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Author SHA1 Message Date
09609cd553 feat(nodes): WIP restricted invocation context 2023-10-16 21:18:13 +11:00
0aedd6d9f0 feat(api): chore: pydantic & fastapi upgrade
Upgrade pydantic and fastapi to latest.

- pydantic~=2.4.2
- fastapi~=103.2
- fastapi-events~=0.9.1

**Big Changes**

There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes.

**Invocations**

The biggest change relates to invocation creation, instantiation and validation.

Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie.

Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`.

With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation.

This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method.

In the end, this implementation is cleaner.

**Invocation Fields**

In pydantic v2, you can no longer directly add or remove fields from a model.

Previously, we did this to add the `type` field to invocations.

**Invocation Decorators**

With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper.

A similar technique is used for `invocation_output()`.

**Minor Changes**

There are a number of minor changes around the pydantic v2 models API.

**Protected `model_` Namespace**

All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_".

Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple.

```py
class IPAdapterModelField(BaseModel):
    model_name: str = Field(description="Name of the IP-Adapter model")
    base_model: BaseModelType = Field(description="Base model")

    model_config = ConfigDict(protected_namespaces=())
```

**Model Serialization**

Pydantic models no longer have `Model.dict()` or `Model.json()`.

Instead, we use `Model.model_dump()` or `Model.model_dump_json()`.

**Model Deserialization**

Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions.

Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model.

```py
adapter_graph = TypeAdapter(Graph)
deserialized_graph_from_json = adapter_graph.validate_json(graph_json)
deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict)
```

**Field Customisation**

Pydantic `Field`s no longer accept arbitrary args.

Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field.

**Schema Customisation**

FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec.

This necessitates two changes:
- Our schema customization logic has been revised
- Schema parsing to build node templates has been revised

The specific aren't important, but this does present additional surface area for bugs.

**Performance Improvements**

Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node.

I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
2023-10-16 17:04:54 +11:00
70a1202deb fix(api): fix socketio breaking change (#4901)
## 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 for breaking change in `python-socketio` 5.10.0 in which
`enter_room` and `leave_room` were made coroutines.

## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
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- Closes #4899
2023-10-16 07:29:31 +05:30
9a1aea9caf fix(api): fix socketio breaking change
Fix for breaking change in `python-socketio` 5.10.0 in which `enter_room` and `leave_room` were made coroutines.
2023-10-16 12:18:46 +11:00
388d36b839 fix(db): use RLock instead of Lock
Fixes issues where a db-accessing service wants to call db-accessing methods with locks.
2023-10-16 11:45:24 +11:00
bedb35af8c translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 100.0% (1217 of 1217 strings)

Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-16 07:57:41 +11:00
dc232438fb translationBot(ui): update translation (Italian)
Currently translated at 97.5% (1187 of 1217 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
2023-10-16 07:57:41 +11:00
d7edf5aaad fix(ui): fix control adapter translation string (#4888)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission

## Description

fix(ui): fix control adapter translation string

Missed this during a previous change

## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
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Reported by @Harvester62 :

https://discord.com/channels/1020123559063990373/1054129386447716433/1162018775437148160
2023-10-15 18:19:41 +05:30
3ad1226d1e Merge branch 'main' into fix/ui/control-adapter-translation-string 2023-10-15 18:16:48 +05:30
86ca9f122d Strip whitespace from model URLs (#4863)
## 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

This PR strips leading and trailing whitespace from URLs that are
entered into either the Web Model Manager import field, or using the
TUI.

## Related Tickets & Documents

Closes #4536


## QA Instructions, Screenshots, Recordings

Try to import a URL with leading or trailing whitespace. Should not work
in current main. This PR should fix it.
2023-10-15 17:53:20 +05:30
2c6772f92f Merge branch 'main' into bugfix/trim-whitespace-from-urls 2023-10-15 17:41:41 +05:30
e6c1e03b8b Bugfix/ignore dot directories on model scan (#4865)
## 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

Mac users have a recurring issue in which a `.DS_Store` directory is
created in their `models` hierarchy, causing the new model scanner to
freak out. This PR skips over any paths that begin with a dot. I haven't
tested it on a Macintosh, so I'm not 100% certain it will do the trick.

## Related Tickets & Documents

- Related Issue #4815 

## QA Instructions, Screenshots, Recordings

Someone with a Mac please try to reproduce the `.DS_Store` crash and
then see if applying this PR addresses the issue.
2023-10-15 17:33:11 +05:30
c9d95e5758 Merge branch 'main' into bugfix/ignore-dot-directories-on-model-scan 2023-10-15 17:23:02 +05:30
10755718b8 fix(ui): reset canvas batchIds on clear/batch cancel (#4890)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission

## Description

This was in the original fix in #4829 but I must have removed it
accidentally.

## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
automatically close the issue.
-->

- Related Issue #
- Closes #4889

## QA Instructions, Screenshots, Recordings

- Start from a fresh canvas session (may need to let a generation finish
or reset web UI if yours is locked)
- Invoke/add to queue
- Immediately cancel current, clear queue, or clear batch (can do this
from the queue tab)
- Canvas should return to normal state

<!-- 
Please provide steps on how to test changes, any hardware or 
software specifications as well as any other pertinent information. 
-->
2023-10-15 17:10:38 +05:30
459c7b3b74 Merge branch 'main' into fix/ui/reset-canvas-batch-on-clear 2023-10-15 17:05:21 +05:30
353719f81d chore(ui): update deps (#4892)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Description

Update all dependencies

Resolves https://github.com/invoke-ai/InvokeAI/security/dependabot/26
2023-10-15 17:05:04 +05:30
bd4b260c23 Merge branch 'main' into fix/ui/reset-canvas-batch-on-clear 2023-10-15 17:03:08 +05:30
3e389d3f60 chore(ui): update deps 2023-10-15 19:30:39 +11:00
ffb01f1345 Update facetools.py
Facetools nodes were cutting off faces that extended beyond chunk boundaries in some cases. All faces found are considered and are coalesced rather than pruned, meaning that you should not see half a face any more.
2023-10-15 19:12:10 +11:00
faa0a8236c Merge branch 'main' into fix/ui/reset-canvas-batch-on-clear 2023-10-15 18:46:46 +11:00
e4d73d3659 Merge branch 'main' into fix/ui/control-adapter-translation-string 2023-10-15 18:46:40 +11:00
6994783c17 translationBot(ui): update translation (Italian)
Currently translated at 91.9% (1119 of 1217 strings)

Co-authored-by: psychedelicious <mabianfu@icloud.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI
2023-10-15 18:42:58 +11:00
3f9708f166 translationBot(ui): update translation (Italian)
Currently translated at 91.9% (1119 of 1217 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
2023-10-15 18:42:58 +11:00
bcf0d8a590 fix(ui): use _other for control adapter collapse 2023-10-15 18:34:25 +11:00
2060ee22f2 fix(ui): reset canvas batchIds on clear/batch cancel
Closes #4889
2023-10-15 18:28:05 +11:00
3fd79b837f fix(ui): fix control adapter translation string 2023-10-15 18:16:10 +11:00
1c099e0abb feat(ui): add tooltip to clear intermediates button when disabled 2023-10-15 17:29:49 +11:00
95cca9493c feat(ui): disable clear intermediates button when queue has items 2023-10-15 17:29:49 +11:00
779c902402 chore(ui): lint 2023-10-15 17:29:49 +11:00
99e6bb48ba fixed problems 2023-10-15 17:29:49 +11:00
c3d6ff5b11 fixed bug #4857 2023-10-15 17:29:49 +11:00
bba962b82f fix(nodes,ui): optional metadata (#4884)
## 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`
2023-10-15 05:42:42 +05:30
78b8cfede3 fix(nodes,ui): optional metadata
- 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`
2023-10-15 10:44:16 +11:00
e9879b9e1f Clean up communityNodes.md (#4870)
* Clean up communityNodes.md

* Update communityNodes.md
2023-10-14 22:01:20 +00:00
e21f3af5ab translationBot(ui): update translation files
Updated by "Remove blank strings" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-15 08:12:17 +11:00
2ab7c5f783 translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 100.0% (1216 of 1216 strings)

Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-15 08:12:17 +11:00
8bbd938be9 translationBot(ui): update translation (Dutch)
Currently translated at 100.0% (1216 of 1216 strings)

Co-authored-by: Dennis <dennis@vanzoerlandt.nl>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-15 08:12:17 +11:00
b4cee46936 translationBot(ui): update translation (Italian)
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
2023-10-15 08:12:17 +11:00
48626c40fd fix(backend): handle systems with glibc < 2.33
`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.
2023-10-15 07:56:55 +11:00
a1001b6d10 Merge branch 'main' into bugfix/ignore-dot-directories-on-model-scan 2023-10-14 10:37:55 -04:00
50df641e1b Upload to pypi whenever a branch starting with "release/" is released (#4875)
## 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.
2023-10-14 10:24:01 -04:00
22dd64dfa4 Merge branch 'main' into chore/update-pypi-from-release-branches 2023-10-14 10:21:33 -04:00
0a929ca3de Fix/UI/sync translations (#4880)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission

## Description

Weblate has some merge conflicts, attempting to resolve them...
2023-10-14 18:38:17 +05:30
8c61cda4b8 Merge branch 'main' into fix/ui/sync-translations 2023-10-14 18:35:48 +05:30
75663ec81e feat (ui, generation): High Resolution Fix MVP in Text2Image Linear Flow (#4819)
* 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>
2023-10-14 10:34:41 +00:00
40a568c060 Hide Metadata in Info View (#4787)
* #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>
2023-10-14 21:25:07 +11:00
8e7aa74a16 Merge remote-tracking branch 'weblate/main' 2023-10-14 20:35:21 +11:00
fcba4382b2 upload to pypi whenever a branch starting with "release/" is released 2023-10-13 12:49:24 -04:00
bf9f7271dd add ref to pypi-release workflow to fix release with unintentional changes
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.
2023-10-13 18:58:36 +11:00
d3821594df Release/v3.3.0 (#4868)
## 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
2023-10-13 17:45:34 +11:00
631ad1596f Updated JS files 2023-10-13 17:27:41 +11:00
dfe32e467d Update version to 3.3.0 2023-10-13 17:27:41 +11:00
3575cf3b3b Enable the ram cache slider in invokeai-configure (#4866)
## 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
2023-10-13 16:08:03 +11:00
15cabc4968 Possibly closes #4815 2023-10-12 23:37:05 -04:00
29c3f49182 enable the ram cache slider in invokeai-configure 2023-10-12 23:04:16 -04:00
21d5969942 strip leading and trailing quotes as well as whitespace 2023-10-12 22:35:02 -04:00
334dcf71c4 Merge branch 'main' into bugfix/trim-whitespace-from-urls 2023-10-12 22:30:44 -04:00
d2149a8380 Fix gratuitous, parasitic, endlessly repeated, pointless menu in version 3.2.0 (#4864)
## 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
2023-10-12 22:27:28 -04:00
6532d9ffa1 closes #4768 2023-10-12 22:04:54 -04:00
52274087f3 close #4536 2023-10-12 21:24:07 -04:00
89db8c83c2 Add a comment to warn about a necessary action before bumping the diffusers version. 2023-10-12 14:48:10 -04:00
fc09ab7e13 chore: typegen 2023-10-12 12:15:06 -04:00
9646157ad5 fix: fix test imports 2023-10-12 12:15:06 -04:00
b89ec2b9c3 chore(ui): regen types 2023-10-12 12:15:06 -04:00
d2fb29cf0d fix(app): remove errant logger line 2023-10-12 12:15:06 -04:00
d1fce4b70b chore: rebase conflicts 2023-10-12 12:15:06 -04:00
f50f95a81d fix: merge conflicts 2023-10-12 12:15:06 -04:00
3611029057 fix(backend): remove logic to create workflows column
Snuck in there while I was organising
2023-10-12 12:15:06 -04:00
402cf9b0ee feat: refactor services folder/module structure
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
2023-10-12 12:15:06 -04:00
88bee96ca3 feat(backend): rename db.py to sqlite.py 2023-10-12 12:15:06 -04:00
5048fc7c9e feat(backend): move pagination models to own file 2023-10-12 12:15:06 -04:00
2a35d93a4d feat(backend): organise service dependencies
**Service Dependencies**

Services that depend on other services now access those services via the `Invoker` object. This object is provided to the service as a kwarg to its `start()` method.

Until now, most services did not utilize this feature, and several services required their dependencies to be initialized and passed in on init.

Additionally, _all_ services are now registered as invocation services - including the low-level services. This obviates issues with inter-dependent services we would otherwise experience as we add workflow storage.

**Database Access**

Previously, we were passing in a separate sqlite connection and corresponding lock as args to services in their init. A good amount of posturing was done in each service that uses the db.

These objects, along with the sqlite startup and cleanup logic, is now abstracted into a simple `SqliteDatabase` class. This creates the shared connection and lock objects, enables foreign keys, and provides a `clean()` method to do startup db maintenance.

This is not a service as it's only used by sqlite services.
2023-10-12 12:15:06 -04:00
10fac5c085 feat(ui): set w/h to multiple of 64 on add t2i 2023-10-12 23:51:01 +11:00
58850ded22 translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 98.0% (1186 of 1210 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 98.0% (1179 of 1203 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 97.9% (1175 of 1199 strings)

Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
f21ebdfaca translationBot(ui): update translation files
Updated by "Remove blank strings" hook in Weblate.

translationBot(ui): update translation files

Updated by "Remove blank strings" hook in Weblate.

translationBot(ui): update translation files

Updated by "Remove blank strings" hook in Weblate.

translationBot(ui): update translation files

Updated by "Remove blank strings" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
c4f1e94cc4 translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 92.0% (1104 of 1199 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 92.1% (1105 of 1199 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 83.2% (998 of 1199 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 83.0% (996 of 1199 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 67.5% (810 of 1199 strings)

Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
dbbcce9f70 translationBot(ui): update translation files
Updated by "Remove blank strings" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
cc52896bd9 translationBot(ui): update translation (Italian)
Currently translated at 85.5% (1026 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 84.7% (1016 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 84.7% (1016 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 84.4% (1012 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 84.3% (1011 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 83.5% (1002 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.5% (978 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 80.8% (969 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 80.7% (968 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.3% (959 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.3% (959 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.3% (959 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.3% (959 of 1179 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
2023-10-12 23:45:46 +11:00
d12314fb8b translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
07b88e3017 translationBot(ui): update translation (Dutch)
Currently translated at 100.0% (605 of 605 strings)

Co-authored-by: Dennis <dennis@vanzoerlandt.nl>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
0b85f2487c translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (607 of 607 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (605 of 605 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
5530d3fcd2 translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 95.7% (579 of 605 strings)

Co-authored-by: nemuruibai <nemuruibai@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
7b1b24900f translationBot(ui): update translation (Russian)
Currently translated at 65.5% (643 of 981 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (605 of 605 strings)

Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
f52fb45276 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
fb9f0339a2 translationBot(ui): update translation (Italian)
Currently translated at 81.2% (958 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.2% (958 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 76.6% (904 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 76.5% (903 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 71.9% (848 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 71.7% (845 of 1177 strings)

translationBot(ui): update translation (Italian)

Currently translated at 71.7% (845 of 1177 strings)

translationBot(ui): update translation (Italian)

Currently translated at 67.8% (799 of 1177 strings)

translationBot(ui): update translation (Italian)

Currently translated at 58.5% (689 of 1177 strings)

translationBot(ui): update translation (Italian)

Currently translated at 59.8% (640 of 1069 strings)

translationBot(ui): update translation (Italian)

Currently translated at 57.2% (612 of 1069 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (607 of 607 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (605 of 605 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (605 of 605 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (602 of 602 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
2023-10-12 23:45:46 +11:00
ac501ee742 translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 96.1% (579 of 602 strings)

Co-authored-by: nemuruibai <nemuruibai@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
2182ccf8d1 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
fc674ff1b8 translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (605 of 605 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
708ac6a511 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
d0e0b64fc8 translationBot(ui): update translation (Dutch)
Currently translated at 99.6% (591 of 593 strings)

Co-authored-by: Arnold Cordewiner <weblate@a14r.be>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
a23580664d translationBot(ui): update translation (Italian)
Currently translated at 97.8% (589 of 602 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (603 of 603 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (599 of 599 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (596 of 596 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (595 of 595 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (595 of 595 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (593 of 593 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (592 of 592 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
2023-10-12 23:45:46 +11:00
0edf01d927 translationBot(ui): update translation (Spanish)
Currently translated at 99.6% (601 of 603 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 99.5% (600 of 603 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (599 of 599 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (596 of 596 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 99.8% (594 of 595 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (593 of 593 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (592 of 592 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
4af5b9cbf7 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
1bf973d46e translationBot(ui): update translation (Polish)
Currently translated at 58.4% (338 of 578 strings)

Co-authored-by: Simona Liliac <simonaliliac@yandex.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/pl/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
72252e3ff7 translationBot(ui): update translation (Dutch)
Currently translated at 100.0% (563 of 563 strings)

translationBot(ui): update translation (Dutch)

Currently translated at 100.0% (563 of 563 strings)

Co-authored-by: Dennis <dennis@vanzoerlandt.nl>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
8d2596c288 translationBot(ui): update translation (Italian)
Currently translated at 100.0% (591 of 591 strings)

translationBot(ui): update translation (Italian)

Currently translated at 99.3% (587 of 591 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (586 of 586 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (578 of 578 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (563 of 563 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (559 of 559 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (559 of 559 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (551 of 551 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
2023-10-12 23:45:46 +11:00
0ffb7ecaa8 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
10f30fc599 translationBot(ui): update translation (Russian)
Currently translated at 99.5% (602 of 605 strings)

translationBot(ui): update translation (Russian)

Currently translated at 99.8% (605 of 606 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (596 of 596 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (595 of 595 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (593 of 593 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (592 of 592 strings)

translationBot(ui): update translation (Russian)

Currently translated at 90.2% (534 of 592 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (543 of 543 strings)

Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
136570aa1d translationBot(ui): update translation (Italian)
Currently translated at 100.0% (550 of 550 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (548 of 548 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (546 of 546 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (541 of 541 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (544 of 544 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (543 of 543 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
2023-10-12 23:45:46 +11:00
5a30b507e0 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
d47fbf283c translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 88.0% (477 of 542 strings)

Co-authored-by: Song, Pengcheng <17528592@qq.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
7c24312d3f translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
905cd8c639 translationBot(ui): update translation (Dutch)
Currently translated at 100.0% (538 of 538 strings)

Co-authored-by: Dennis <dennis@vanzoerlandt.nl>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
b13ba55c26 translationBot(ui): update translation (Chinese (Traditional))
Currently translated at 8.9% (48 of 536 strings)

Co-authored-by: nekowaiz <nekowaiz@hotmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hant/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
82747e2260 translationBot(ui): update translation (Russian)
Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Russian)

Currently translated at 98.8% (536 of 542 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (533 of 533 strings)

Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
910553f49a translationBot(ui): update translation (Italian)
Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (540 of 540 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (538 of 538 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Italian)

Currently translated at 99.8% (535 of 536 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (533 of 533 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (533 of 533 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
2023-10-12 23:45:46 +11:00
faabd83717 translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (591 of 591 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (586 of 586 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (578 of 578 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (563 of 563 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (550 of 550 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (550 of 550 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (548 of 548 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (546 of 546 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (544 of 544 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (543 of 543 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (540 of 540 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (533 of 533 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 99.8% (532 of 533 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
5ad77ece4b translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
6b3c413a5b translationBot(ui): update translation (Russian)
Currently translated at 100.0% (526 of 526 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (519 of 519 strings)

Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
2a923d1f69 translationBot(ui): update translation (French)
Currently translated at 80.7% (419 of 519 strings)

Co-authored-by: pand4z31 <pand4zdev31@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/fr/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
c54a5ce10e translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
14fbe41834 translationBot(ui): update translation (Italian)
Currently translated at 100.0% (526 of 526 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (523 of 523 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (519 of 519 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (515 of 515 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
2023-10-12 23:45:46 +11:00
64ebe042b5 translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (526 of 526 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (523 of 523 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (519 of 519 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (515 of 515 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 23:45:46 +11:00
5b2ed4ffb4 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:45:13 +00:00
a49b8febed translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 98.0% (1186 of 1210 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 98.0% (1179 of 1203 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 97.9% (1175 of 1199 strings)

Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 12:45:12 +00:00
e543db5a5d translationBot(ui): update translation files
Updated by "Remove blank strings" hook in Weblate.

translationBot(ui): update translation files

Updated by "Remove blank strings" hook in Weblate.

translationBot(ui): update translation files

Updated by "Remove blank strings" hook in Weblate.

translationBot(ui): update translation files

Updated by "Remove blank strings" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:45:10 +00:00
670f3aa165 translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 92.0% (1104 of 1199 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 92.1% (1105 of 1199 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 83.2% (998 of 1199 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 83.0% (996 of 1199 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 67.5% (810 of 1199 strings)

Co-authored-by: Surisen <zhonghx0804@outlook.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 12:45:09 +00:00
c0534d6519 translationBot(ui): update translation files
Updated by "Remove blank strings" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:45:07 +00:00
7bc6c23dfa translationBot(ui): update translation (Italian)
Currently translated at 87.1% (1054 of 1210 strings)

translationBot(ui): update translation (Italian)

Currently translated at 85.5% (1026 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 84.7% (1016 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 84.7% (1016 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 84.4% (1012 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 84.3% (1011 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 83.5% (1002 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.5% (978 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 80.8% (969 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 80.7% (968 of 1199 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.3% (959 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.3% (959 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.3% (959 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.3% (959 of 1179 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
2023-10-12 12:45:05 +00:00
851ce36250 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:45:04 +00:00
d631088566 translationBot(ui): update translation (Dutch)
Currently translated at 100.0% (605 of 605 strings)

