Accept the best guess; you can fix it in the Web UI later
select [3]>
```
Note that one can also put the appropriate config file into the same
directory as the checkpoint you wish to import. Give it the same name as
the model file, but with the extension `.yaml`. For example
`v1-5-pruned-emaonly.yaml`. The system will notice the yaml file and use
that, suppressing the quiz entirely.
## Related Tickets & Documents
- Closes#4768
- Closes#4827
Refactor services folder/module structure.
**Motivation**
While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward.
**Services**
Services are now in their own folder with a few files:
- `services/{service_name}/__init__.py`: init as needed, mostly empty now
- `services/{service_name}/{service_name}_base.py`: the base class for the service
- `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory`
- `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc
Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename.
There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`.
**Shared**
Things that are used across disparate services are in `services/shared/`:
- `default_graphs.py`: previously in `services/`
- `graphs.py`: previously in `services/`
- `paginatation`: generic pagination models used in a few services
- `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