Co-authored-by: Dennis <dennis@vanzoerlandt.nl>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-12 12:45:01 +00:00
f0bf733309 translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (607 of 607 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (605 of 605 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 12:45:00 +00:00
65af7dd8f8 translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 95.7% (579 of 605 strings)

Co-authored-by: nemuruibai <nemuruibai@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 12:44:59 +00:00
74c666aaa2 translationBot(ui): update translation (Russian)
Currently translated at 65.5% (643 of 981 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (605 of 605 strings)

Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
2023-10-12 12:44:58 +00:00
45f9aca7e5 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:44:56 +00:00
9fb624f390 translationBot(ui): update translation (Italian)
Currently translated at 81.2% (958 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 81.2% (958 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 76.6% (904 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 76.5% (903 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 71.9% (848 of 1179 strings)

translationBot(ui): update translation (Italian)

Currently translated at 71.7% (845 of 1177 strings)

translationBot(ui): update translation (Italian)

Currently translated at 71.7% (845 of 1177 strings)

translationBot(ui): update translation (Italian)

Currently translated at 67.8% (799 of 1177 strings)

translationBot(ui): update translation (Italian)

Currently translated at 58.5% (689 of 1177 strings)

translationBot(ui): update translation (Italian)

Currently translated at 59.8% (640 of 1069 strings)

translationBot(ui): update translation (Italian)

Currently translated at 57.2% (612 of 1069 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (607 of 607 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (605 of 605 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (605 of 605 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (602 of 602 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
2023-10-12 12:44:53 +00:00
962e51320b translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 96.1% (579 of 602 strings)

Co-authored-by: nemuruibai <nemuruibai@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 12:44:52 +00:00
44932923eb translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:44:50 +00:00
ffcf6dfde6 translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (605 of 605 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 12:44:46 +00:00
be52eb153c translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:44:44 +00:00
bd97c6b708 translationBot(ui): update translation (Dutch)
Currently translated at 99.6% (591 of 593 strings)

Co-authored-by: Arnold Cordewiner <weblate@a14r.be>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-12 12:44:41 +00:00
9940cbfa87 translationBot(ui): update translation (Italian)
Currently translated at 97.8% (589 of 602 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (603 of 603 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (599 of 599 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (596 of 596 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (595 of 595 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (595 of 595 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (593 of 593 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (592 of 592 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
2023-10-12 12:44:40 +00:00
77aeb9a421 translationBot(ui): update translation (Spanish)
Currently translated at 99.6% (601 of 603 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 99.5% (600 of 603 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (599 of 599 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (596 of 596 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 99.8% (594 of 595 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (593 of 593 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (592 of 592 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 12:44:38 +00:00
2bad8b9f29 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:44:36 +00:00
8e943b2ce1 translationBot(ui): update translation (Polish)
Currently translated at 58.4% (338 of 578 strings)

Co-authored-by: Simona Liliac <simonaliliac@yandex.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/pl/
Translation: InvokeAI/Web UI
2023-10-12 12:44:33 +00:00
5d3ab4f333 translationBot(ui): update translation (Dutch)
Currently translated at 100.0% (563 of 563 strings)

translationBot(ui): update translation (Dutch)

Currently translated at 100.0% (563 of 563 strings)

Co-authored-by: Dennis <dennis@vanzoerlandt.nl>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-12 12:44:32 +00:00
1047d08835 translationBot(ui): update translation (Italian)
Currently translated at 100.0% (591 of 591 strings)

translationBot(ui): update translation (Italian)

Currently translated at 99.3% (587 of 591 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (586 of 586 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (578 of 578 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (563 of 563 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (559 of 559 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (559 of 559 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (551 of 551 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
2023-10-12 12:44:30 +00:00
516cc258f9 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:44:28 +00:00
7c2aa1dc20 translationBot(ui): update translation (Russian)
Currently translated at 99.5% (602 of 605 strings)

translationBot(ui): update translation (Russian)

Currently translated at 99.8% (605 of 606 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (596 of 596 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (595 of 595 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (593 of 593 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (592 of 592 strings)

translationBot(ui): update translation (Russian)

Currently translated at 90.2% (534 of 592 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (543 of 543 strings)

Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
2023-10-12 12:44:25 +00:00
035f1e12e1 translationBot(ui): update translation (Italian)
Currently translated at 100.0% (550 of 550 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (548 of 548 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (546 of 546 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (541 of 541 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (544 of 544 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (543 of 543 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
2023-10-12 12:44:23 +00:00
4c93202ee4 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:44:20 +00:00
227046bdb0 translationBot(ui): update translation (Chinese (Simplified))
Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Chinese (Simplified))

Currently translated at 88.0% (477 of 542 strings)

Co-authored-by: Song, Pengcheng <17528592@qq.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hans/
Translation: InvokeAI/Web UI
2023-10-12 12:44:17 +00:00
83b123f1f6 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:44:15 +00:00
320ef15ee9 translationBot(ui): update translation (Dutch)
Currently translated at 100.0% (538 of 538 strings)

Co-authored-by: Dennis <dennis@vanzoerlandt.nl>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/nl/
Translation: InvokeAI/Web UI
2023-10-12 12:44:11 +00:00
6905c61912 translationBot(ui): update translation (Chinese (Traditional))
Currently translated at 8.9% (48 of 536 strings)

Co-authored-by: nekowaiz <nekowaiz@hotmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/zh_Hant/
Translation: InvokeAI/Web UI
2023-10-12 12:44:09 +00:00
494bde785e translationBot(ui): update translation (Russian)
Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Russian)

Currently translated at 98.8% (536 of 542 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (533 of 533 strings)

Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
2023-10-12 12:44:08 +00:00
732ab38ca6 translationBot(ui): update translation (Italian)
Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (540 of 540 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (538 of 538 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Italian)

Currently translated at 99.8% (535 of 536 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (533 of 533 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (533 of 533 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
2023-10-12 12:44:07 +00:00
ba38aa56a5 translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (591 of 591 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (586 of 586 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (578 of 578 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (563 of 563 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (550 of 550 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (550 of 550 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (548 of 548 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (546 of 546 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (544 of 544 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (543 of 543 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (542 of 542 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (540 of 540 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (536 of 536 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (533 of 533 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 99.8% (532 of 533 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 12:44:04 +00:00
0a48c5a712 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

translationBot(ui): update translation files

Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:44:01 +00:00
133ab91c8d translationBot(ui): update translation (Russian)
Currently translated at 100.0% (526 of 526 strings)

translationBot(ui): update translation (Russian)

Currently translated at 100.0% (519 of 519 strings)

Co-authored-by: System X - Files <vasyasos@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI
2023-10-12 12:43:56 +00:00
7a672bd2b2 translationBot(ui): update translation (French)
Currently translated at 80.7% (419 of 519 strings)

Co-authored-by: pand4z31 <pand4zdev31@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/fr/
Translation: InvokeAI/Web UI
2023-10-12 12:43:51 +00:00
7dee6f51a3 translationBot(ui): update translation files
Updated by "Cleanup translation files" hook in Weblate.

Co-authored-by: Hosted Weblate <hosted@weblate.org>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/
Translation: InvokeAI/Web UI
2023-10-12 12:43:50 +00:00
3c029eee29 translationBot(ui): update translation (Italian)
Currently translated at 100.0% (526 of 526 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (523 of 523 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (519 of 519 strings)

translationBot(ui): update translation (Italian)

Currently translated at 100.0% (515 of 515 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
2023-10-12 12:43:47 +00:00
1a8f9d1ecb translationBot(ui): update translation (Spanish)
Currently translated at 100.0% (526 of 526 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (523 of 523 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (519 of 519 strings)

translationBot(ui): update translation (Spanish)

Currently translated at 100.0% (515 of 515 strings)

Co-authored-by: gallegonovato <fran-carro@hotmail.es>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/es/
Translation: InvokeAI/Web UI
2023-10-12 12:43:45 +00:00
80d329c900 fix(ui): fix plurals (#4860) 2023-10-12 18:07:22 +05:30
89db749d89 fix(ui): add missing translation strings 2023-10-12 22:46:47 +11:00
18164fc72a fix(ui): prettier ignores translation files 2023-10-12 21:37:45 +11:00
75de20af6a fix(ui): fix plurals in translation 2023-10-12 21:34:24 +11:00
cb1509bf52 feat(ui): add translation strings for clear intermediates (#4856)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Description

feat(ui): add translation strings for clear intermediates

## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
automatically close the issue.
-->

- Related Issue #
- Closes #4851

## [optional] Are there any post deployment tasks we need to perform?

@Millu this can go into 3.3.0
2023-10-12 13:16:54 +05:30
10cd814cf7 feat(ui): add translation strings for clear intermediates 2023-10-12 18:35:33 +11:00
8ef38ecc7c fix(ui): only count enabled control adapters in collapse heading 2023-10-12 16:48:01 +11:00
69937d68d2 Maryhipp/dummy bulk download (#4852)
* UI for bulk downloading boards or groups of images

* placeholder route for bulk downloads that does nothing

* lint

---------

Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
2023-10-11 23:27:22 +00:00
40f9e49b5e Demote model cache logs from warning to debug based on the conversation here: https://discord.com/channels/1020123559063990373/1049495067846524939/1161647290189090816 2023-10-11 12:02:46 -04:00
98fa234529 Bump safetensors to ~=0.4.0 (#4844)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission

## Description

@Millu pointed out this safetensors PR a few weeks ago, which claimed to
offer a performance benefit:
https://github.com/huggingface/safetensors/pull/362 . It was superseded
by https://github.com/huggingface/safetensors/pull/363 and included in
the latest [safetensors 0.4.0
release](https://github.com/huggingface/safetensors/releases/tag/v0.4.0).

Here are the results from my local performance comparison:
```
Before(0.3.1) / After(0.4.0)

sdxl:main:tokenizer from disk to cpu in                              0.46s / 0.46s
sdxl:main:text_encoder from disk to cpu in                           2.12s / 2.32s
embroidered_style_v1_sdxl.safetensors:sdxl:lora' from disk to cpu in 0.67s / 0.36s
VoxelXL_v1.safetensors:sdxl:lora' from disk to cpu in                1.64s / 0.60s
ryan_db_sdxl_epoch640.safetensors:sdxl:lora' from disk to cpu in     2.46s / 1.40s
sdxl:main:tokenizer_2 from disk to cpu in                            0.37s / 0.39s
sdxl:main:text_encoder_2 from disk to cpu in                         3.78s / 4.70s
sdxl:main:unet from disk to cpu in                                   4.66s / 3.08s
sdxl:main:scheduler from disk to cpu in                              0.34s / 0.33s
sdxl:main:vae from disk to cpu in                                    0.66s / 0.51s

TOTAL GRAPH EXECUTION TIME:                                        56.489s / 53.416s
```

The benefit was marginal on my system (maybe even within measurement
error), but I figured we might as well pull it.
2023-10-11 09:40:47 -04:00
fe889235cc Bump safetensors to ~=0.4.0 2023-10-10 18:00:15 -04:00
462c1d4c9b Improve model load times from disk: skip unnecessary weight init (#4840)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
      
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No


## Description

This PR optimizes the time to load models from disk.
In my local testing, SDXL text_encoder_2 models saw the greatest
improvement:
- Before change, load time (disk to cpu): 14 secs
- After change, load time (disk to cpu): 4 secs

See the in-code documentation for an explanation of how this speedup is
achieved.

## Related Tickets & Documents

This change was previously proposed on the HF transformers repo, but did
not get any traction:
https://github.com/huggingface/transformers/issues/18505#issue-1330728188

## QA Instructions, Screenshots, Recordings

I don't expect any adverse effects, but the new context manager is
applied while loading **all** models, so it would make sense to exercise
everything.

## Added/updated tests?

- [x] Yes
- [ ] No
2023-10-10 13:40:20 -04:00
0ed36158c8 Merge branch 'main' into ryan/optimize-model-load 2023-10-10 13:31:08 -04:00
f3c138a208 (minor) Fix Flake8. 2023-10-10 10:06:53 -04:00
61242bf86a Fix bug in skip_torch_weight_init() where the original behavior of torch.nn.Conv*d modules wasn't being restored correctly. 2023-10-10 10:05:50 -04:00
d118d02df4 feat(ui): add mapping for sketch and scribble control adapter processors 2023-10-09 23:24:56 -04:00
58b56e9b1e Add a skip_torch_weight_init() context manager to improve model load times (from disk). 2023-10-09 14:12:56 -04:00
1f751f8c21 fix(ui): remove extraneous cache update 2023-10-09 20:11:21 +11:00
ca95a3bd0d fix(ui): fix canvas soft-lock if canceled before first generation
The canvas needs to be set to staging mode as soon as a canvas-destined batch is enqueued. If the batch is is fully canceled before an image is generated, we need to remove that batch from the canvas `batchIds` watchlist, else canvas gets stuck in staging mode with no way to exit.

The changes here allow the batch status to be tracked, and if a batch has all its items completed, we can remove it from the `batchIds` watchlist. The `batchIds` watchlist now accurately represents *incomplete* canvas batches, fixing this cause of soft lock.
2023-10-09 20:11:21 +11:00
55b40a9425 feat(events): add batch status and queue status to queue item status changed events
The UI will always re-fetch queue and batch status on receiving this event, so we may as well jsut include that data in the event and save the extra network roundtrips.
2023-10-09 20:11:21 +11:00
90083cc88d fix(ui): fix use all hotkey 2023-10-09 20:03:14 +11:00
ead754432a add a lists of t2i adapters to startup set (#4828)
## What type of PR is this? (check all applicable)

- [X] Feature

## Have you discussed this change with the InvokeAI team?
- [X] No, because: Non-controversial

      
## Have you updated all relevant documentation?
- [ ] Yes
- [X] N/A


## Description

This adds a list of T2I adapters to the “starter models” offered by the
TUI installer. None of the models is selected by default; this can be
done easily if requested. The models offered to the user are:

```
TencentARC/t2iadapter_canny_sd15v2
TencentARC/t2iadapter_sketch_sd15v2
TencentARC/t2iadapter_depth_sd15v2
TencentARC/t2iadapter_zoedepth_sd15v1
TencentARC/t2i-adapter-canny-sdxl-1.0
TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
TencentARC/t2i-adapter-lineart-sdxl-1.0
TencentARC/t2i-adapter-sketch-sdxl-1.0
```

## Related Tickets & Documents

PR #4612 

## QA Instructions, Screenshots, Recordings

The revised installer has a new IP-ADAPTERS tab that looks like this:


![IMG_0255](https://github.com/invoke-ai/InvokeAI/assets/111189/0e01b1f6-7191-49a1-ac63-2c913826d299)

## Added/updated tests?

- [ ] Yes
- [X] No : It would be good to have a suite of model download tests, but
not set up yet.
2023-10-08 19:49:43 -04:00
fa9ea93477 add a lists of t2i adapters to startup set 2023-10-08 18:53:21 -04:00
fe0cf2c160 remove hardcoded subfolder name from model downloader 2023-10-08 17:45:39 -04:00
a681fa4b03 fix(ui): invalidate query cache for all models on sync models
Also realised the tags were set up incorrectly, fixed that to get type safety with tags.
2023-10-07 22:30:15 +11:00
1cc686734b feat(ui): on base model change, disable control adapters
Previously it deleted them entirely.
2023-10-07 22:30:15 +11:00
82e8b92ba0 feat(ui): display toast when enabling t2i/controlnet and disabling the other 2023-10-07 22:30:15 +11:00
e86658f864 feat(ui): disable invoke button if enabled control adapter model does not match base model 2023-10-07 22:30:15 +11:00
ad136c2680 fix(ui): do not add control adapters with incompatible models to graph 2023-10-07 22:30:15 +11:00
35374ec531 feat(ui): update graphs for multi ip adapter 2023-10-07 22:30:15 +11:00
ed82bf6bb8 feat(ui): disable control adapter buttons if no models available 2023-10-07 22:30:15 +11:00
078c9b6964 feat(nodes,ui): add t2i to linear UI
- Update backend metadata for t2i adapter
- Fix typo in `T2IAdapterInvocation`: `ip_adapter_model` -> `t2i_adapter_model`
- Update linear graphs to use t2i adapter
- Add client metadata recall for t2i adapter
- Fix bug with controlnet metadata recall - processor should be set to 'none' when recalling a control adapter
2023-10-07 22:30:15 +11:00
1a9d2f1701 feat(ui): spruce up control adapter ui 2023-10-07 22:30:15 +11:00
3e93159bce fix(ui): enable duplicated control adapter 2023-10-07 22:30:15 +11:00
b57ebe52e4 chore(ui): "controlnet" -> "controladapters" 2023-10-07 22:30:15 +11:00
ba4616ff89 feat(ui): add limits to enabled control adapters
- only 1 ip adapter at a time
- controlnet and t2i cannot both be active at once
2023-10-07 22:30:15 +11:00
dcfbd49e1b fix(ui): fix control adapters recall 2023-10-07 22:30:15 +11:00
913fc83cbf fix(ui): fix control adapter autoprocess 2023-10-07 22:30:15 +11:00
6b8ce34eb3 fix(ui): fix excessive re-renders 2023-10-07 22:30:15 +11:00
9508e0c9db feat(ui): refactor control adapters
Control adapters logic/state/ui is now generalized to hold controlnet, ip_adapter and t2i_adapter. In the future, other control adapter types can be added.

TODO:
- Limit IP adapter to 1
- Add T2I adapter to linear graphs
- Fix autoprocess
- T2I metadata saving & recall
- Improve on control adapters UI
2023-10-07 22:30:15 +11:00
9c720da021 Bump DenoiseLatentsInvocation version. 2023-10-06 20:43:43 -04:00
e1b576c72d yarn build 2023-10-06 20:43:43 -04:00
971ccfb081 Refactor multi-IP-Adapter to clean up the interface around changing scales. 2023-10-06 20:43:43 -04:00
43a3c3c7ea Fix typo in setting IP-Adapter scales. 2023-10-06 20:43:43 -04:00
4df1cdb34d Tidy _prepare_attention_processors(...) logic. 2023-10-06 20:43:43 -04:00
3f860c3523 Fixup IP-Adapter locale strings. 2023-10-06 20:43:43 -04:00
d8d0c9af09 Fix handling of scales with multiple IP-Adapters. 2023-10-06 20:43:43 -04:00
9403672ac0 Bugfix for multi-ip-adapter in DenoiseLatentsInvocation. 2023-10-06 20:43:43 -04:00
94591840a7 Frontend changes to enable multiple IP-Adapters in the workflow editor. 2023-10-06 20:43:43 -04:00
26b91a538a Fixes to get IP-Adapter tests working with new multi-IP-Adapter support. 2023-10-06 20:43:43 -04:00
7ca456d674 Update IP-Adapter model to enable running multiple IP-Adapters at once. (Not tested yet.) 2023-10-06 20:43:43 -04:00
78828b6b9c WIP - Accept a list of IPAdapterFields in DenoiseLatents. 2023-10-06 20:43:43 -04:00
166ff9d301 Proposal: Support slow tests that depend on models (#4813)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [x] Feature
- [ ] 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

This PR adds support for slow unit tests that depend on models. It
includes:
- Documentation explaining the handling of fast vs. slow unit tests.
- Utilities to assist with writing tests that depend on models.
- A sample test that loads and runs an IP-Adapter model. This is far
from complete test coverage of IP-Adapter - it's just intended as a
first example of how to write tests with models.

**Suggestion for reviewers**: Start with docs/contributing/TESTS.md

## QA Instructions, Screenshots, Recordings

I've tested it all, but it would make sense for others to try running
both the fast tests and the slow tests.

## Added/updated tests?

- [x] Yes
- [ ] No
2023-10-06 19:55:38 -04:00
4f97bd4418 Merge branch 'main' into ryan/model-tests 2023-10-06 19:47:28 -04:00
e0e001758a Remove @slow decorator in favor of @pytest.mark.slow. 2023-10-06 18:26:06 -04:00
c1887135b3 Improve model cache debug logging (#4784)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [x] 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

This PR adds detailed debug logging to the model cache in order to give
more visibility into the model cache's memory utilization. **This PR
does not make any functional changes to the model cache.**

Every time a model is moved from disk to CPU, or between CPU/CUDA, a log
like this is emitted:
```bash
[2023-10-03 15:17:20,599]::[InvokeAI]::DEBUG --> Moved model '/home/ryan/invokeai/models/.cache/63742ed45b499e55620c402d6df26a20:sdxl:main:unet' from cpu to cuda in 1.23s.
Estimated model size: 4.782 GB.
Process RAM                    (-4.722): 6.987GB -> 2.265GB
libc mmap allocated            (-4.722): 6.030GB -> 1.308GB
libc arena used                (-0.061): 0.402GB -> 0.341GB
libc arena free                (+0.061): 0.006GB -> 0.067GB
libc total allocated           (-4.722): 6.439GB -> 1.717GB
libc total used                (-4.783): 6.433GB -> 1.649GB
VRAM                           (+4.881): 1.538GB -> 6.418GB
```

## Related Tickets & Documents

https://github.com/invoke-ai/InvokeAI/pull/4694 contains related fixes
to some known memory issues.

## QA Instructions, Screenshots, Recordings

Make sure debug logs are enabled and you should see the new logs.

We should test each of the following environments:
- [x] Linux
- [x] Mac OS + MPS
- [x] Windows

## Added/updated tests?

- [x] Yes
- [ ] No

Added unit tests for the new utilities. Test coverage is still low for
the ModelCache, but not worse than before.
2023-10-06 10:21:42 -04:00
096d195d6e Merge branch 'main' into ryan/model-cache-logging-only 2023-10-06 09:52:45 -04:00
7870b90717 Add TESTS.md documentation. 2023-10-05 15:38:25 -04:00
9854b244fd Fix Flake8 errors by using a pytest conftest.py file. 2023-10-05 15:36:15 -04:00
7d800e1ce3 Fix broken link in documentation to 'Frontend Documentation'. 2023-10-05 15:36:15 -04:00
1c8b1fbc53 POC of a test that depends on models. 2023-10-05 15:35:58 -04:00
594a3aef93 Set MALLOC_MMAP_THRESHOLD_=1048576 by default in invoke.sh. And add it to the manual installation docs. 2023-10-05 14:26:45 -04:00
78377469db Add support for T2I-Adapter in node workflows (#4612)
* Bump diffusers to 0.21.2.

* Add T2IAdapterInvocation boilerplate.

* Add T2I-Adapter model to model-management.

* (minor) Tidy prepare_control_image(...).

* Add logic to run the T2I-Adapter models at the start of the DenoiseLatentsInvocation.

* Add logic for applying T2I-Adapter weights and accumulating.

* Add T2IAdapter to MODEL_CLASSES map.

* yarn typegen

* Add model probes for T2I-Adapter models.

* Add all of the frontend boilerplate required to use T2I-Adapter in the nodes editor.

* Add T2IAdapterModel.convert_if_required(...).

* Fix errors in T2I-Adapter input image sizing logic.

* Fix bug with handling of multiple T2I-Adapters.

* black / flake8

* Fix typo

* yarn build

* Add num_channels param to prepare_control_image(...).

* Link to upstream diffusers bugfix PR that currently requires a workaround.

* feat: Add Color Map Preprocessor

Needed for the color T2I Adapter

* feat: Add Color Map Preprocessor to Linear UI

* Revert "feat: Add Color Map Preprocessor"

This reverts commit a1119a00bf.

* Revert "feat: Add Color Map Preprocessor to Linear UI"

This reverts commit bd8a9b82d8.

* Fix T2I-Adapter field rendering in workflow editor.

* yarn build, yarn typegen

---------

Co-authored-by: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2023-10-05 16:29:16 +11:00
fbe6452c45 Add support for IPAdapterPlusXL based on 6219530507. 2023-10-04 22:35:17 -04:00
3f4ea073d1 fix(ui): throw on fetch err when copying image 2023-10-05 10:43:59 +11:00
8b7f8eaea2 chore: flake8 2023-10-05 09:32:29 +11:00
88e16ce051 fix(nodes): mark session queue items failed on processor error
When the processor has an error and it has a queue item, mark that item failed.

This addresses processor errors resulting in `in_progress` queue items, which create a soft lock of the processor, requiring the user to cancel the `in_progress` item before anything else processes.
2023-10-05 09:32:29 +11:00
421440cae0 feat(nodes): exhaustive graph validation
Makes graph validation logic more rigorous, validating graphs when they are created as part of a session or batch.

`validate_self()` method added to `Graph` model. It does all the validation that `is_valid()` did, plus a few extras:
- unique `node.id` values across graph
- node ids match their key in `Graph.nodes`
- recursively validate subgraphs
- validate all edges
- validate graph is acyclical

The new method is required because `is_valid()` just returned a boolean. That behaviour is retained, but `validate_self()` now raises appropriate exceptions for validation errors. This are then surfaced to the client.

The function is named `validate_self()` because pydantic reserves `validate()`.

There are two main places where graphs are created - in batches and in sessions.

Field validators are added to each of these for their `graph` fields, which call the new validation logic.

**Closes #4744**

In this issue, a batch is enqueued with an invalid graph. The output field is typed as optional while the input field is required. The field types themselves are not relevant - this change addresses the case where an invalid graph was created.

The mismatched types problem is not noticed until we attempt to invoke the graph, because the graph was never *fully* validated. An error is raised during the call to `graph_execution_state.next()` in `invoker.py`. This function prepares the edges and validates them, raising an exception due to the mismatched types.

This exception is caught by the session processor, but it doesn't handle this situation well - the graph is not marked as having an error and the queue item status is never changed. The queue item is therefore forever `in_progress`, so no new queue items are popped - the app won't do anything until the queue item is canceled manually.

This commit addresses this by preventing invalid graphs from being created in the first place, addressing a substantial number of fail cases.
2023-10-05 09:32:29 +11:00
421021cede Add 'make 3d' plugin / community node (#4794)
* Add 'make 3d' plugin.

* Update communityNodes.md

Updated to Repo Link

---------

Co-authored-by: Jordan <srcrr-gitlab@ipriva.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
2023-10-04 21:41:21 +00:00
020d4302d1 Change version bump from patch to minor
Because this adds a new field, it's a minor version bump
2023-10-05 08:24:52 +11:00
8c59d2e5af chore: isort 2023-10-05 08:24:52 +11:00
17d451eaa7 feat(images): add png_compress_level config
The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize

Closes #4786
2023-10-05 08:24:52 +11:00
23a06fd06d feat(nodes): clear torch cache after upscaling
This can use many GB of VRAM, so we need to clean up after ourselves.
2023-10-05 08:24:52 +11:00
010c8e8038 Roll back change to buildAdHocUpscaleGraph.ts
Undo the change made here which was causing automated tests to fail.
2023-10-05 08:24:52 +11:00
dfc635223c Update upscale.py with minor style correction 2023-10-05 08:24:52 +11:00
37121a3a24 Add tile_size parameter to ESERGAN node in buildAdHocUpscaleGraph.ts
Adds tile_size parameter to support the changed ESRGAN node in invokeai/app/invocations/upscale.py
2023-10-05 08:24:52 +11:00
51b5de799a Update upscale.py to support tile kwarg of RealESRGANer
Adds tile_size field to the ESRGAN Upscaler node, which sends the tile kwarg to RealESRGANer's constructor, enabling tiled upscaling (default=512)
2023-10-05 08:24:52 +11:00
eadbe6abf7 handle 0 images/assets 2023-10-05 08:11:52 +11:00
16f48a816f fix(ui): add dnd validation logic for multi-select board move 2023-10-05 08:11:52 +11:00
95838e5559 fix(ui): fix remove from board dnd validation
This is fired when the dnd image is moved over the 'none' board. Weren't defaulting to 'none' for the image's board_id, resulting in it being possible to drag a 'none' image onto 'none'.
2023-10-05 08:11:52 +11:00
3e8d62b1d1 fix(ui): fix duplicate image selection
Selections were not being `uniqBy()`'d, or were `uniqBy()`'d without a proper iteratee. This results in duplicate images in selections in certain situations.

Add correct `uniqBy()` to the reducer to prevent this in the future.
2023-10-05 08:11:52 +11:00
2acc93eb8e feat(ui): remove all calls to getBoardImagesTotals/getBoardAssetsTotals
This caused a crapload of network requests any time an image was generated.

The counts are necessary to handle the logic for inserting images into existing image list caches; we have to keep track of the counts.

Replace tag invalidation with manual cache updates in all cases, except the initial request (which is necessary to get the initial image counts).

One subtle change is to make the counts an object instead of a number. This is required for `immer` to handle draft states. This should be raised as a bug with RTK Query, as no error is thrown when attempting to update a primitive immer draft.
2023-10-05 08:11:52 +11:00
fbb61f2334 Revert "Updated js files"
This reverts commit a0e936f3a7.
2023-10-04 22:32:00 +11:00
be85c7972b Updated js files 2023-10-04 22:32:00 +11:00
3a586fc9c4 Prevent caching to ensure updated UI is shown 2023-10-04 22:32:00 +11:00
dedead672f chore(facetools): bump node patch versions
The helper function `generate_face_box_mask()` had a bug that prevented larger faces from being detected in some situations. This is resolved, and its dependent nodes (all the FaceTools nodes) have a patch version bump.
2023-10-04 09:33:14 +11:00
67366921c0 add checkbounds bool
- don't check bounds on first detection before chunking, allows larger faces to be detected
2023-10-04 09:33:14 +11:00
5a1019d858 sort by starred and then created_at to get board cover image 2023-10-04 08:54:47 +11:00
f4ba7be918 refetch baord list when image is starred or unstarred 2023-10-04 08:54:47 +11:00
069d8b5812 feat(ui): move initial IP adapter model selection to listener 2023-10-04 08:41:37 +11:00
24d73d484a IP adapter UI 2023-10-04 08:41:37 +11:00
2479a59e5e Re-enable garbage collection in model cache MemorySnapshots. 2023-10-03 15:18:47 -04:00
7d0ac2c36d (minor) clean up typos. 2023-10-03 15:00:03 -04:00
519b892f0c Add unit test for Struct_mallinfo2.__str__() 2023-10-03 14:25:34 -04:00
763dcacfd3 Add unit test for get_pretty_snapshot_diff(...). 2023-10-03 14:25:34 -04:00
3599d546e6 Add unit test for LibcUtil().mallinfo2(). 2023-10-03 14:25:34 -04:00
22a84930f6 Disable garbage collection in ModelCache calls to MemorySnapshot in order minimize snapshot overhead. 2023-10-03 14:25:34 -04:00
d64e17e043 Add README with info about glib memory fragmentation caused by the model cache. 2023-10-03 14:25:34 -04:00
ba54277011 Catch a more specific exception in environments that do not have a libc shared library. 2023-10-03 14:25:34 -04:00
5915a4a51c Minor fixes. 2023-10-03 14:25:34 -04:00
4580ba0d87 Remove logic to update model cache size estimates dynamically. 2023-10-03 14:25:34 -04:00
b9fd2e9e76 Improve get_pretty_snapshot_diff(...) message formatting. 2023-10-03 14:25:34 -04:00
75b65597af Add malloc info to MemorySnapshot. 2023-10-03 14:25:34 -04:00
2a3c0ab5d2 Move MemorySnapshot to its own file. 2023-10-03 14:25:34 -04:00
7d61373b82 Add LibcUtil class. 2023-10-03 14:25:34 -04:00
7d65555a5a Fix type error in torch device comparison. 2023-10-03 14:25:34 -04:00
123f2b2dbc Update cache model size estimates based on changes in VRAM when moving models to/from CUDA. 2023-10-03 14:25:34 -04:00
1e4e42556e Update model cache device comparison to treat 'cuda' and 'cuda:0' as the same device type. 2023-10-03 14:25:34 -04:00
1f6699ac43 Consolidate all model.to(...) calls in the model cache to use a utility function with better logging. 2023-10-03 14:25:34 -04:00
ace8665411 Add warning log if moving a model from cuda to cpu causes unexpected change in VRAM usage. 2023-10-03 14:25:34 -04:00
7fa5bae8fd Add warning log if moving model from RAM to VRAM causes an unexpected change in VRAM usage. 2023-10-03 14:25:34 -04:00
f9faca7c91 Add warning log if model mis-reports its required cache memory before load from disk. 2023-10-03 14:25:34 -04:00
594fd3ba6d Add debug logging of changes in RAM and VRAM for all model cache operations. 2023-10-03 14:25:34 -04:00
44d68f5ed5 Auto-format model_cache.py. 2023-10-03 14:25:34 -04:00
4bda7d7df5 Add font Inter-Regular.ttf to installed assets (#4775)
## What type of PR is this? (check all applicable)

- [X] Bug Fix


## Have you discussed this change with the InvokeAI team?
- [X] Yes

      
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No


## Description

This PR causes the font "Inter-Regular.ttf", which is needed by the
facetools Face Identifier node, to be installed along with other assets
in the virtual environment. It also fixes the font path resolution logic
in the invocation to work with both package and editable installs.

## Related Tickets & Documents

Closes #4771
2023-10-03 09:05:51 -04:00
920c5dd686 remove unneeded os import 2023-10-03 08:53:47 -04:00
4ce00a32f4 add font Inter-Regular.ttf to installed assets 2023-10-03 08:48:50 -04:00
dcbb25dfea feat(ui): staging styling tweak 2023-10-03 13:46:01 +11:00
6c8270dae2 fix(ui): canvas staging area works after undo 2023-10-03 13:46:01 +11:00
b19572199f Release/v3.2.0 (#4766)
## What type of PR is this? (check all applicable)

Release v3.2.0

## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No

Need to update prompting docs 

## Description
3.2.0 release version

## [optional] Are there any post deployment tasks we need to perform?
2023-10-03 11:59:19 +11:00
a673c0aa14 Update JS files 2023-10-03 10:31:35 +11:00
955ef3bc54 Update version to 3.2.0 2023-10-03 10:29:27 +11:00
f002ae8da5 feat(ui): max upscale pixels config (#4765)
* feat(ui): max upscale pixels config

Add `maxUpscalePixels: number` to the app config. The number should be the *total* number of pixels eg `maxUpscalePixels: 4096 * 4096`.

If not provided, any size image may be upscaled.

If the config is provided, users will see be advised if their image is too large for either model, or told to switch to an x2 model if it's only too large for x4.

The message is via tooltip in the popover and via toast if the user uses the hotkey to upscale.

* feat(ui): "mayUpscale" -> "isAllowedToUpscale"
2023-10-02 23:25:05 +00:00
208bf68ba2 fix missing toast message 2023-10-03 07:45:26 +11:00
1aba369c83 invalidate board cache when an image is added to a board 2023-10-02 19:40:11 +11:00
9ac11e793c Added GridtoGif to communityNodes.md (#4755)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [x] 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
Grid to Gif is two custom nodes, one that divides a grid image into an
image collection, the other converts an image collection into a animated
gif
2023-10-02 10:44:55 +11:00
9b39888e2f Added GridtoGif to communityNodes.md 2023-10-01 17:42:36 -05:00
c1715144f0 add Character Art Node's to communityNodes.md 2023-10-01 11:10:36 -04:00
929557bc6f Fix typo of Psychedelicious name (#4746)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ x ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ x ] No, because:

      
## Have you updated all relevant documentation?
- [x  ] Yes
- [ ] No
2023-09-30 22:48:30 +05:30
811dd93912 Fix typo of Psychedelicious name 2023-09-30 12:35:49 -04:00
9a60dbd5cb add version to cv2 infill (#4741)
cv2 infill node was missing a version in its decorator, resulting in a
red exclamation mark on the node

## 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?
- [ ] Yes
- [x] No, because: is tiny

      
## Have you updated all relevant documentation?
- [ ] Yes
- [x] No
2023-09-29 20:36:51 +05:30
637c5b0747 add version to cv2 infill
- cv2 infill was missing a version in its decorator, resulting in a red exclamation mark on the node
2023-09-29 16:58:19 +02:00
27164de8b8 Fix absolute path for font file
Make the font file relative to this source file. Not ideal, but it will work no matter where InvokeAI is launched.
2023-09-29 22:05:04 +10:00
08e40d6d16 fix(ui): fit ip adapter image to panel (#4737)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission

## Description

Very tall IP adapter images didn't get fit to the panel. Now they do
2023-09-29 14:29:39 +05:30
d905c54795 fix(ui): fit ip adapter image to panel 2023-09-29 18:54:34 +10:00
dc1e804887 Workflow editor improvements - add node from empty connection and auto-connect to empy handle. (#4684)
* Initial commit of edge drag feature.

* Fixed build warnings

* code cleanup and drag to existing node

* improved isValidConnection check

* fixed build issues, removed cyclic dependency

* edge created nodes now spawn at cursor

* Add Node popover will no longer show when using drag to delete an edge.

* Fixed collection handling, added priority for handles matching name of source handle, removed current image/notes nodes from filtered list

* Fixed not properly clearing startParams when closing the Add Node popover

* fix(ui): do not allow Collect -> Iterate connection

This can be removed when #3956 is resolved

* feat(ui): use existing node validation logic in add-node-on-drop

This logic handles a number of special cases

---------

Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2023-09-29 18:12:57 +10:00
95fd2ee6ff Nodes-FaceTools (FaceIdentifier, FaceOff, FaceMask) (#4576)
* node-FaceTools

* Added more documentation for facetools

* invert FaceMask masking

- FaceMask had face protected and surroundings change by default (face white, else black)
- Change to how FaceOff/others work: the opposite where surroundings protected, face changes by default (face black, else white)

* reflect changed facemask behaviour in docs

* add FaceOff+FaceMask workflows

- Add FaceOff and FaceMask example workflows to docs/workflows

* add FaceMask+FaceOff workflows to exampleworkflows.md

- used invokeai URL paths mimicking other workflow URLs, hopefully they translate when/if merged

* inheriting, typehints, black/isort/flake8

- modified FaceMask and FaceOff output classes to inherit base image, height, width from ImageOutput
- Added type annotations to helper functions, required some reworking of code's stored data

* remove credit header

- Was in my personal/repo copy, don't think it's necessary if merged.

* Optionals & image declaration duplication

- Added Optional[] to optional outputs and types
- removed duplication of image = context.services.images.get_pil_images(self.image.image_name) declaration
- Still need to find a way to deal with mask_pil None typing errors

* face(facetools): fix typing issues, add validation, clean up structure

* feat(facetools): update field descriptions

* Update FaceOff_FaceScale2x.json

- update FaceOff workflow after Bounded Image field removed in place of inheriting Image out field from ImageOutput

* feat(facetools): pass through original image on facemask if invalid face ids requested

* feat(facetools): tidy variable names & fn calls

* feat(facetools): bundle inter font, draw ids with it

Inter is a SIL Open Font license. The license is included and is fully permissive. Inter is the same font the UI and commercial application already uses.

Only the "regular" version is bundled.

* chore(facetools): isort & fix mypy issues

* docs(facetools): update and format docs

---------

Co-authored-by: Millun Atluri <millun.atluri@gmail.com>
Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2023-09-29 17:54:13 +10:00
5f4eb0c3b3 update communitynodes.md to add Rotate/Flip Image to composition pack (#4735)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [X] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:

      
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No


## Description
Adds another node description (Rotate/Flip Image) to Image and Mask
Composition Pack

## Related Tickets & Documents
n/a

## QA Instructions, Screenshots, Recordings
n/a
## Added/updated tests?

- [ ] Yes
- [X] No : n/a
2023-09-29 15:19:48 +10:00
d464ce509b update communitynodes.md to add Rotate/Flip Image to composition pack 2023-09-29 00:37:40 -04:00
3909e68527 fix(ui): data-testId -> data-testid
Must be strict kebab-case for react to pass the attribute to DOM
2023-09-29 12:44:00 +10:00
848e51f72b Update communityNodes.md (#4729)
Added thresholding and halftone nodes.
2023-09-28 23:48:07 +00:00
52f8c9e16f add data-testids to UI components that may be hard to target with automation 2023-09-29 08:58:31 +10:00
5174f382b9 Update LOCAL_DEVELOPMENT.md
add LSP and type checking notes
2023-09-29 00:34:39 +10:00
c7f80cd163 Use metadata ip adapter (#4715)
* add control net to useRecallParams

* got recall controlnets working

* fix metadata viewer controlnet

* fix type errors

* fix controlnet metadata viewer

* add ip adapter to metadata

* added ip adapter to recall parameters

* got ip adapter recall working, still need to fix type errors

* fix type issues

* clean up logs

* python formatting

* cleanup

* fix(ui): only store `image_name` as ip adapter image

* fix(ui): use nullish coalescing operator for numbers

Need to use the nullish coalescing operator `??` instead of false-y coalescing operator `||` when the value being check is a number. This prevents unintended coalescing when the value is zero and therefore false-y.

* feat(ui): fall back on default values for ip adapter metadata

* fix(ui): remove unused schema

* feat(ui): re-use existing schemas in metadata schema

* fix(ui): do not disable invocationCache

---------

Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2023-09-28 09:05:32 +00:00
309e2414ce enable downloading from subfolders for repo_ids (#4725)
[## What type of PR is this? (check all applicable)

- [X] Feature

## Have you discussed this change with the InvokeAI team?
- [X] Yes
      
## Have you updated all relevant documentation?
- [X] Yes

## Description

Very rarely a model lives in the subfolder of a non-pipeline HuggingFace
repo_id. The example I've been working with is
https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster/tree/main,
where the improved monster QR code controlnet model lives in the `v2`
subdirectory.

In order to accommodate installing such files, I have made two changes
to the model installer.

1. At installation/configuration time, if a stanza in
`INITIAL_MODELS.yaml` contains the field `subfolder`, then the model
will be installed from the indicated subfolder. The syntax in this case
is:
```
sd-1/controlnet/qrcode_monster:
   repo_id: monster-labs/control_v1p_sd15_qrcode_monster
   subfolder: v2
```
2. From within the Web GUI or the installer TUI, if you wish to indicate
that the model resides in a subfolder, you can tack ":_subfoldername_"
to the end of the repo_id. The resulting repo_id will look like:
```
monster-labs/control_v1p_sd15_qrcode_monster:v2
```

The code for introducing these changes is obscure and somewhat hacky.
However, the whole installer code base has been rewritten for the model
manager refactor (#4252 ) and I will reimplement this feature in a more
elegant way in that PR.
2023-09-28 15:26:18 +10:00
6704f77d87 Merge branch 'main' into feat/install-repoid-folders 2023-09-28 13:49:57 +10:00
045d3f6139 chore: flake8 2023-09-28 13:49:31 +10:00
a0bd8c638e chore(ui): lint 2023-09-28 12:39:00 +10:00
de04a5f441 cleanup 2023-09-28 12:39:00 +10:00
40ed218c26 surface usage errors for cnet and upscale, handle clearing cnet if error occurs 2023-09-28 12:39:00 +10:00
807c6b41c5 surface usage errors for enqueuing batch 2023-09-28 12:39:00 +10:00
f6bbcd0589 remove dangling debug statement 2023-09-27 22:26:26 -04:00
ada22a799e remove dangling debug statement 2023-09-27 22:26:06 -04:00
a42ef9c855 add documentation on syntax to use for subfolder repo_ids 2023-09-27 22:17:29 -04:00
034af2d9f8 enable downloading from subfolders for repo_ids 2023-09-27 22:11:56 -04:00
676ccd8ebb Add IP-Adapter to docs (#4703)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No


## Description


## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
automatically close the issue.
-->

- Related Issue #
- Closes #

## QA Instructions, Screenshots, Recordings

<!-- 
Please provide steps on how to test changes, any hardware or 
software specifications as well as any other pertinent information. 
-->

## Added/updated tests?

- [ ] Yes
- [ ] No : _please replace this line with details on why tests
      have not been included_

## [optional] Are there any post deployment tasks we need to perform?
2023-09-28 11:11:24 +10:00
a263a4f4cc Update CONTROLNET.md 2023-09-27 20:51:02 -04:00
ef0754cdec Merge branch 'invoke-ai:main' into main 2023-09-28 09:41:29 +10:00
8158124679 fix(ui): usePreselectedImage causing re-renders
This hook was rerendering any time anything changed. Moved it to a logical component, put its useEffects inside the component. This reduces the effect of the rerenders to just that tiny always-null component.
2023-09-28 09:02:45 +10:00
5d31df0cb7 Fix IP-Adapter calculation of memory footprint (#4692)
## 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?
- [ ] Yes
- [x] No, because:

      
## Have you updated all relevant documentation?
- [x] Yes
- [ ] No


## Description

The IP-Adapter memory footprint was not being calculated correctly.

I think we could put checks in place to catch this type of error in the
future, but for now I'm just fixing the bug.

## QA Instructions, Screenshots, Recordings

I tested manually in a debugger. There are 3 pathways for calculating
the model size. All were tested:
- From file
- From state_dict
- From model weights

## Added/updated tests?

- [ ] Yes
- [x] No : This would require the ability to run tests that depend on
models. I'm working on this in another branch, but not ready quite yet.
2023-09-27 12:03:04 -04:00
bd63454e51 Merge branch 'main' into bug/ip-adapter-calc-size 2023-09-27 11:55:55 -04:00
062df07de2 fix(ui): fix loading queue item translation 2023-09-27 11:18:43 -04:00
0fc14afcf0 Merge branch 'main' into bug/ip-adapter-calc-size 2023-09-27 09:42:51 -04:00
4a0a1c30db use controlnet from metadata if available (#4658)
* add control net to useRecallParams

* got recall controlnets working

* fix metadata viewer controlnet

* fix type errors

* fix controlnet metadata viewer

* set control image and use correct processor type and node

* clean up logs

* recall processor using substring

* feat(ui): enable controlNet when recalling one

---------

Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2023-09-27 19:30:50 +10:00
3432fd72f8 fix auto-switch alongside starred images (#4708)
* add skeleton loading state for queue lit

* add optional selectedImage when switching a board

* unstage

---------

Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2023-09-27 07:51:37 +00:00
05a43c41f9 feat: Improve Staging Toolbar Styling 2023-09-27 17:45:39 +10:00
bb48617101 fix(ui): memoize canvas context menu callback 2023-09-27 17:45:39 +10:00
aa2f68f608 fix(ui): use theme colors for canvas error fallback 2023-09-27 17:45:39 +10:00
fbccce7573 feat(ui): staging area toolbar enhancements
- Current image number & total are displayed
- Left/right wrap around instead of stopping on first/last image
- Disable the left/right/number buttons when showing base layer
- improved translations
2023-09-27 17:45:39 +10:00
a35087ee6e feat(ui): hide mask when staging
Now you can compare inpainted area with new image data
2023-09-27 17:45:39 +10:00
03e463dc89 fix(ui): reset canvas batchIds on staging area init/discard/commit
This prevents the bbox from being used inadvertantly during canvas generation
2023-09-27 17:45:39 +10:00
d467e138a4 fix(ui): canvas is staging if is listening for batch ids 2023-09-27 17:45:39 +10:00
ba4aaea45b fix(ui): memoize event handlers on bounding box 2023-09-27 17:45:39 +10:00
53eb23b8b6 fix(ui): fix canvas staging images offset from bounding box
The staging area used the stage bbox, not the staging area bbox.
2023-09-27 17:45:39 +10:00
8b969053e7 fix: SDXL Refiner using the incorrect node during inpainting 2023-09-27 17:42:42 +10:00
98a076260b fix(ui): only disable cancel item button if value is null/undefined
0 is falsy and the `item_id` is an integer
2023-09-27 14:28:26 +10:00
164877b610 Merge branch 'main' into main 2023-09-27 12:28:24 +10:00
b3f4f28d76 fix: Canvas pull getting cropped for Control Images 2023-09-27 12:25:45 +10:00
acee4bd282 fix: Always use bbox bounds for Controlnet Image (canvas) 2023-09-27 12:25:45 +10:00
fc9a7320eb Update to be more accurate 2023-09-27 12:21:20 +10:00
7c0a083b13 Merge branch 'invoke-ai:main' into main 2023-09-27 11:26:26 +10:00
50d254fdb7 fix(ui): fix types for cache setting 2023-09-27 10:29:19 +10:00
0cfc1c5f86 fix(ui): save cache setting to workflow
Do not strip out unknown values. Quick fix, probably not the best way to handle this.
2023-09-27 10:29:19 +10:00
f35dfa06bb Merge branch 'invoke-ai:main' into main 2023-09-27 10:10:52 +10:00
407bca5063 fix merges 2023-09-27 10:10:09 +10:00
1419977e89 feat(ui): update cache status on queue event
It was polling every 5s before. No need - just invalidate the tag when we have a queue item status change event.
2023-09-27 08:56:14 +10:00
a953944894 feat(ui): updatable edges in workflow editor (#4701)
- Drag the end of an edge away from its handle to disconnect it
- Drop in empty space to delete the edge
- Drop on valid handle to reconnect it
- Update connection logic slightly to allow edge updates
2023-09-26 15:54:35 +00:00
a4cdaa245e feat(ui): improve error handling (#4699)
* feat(ui): add error handling for enqueueBatch route, remove sessions

This re-implements the handling for the session create/invoke errors, but for batches.

Also remove all references to the old sessions routes in the UI.

* feat(ui): improve canvas image error UI

* make canvas error state gray instead of red

---------

Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
2023-09-26 15:24:53 +00:00
105a4234b0 fix(ui): fix color picker on canvas (#4706)
Resolves  #4667

Co-authored-by: Mary Hipp Rogers <maryhipp@gmail.com>
2023-09-26 14:11:12 +00:00
34c563060f feat(ui): store active tab as name, not index (#4697)
This fixes an issue with tab changing when some tabs are disabled.
2023-09-26 14:06:39 +00:00
d45c47db81 fix(backend): remove extra cache arg (#4698) 2023-09-26 10:03:48 -04:00
c771a4027f Give user option to disable the configure TUI during installation (#4676)
## What type of PR is this? (check all applicable)

- [X] Feature


## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [X] No - this should go into release notes.

## Description

During installation, the installer will now ask the user whether they
wish to perform a manual or automatic configuration of invokeai. If they
choose automatic (the default), then the install is performed without
running the TUI of the `invokeai-configure` script. Otherwise the
console-based interface is activated as usual.

This script also bumps up the default model RAM cache size to 7.5, which
improves performance on SDXL models.
2023-09-26 08:15:48 -04:00
3fd27b1aa9 run correct version of black 2023-09-26 08:03:34 -04:00
d59e534cad use heuristic to select RAM cache size during headless install; blackified 2023-09-26 08:03:34 -04:00
0c97a1e7e7 give user option to disable the configure TUI during installation 2023-09-26 08:03:34 -04:00
c8b306d9f8 Update CONTROLNET.md 2023-09-26 19:20:03 +10:00
edd2c54b9e add cache 2023-09-26 18:28:52 +10:00
727cc0dafe add pics 2023-09-26 17:51:08 +10:00
4530bd46dc Added IP-Adapter 2023-09-26 17:30:34 +10:00
c8b109f52e Add 'Random Float' node <3 (#4581)
* Add 'Random Float' node <3

does what it says on the tin :)

* Add random float + random seeded float nodes

altered my random float node as requested by Millu, kept the seeded version as an alternate variant for those that would like to control the randomization seed :)

* Update math.py

* Update math.py

* feat(nodes): standardize fields to match other nodes

---------

Co-authored-by: Millun Atluri <Millu@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2023-09-26 05:57:44 +00:00
a2613948d8 Feature/lru caching 2 (#4657)
* fix(nodes): do not disable invocation cache delete methods

When the runtime disabled flag is on, do not skip the delete methods. This could lead to a hit on a missing resource.

Do skip them when the cache size is 0, because the user cannot change this (must restart app to change it).

* fix(nodes): do not use double-underscores in cache service

* Thread lock for cache

* Making cache LRU

* Bug fixes

* bugfix

* Switching to one Lock and OrderedDict cache

* Removing unused imports

* Move lock cache instance

* Addressing PR comments

---------

Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: Martin Kristiansen <martin@modyfi.io>
2023-09-26 03:42:09 +00:00
f8392b2f78 Maryhipp/hide use cache checkbox if disabled (#4691)
* add skeleton loading state for queue lit

* hide use cache checkbox if cache is disabled

* undo accidental add

* feat(ui): hide node footer entirely if nothing to show there

---------

Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
2023-09-26 03:26:15 +00:00
358116bc22 feat(ui): use spinner for queue loading state
Skeletons are for when we know the number of specific content items that are loading. When the queue is loading, we don't know how many items there are, or how many will load, so the whole list should be replaced with loading state.

The previous behaviour rendered a static number of skeletons. That number would rarely be the right number - the app shouldn't say "I'm loading 7 queue items", then load none, or load 50.

A future enhancement could use the queue item skeleton component and go by the total number of queue items, as reported by the queue status. I tried this but had some layout jankiness, not worth the effort right now.

The queue item skeleton component's styling was updated to support this future enhancement, making it exactly the same size as a queue item (it was a bit smaller before).
2023-09-26 13:19:49 +10:00
1e3590111d Remove dangling debug statement (#4695)
## What type of PR is this? (check all applicable)

- [X] Bug Fix

## Description

I left a dangling debug statement in a recent merged PR (#4674 ). This
removes it.
2023-09-26 11:08:10 +10:00
063b800280 Merge branch 'main' into bugfix/remove-debug-statement 2023-09-26 10:39:29 +10:00
3935bf92c8 Add image enhance node to composition pack in communitynods, 9 more n… (#4693)
Updates my Image & Mask Composition Pack from 4 to 14 nodes, and moves
the Enhance Image node into it.

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [X] Documentation Update
- [X] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] No, because:
This is an update of my existing community nodes entries.
      
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No


## Description
Adds 9 more nodes to my Image & Mask Composition pack including Clipseg,
Image Layer Blend, Masked Latent/Noise Blend, Image Dilate/Erode,
Shadows/Highlights/Midtones masks from image, and more.

## Related Tickets & Documents

n/a

## QA Instructions, Screenshots, Recordings

<!-- 
Please provide steps on how to test changes, any hardware or 
software specifications as well as any other pertinent information. 
-->

## Added/updated tests?

- [ ] Yes
- [X] No : out of scope, tested the nodes, will integrate tests with my
own repo in time as is helpful
2023-09-26 09:41:28 +10:00
066e09b517 remove dangling debug statement 2023-09-25 19:30:41 -04:00
869b4a8d49 Add image enhance node to composition pack in communitynods, 9 more nodes
Adds 9 more of my nodes to the Image & Mask Composition Pack in the community nodes page, and integrates the Enhance Image node into that pack as well (formerly it was its own entry).
2023-09-25 18:49:04 -04:00
399ebe443e Fix IP-Adapter calculation of memory footprint. 2023-09-25 18:28:10 -04:00
13919ff300 remove unused vars 2023-09-25 17:45:29 -04:00
634e5652ef add skeleton loading state for queue lit 2023-09-25 17:45:29 -04:00
9bdc718df5 Update 020_INSTALL_MANUAL.md (#4685)
Add some instructions about installing the frontend toolchain when doing
a git-based install.

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [x] Documentation Update
- [ ] Community Node Submission

## Description

[Update
020_INSTALL_MANUAL.md](73ca8ccdb3)

Add some instructions about installing the frontend toolchain when doing
a git-based install.
2023-09-25 21:43:08 +10:00
73ca8ccdb3 Update 020_INSTALL_MANUAL.md
Add some instructions about installing the frontend toolchain when doing a git-based install.
2023-09-25 21:17:11 +10:00
f37ffda966 replace case statements with if/else to support python 3.9 2023-09-25 18:33:39 +10:00
5a9777d443 fix: Auto switch Control Adapter processor to Color on relevant models (#4683)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:

      
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No


## Description


## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
automatically close the issue.
-->

- Related Issue #
- Closes #

## QA Instructions, Screenshots, Recordings

<!-- 
Please provide steps on how to test changes, any hardware or 
software specifications as well as any other pertinent information. 
-->

## Added/updated tests?

- [ ] Yes
- [ ] No : _please replace this line with details on why tests
      have not been included_

## [optional] Are there any post deployment tasks we need to perform?
2023-09-25 12:48:24 +05:30
8072c05ee0 Merge branch 'main' into color-map-auto 2023-09-25 12:48:12 +05:30
75ff4f4ca3 fix: Auto switch Control Adapter processor to Color on relevant models 2023-09-25 12:47:43 +05:30
30df123221 fix(ui): fix circular dependency (#4679)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission

## Description

This is actually a platform-specific issue. `madge` is complaining about
a circular dependency on a single file -
`invokeai/frontend/web/src/features/queue/store/nanoStores.ts`. In that
file, we import from the `nanostores` package. Very similar name to the
file itself.

The error only appears on Windows and macOS, I imagine because those
systems both resolve `nanostores` to itself before resolving to the
package.

The solution is simple - rename `nanoStores.ts`. It's now
`queueNanoStore.ts`.


## Related Tickets & Documents

https://discord.com/channels/1020123559063990373/1155434451979993140

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
automatically close the issue.
-->
2023-09-25 12:47:05 +05:30
06193ddbe8 Merge branch 'main' into fix/ui/fix-circular-dep 2023-09-25 12:45:01 +05:30
ce5122f87c Add installer support for ip-adapters (#4677)
## What type of PR is this? (check all applicable)

- [X] Feature


## Have you discussed this change with the InvokeAI team?
- [X] Yes

      
## Have you updated all relevant documentation?
- [X] Yes

## Description

This PR adds support for selecting and installing IP-Adapters at
configure time. The user is offered the four existing InvokeAI IP
Adapters in the UI as shown below. The matching image encoders are
selected and installed behind the scenes. That is, if the user selects
one of the three sd15 adapters, then the SD encoder will be installed.
If they select the sdxl adapter, then the SDXL encoder will be
installed.


![image](https://github.com/invoke-ai/InvokeAI/assets/111189/19f46401-99fb-4f7b-9a5e-8f2efd0a5b77)

Note that the automatic selection of the encoder does not work when the
installer is run in headless mode. I may be able to fix that soon, but
I'm out of time today.
2023-09-24 23:29:57 -04:00
43ebd68313 Merge branch 'main' into install/install-ip-adapters 2023-09-24 23:19:25 -04:00
ec19fcafb1 fix(ui): fix circular dependency
This is actually a platform-specific issue. `madge` is complaining about a circular dependency on a single file - `invokeai/frontend/web/src/features/queue/store/nanoStores.ts`. In that file, we import from the `nanostores` package. Very similar name to the file itself.

The error only appears on Windows and macOS, I imagine because those systems both resolve `nanostores` to itself before resolving to the package.

The solution is simple - rename `nanoStores.ts`. It's now `queueNanoStore.ts`.
2023-09-25 10:45:38 +10:00
6fcc7d4c4b Re-enable button for seeds set to zero
Change the statement to explicitly look for null and undefined so it doesn't fail to re-enable the button on images with seeds set to zero.
2023-09-25 10:33:35 +10:00
912087e4dc blackify 2023-09-24 19:00:38 -04:00
593fb95213 ip_adapter_sd15 & its encoder will now be installed by default during headless install 2023-09-24 19:00:21 -04:00
6d821b32d3 fix(ui): fix hidden dropdowns
Notably in the change board modal.
2023-09-25 08:13:16 +10:00
297f96c16b add installer support for ip-adapters 2023-09-24 17:31:08 -04:00
0e53b27655 Removing logging import from api_api.py 2023-09-25 07:25:32 +10:00
35ae9f6e71 fix probing for ip_adapter folders (#4669)
## What type of PR is this? (check all applicable)

- [X] Bug Fix
- [ ] Optimizatio

## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [X] Np

      
## Have you updated all relevant documentation?
- [ ] Yes
- [X] No


## Description

ip_adapter models live in a folder containing the file
`image_encoder.txt` and a safetensors file. The load-time probe for new
models was detecting the files contained within the folder rather than
the folder itself, and so models.yaml was not getting correctly updated.
This fixes the issue.

## Added/updated tests?

- [ ] Yes
- [ ] No : _please replace this line with details on why tests
      have not been included_

## [optional] Are there any post deployment tasks we need to perform?
2023-09-24 15:45:46 -04:00
a1d9e6b871 Merge branch 'main' into bugfix/probe_ip_adapter 2023-09-24 15:39:43 -04:00
f05379f965 Enable v_prediction for sd-1 models (#4674)
## What type of PR is this? (check all applicable)

- [X] Feature

## Have you discussed this change with the InvokeAI team?
- [X] Yes

      
## Have you updated all relevant documentation?
- [X] Yes

## Description

It turns out that there are a few SD-1 models that use the
`v_prediction` SchedulerPredictionType. Examples here:
https://huggingface.co/zatochu/EasyFluff/tree/main . Previously we only
allowed the user to set the prediction type for sd-2 models. This PR
does three things:

1. Add a new checkpoint configuration file `v1-inference-v.yaml`. This
will install automatically on new installs, but for existing installs
users will need to update and then run `invokeai-configure` to get it.
2. Change the prompt on the web model install page to indicate that some
SD-1 models use the "v_prediction" method
3. Provide backend support for sd-1 models that use the v_prediction
method.

## Related Tickets & Documents

<!--
For pull requests that relate or close an issue, please include them
below. 

For example having the text: "closes #1234" would connect the current
pull
request to issue 1234.  And when we merge the pull request, Github will
automatically close the issue.
-->

- Related Issue #
- Closes #4277 

## QA Instructions, Screenshots, Recordings

Update, run `invoke-ai-configure --yes --skip-sd --skip-support`, and
then use the web interface to install
https://huggingface.co/zatochu/EasyFluff/resolve/main/EasyFluffV11.2.safetensors
with the prediction type set to "v_prediction." Check that the installed
model uses configuration `v1-inference-v.yaml`.

If "None" is selected from the install menu, check that SD-1 models
default to `v1-inference.yaml` and SD-2 default to
`v2-inference-v.yaml`.

Also try installing a checkpoint at a local path if a like-named config
.yaml file is located next to it in the same directory. This should
override everything else and use the local path .yaml.

## Added/updated tests?

- [ ] Yes
- [X] No
2023-09-24 15:24:36 -04:00
e34e6d6e80 enable v_prediction for sd-1 models 2023-09-24 12:22:29 -04:00
86cb53342a fix probing for ip_adapter folders 2023-09-23 22:32:03 -04:00
e3de996525 Rename getLogger() to get_logger() (#4275)
## What type of PR is this? (check all applicable)

- [X] Refactor
## Have you discussed this change with the InvokeAI team?

- [ ] Yes
- [X] No, because: trivial fix

      
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No

## Description

It annoyed me that the class method to get the invokeai logger was
`InvokeAILogger.getLogger()`. We do not use camelCase anywhere else. So
this PR renames the method `get_logger()`.
2023-09-23 14:56:23 -07:00
25a71a1791 Merge branch 'main' into refactor/rename-get-logger 2023-09-23 14:49:07 -07:00
d16583ad1c Unpin Safetensors dependencies, safeguard against breaking changes 2023-09-23 10:23:05 -04:00
46db1dd18f feat(ui): allow numbers to connect to strings (#4653)
## What type of PR is this? (check all applicable)

- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Description

Pydantic handles the casting so this is always safe.

Also de-duplicate some validation logic code that was needlessly
duplicated.
2023-09-23 10:09:59 +05:30
4c9344b0ee Merge branch 'main' into feat/ui/allow-number-to-string 2023-09-22 21:02:28 -05:00
cba31efd78 fix(ui): do not process gallery logic for image primitive node 2023-09-23 10:02:55 +10:00
4d01b5c0f2 fix(ui): hide workflow and gallery checkboxes on image primitive
This node doesn't actually *save* the image, so these checkboxes do nothing on it.
2023-09-23 10:02:55 +10:00
e02af8f518 fix(ui): fix node glow styling 2023-09-23 10:02:55 +10:00
c485cf568b feat: Add Color PreProcessor to Linear UI 2023-09-22 17:30:12 -04:00
51451cbf21 fix: Handle cases where tile size > image size 2023-09-22 17:30:12 -04:00
0363a06963 feat: Add Color Map Preprocessor 2023-09-22 17:30:12 -04:00
cc280cbef1 feat(ui): refactor informational popover
- Change translations to use arrays of paragraphs instead of a single paragraph.
- Change component to accept a `feature` prop to identify the feature which the popover describes.
- Add optional `wrapperProps`: passed to the wrapper element, allowing more flexibility when using the popover
- Add optional `popoverProps`: passed to the `<Popover />` component, allowing for overriding individual instances of the popover's props
- Move definitions of features and popover settings to `invokeai/frontend/web/src/common/components/IAIInformationalPopover/constants.ts`
  - Add some type safety to the `feature` prop
  - Edit `POPOVER_DATA` to provide `image`, `href`, `buttonLabel`, and any popover props. The popover props are applied to all instances of the popover for the given feature. Note that the component prop `popoverProps` will override settings here.
- Remove the popover's arrow. Because the popover is wrapping groups of components, sometimes the error ends up pointing to nothing, which looks kinda janky. I've just removed the arrow entirely, but feel free to add it back if you think it looks better.
- Use a `link` variant button with external link icon to better communicate that clicking the button will open a new tab.
- Default the link button label to "Learn More" (if a label is provided, that will be used instead)
- Make default position `top`, but set manually set some to `right` - namely, anything with a dropdown. This prevents the popovers from obscuring or being obscured by the dropdowns.
- Do a bit more restructuring of the Popover component itself, and how it is integrated with other components
- More ref forwarding
- Make the open delay 1s
- Set the popovers to use lazy mounting (eg do not mount until the user opens the thing)
- Update the verbiage for many popover items and add missing dynamic prompts stuff
2023-09-22 13:23:26 -04:00
7544eadd48 fix(nodes): do not use double-underscores in cache service 2023-09-22 13:15:03 -04:00
7d683b4db6 fix(nodes): do not disable invocation cache delete methods
When the runtime disabled flag is on, do not skip the delete methods. This could lead to a hit on a missing resource.

Do skip them when the cache size is 0, because the user cannot change this (must restart app to change it).
2023-09-22 13:15:03 -04:00
60b3c6a201 feat(nodes): provide board_id in image creation 2023-09-22 10:11:20 -04:00
88c8cb61f0 feat(ui): update linear UI to use new board field on save_image
- No longer need to make network request to add image to board after it's finished - removed
- Update linear graphs & upscale graph to save image to the board
- Update autoSwitch logic so when image is generated we still switch to the right board
2023-09-22 10:11:20 -04:00
43fbac26df feat: move board logic to save_image node
- Remove the add-to-board node
- Create `BoardField` field type & add it to `save_image` node
- Add UI for `BoardField`
- Tighten up some loose types
- Make `save_image` node, in workflow editor, default to not intermediate
- Patch bump `save_image`
2023-09-22 10:11:20 -04:00
627444e17c Add images to a board through nodes 2023-09-22 10:11:20 -04:00
5601858f4f feat(ui): allow numbers to connect to strings
Pydantic handles the casting so this is always safe.

Also de-duplicate some validation logic code that was needlessly duplicated.
2023-09-22 21:51:08 +10:00
b5e1ba34b3 Merge branch 'main' into refactor/rename-get-logger 2023-09-07 23:19:59 +10:00
58aa159a50 fix(backend): fix remaining instances of getLogger() 2023-09-05 10:43:30 +10:00
d8f7c19030 Merge branch 'main' into refactor/rename-get-logger 2023-09-05 10:37:53 +10:00
24132a7950 Merge branch 'main' into refactor/rename-get-logger 2023-08-28 11:38:37 +10:00
45d172d5a8 Merge branch 'main' into refactor/rename-get-logger 2023-08-20 16:08:32 -04:00
3cb6d333f6 Merge branch 'main' into refactor/rename-get-logger 2023-08-17 20:31:30 -04:00
4570702dd0 hotfix for crashing api 2023-08-17 20:17:10 -04:00
1d107f30e5 remove getLogger() completely 2023-08-17 19:17:38 -04:00
79084e9e20 Merge branch 'main' into refactor/rename-get-logger 2023-08-17 19:01:17 -04:00
fc9b4539a3 Merge branch 'main' into refactor/rename-get-logger 2023-08-16 09:19:52 -04:00
09ef57718e fix docs 2023-08-14 20:20:35 -04:00
cab8239ba8 add get_logger() as alias for getLogger() 2023-08-14 20:18:09 -04:00
544 changed files with 26664 additions and 12355 deletions

View File

@ -28,7 +28,7 @@ jobs:
run: twine check dist/*
- name: check PyPI versions
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3'
if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')
run: |
pip install --upgrade requests
python -c "\

View File

@ -47,34 +47,9 @@ pip install ".[dev,test]"
These are optional groups of packages which are defined within the `pyproject.toml`
and will be required for testing the changes you make to the code.
### Running Tests
We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
be found under the `./tests` folder and can be run with a single `pytest`
command. Optionally, to review test coverage you can append `--cov`.
```zsh
pytest --cov
```
Test outcomes and coverage will be reported in the terminal. In addition a more
detailed report is created in both XML and HTML format in the `./coverage`
folder. The HTML one in particular can help identify missing statements
requiring tests to ensure coverage. This can be run by opening
`./coverage/html/index.html`.
For example.
```zsh
pytest --cov; open ./coverage/html/index.html
```
??? info "HTML coverage report output"
![html-overview](../assets/contributing/html-overview.png)
![html-detail](../assets/contributing/html-detail.png)
### Tests
See the [tests documentation](./TESTS.md) for information about running and writing tests.
### Reloading Changes
Experimenting with changes to the Python source code is a drag if you have to re-start the server —
@ -167,6 +142,23 @@ and so you'll have access to the same python environment as the InvokeAI app.
This is _super_ handy.
#### Enabling Type-Checking with Pylance
We use python's typing system in InvokeAI. PR reviews will include checking that types are present and correct. We don't enforce types with `mypy` at this time, but that is on the horizon.
Using a code analysis tool to automatically type check your code (and types) is very important when writing with types. These tools provide immediate feedback in your editor when types are incorrect, and following their suggestions lead to fewer runtime bugs.
Pylance, installed at the beginning of this guide, is the de-facto python LSP (language server protocol). It provides type checking in the editor (among many other features). Once installed, you do need to enable type checking manually:
- Open a python file
- Look along the status bar in VSCode for `{ } Python`
- Click the `{ }`
- Turn type checking on - basic is fine
You'll now see red squiggly lines where type issues are detected. Hover your cursor over the indicated symbols to see what's wrong.
In 99% of cases when the type checker says there is a problem, there really is a problem, and you should take some time to understand and resolve what it is pointing out.
#### Debugging configs with `launch.json`
Debugging configs are managed in a `launch.json` file. Like most VSCode configs,

View File

@ -0,0 +1,89 @@
# InvokeAI Backend Tests
We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
## Fast vs. Slow
All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
'Fast' tests are run to validate every PR, and are fast enough that they can be run routinely during development.
'Slow' tests are currently only run manually on an ad-hoc basis. In the future, they may be automated to run nightly. Most developers are only expected to run the 'slow' tests that directly relate to the feature(s) that they are working on.
As a rule of thumb, tests should be marked as 'slow' if there is a chance that they take >1s (e.g. on a CPU-only machine with slow internet connection). Common examples of slow tests are tests that depend on downloading a model, or running model inference.
## Running Tests
Below are some common test commands:
```bash
# Run the fast tests. (This implicitly uses the configured default option: `-m "not slow"`.)
pytest tests/
# Equivalent command to run the fast tests.
pytest tests/ -m "not slow"
# Run the slow tests.
pytest tests/ -m "slow"
# Run the slow tests from a specific file.
pytest tests/path/to/slow_test.py -m "slow"
# Run all tests (fast and slow).
pytest tests -m ""
```
## Test Organization
All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
## Tests that depend on models
There are a few things to keep in mind when adding tests that depend on models.
1. If a required model is not already present, it should automatically be downloaded as part of the test setup.
2. If a model is already downloaded, it should not be re-downloaded unnecessarily.
3. Take reasonable care to keep the total number of models required for the tests low. Whenever possible, re-use models that are already required for other tests. If you are adding a new model, consider including a comment to explain why it is required/unique.
There are several utilities to help with model setup for tests. Here is a sample test that depends on a model:
```python
import pytest
import torch
from invokeai.backend.model_management.models.base import BaseModelType, ModelType
from invokeai.backend.util.test_utils import install_and_load_model
@pytest.mark.slow
def test_model(model_installer, torch_device):
model_info = install_and_load_model(
model_installer=model_installer,
model_path_id_or_url="HF/dummy_model_id",
model_name="dummy_model",
base_model=BaseModelType.StableDiffusion1,
model_type=ModelType.Dummy,
)
dummy_input = build_dummy_input(torch_device)
with torch.no_grad(), model_info as model:
model.to(torch_device, dtype=torch.float32)
output = model(dummy_input)
# Validate output...
```
## Test Coverage
To review test coverage, append `--cov` to your pytest command:
```bash
pytest tests/ --cov
```
Test outcomes and coverage will be reported in the terminal. In addition, a more detailed report is created in both XML and HTML format in the `./coverage` folder. The HTML output is particularly helpful in identifying untested statements where coverage should be improved. The HTML report can be viewed by opening `./coverage/html/index.html`.
??? info "HTML coverage report output"
![html-overview](../assets/contributing/html-overview.png)
![html-detail](../assets/contributing/html-detail.png)

View File

@ -12,7 +12,7 @@ To get started, take a look at our [new contributors checklist](newContributorCh
Once you're setup, for more information, you can review the documentation specific to your area of interest:
* #### [InvokeAI Architecure](../ARCHITECTURE.md)
* #### [Frontend Documentation](development_guides/contributingToFrontend.md)
* #### [Frontend Documentation](./contributingToFrontend.md)
* #### [Node Documentation](../INVOCATIONS.md)
* #### [Local Development](../LOCAL_DEVELOPMENT.md)
@ -38,9 +38,9 @@ There are two paths to making a development contribution:
If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
For frontend related work, **@pyschedelicious** is the best person to reach out to.
For frontend related work, **@psychedelicious** is the best person to reach out to.
For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.
For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@psychedelicious**.
## **What does the Code of Conduct mean for me?**

View File

@ -10,4 +10,4 @@ When updating or creating documentation, please keep in mind InvokeAI is a tool
## Help & Questions
Please ping @imic1 or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.
Please ping @imic or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.

View File

@ -1,13 +1,11 @@
---
title: ControlNet
title: Control Adapters
---
# :material-loupe: ControlNet
# :material-loupe: Control Adapters
## ControlNet
ControlNet
ControlNet is a powerful set of features developed by the open-source
community (notably, Stanford researcher
[**@ilyasviel**](https://github.com/lllyasviel)) that allows you to
@ -20,7 +18,7 @@ towards generating images that better fit your desired style or
outcome.
### How it works
#### How it works
ControlNet works by analyzing an input image, pre-processing that
image to identify relevant information that can be interpreted by each
@ -30,7 +28,7 @@ composition, or other aspects of the image to better achieve a
specific result.
### Models
#### Models
InvokeAI provides access to a series of ControlNet models that provide
different effects or styles in your generated images. Currently
@ -96,6 +94,8 @@ A model that generates normal maps from input images, allowing for more realisti
**Image Segmentation**:
A model that divides input images into segments or regions, each of which corresponds to a different object or part of the image. (More details coming soon)
**QR Code Monster**:
A model that helps generate creative QR codes that still scan. Can also be used to create images with text, logos or shapes within them.
**Openpose**:
The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image.
@ -120,7 +120,7 @@ With Pix2Pix, you can input an image into the controlnet, and then "instruct" th
Each of these models can be adjusted and combined with other ControlNet models to achieve different results, giving you even more control over your image generation process.
## Using ControlNet
### Using ControlNet
To use ControlNet, you can simply select the desired model and adjust both the ControlNet and Pre-processor settings to achieve the desired result. You can also use multiple ControlNet models at the same time, allowing you to achieve even more complex effects or styles in your generated images.
@ -132,3 +132,31 @@ Weight - Strength of the Controlnet model applied to the generation for the sect
Start/End - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke.
## IP-Adapter
[IP-Adapter](https://ip-adapter.github.io) is a tooling that allows for image prompt capabilities with text-to-image diffusion models. IP-Adapter works by analyzing the given image prompt to extract features, then passing those features to the UNet along with any other conditioning provided.
![IP-Adapter + T2I](https://github.com/tencent-ailab/IP-Adapter/raw/main/assets/demo/ip_adpter_plus_multi.jpg)
![IP-Adapter + IMG2IMG](https://github.com/tencent-ailab/IP-Adapter/blob/main/assets/demo/image-to-image.jpg)
#### Installation
There are several ways to install IP-Adapter models with an existing InvokeAI installation:
1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [5] to download models.
2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.
3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder.
#### Using IP-Adapter
IP-Adapter can be used by navigating to the *Control Adapters* options and enabling IP-Adapter.
IP-Adapter requires an image to be used as the Image Prompt. It can also be used in conjunction with text prompts, Image-to-Image, Inpainting, Outpainting, ControlNets and LoRAs.
Each IP-Adapter has two settings that are applied to the IP-Adapter:
* Weight - Strength of the IP-Adapter model applied to the generation for the section, defined by start/end
* Start/End - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the IP-Adapter applied.

View File

@ -256,6 +256,10 @@ manager, please follow these steps:
*highly recommended** if your virtual environment is located outside of
your runtime directory.
!!! tip
On linux, it is recommended to run invokeai with the following env var: `MALLOC_MMAP_THRESHOLD_=1048576`. For example: `MALLOC_MMAP_THRESHOLD_=1048576 invokeai --web`. This helps to prevent memory fragmentation that can lead to memory accumulation over time. This env var is set automatically when running via `invoke.sh`.
10. Render away!
Browse the [features](../features/index.md) section to learn about all the
@ -296,8 +300,18 @@ code for InvokeAI. For this to work, you will need to install the
on your system, please see the [Git Installation
Guide](https://github.com/git-guides/install-git)
You will also need to install the [frontend development toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md).
If you have a "normal" installation, you should create a totally separate virtual environment for the git-based installation, else the two may interfere.
> **Why do I need the frontend toolchain**?
>
> The InvokeAI project uses trunk-based development. That means our `main` branch is the development branch, and releases are tags on that branch. Because development is very active, we don't keep an updated build of the UI in `main` - we only build it for production releases.
>
> That means that between releases, to have a functioning application when running directly from the repo, you will need to run the UI in dev mode or build it regularly (any time the UI code changes).
1. Create a fork of the InvokeAI repository through the GitHub UI or [this link](https://github.com/invoke-ai/InvokeAI/fork)
1. From the command line, run this command:
2. From the command line, run this command:
```bash
git clone https://github.com/<your_github_username>/InvokeAI.git
```
@ -305,10 +319,10 @@ Guide](https://github.com/git-guides/install-git)
This will create a directory named `InvokeAI` and populate it with the
full source code from your fork of the InvokeAI repository.
2. Activate the InvokeAI virtual environment as per step (4) of the manual
3. Activate the InvokeAI virtual environment as per step (4) of the manual
installation protocol (important!)
3. Enter the InvokeAI repository directory and run one of these
4. Enter the InvokeAI repository directory and run one of these
commands, based on your GPU:
=== "CUDA (NVidia)"
@ -334,11 +348,15 @@ installation protocol (important!)
Be sure to pass `-e` (for an editable install) and don't forget the
dot ("."). It is part of the command.
You can now run `invokeai` and its related commands. The code will be
5. Install the [frontend toolchain](https://github.com/invoke-ai/InvokeAI/blob/main/docs/contributing/contribution_guides/contributingToFrontend.md) and do a production build of the UI as described.
6. You can now run `invokeai` and its related commands. The code will be
read from the repository, so that you can edit the .py source files
and watch the code's behavior change.
4. If you wish to contribute to the InvokeAI project, you are
When you pull in new changes to the repo, be sure to re-build the UI.
7. If you wish to contribute to the InvokeAI project, you are
encouraged to establish a GitHub account and "fork"
https://github.com/invoke-ai/InvokeAI into your own copy of the
repository. You can then use GitHub functions to create and submit

View File

@ -171,3 +171,16 @@ subfolders and organize them as you wish.
The location of the autoimport directories are controlled by settings
in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).
### Installing models that live in HuggingFace subfolders
On rare occasions you may need to install a diffusers-style model that
lives in a subfolder of a HuggingFace repo id. In this event, simply
add ":_subfolder-name_" to the end of the repo id. For example, if the
repo id is "monster-labs/control_v1p_sd15_qrcode_monster" and the model
you wish to fetch lives in a subfolder named "v2", then the repo id to
pass to the various model installers should be
```
monster-labs/control_v1p_sd15_qrcode_monster:v2
```

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@ -4,12 +4,12 @@ The workflow editor is a blank canvas allowing for the use of individual functio
If you're not familiar with Diffusion, take a look at our [Diffusion Overview.](../help/diffusion.md) Understanding how diffusion works will enable you to more easily use the Workflow Editor and build workflows to suit your needs.
## UI Features
## Features
### Linear View
The Workflow Editor allows you to create a UI for your workflow, to make it easier to iterate on your generations.
To add an input to the Linear UI, right click on the input and select "Add to Linear View".
To add an input to the Linear UI, right click on the input label and select "Add to Linear View".
The Linear UI View will also be part of the saved workflow, allowing you share workflows and enable other to use them, regardless of complexity.
@ -25,6 +25,10 @@ Any node or input field can be renamed in the workflow editor. If the input fiel
* Backspace/Delete to delete a node
* Shift+Click to drag and select multiple nodes
### Node Caching
Nodes have a "Use Cache" option in their footer. This allows for performance improvements by using the previously cached values during the workflow processing.
## Important Concepts

View File

@ -8,26 +8,42 @@ To download a node, simply download the `.py` node file from the link and add it
To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor.
## Community Nodes
- Community Nodes
+ [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
+ [Film Grain](#film-grain)
+ [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
+ [GPT2RandomPromptMaker](#gpt2randompromptmaker)
+ [Grid to Gif](#grid-to-gif)
+ [Halftone](#halftone)
+ [Ideal Size](#ideal-size)
+ [Image and Mask Composition Pack](#image-and-mask-composition-pack)
+ [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
+ [Image Picker](#image-picker)
+ [Load Video Frame](#load-video-frame)
+ [Make 3D](#make-3d)
+ [Oobabooga](#oobabooga)
+ [Prompt Tools](#prompt-tools)
+ [Retroize](#retroize)
+ [Size Stepper Nodes](#size-stepper-nodes)
+ [Text font to Image](#text-font-to-image)
+ [Thresholding](#thresholding)
+ [XY Image to Grid and Images to Grids nodes](#xy-image-to-grid-and-images-to-grids-nodes)
- [Example Node Template](#example-node-template)
- [Disclaimer](#disclaimer)
- [Help](#help)
### FaceTools
**Description:** FaceTools is a collection of nodes created to manipulate faces as you would in Unified Canvas. It includes FaceMask, FaceOff, and FacePlace. FaceMask autodetects a face in the image using MediaPipe and creates a mask from it. FaceOff similarly detects a face, then takes the face off of the image by adding a square bounding box around it and cropping/scaling it. FacePlace puts the bounded face image from FaceOff back onto the original image. Using these nodes with other inpainting node(s), you can put new faces on existing things, put new things around existing faces, and work closer with a face as a bounded image. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control on FaceMask and FaceOff. See GitHub repository below for usage examples.
**Node Link:** https://github.com/ymgenesis/FaceTools/
**FaceMask Output Examples**
![5cc8abce-53b0-487a-b891-3bf94dcc8960](https://github.com/invoke-ai/InvokeAI/assets/25252829/43f36d24-1429-4ab1-bd06-a4bedfe0955e)
![b920b710-1882-49a0-8d02-82dff2cca907](https://github.com/invoke-ai/InvokeAI/assets/25252829/7660c1ed-bf7d-4d0a-947f-1fc1679557ba)
![71a91805-fda5-481c-b380-264665703133](https://github.com/invoke-ai/InvokeAI/assets/25252829/f8f6a2ee-2b68-4482-87da-b90221d5c3e2)
--------------------------------
### Ideal Size
### Depth Map from Wavefront OBJ
**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
**Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation.
**Node Link:** https://github.com/JPPhoto/ideal-size-node
To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations.
**Node Link:** https://github.com/dwringer/depth-from-obj-node
**Example Usage:**
</br><img src="https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg" width="500" />
--------------------------------
### Film Grain
@ -37,22 +53,19 @@ To use a community workflow, download the the `.json` node graph file and load i
**Node Link:** https://github.com/JPPhoto/film-grain-node
--------------------------------
### Image Picker
### Generative Grammar-Based Prompt Nodes
**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose.
**Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no nonterminal terms remain in the string.
**Node Link:** https://github.com/JPPhoto/image-picker-node
This includes 3 Nodes:
- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output)
- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading
- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts.
--------------------------------
### Retroize
**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes
**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images.
**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/
**Retroize Output Examples**
![image](https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974)
**Example Usage:**
</br><img src="https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg" width="500" />
--------------------------------
### GPT2RandomPromptMaker
@ -65,31 +78,133 @@ To use a community workflow, download the the `.json` node graph file and load i
Generated Prompt: An enchanted weapon will be usable by any character regardless of their alignment.
![9acf5aef-7254-40dd-95b3-8eac431dfab0 (1)](https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c)
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/8496ba09-bcdd-4ff7-8076-ff213b6a1e4c" width="200" />
--------------------------------
### Grid to Gif
**Description:** One node that turns a grid image into an image collection, one node that turns an image collection into a gif.
**Node Link:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/GridToGif.py
**Example Node Graph:** https://github.com/mildmisery/invokeai-GridToGifNode/blob/main/Grid%20to%20Gif%20Example%20Workflow.json
**Output Examples**
<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/input.png" width="300" />
<img src="https://raw.githubusercontent.com/mildmisery/invokeai-GridToGifNode/main/output.gif" width="300" />
--------------------------------
### Halftone
**Description**: Halftone converts the source image to grayscale and then performs halftoning. CMYK Halftone converts the image to CMYK and applies a per-channel halftoning to make the source image look like a magazine or newspaper. For both nodes, you can specify angles and halftone dot spacing.
**Node Link:** https://github.com/JPPhoto/halftone-node
**Example**
Input:
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/fd5efb9f-4355-4409-a1c2-c1ca99e0cab4" width="300" />
Halftone Output:
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/7e606f29-e68f-4d46-b3d5-97f799a4ec2f" width="300" />
CMYK Halftone Output:
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c59c578f-db8e-4d66-8c66-2851752d75ea" width="300" />
--------------------------------
### Ideal Size
**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
**Node Link:** https://github.com/JPPhoto/ideal-size-node
--------------------------------
### Image and Mask Composition Pack
**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
This includes 15 Nodes:
- *Adjust Image Hue Plus* - Rotate the hue of an image in one of several different color spaces.
- *Blend Latents/Noise (Masked)* - Use a mask to blend part of one latents tensor [including Noise outputs] into another. Can be used to "renoise" sections during a multi-stage [masked] denoising process.
- *Enhance Image* - Boost or reduce color saturation, contrast, brightness, sharpness, or invert colors of any image at any stage with this simple wrapper for pillow [PIL]'s ImageEnhance module.
- *Equivalent Achromatic Lightness* - Calculates image lightness accounting for Helmholtz-Kohlrausch effect based on a method described by High, Green, and Nussbaum (2023).
- *Text to Mask (Clipseg)* - Input a prompt and an image to generate a mask representing areas of the image matched by the prompt.
- *Text to Mask Advanced (Clipseg)* - Output up to four prompt masks combined with logical "and", logical "or", or as separate channels of an RGBA image.
- *Image Layer Blend* - Perform a layered blend of two images using alpha compositing. Opacity of top layer is selectable, with optional mask and several different blend modes/color spaces.
- *Image Compositor* - Take a subject from an image with a flat backdrop and layer it on another image using a chroma key or flood select background removal.
- *Image Dilate or Erode* - Dilate or expand a mask (or any image!). This is equivalent to an expand/contract operation.
- *Image Value Thresholds* - Clip an image to pure black/white beyond specified thresholds.
- *Offset Latents* - Offset a latents tensor in the vertical and/or horizontal dimensions, wrapping it around.
- *Offset Image* - Offset an image in the vertical and/or horizontal dimensions, wrapping it around.
- *Rotate/Flip Image* - Rotate an image in degrees clockwise/counterclockwise about its center, optionally resizing the image boundaries to fit, or flipping it about the vertical and/or horizontal axes.
- *Shadows/Highlights/Midtones* - Extract three masks (with adjustable hard or soft thresholds) representing shadows, midtones, and highlights regions of an image.
- *Text Mask (simple 2D)* - create and position a white on black (or black on white) line of text using any font locally available to Invoke.
**Node Link:** https://github.com/dwringer/composition-nodes
</br><img src="https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_pack_overview.jpg" width="500" />
--------------------------------
### Image to Character Art Image Nodes
**Description:** Group of nodes to convert an input image into ascii/unicode art Image
**Node Link:** https://github.com/mickr777/imagetoasciiimage
**Output Examples**
<img src="https://user-images.githubusercontent.com/115216705/271817646-8e061fcc-9a2c-4fa9-bcc7-c0f7b01e9056.png" width="300" /><img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/3c4990eb-2f42-46b9-90f9-0088b939dc6a" width="300" /></br>
<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/fee7f800-a4a8-41e2-a66b-c66e4343307e" width="300" />
<img src="https://github.com/mickr777/imagetoasciiimage/assets/115216705/1d9c1003-a45f-45c2-aac7-46470bb89330" width="300" />
--------------------------------
### Image Picker
**Description:** This InvokeAI node takes in a collection of images and randomly chooses one. This can be useful when you have a number of poses to choose from for a ControlNet node, or a number of input images for another purpose.
**Node Link:** https://github.com/JPPhoto/image-picker-node
--------------------------------
### Load Video Frame
**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs.
**Description:** This is a video frame image provider + indexer/video creation nodes for hooking up to iterators and ranges and ControlNets and such for invokeAI node experimentation. Think animation + ControlNet outputs.
**Node Link:** https://github.com/helix4u/load_video_frame
**Example Node Graph:** https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json
**Output Example:**
=======
![Example animation](https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif)
<img src="https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif" width="500" />
[Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4)
--------------------------------
### Make 3D
**Description:** Create compelling 3D stereo images from 2D originals.
**Node Link:** [https://gitlab.com/srcrr/shift3d/-/raw/main/make3d.py](https://gitlab.com/srcrr/shift3d)
**Example Node Graph:** https://gitlab.com/srcrr/shift3d/-/raw/main/example-workflow.json?ref_type=heads&inline=false
**Output Examples**
<img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png" width="300" />
<img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png" width="300" />
--------------------------------
### Oobabooga
**Description:** asks a local LLM running in Oobabooga's Text-Generation-Webui to write a prompt based on the user input.
**Link:** https://github.com/sammyf/oobabooga-node
**Example:**
"describe a new mystical creature in its natural environment"
@ -99,7 +214,7 @@ Generated Prompt: An enchanted weapon will be usable by any character regardless
"The mystical creature I am describing to you is called the "Glimmerwing". It is a majestic, iridescent being that inhabits the depths of the most enchanted forests and glimmering lakes. Its body is covered in shimmering scales that reflect every color of the rainbow, and it has delicate, translucent wings that sparkle like diamonds in the sunlight. The Glimmerwing's home is a crystal-clear lake, surrounded by towering trees with leaves that shimmer like jewels. In this serene environment, the Glimmerwing spends its days swimming gracefully through the water, chasing schools of glittering fish and playing with the gentle ripples of the lake's surface.
As the sun sets, the Glimmerwing perches on a branch of one of the trees, spreading its wings to catch the last rays of light. The creature's scales glow softly, casting a rainbow of colors across the forest floor. The Glimmerwing sings a haunting melody, its voice echoing through the stillness of the night air. Its song is said to have the power to heal the sick and bring peace to troubled souls. Those who are lucky enough to hear the Glimmerwing's song are forever changed by its beauty and grace."
![glimmerwing_small](https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed)
<img src="https://github.com/sammyf/oobabooga-node/assets/42468608/cecdd820-93dd-4c35-abbf-607e001fb2ed" width="300" />
**Requirement**
@ -107,98 +222,12 @@ a Text-Generation-Webui instance (might work remotely too, but I never tried it)
**Note**
This node works best with SDXL models, especially as the style can be described independantly of the LLM's output.
This node works best with SDXL models, especially as the style can be described independently of the LLM's output.
--------------------------------
### Depth Map from Wavefront OBJ
**Description:** Render depth maps from Wavefront .obj files (triangulated) using this simple 3D renderer utilizing numpy and matplotlib to compute and color the scene. There are simple parameters to change the FOV, camera position, and model orientation.
To be imported, an .obj must use triangulated meshes, so make sure to enable that option if exporting from a 3D modeling program. This renderer makes each triangle a solid color based on its average depth, so it will cause anomalies if your .obj has large triangles. In Blender, the Remesh modifier can be helpful to subdivide a mesh into small pieces that work well given these limitations.
**Node Link:** https://github.com/dwringer/depth-from-obj-node
**Example Usage:**
![depth from obj usage graph](https://raw.githubusercontent.com/dwringer/depth-from-obj-node/main/depth_from_obj_usage.jpg)
--------------------------------
### Enhance Image (simple adjustments)
**Description:** Boost or reduce color saturation, contrast, brightness, sharpness, or invert colors of any image at any stage with this simple wrapper for pillow [PIL]'s ImageEnhance module.
Color inversion is toggled with a simple switch, while each of the four enhancer modes are activated by entering a value other than 1 in each corresponding input field. Values less than 1 will reduce the corresponding property, while values greater than 1 will enhance it.
**Node Link:** https://github.com/dwringer/image-enhance-node
**Example Usage:**
![enhance image usage graph](https://raw.githubusercontent.com/dwringer/image-enhance-node/main/image_enhance_usage.jpg)
--------------------------------
### Generative Grammar-Based Prompt Nodes
**Description:** This set of 3 nodes generates prompts from simple user-defined grammar rules (loaded from custom files - examples provided below). The prompts are made by recursively expanding a special template string, replacing nonterminal "parts-of-speech" until no more nonterminal terms remain in the string.
This includes 3 Nodes:
- *Lookup Table from File* - loads a YAML file "prompt" section (or of a whole folder of YAML's) into a JSON-ified dictionary (Lookups output)
- *Lookups Entry from Prompt* - places a single entry in a new Lookups output under the specified heading
- *Prompt from Lookup Table* - uses a Collection of Lookups as grammar rules from which to randomly generate prompts.
**Node Link:** https://github.com/dwringer/generative-grammar-prompt-nodes
**Example Usage:**
![lookups usage example graph](https://raw.githubusercontent.com/dwringer/generative-grammar-prompt-nodes/main/lookuptables_usage.jpg)
--------------------------------
### Image and Mask Composition Pack
**Description:** This is a pack of nodes for composing masks and images, including a simple text mask creator and both image and latent offset nodes. The offsets wrap around, so these can be used in conjunction with the Seamless node to progressively generate centered on different parts of the seamless tiling.
This includes 4 Nodes:
- *Text Mask (simple 2D)* - create and position a white on black (or black on white) line of text using any font locally available to Invoke.
- *Image Compositor* - Take a subject from an image with a flat backdrop and layer it on another image using a chroma key or flood select background removal.
- *Offset Latents* - Offset a latents tensor in the vertical and/or horizontal dimensions, wrapping it around.
- *Offset Image* - Offset an image in the vertical and/or horizontal dimensions, wrapping it around.
**Node Link:** https://github.com/dwringer/composition-nodes
**Example Usage:**
![composition nodes usage graph](https://raw.githubusercontent.com/dwringer/composition-nodes/main/composition_nodes_usage.jpg)
--------------------------------
### Size Stepper Nodes
**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes.
A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection.
**Node Link:** https://github.com/dwringer/size-stepper-nodes
**Example Usage:**
![size stepper usage graph](https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg)
--------------------------------
### Text font to Image
**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line
**Node Link:** https://github.com/mickr777/textfontimage
**Output Examples**
![a3609d48-d9b7-41f0-b280-063d857986fb](https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36)
Results after using the depth controlnet
![9133eabb-bcda-4326-831e-1b641228b178](https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a)
![4f9a3fa8-9be9-4236-8a3e-fcec66decd2a](https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc)
![babd69c4-9d60-4a55-a834-5e8397f62610](https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89)
--------------------------------
### Prompt Tools
**Description:** A set of InvokeAI nodes that add general prompt manipulation tools. These where written to accompany the PromptsFromFile node and other prompt generation nodes.
**Description:** A set of InvokeAI nodes that add general prompt manipulation tools. These were written to accompany the PromptsFromFile node and other prompt generation nodes.
1. PromptJoin - Joins to prompts into one.
2. PromptReplace - performs a search and replace on a prompt. With the option of using regex.
@ -215,21 +244,83 @@ See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/mai
**Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
--------------------------------
### Retroize
**Description:** Retroize is a collection of nodes for InvokeAI to "Retroize" images. Any image can be given a fresh coat of retro paint with these nodes, either from your gallery or from within the graph itself. It includes nodes to pixelize, quantize, palettize, and ditherize images; as well as to retrieve palettes from existing images.
**Node Link:** https://github.com/Ar7ific1al/invokeai-retroizeinode/
**Retroize Output Examples**
<img src="https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974" width="500" />
--------------------------------
### Size Stepper Nodes
**Description:** This is a set of nodes for calculating the necessary size increments for doing upscaling workflows. Use the *Final Size & Orientation* node to enter your full size dimensions and orientation (portrait/landscape/random), then plug that and your initial generation dimensions into the *Ideal Size Stepper* and get 1, 2, or 3 intermediate pairs of dimensions for upscaling. Note this does not output the initial size or full size dimensions: the 1, 2, or 3 outputs of this node are only the intermediate sizes.
A third node is included, *Random Switch (Integers)*, which is just a generic version of Final Size with no orientation selection.
**Node Link:** https://github.com/dwringer/size-stepper-nodes
**Example Usage:**
</br><img src="https://raw.githubusercontent.com/dwringer/size-stepper-nodes/main/size_nodes_usage.jpg" width="500" />
--------------------------------
### Text font to Image
**Description:** text font to text image node for InvokeAI, download a font to use (or if in font cache uses it from there), the text is always resized to the image size, but can control that with padding, optional 2nd line
**Node Link:** https://github.com/mickr777/textfontimage
**Output Examples**
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/c21b0af3-d9c6-4c16-9152-846a23effd36" width="300" />
Results after using the depth controlnet
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/915f1a53-968e-43eb-aa61-07cd8f1a733a" width="300" />
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/821ef89e-8a60-44f5-b94e-471a9d8690cc" width="300" />
<img src="https://github.com/mickr777/InvokeAI/assets/115216705/2befcb6d-49f4-4bfd-b5fc-1fee19274f89" width="300" />
--------------------------------
### Thresholding
**Description:** This node generates masks for highlights, midtones, and shadows given an input image. You can optionally specify a blur for the lookup table used in making those masks from the source image.
**Node Link:** https://github.com/JPPhoto/thresholding-node
**Examples**
Input:
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/c88ada13-fb3d-484c-a4fe-947b44712632" width="300" />
Highlights/Midtones/Shadows:
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/727021c1-36ff-4ec8-90c8-105e00de986d" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0b721bfc-f051-404e-b905-2f16b824ddfe" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/04c1297f-1c88-42b6-a7df-dd090b976286" width="300" />
Highlights/Midtones/Shadows (with LUT blur enabled):
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/19aa718a-70c1-4668-8169-d68f4bd13771" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" width="300" />
--------------------------------
### XY Image to Grid and Images to Grids nodes
**Description:** Image to grid nodes and supporting tools.
1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then mutilple grids will be created until it runs out of images.
2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporoting nodes. See example node setups for more details.
1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then multiple grids will be created until it runs out of images.
2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporting nodes. See example node setups for more details.
See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
**Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
--------------------------------
### Example Node Template
**Description:** This node allows you to do super cool things with InvokeAI.
@ -240,7 +331,7 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
**Output Examples**
![Example Image](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png){: style="height:115px;width:240px"}
</br><img src="https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png" width="500" />
## Disclaimer

View File

@ -1,6 +1,6 @@
# List of Default Nodes
The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
The table below contains a list of the default nodes shipped with InvokeAI and their descriptions.
| Node <img width=160 align="right"> | Function |
|: ---------------------------------- | :--------------------------------------------------------------------------------------|
@ -17,11 +17,12 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
|Conditioning Primitive | A conditioning tensor primitive value|
|Content Shuffle Processor | Applies content shuffle processing to image|
|ControlNet | Collects ControlNet info to pass to other nodes|
|OpenCV Inpaint | Simple inpaint using opencv.|
|Denoise Latents | Denoises noisy latents to decodable images|
|Divide Integers | Divides two numbers|
|Dynamic Prompt | Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator|
|Upscale (RealESRGAN) | Upscales an image using RealESRGAN.|
|[FaceMask](./detailedNodes/faceTools.md#facemask) | Generates masks for faces in an image to use with Inpainting|
|[FaceIdentifier](./detailedNodes/faceTools.md#faceidentifier) | Identifies and labels faces in an image|
|[FaceOff](./detailedNodes/faceTools.md#faceoff) | Creates a new image that is a scaled bounding box with a mask on the face for Inpainting|
|Float Math | Perform basic math operations on two floats|
|Float Primitive Collection | A collection of float primitive values|
|Float Primitive | A float primitive value|
@ -76,6 +77,7 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
|ONNX Prompt (Raw) | A node to process inputs and produce outputs. May use dependency injection in __init__ to receive providers.|
|ONNX Text to Latents | Generates latents from conditionings.|
|ONNX Model Loader | Loads a main model, outputting its submodels.|
|OpenCV Inpaint | Simple inpaint using opencv.|
|Openpose Processor | Applies Openpose processing to image|
|PIDI Processor | Applies PIDI processing to image|
|Prompts from File | Loads prompts from a text file|
@ -97,5 +99,6 @@ The table below contains a list of the default nodes shipped with InvokeAI and t
|String Primitive | A string primitive value|
|Subtract Integers | Subtracts two numbers|
|Tile Resample Processor | Tile resampler processor|
|Upscale (RealESRGAN) | Upscales an image using RealESRGAN.|
|VAE Loader | Loads a VAE model, outputting a VaeLoaderOutput|
|Zoe (Depth) Processor | Applies Zoe depth processing to image|

View File

@ -0,0 +1,154 @@
# Face Nodes
## FaceOff
FaceOff mimics a user finding a face in an image and resizing the bounding box
around the head in Canvas.
Enter a face ID (found with FaceIdentifier) to choose which face to mask.
Just as you would add more context inside the bounding box by making it larger
in Canvas, the node gives you a padding input (in pixels) which will
simultaneously add more context, and increase the resolution of the bounding box
so the face remains the same size inside it.
The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
threshold a detected face must reach for it to be processed. Lowering this value
may help if detection is failing. If the detected masks are imperfect and stray
too far outside/inside of faces, the node gives you X & Y offsets to shrink/grow
the masks by a multiplier.
FaceOff will output the face in a bounded image, taking the face off of the
original image for input into any node that accepts image inputs. The node also
outputs a face mask with the dimensions of the bounded image. The X & Y outputs
are for connecting to the X & Y inputs of the Paste Image node, which will place
the bounded image back on the original image using these coordinates.
###### Inputs/Outputs
| Input | Description |
| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Image | Image for face detection |
| Face ID | The face ID to process, numbered from 0. Multiple faces not supported. Find a face's ID with FaceIdentifier node. |
| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing) |
| X Offset | X-axis offset of the mask |
| Y Offset | Y-axis offset of the mask |
| Padding | All-axis padding around the mask in pixels |
| Chunk | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
| Output | Description |
| ------------- | ------------------------------------------------ |
| Bounded Image | Original image bound, cropped, and resized |
| Width | The width of the bounded image in pixels |
| Height | The height of the bounded image in pixels |
| Mask | The output mask |
| X | The x coordinate of the bounding box's left side |
| Y | The y coordinate of the bounding box's top side |
## FaceMask
FaceMask mimics a user drawing masks on faces in an image in Canvas.
The "Face IDs" input allows the user to select specific faces to be masked.
Leave empty to detect and mask all faces, or a comma-separated list for a
specific combination of faces (ex: `1,2,4`). A single integer will detect and
mask that specific face. Find face IDs with the FaceIdentifier node.
The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
threshold a detected face must reach for it to be processed. Lowering this value
may help if detection is failing.
If the detected masks are imperfect and stray too far outside/inside of faces,
the node gives you X & Y offsets to shrink/grow the masks by a multiplier. All
masks shrink/grow together by the X & Y offset values.
By default, masks are created to change faces. When masks are inverted, they
change surrounding areas, protecting faces.
###### Inputs/Outputs
| Input | Description |
| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Image | Image for face detection |
| Face IDs | Comma-separated list of face ids to mask eg '0,2,7'. Numbered from 0. Leave empty to mask all. Find face IDs with FaceIdentifier node. |
| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing) |
| X Offset | X-axis offset of the mask |
| Y Offset | Y-axis offset of the mask |
| Chunk | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
| Invert Mask | Toggle to invert the face mask |
| Output | Description |
| ------ | --------------------------------- |
| Image | The original image |
| Width | The width of the image in pixels |
| Height | The height of the image in pixels |
| Mask | The output face mask |
## FaceIdentifier
FaceIdentifier outputs an image with detected face IDs printed in white numbers
onto each face.
Face IDs can then be used in FaceMask and FaceOff to selectively mask all, a
specific combination, or single faces.
The FaceIdentifier output image is generated for user reference, and isn't meant
to be passed on to other image-processing nodes.
The "Minimum Confidence" input defaults to 0.5 (50%), and represents a pass/fail
threshold a detected face must reach for it to be processed. Lowering this value
may help if detection is failing. If an image is changed in the slightest, run
it through FaceIdentifier again to get updated FaceIDs.
###### Inputs/Outputs
| Input | Description |
| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Image | Image for face detection |
| Minimum Confidence | Minimum confidence for face detection (lower if detection is failing) |
| Chunk | Chunk (or divide) the image into sections to greatly improve face detection success. Defaults to off, but will activate if no faces are detected normally. Activate to chunk by default. |
| Output | Description |
| ------ | ------------------------------------------------------------------------------------------------ |
| Image | The original image with small face ID numbers printed in white onto each face for user reference |
| Width | The width of the original image in pixels |
| Height | The height of the original image in pixels |
## Tips
- If not all target faces are being detected, activate Chunk to bypass full
image face detection and greatly improve detection success.
- Final results will vary between full-image detection and chunking for faces
that are detectable by both due to the nature of the process. Try either to
your taste.
- Be sure Minimum Confidence is set the same when using FaceIdentifier with
FaceOff/FaceMask.
- For FaceOff, use the color correction node before faceplace to correct edges
being noticeable in the final image (see example screenshot).
- Non-inpainting models may struggle to paint/generate correctly around faces.
- If your face won't change the way you want it to no matter what you change,
consider that the change you're trying to make is too much at that resolution.
For example, if an image is only 512x768 total, the face might only be 128x128
or 256x256, much smaller than the 512x512 your SD1.5 model was probably
trained on. Try increasing the resolution of the image by upscaling or
resizing, add padding to increase the bounding box's resolution, or use an
image where the face takes up more pixels.
- If the resulting face seems out of place pasted back on the original image
(ie. too large, not proportional), add more padding on the FaceOff node to
give inpainting more context. Context and good prompting are important to
keeping things proportional.
- If you find the mask is too big/small and going too far outside/inside the
area you want to affect, adjust the x & y offsets to shrink/grow the mask area
- Use a higher denoise start value to resemble aspects of the original face or
surroundings. Denoise start = 0 & denoise end = 1 will make something new,
while denoise start = 0.50 & denoise end = 1 will be 50% old and 50% new.
- mediapipe isn't good at detecting faces with lots of face paint, hair covering
the face, etc. Anything that obstructs the face will likely result in no faces
being detected.
- If you find your face isn't being detected, try lowering the minimum
confidence value from 0.5. This could result in false positives, however
(random areas being detected as faces and masked).
- After altering an image and wanting to process a different face in the newly
altered image, run the altered image through FaceIdentifier again to see the
new Face IDs. MediaPipe will most likely detect faces in a different order
after an image has been changed in the slightest.

View File

@ -9,5 +9,6 @@ If you're interested in finding more workflows, checkout the [#share-your-workfl
* [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json)
* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
* [SDXL (with Refiner) Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json)ß
* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json)
* [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json)
* [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json)

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@ -332,6 +332,7 @@ class InvokeAiInstance:
Configure the InvokeAI runtime directory
"""
auto_install = False
# set sys.argv to a consistent state
new_argv = [sys.argv[0]]
for i in range(1, len(sys.argv)):
@ -340,13 +341,17 @@ class InvokeAiInstance:
new_argv.append(el)
new_argv.append(sys.argv[i + 1])
elif el in ["-y", "--yes", "--yes-to-all"]:
new_argv.append(el)
auto_install = True
sys.argv = new_argv
import messages
import requests # to catch download exceptions
from messages import introduction
introduction()
auto_install = auto_install or messages.user_wants_auto_configuration()
if auto_install:
sys.argv.append("--yes")
else:
messages.introduction()
from invokeai.frontend.install.invokeai_configure import invokeai_configure

View File

@ -7,7 +7,7 @@ import os
import platform
from pathlib import Path
from prompt_toolkit import prompt
from prompt_toolkit import HTML, prompt
from prompt_toolkit.completion import PathCompleter
from prompt_toolkit.validation import Validator
from rich import box, print
@ -65,17 +65,50 @@ def confirm_install(dest: Path) -> bool:
if dest.exists():
print(f":exclamation: Directory {dest} already exists :exclamation:")
dest_confirmed = Confirm.ask(
":stop_sign: Are you sure you want to (re)install in this location?",
":stop_sign: (re)install in this location?",
default=False,
)
else:
print(f"InvokeAI will be installed in {dest}")
dest_confirmed = not Confirm.ask("Would you like to pick a different location?", default=False)
dest_confirmed = Confirm.ask("Use this location?", default=True)
console.line()
return dest_confirmed
def user_wants_auto_configuration() -> bool:
"""Prompt the user to choose between manual and auto configuration."""
console.rule("InvokeAI Configuration Section")
console.print(
Panel(
Group(
"\n".join(
[
"Libraries are installed and InvokeAI will now set up its root directory and configuration. Choose between:",
"",
" * AUTOMATIC configuration: install reasonable defaults and a minimal set of starter models.",
" * MANUAL configuration: manually inspect and adjust configuration options and pick from a larger set of starter models.",
"",
"Later you can fine tune your configuration by selecting option [6] 'Change InvokeAI startup options' from the invoke.bat/invoke.sh launcher script.",
]
),
),
box=box.MINIMAL,
padding=(1, 1),
)
)
choice = (
prompt(
HTML("Choose <b>&lt;a&gt;</b>utomatic or <b>&lt;m&gt;</b>anual configuration [a/m] (a): "),
validator=Validator.from_callable(
lambda n: n == "" or n.startswith(("a", "A", "m", "M")), error_message="Please select 'a' or 'm'"
),
)
or "a"
)
return choice.lower().startswith("a")
def dest_path(dest=None) -> Path:
"""
Prompt the user for the destination path and create the path

View File

@ -46,6 +46,9 @@ if [ "$(uname -s)" == "Darwin" ]; then
export PYTORCH_ENABLE_MPS_FALLBACK=1
fi
# Avoid glibc memory fragmentation. See invokeai/backend/model_management/README.md for details.
export MALLOC_MMAP_THRESHOLD_=1048576
# Primary function for the case statement to determine user input
do_choice() {
case $1 in

View File

@ -1,35 +1,35 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import sqlite3
from logging import Logger
from invokeai.app.services.board_image_record_storage import SqliteBoardImageRecordStorage
from invokeai.app.services.board_images import BoardImagesService, BoardImagesServiceDependencies
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.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.session_processor.session_processor_default import DefaultSessionProcessor
from invokeai.app.services.session_queue.session_queue_sqlite import SqliteSessionQueue
from invokeai.app.services.urls import LocalUrlService
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
from ..services.default_graphs import create_system_graphs
from ..services.graph import GraphExecutionState, LibraryGraph
from ..services.image_file_storage import DiskImageFileStorage
from ..services.invocation_queue import MemoryInvocationQueue
from ..services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
from ..services.board_images.board_images_default import BoardImagesService
from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
from ..services.boards.boards_default import BoardService
from ..services.config import InvokeAIAppConfig
from ..services.image_files.image_files_disk import DiskImageFileStorage
from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
from ..services.images.images_default import ImageService
from ..services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from ..services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
from ..services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
from ..services.invocation_services import InvocationServices
from ..services.invocation_stats import InvocationStatsService
from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
from ..services.invoker import Invoker
from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
from ..services.model_manager_service import ModelManagerService
from ..services.processor import DefaultInvocationProcessor
from ..services.sqlite import SqliteItemStorage
from ..services.thread import lock
from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
from ..services.model_manager.model_manager_default import ModelManagerService
from ..services.names.names_default import SimpleNameService
from ..services.session_processor.session_processor_default import DefaultSessionProcessor
from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
from ..services.shared.default_graphs import create_system_graphs
from ..services.shared.graph import GraphExecutionState, LibraryGraph
from ..services.shared.sqlite import SqliteDatabase
from ..services.urls.urls_default import LocalUrlService
from .events import FastAPIEventService
@ -49,7 +49,7 @@ def check_internet() -> bool:
return False
logger = InvokeAILogger.getLogger()
logger = InvokeAILogger.get_logger()
class ApiDependencies:
@ -63,100 +63,64 @@ class ApiDependencies:
logger.info(f"Root directory = {str(config.root_path)}")
logger.debug(f"Internet connectivity is {config.internet_available}")
events = FastAPIEventService(event_handler_id)
output_folder = config.output_path
# TODO: build a file/path manager?
if config.use_memory_db:
db_location = ":memory:"
else:
db_path = config.db_path
db_path.parent.mkdir(parents=True, exist_ok=True)
db_location = str(db_path)
db = SqliteDatabase(config, logger)
logger.info(f"Using database at {db_location}")
db_conn = sqlite3.connect(db_location, check_same_thread=False) # TODO: figure out a better threading solution
configuration = config
logger = logger
if config.log_sql:
db_conn.set_trace_callback(print)
db_conn.execute("PRAGMA foreign_keys = ON;")
graph_execution_manager = SqliteItemStorage[GraphExecutionState](
conn=db_conn, table_name="graph_executions", lock=lock
)
urls = LocalUrlService()
image_record_storage = SqliteImageRecordStorage(conn=db_conn, lock=lock)
image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
names = SimpleNameService()
board_image_records = SqliteBoardImageRecordStorage(db=db)
board_images = BoardImagesService()
board_records = SqliteBoardRecordStorage(db=db)
boards = BoardService()
events = FastAPIEventService(event_handler_id)
graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
graph_library = SqliteItemStorage[LibraryGraph](db=db, table_name="graphs")
image_files = DiskImageFileStorage(f"{output_folder}/images")
image_records = SqliteImageRecordStorage(db=db)
images = ImageService()
invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
board_record_storage = SqliteBoardRecordStorage(conn=db_conn, lock=lock)
board_image_record_storage = SqliteBoardImageRecordStorage(conn=db_conn, lock=lock)
boards = BoardService(
services=BoardServiceDependencies(
board_image_record_storage=board_image_record_storage,
board_record_storage=board_record_storage,
image_record_storage=image_record_storage,
url=urls,
logger=logger,
)
)
board_images = BoardImagesService(
services=BoardImagesServiceDependencies(
board_image_record_storage=board_image_record_storage,
board_record_storage=board_record_storage,
image_record_storage=image_record_storage,
url=urls,
logger=logger,
)
)
images = ImageService(
services=ImageServiceDependencies(
board_image_record_storage=board_image_record_storage,
image_record_storage=image_record_storage,
image_file_storage=image_file_storage,
url=urls,
logger=logger,
names=names,
graph_execution_manager=graph_execution_manager,
)
)
model_manager = ModelManagerService(config, logger)
names = SimpleNameService()
performance_statistics = InvocationStatsService()
processor = DefaultInvocationProcessor()
queue = MemoryInvocationQueue()
session_processor = DefaultSessionProcessor()
session_queue = SqliteSessionQueue(db=db)
urls = LocalUrlService()
services = InvocationServices(
model_manager=ModelManagerService(config, logger),
events=events,
latents=latents,
images=images,
boards=boards,
board_image_records=board_image_records,
board_images=board_images,
queue=MemoryInvocationQueue(),
graph_library=SqliteItemStorage[LibraryGraph](conn=db_conn, lock=lock, table_name="graphs"),
board_records=board_records,
boards=boards,
configuration=configuration,
events=events,
graph_execution_manager=graph_execution_manager,
processor=DefaultInvocationProcessor(),
configuration=config,
performance_statistics=InvocationStatsService(graph_execution_manager),
graph_library=graph_library,
image_files=image_files,
image_records=image_records,
images=images,
invocation_cache=invocation_cache,
latents=latents,
logger=logger,
session_queue=SqliteSessionQueue(conn=db_conn, lock=lock),
session_processor=DefaultSessionProcessor(),
invocation_cache=MemoryInvocationCache(max_cache_size=config.node_cache_size),
model_manager=model_manager,
names=names,
performance_statistics=performance_statistics,
processor=processor,
queue=queue,
session_processor=session_processor,
session_queue=session_queue,
urls=urls,
)
create_system_graphs(services.graph_library)
ApiDependencies.invoker = Invoker(services)
try:
lock.acquire()
db_conn.execute("VACUUM;")
db_conn.commit()
logger.info("Cleaned database")
finally:
lock.release()
db.clean()
@staticmethod
def shutdown():

View File

@ -7,7 +7,7 @@ from typing import Any
from fastapi_events.dispatcher import dispatch
from ..services.events import EventServiceBase
from ..services.events.events_base import EventServiceBase
class FastAPIEventService(EventServiceBase):

View File

@ -4,9 +4,9 @@ from fastapi import Body, HTTPException, Path, Query
from fastapi.routing import APIRouter
from pydantic import BaseModel, Field
from invokeai.app.services.board_record_storage import BoardChanges
from invokeai.app.services.image_record_storage import OffsetPaginatedResults
from invokeai.app.services.models.board_record import BoardDTO
from invokeai.app.services.board_records.board_records_common import BoardChanges
from invokeai.app.services.boards.boards_common import BoardDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from ..dependencies import ApiDependencies

View File

@ -8,9 +8,9 @@ from PIL import Image
from pydantic import BaseModel, Field
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from invokeai.app.services.image_record_storage import OffsetPaginatedResults
from invokeai.app.services.models.image_record import ImageDTO, ImageRecordChanges, ImageUrlsDTO
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from ..dependencies import ApiDependencies
@ -42,7 +42,7 @@ async def upload_image(
crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
) -> ImageDTO:
"""Uploads an image"""
if not file.content_type.startswith("image"):
if not file.content_type or not file.content_type.startswith("image"):
raise HTTPException(status_code=415, detail="Not an image")
contents = await file.read()
@ -322,3 +322,20 @@ async def unstar_images_in_list(
return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
except Exception:
raise HTTPException(status_code=500, detail="Failed to unstar images")
class ImagesDownloaded(BaseModel):
response: Optional[str] = Field(
description="If defined, the message to display to the user when images begin downloading"
)
@images_router.post("/download", operation_id="download_images_from_list", response_model=ImagesDownloaded)
async def download_images_from_list(
image_names: list[str] = Body(description="The list of names of images to download", embed=True),
board_id: Optional[str] = Body(
default=None, description="The board from which image should be downloaded from", embed=True
),
) -> ImagesDownloaded:
# return ImagesDownloaded(response="Your images are downloading")
raise HTTPException(status_code=501, detail="Endpoint is not yet implemented")

View File

@ -2,11 +2,11 @@
import pathlib
from typing import List, Literal, Optional, Union
from typing import Annotated, List, Literal, Optional, Union
from fastapi import Body, Path, Query, Response
from fastapi.routing import APIRouter
from pydantic import BaseModel, parse_obj_as
from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
from starlette.exceptions import HTTPException
from invokeai.backend import BaseModelType, ModelType
@ -23,8 +23,14 @@ from ..dependencies import ApiDependencies
models_router = APIRouter(prefix="/v1/models", tags=["models"])
UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
update_models_response_adapter = TypeAdapter(UpdateModelResponse)
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
import_models_response_adapter = TypeAdapter(ImportModelResponse)
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
convert_models_response_adapter = TypeAdapter(ConvertModelResponse)
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
@ -32,6 +38,11 @@ ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
class ModelsList(BaseModel):
models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
model_config = ConfigDict(use_enum_values=True)
models_list_adapter = TypeAdapter(ModelsList)
@models_router.get(
"/",
@ -49,7 +60,7 @@ async def list_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})
models = models_list_adapter.validate_python({"models": models_raw})
return models
@ -105,11 +116,14 @@ async def update_model(
info.path = new_info.get("path")
# replace empty string values with None/null to avoid phenomenon of vae: ''
info_dict = info.dict()
info_dict = info.model_dump()
info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
ApiDependencies.invoker.services.model_manager.update_model(
model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info_dict
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(
@ -117,7 +131,7 @@ async def update_model(
base_model=base_model,
model_type=model_type,
)
model_response = parse_obj_as(UpdateModelResponse, model_raw)
model_response = update_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=str(e))
except ValueError as e:
@ -146,18 +160,21 @@ async def update_model(
async def import_model(
location: str = Body(description="A model path, repo_id or URL to import"),
prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
description="Prediction type for SDv2 checkpoint files", default="v_prediction"
description="Prediction type for SDv2 checkpoints and rare SDv1 checkpoints",
default=None,
),
) -> ImportModelResponse:
"""Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
location = location.strip("\"' ")
items_to_import = {location}
prediction_types = {x.value: x for x in SchedulerPredictionType}
logger = ApiDependencies.invoker.services.logger
try:
installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type)
items_to_import=items_to_import,
prediction_type_helper=lambda x: prediction_types.get(prediction_type),
)
info = installed_models.get(location)
@ -169,7 +186,7 @@ async def import_model(
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.name, base_model=info.base_model, model_type=info.model_type
)
return parse_obj_as(ImportModelResponse, model_raw)
return import_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
@ -203,13 +220,18 @@ async def add_model(
try:
ApiDependencies.invoker.services.model_manager.add_model(
info.model_name, info.base_model, info.model_type, model_attributes=info.dict()
info.model_name,
info.base_model,
info.model_type,
model_attributes=info.model_dump(),
)
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
model_name=info.model_name,
base_model=info.base_model,
model_type=info.model_type,
)
return parse_obj_as(ImportModelResponse, model_raw)
return import_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
@ -221,7 +243,10 @@ async def add_model(
@models_router.delete(
"/{base_model}/{model_type}/{model_name}",
operation_id="del_model",
responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}},
responses={
204: {"description": "Model deleted successfully"},
404: {"description": "Model not found"},
},
status_code=204,
response_model=None,
)
@ -277,7 +302,7 @@ async def convert_model(
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)
response = convert_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
except ValueError as e:
@ -300,7 +325,8 @@ async def 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"
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)
@ -335,6 +361,26 @@ async def sync_to_config() -> bool:
return True
# There's some weird pydantic-fastapi behaviour that requires this to be a separate class
# TODO: After a few updates, see if it works inside the route operation handler?
class MergeModelsBody(BaseModel):
model_names: List[str] = Field(description="model name", min_length=2, max_length=3)
merged_model_name: Optional[str] = Field(description="Name of destination model")
alpha: Optional[float] = Field(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5)
interp: Optional[MergeInterpolationMethod] = Field(description="Interpolation method")
force: Optional[bool] = Field(
description="Force merging of models created with different versions of diffusers",
default=False,
)
merge_dest_directory: Optional[str] = Field(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
)
model_config = ConfigDict(protected_namespaces=())
@models_router.put(
"/merge/{base_model}",
operation_id="merge_models",
@ -347,31 +393,23 @@ async def sync_to_config() -> bool:
response_model=MergeModelResponse,
)
async def merge_models(
body: Annotated[MergeModelsBody, Body(description="Model configuration", embed=True)],
base_model: BaseModelType = Path(description="Base model"),
model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
merged_model_name: Optional[str] = Body(description="Name of destination model"),
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} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
logger.info(
f"Merging models: {body.model_names} into {body.merge_dest_directory or '<MODELS>'}/{body.merged_model_name}"
)
dest = pathlib.Path(body.merge_dest_directory) if body.merge_dest_directory else None
result = ApiDependencies.invoker.services.model_manager.merge_models(
model_names,
base_model,
merged_model_name=merged_model_name or "+".join(model_names),
alpha=alpha,
interp=interp,
force=force,
model_names=body.model_names,
base_model=base_model,
merged_model_name=body.merged_model_name or "+".join(body.model_names),
alpha=body.alpha,
interp=body.interp,
force=body.force,
merge_dest_directory=dest,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
@ -379,9 +417,12 @@ async def merge_models(
base_model=base_model,
model_type=ModelType.Main,
)
response = parse_obj_as(ConvertModelResponse, model_raw)
response = convert_models_response_adapter.validate_python(model_raw)
except ModelNotFoundException:
raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")
raise HTTPException(
status_code=404,
detail=f"One or more of the models '{body.model_names}' not found",
)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
return response

View File

@ -18,9 +18,9 @@ from invokeai.app.services.session_queue.session_queue_common import (
SessionQueueItemDTO,
SessionQueueStatus,
)
from invokeai.app.services.shared.models import CursorPaginatedResults
from invokeai.app.services.shared.graph import Graph
from invokeai.app.services.shared.pagination import CursorPaginatedResults
from ...services.graph import Graph
from ..dependencies import ApiDependencies
session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])

View File

@ -6,11 +6,12 @@ from fastapi import Body, HTTPException, Path, Query, Response
from fastapi.routing import APIRouter
from pydantic.fields import Field
from invokeai.app.services.shared.pagination import PaginatedResults
# Importing * is bad karma but needed here for node detection
from ...invocations import * # noqa: F401 F403
from ...invocations.baseinvocation import BaseInvocation
from ...services.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
from ...services.item_storage import PaginatedResults
from ...services.shared.graph import Edge, EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
from ..dependencies import ApiDependencies
session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])

View File

@ -1,4 +1,4 @@
from typing import Optional
from typing import Optional, Union
from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
from fastapi import Body
@ -27,6 +27,7 @@ async def parse_dynamicprompts(
combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
) -> DynamicPromptsResponse:
"""Creates a batch process"""
generator: Union[RandomPromptGenerator, CombinatorialPromptGenerator]
try:
error: Optional[str] = None
if combinatorial:

View File

@ -5,7 +5,7 @@ from fastapi_events.handlers.local import local_handler
from fastapi_events.typing import Event
from socketio import ASGIApp, AsyncServer
from ..services.events import EventServiceBase
from ..services.events.events_base import EventServiceBase
class SocketIO:
@ -30,8 +30,8 @@ class SocketIO:
async def _handle_sub_queue(self, sid, data, *args, **kwargs):
if "queue_id" in data:
self.__sio.enter_room(sid, data["queue_id"])
await self.__sio.enter_room(sid, data["queue_id"])
async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
if "queue_id" in data:
self.__sio.enter_room(sid, data["queue_id"])
await self.__sio.enter_room(sid, data["queue_id"])

View File

@ -8,7 +8,6 @@ app_config.parse_args()
if True: # hack to make flake8 happy with imports coming after setting up the config
import asyncio
import logging
import mimetypes
import socket
from inspect import signature
@ -23,7 +22,7 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from pydantic.schema import schema
from pydantic.json_schema import models_json_schema
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
@ -32,7 +31,7 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
from ..backend.util.logging import InvokeAILogger
from .api.dependencies import ApiDependencies
from .api.routers import app_info, board_images, boards, images, models, session_queue, sessions, utilities
from .api.routers import app_info, board_images, boards, images, models, session_queue, utilities
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
@ -41,7 +40,9 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
logger = InvokeAILogger.getLogger(config=app_config)
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
logger = InvokeAILogger.get_logger(config=app_config)
# fix for windows mimetypes registry entries being borked
# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
@ -50,7 +51,7 @@ mimetypes.add_type("text/css", ".css")
# Create the app
# TODO: create this all in a method so configuration/etc. can be passed in?
app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
# Add event handler
event_handler_id: int = id(app)
@ -62,18 +63,18 @@ app.add_middleware(
socket_io = SocketIO(app)
app.add_middleware(
CORSMiddleware,
allow_origins=app_config.allow_origins,
allow_credentials=app_config.allow_credentials,
allow_methods=app_config.allow_methods,
allow_headers=app_config.allow_headers,
)
# Add startup event to load dependencies
@app.on_event("startup")
async def startup_event():
app.add_middleware(
CORSMiddleware,
allow_origins=app_config.allow_origins,
allow_credentials=app_config.allow_credentials,
allow_methods=app_config.allow_methods,
allow_headers=app_config.allow_headers,
)
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
@ -84,12 +85,7 @@ async def shutdown_event():
# Include all routers
# TODO: REMOVE
# app.include_router(
# invocation.invocation_router,
# prefix = '/api')
app.include_router(sessions.session_router, prefix="/api")
# app.include_router(sessions.session_router, prefix="/api")
app.include_router(utilities.utilities_router, prefix="/api")
@ -116,6 +112,7 @@ def custom_openapi():
description="An API for invoking AI image operations",
version="1.0.0",
routes=app.routes,
separate_input_output_schemas=False, # https://fastapi.tiangolo.com/how-to/separate-openapi-schemas/
)
# Add all outputs
@ -126,29 +123,32 @@ def custom_openapi():
output_type = signature(invoker.invoke).return_annotation
output_types.add(output_type)
output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
for schema_key, output_schema in output_schemas["definitions"].items():
output_schema["class"] = "output"
openapi_schema["components"]["schemas"][schema_key] = output_schema
output_schemas = models_json_schema(
models=[(o, "serialization") for o in output_types], ref_template="#/components/schemas/{model}"
)
for schema_key, output_schema in output_schemas[1]["$defs"].items():
# TODO: note that we assume the schema_key here is the TYPE.__name__
# This could break in some cases, figure out a better way to do it
output_type_titles[schema_key] = output_schema["title"]
# Add Node Editor UI helper schemas
ui_config_schemas = schema([UIConfigBase, _InputField, _OutputField], ref_prefix="#/components/schemas/")
for schema_key, ui_config_schema in ui_config_schemas["definitions"].items():
ui_config_schemas = models_json_schema(
[(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
ref_template="#/components/schemas/{model}",
)
for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
openapi_schema["components"]["schemas"][schema_key] = ui_config_schema
# Add a reference to the output type to additionalProperties of the invoker schema
for invoker in all_invocations:
invoker_name = invoker.__name__
output_type = signature(invoker.invoke).return_annotation
output_type = signature(obj=invoker.invoke).return_annotation
output_type_title = output_type_titles[output_type.__name__]
invoker_schema = openapi_schema["components"]["schemas"][invoker_name]
invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
outputs_ref = {"$ref": f"#/components/schemas/{output_type_title}"}
invoker_schema["output"] = outputs_ref
invoker_schema["class"] = "invocation"
openapi_schema["components"]["schemas"][f"{output_type_title}"]["class"] = "output"
from invokeai.backend.model_management.models import get_model_config_enums
@ -171,7 +171,7 @@ def custom_openapi():
return app.openapi_schema
app.openapi = custom_openapi
app.openapi = custom_openapi # type: ignore [method-assign] # this is a valid assignment
# Override API doc favicons
app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
@ -223,7 +223,7 @@ def invoke_api():
exc_info=e,
)
else:
jurigged.watch(logger=InvokeAILogger.getLogger(name="jurigged").info)
jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
port = find_port(app_config.port)
if port != app_config.port:
@ -242,7 +242,7 @@ def invoke_api():
# replace uvicorn's loggers with InvokeAI's for consistent appearance
for logname in ["uvicorn.access", "uvicorn"]:
log = logging.getLogger(logname)
log = InvokeAILogger.get_logger(logname)
log.handlers.clear()
for ch in logger.handlers:
log.addHandler(ch)

View File

@ -24,8 +24,8 @@ def add_field_argument(command_parser, name: str, field, default_override=None):
if field.default_factory is None
else field.default_factory()
)
if get_origin(field.type_) == Literal:
allowed_values = get_args(field.type_)
if get_origin(field.annotation) == Literal:
allowed_values = get_args(field.annotation)
allowed_types = set()
for val in allowed_values:
allowed_types.add(type(val))
@ -38,15 +38,15 @@ def add_field_argument(command_parser, name: str, field, default_override=None):
type=field_type,
default=default,
choices=allowed_values,
help=field.field_info.description,
help=field.description,
)
else:
command_parser.add_argument(
f"--{name}",
dest=name,
type=field.type_,
type=field.annotation,
default=default,
help=field.field_info.description,
help=field.description,
)
@ -142,7 +142,6 @@ class BaseCommand(ABC, BaseModel):
"""A CLI command"""
# All commands must include a type name like this:
# type: Literal['your_command_name'] = 'your_command_name'
@classmethod
def get_all_subclasses(cls):

View File

@ -7,8 +7,6 @@ from .services.config import InvokeAIAppConfig
# parse_args() must be called before any other imports. if it is not called first, consumers of the config
# which are imported/used before parse_args() is called will get the default config values instead of the
# values from the command line or config file.
config = InvokeAIAppConfig.get_config()
config.parse_args()
if True: # hack to make flake8 happy with imports coming after setting up the config
import argparse
@ -61,8 +59,9 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
logger = InvokeAILogger().getLogger(config=config)
config = InvokeAIAppConfig.get_config()
config.parse_args()
logger = InvokeAILogger().get_logger(config=config)
class CliCommand(BaseModel):

File diff suppressed because it is too large Load Diff

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@ -2,7 +2,7 @@
import numpy as np
from pydantic import validator
from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import IntegerCollectionOutput
from invokeai.app.util.misc import SEED_MAX, get_random_seed
@ -20,9 +20,9 @@ class RangeInvocation(BaseInvocation):
stop: int = InputField(default=10, description="The stop of the range")
step: int = InputField(default=1, description="The step of the range")
@validator("stop")
def stop_gt_start(cls, v, values):
if "start" in values and v <= values["start"]:
@field_validator("stop")
def stop_gt_start(cls, v: int, info: ValidationInfo):
if "start" in info.data and v <= info.data["start"]:
raise ValueError("stop must be greater than start")
return v

View File

@ -1,6 +1,6 @@
import re
from dataclasses import dataclass
from typing import List, Union
from typing import List, Optional, Union
import torch
from compel import Compel, ReturnedEmbeddingsType
@ -43,7 +43,13 @@ class ConditioningFieldData:
# PerpNeg = "perp_neg"
@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning", version="1.0.0")
@invocation(
"compel",
title="Prompt",
tags=["prompt", "compel"],
category="conditioning",
version="1.0.0",
)
class CompelInvocation(BaseInvocation):
"""Parse prompt using compel package to conditioning."""
@ -60,23 +66,21 @@ class CompelInvocation(BaseInvocation):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(),
context=context,
tokenizer_info = context.get_model(
**self.clip.tokenizer.model_dump(),
)
text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(),
context=context,
text_encoder_info = context.get_model(
**self.clip.text_encoder.model_dump(),
)
def _lora_loader():
for lora in self.clip.loras:
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
lora_info = context.get_model(**lora.model_dump(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]
# loras = [(context.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., _-]+>", self.prompt):
@ -85,11 +89,10 @@ class CompelInvocation(BaseInvocation):
ti_list.append(
(
name,
context.services.model_manager.get_model(
context.get_model(
model_name=name,
base_model=self.clip.text_encoder.base_model,
model_type=ModelType.TextualInversion,
context=context,
).context.model,
)
)
@ -118,7 +121,7 @@ class CompelInvocation(BaseInvocation):
conjunction = Compel.parse_prompt_string(self.prompt)
if context.services.configuration.log_tokenization:
if context.config.log_tokenization:
log_tokenization_for_conjunction(conjunction, tokenizer)
c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
@ -139,8 +142,7 @@ class CompelInvocation(BaseInvocation):
]
)
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
context.services.latents.save(conditioning_name, conditioning_data)
conditioning_name = context.save_conditioning(conditioning_data)
return ConditioningOutput(
conditioning=ConditioningField(
@ -160,11 +162,11 @@ class SDXLPromptInvocationBase:
zero_on_empty: bool,
):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(),
**clip_field.tokenizer.model_dump(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(),
**clip_field.text_encoder.model_dump(),
context=context,
)
@ -172,7 +174,11 @@ class SDXLPromptInvocationBase:
if prompt == "" and zero_on_empty:
cpu_text_encoder = text_encoder_info.context.model
c = torch.zeros(
(1, cpu_text_encoder.config.max_position_embeddings, cpu_text_encoder.config.hidden_size),
(
1,
cpu_text_encoder.config.max_position_embeddings,
cpu_text_encoder.config.hidden_size,
),
dtype=text_encoder_info.context.cache.precision,
)
if get_pooled:
@ -186,7 +192,9 @@ class SDXLPromptInvocationBase:
def _lora_loader():
for lora in clip_field.loras:
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
lora_info = context.services.model_manager.get_model(
**lora.model_dump(exclude={"weight"}), context=context
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
@ -273,8 +281,16 @@ class SDXLPromptInvocationBase:
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
prompt: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
style: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
prompt: str = InputField(
default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
)
style: str = InputField(
default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
)
original_width: int = InputField(default=1024, description="")
original_height: int = InputField(default=1024, description="")
crop_top: int = InputField(default=0, description="")
@ -310,7 +326,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
[
c1,
torch.zeros(
(c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]), device=c1.device, dtype=c1.dtype
(c1.shape[0], c2.shape[1] - c1.shape[1], c1.shape[2]),
device=c1.device,
dtype=c1.dtype,
),
],
dim=1,
@ -321,7 +339,9 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
[
c2,
torch.zeros(
(c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]), device=c2.device, dtype=c2.dtype
(c2.shape[0], c1.shape[1] - c2.shape[1], c2.shape[2]),
device=c2.device,
dtype=c2.dtype,
),
],
dim=1,
@ -359,7 +379,9 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
"""Parse prompt using compel package to conditioning."""
style: str = InputField(
default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea
default="",
description=FieldDescriptions.compel_prompt,
ui_component=UIComponent.Textarea,
) # TODO: ?
original_width: int = InputField(default=1024, description="")
original_height: int = InputField(default=1024, description="")
@ -403,10 +425,16 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
class ClipSkipInvocationOutput(BaseInvocationOutput):
"""Clip skip node output"""
clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning", version="1.0.0")
@invocation(
"clip_skip",
title="CLIP Skip",
tags=["clipskip", "clip", "skip"],
category="conditioning",
version="1.0.0",
)
class ClipSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model."""
@ -421,7 +449,9 @@ class ClipSkipInvocation(BaseInvocation):
def get_max_token_count(
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
tokenizer,
prompt: Union[FlattenedPrompt, Blend, Conjunction],
truncate_if_too_long=False,
) -> int:
if type(prompt) is Blend:
blend: Blend = prompt

View File

@ -2,7 +2,7 @@
# initial implementation by Gregg Helt, 2023
# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
from builtins import bool, float
from typing import Dict, List, Literal, Optional, Union
from typing import Dict, List, Literal, Union
import cv2
import numpy as np
@ -24,12 +24,12 @@ from controlnet_aux import (
)
from controlnet_aux.util import HWC3, ade_palette
from PIL import Image
from pydantic import BaseModel, Field, validator
from pydantic import BaseModel, ConfigDict, Field, field_validator
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from ...backend.model_management import BaseModelType
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
@ -57,6 +57,8 @@ class ControlNetModelField(BaseModel):
model_name: str = Field(description="Name of the ControlNet model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class ControlField(BaseModel):
image: ImageField = Field(description="The control image")
@ -71,7 +73,7 @@ class ControlField(BaseModel):
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")
@field_validator("control_weight")
def validate_control_weight(cls, v):
"""Validate that all control weights in the valid range"""
if isinstance(v, list):
@ -124,9 +126,7 @@ class ControlNetInvocation(BaseInvocation):
)
@invocation(
"image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet", version="1.0.0"
)
# This invocation exists for other invocations to subclass it - do not register with @invocation!
class ImageProcessorInvocation(BaseInvocation):
"""Base class for invocations that preprocess images for ControlNet"""
@ -393,9 +393,9 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
h: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
w: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
f: Optional[int] = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
def run_processor(self, image):
content_shuffle_processor = ContentShuffleDetector()
@ -559,3 +559,33 @@ class SamDetectorReproducibleColors(SamDetector):
img[:, :] = ann_color
final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
return np.array(final_img, dtype=np.uint8)
@invocation(
"color_map_image_processor",
title="Color Map Processor",
tags=["controlnet"],
category="controlnet",
version="1.0.0",
)
class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
"""Generates a color map from the provided image"""
color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
def run_processor(self, image: Image.Image):
image = image.convert("RGB")
np_image = np.array(image, dtype=np.uint8)
height, width = np_image.shape[:2]
width_tile_size = min(self.color_map_tile_size, width)
height_tile_size = min(self.color_map_tile_size, height)
color_map = cv2.resize(
np_image,
(width // width_tile_size, height // height_tile_size),
interpolation=cv2.INTER_CUBIC,
)
color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
color_map = Image.fromarray(color_map)
return color_map

View File

@ -6,7 +6,7 @@ import numpy
from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation

View File

@ -0,0 +1,724 @@
import math
import re
from pathlib import Path
from typing import Optional, TypedDict
import cv2
import numpy as np
from mediapipe.python.solutions.face_mesh import FaceMesh # type: ignore[import]
from PIL import Image, ImageDraw, ImageFilter, ImageFont, ImageOps
from PIL.Image import Image as ImageType
from pydantic import field_validator
import invokeai.assets.fonts as font_assets
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
InputField,
InvocationContext,
OutputField,
invocation,
invocation_output,
)
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
@invocation_output("face_mask_output")
class FaceMaskOutput(ImageOutput):
"""Base class for FaceMask output"""
mask: ImageField = OutputField(description="The output mask")
@invocation_output("face_off_output")
class FaceOffOutput(ImageOutput):
"""Base class for FaceOff Output"""
mask: ImageField = OutputField(description="The output mask")
x: int = OutputField(description="The x coordinate of the bounding box's left side")
y: int = OutputField(description="The y coordinate of the bounding box's top side")
class FaceResultData(TypedDict):
image: ImageType
mask: ImageType
x_center: float
y_center: float
mesh_width: int
mesh_height: int
chunk_x_offset: int
chunk_y_offset: int
class FaceResultDataWithId(FaceResultData):
face_id: int
class ExtractFaceData(TypedDict):
bounded_image: ImageType
bounded_mask: ImageType
x_min: int
y_min: int
x_max: int
y_max: int
class FaceMaskResult(TypedDict):
image: ImageType
mask: ImageType
def create_white_image(w: int, h: int) -> ImageType:
return Image.new("L", (w, h), color=255)
def create_black_image(w: int, h: int) -> ImageType:
return Image.new("L", (w, h), color=0)
FONT_SIZE = 32
FONT_STROKE_WIDTH = 4
def coalesce_faces(face1: FaceResultData, face2: FaceResultData) -> FaceResultData:
face1_x_offset = face1["chunk_x_offset"] - min(face1["chunk_x_offset"], face2["chunk_x_offset"])
face2_x_offset = face2["chunk_x_offset"] - min(face1["chunk_x_offset"], face2["chunk_x_offset"])
face1_y_offset = face1["chunk_y_offset"] - min(face1["chunk_y_offset"], face2["chunk_y_offset"])
face2_y_offset = face2["chunk_y_offset"] - min(face1["chunk_y_offset"], face2["chunk_y_offset"])
new_im_width = (
max(face1["image"].width, face2["image"].width)
+ max(face1["chunk_x_offset"], face2["chunk_x_offset"])
- min(face1["chunk_x_offset"], face2["chunk_x_offset"])
)
new_im_height = (
max(face1["image"].height, face2["image"].height)
+ max(face1["chunk_y_offset"], face2["chunk_y_offset"])
- min(face1["chunk_y_offset"], face2["chunk_y_offset"])
)
pil_image = Image.new(mode=face1["image"].mode, size=(new_im_width, new_im_height))
pil_image.paste(face1["image"], (face1_x_offset, face1_y_offset))
pil_image.paste(face2["image"], (face2_x_offset, face2_y_offset))
# Mask images are always from the origin
new_mask_im_width = max(face1["mask"].width, face2["mask"].width)
new_mask_im_height = max(face1["mask"].height, face2["mask"].height)
mask_pil = create_white_image(new_mask_im_width, new_mask_im_height)
black_image = create_black_image(face1["mask"].width, face1["mask"].height)
mask_pil.paste(black_image, (0, 0), ImageOps.invert(face1["mask"]))
black_image = create_black_image(face2["mask"].width, face2["mask"].height)
mask_pil.paste(black_image, (0, 0), ImageOps.invert(face2["mask"]))
new_face = FaceResultData(
image=pil_image,
mask=mask_pil,
x_center=max(face1["x_center"], face2["x_center"]),
y_center=max(face1["y_center"], face2["y_center"]),
mesh_width=max(face1["mesh_width"], face2["mesh_width"]),
mesh_height=max(face1["mesh_height"], face2["mesh_height"]),
chunk_x_offset=max(face1["chunk_x_offset"], face2["chunk_x_offset"]),
chunk_y_offset=max(face2["chunk_y_offset"], face2["chunk_y_offset"]),
)
return new_face
def prepare_faces_list(
face_result_list: list[FaceResultData],
) -> list[FaceResultDataWithId]:
"""Deduplicates a list of faces, adding IDs to them."""
deduped_faces: list[FaceResultData] = []
if len(face_result_list) == 0:
return list()
for candidate in face_result_list:
should_add = True
candidate_x_center = candidate["x_center"]
candidate_y_center = candidate["y_center"]
for idx, face in enumerate(deduped_faces):
face_center_x = face["x_center"]
face_center_y = face["y_center"]
face_radius_w = face["mesh_width"] / 2
face_radius_h = face["mesh_height"] / 2
# Determine if the center of the candidate_face is inside the ellipse of the added face
# p < 1 -> Inside
# p = 1 -> Exactly on the ellipse
# p > 1 -> Outside
p = (math.pow((candidate_x_center - face_center_x), 2) / math.pow(face_radius_w, 2)) + (
math.pow((candidate_y_center - face_center_y), 2) / math.pow(face_radius_h, 2)
)
if p < 1: # Inside of the already-added face's radius
deduped_faces[idx] = coalesce_faces(face, candidate)
should_add = False
break
if should_add is True:
deduped_faces.append(candidate)
sorted_faces = sorted(deduped_faces, key=lambda x: x["y_center"])
sorted_faces = sorted(sorted_faces, key=lambda x: x["x_center"])
# add face_id for reference
sorted_faces_with_ids: list[FaceResultDataWithId] = []
face_id_counter = 0
for face in sorted_faces:
sorted_faces_with_ids.append(
FaceResultDataWithId(
**face,
face_id=face_id_counter,
)
)
face_id_counter += 1
return sorted_faces_with_ids
def generate_face_box_mask(
context: InvocationContext,
minimum_confidence: float,
x_offset: float,
y_offset: float,
pil_image: ImageType,
chunk_x_offset: int = 0,
chunk_y_offset: int = 0,
draw_mesh: bool = True,
) -> list[FaceResultData]:
result = []
mask_pil = None
# Convert the PIL image to a NumPy array.
np_image = np.array(pil_image, dtype=np.uint8)
# Check if the input image has four channels (RGBA).
if np_image.shape[2] == 4:
# Convert RGBA to RGB by removing the alpha channel.
np_image = np_image[:, :, :3]
# Create a FaceMesh object for face landmark detection and mesh generation.
face_mesh = FaceMesh(
max_num_faces=999,
min_detection_confidence=minimum_confidence,
min_tracking_confidence=minimum_confidence,
)
# Detect the face landmarks and mesh in the input image.
results = face_mesh.process(np_image)
# Check if any face is detected.
if results.multi_face_landmarks: # type: ignore # this are via protobuf and not typed
# Search for the face_id in the detected faces.
for face_id, face_landmarks in enumerate(results.multi_face_landmarks): # type: ignore #this are via protobuf and not typed
# Get the bounding box of the face mesh.
x_coordinates = [landmark.x for landmark in face_landmarks.landmark]
y_coordinates = [landmark.y for landmark in face_landmarks.landmark]
x_min, x_max = min(x_coordinates), max(x_coordinates)
y_min, y_max = min(y_coordinates), max(y_coordinates)
# Calculate the width and height of the face mesh.
mesh_width = int((x_max - x_min) * np_image.shape[1])
mesh_height = int((y_max - y_min) * np_image.shape[0])
# Get the center of the face.
x_center = np.mean([landmark.x * np_image.shape[1] for landmark in face_landmarks.landmark])
y_center = np.mean([landmark.y * np_image.shape[0] for landmark in face_landmarks.landmark])
face_landmark_points = np.array(
[
[landmark.x * np_image.shape[1], landmark.y * np_image.shape[0]]
for landmark in face_landmarks.landmark
]
)
# Apply the scaling offsets to the face landmark points with a multiplier.
scale_multiplier = 0.2
x_center = np.mean(face_landmark_points[:, 0])
y_center = np.mean(face_landmark_points[:, 1])
if draw_mesh:
x_scaled = face_landmark_points[:, 0] + scale_multiplier * x_offset * (
face_landmark_points[:, 0] - x_center
)
y_scaled = face_landmark_points[:, 1] + scale_multiplier * y_offset * (
face_landmark_points[:, 1] - y_center
)
convex_hull = cv2.convexHull(np.column_stack((x_scaled, y_scaled)).astype(np.int32))
# Generate a binary face mask using the face mesh.
mask_image = np.ones(np_image.shape[:2], dtype=np.uint8) * 255
cv2.fillConvexPoly(mask_image, convex_hull, 0)
# Convert the binary mask image to a PIL Image.
init_mask_pil = Image.fromarray(mask_image, mode="L")
w, h = init_mask_pil.size
mask_pil = create_white_image(w + chunk_x_offset, h + chunk_y_offset)
mask_pil.paste(init_mask_pil, (chunk_x_offset, chunk_y_offset))
x_center = float(x_center)
y_center = float(y_center)
face = FaceResultData(
image=pil_image,
mask=mask_pil or create_white_image(*pil_image.size),
x_center=x_center + chunk_x_offset,
y_center=y_center + chunk_y_offset,
mesh_width=mesh_width,
mesh_height=mesh_height,
chunk_x_offset=chunk_x_offset,
chunk_y_offset=chunk_y_offset,
)
result.append(face)
return result
def extract_face(
context: InvocationContext,
image: ImageType,
face: FaceResultData,
padding: int,
) -> ExtractFaceData:
mask = face["mask"]
center_x = face["x_center"]
center_y = face["y_center"]
mesh_width = face["mesh_width"]
mesh_height = face["mesh_height"]
# Determine the minimum size of the square crop
min_size = min(mask.width, mask.height)
# Calculate the crop boundaries for the output image and mask.
mesh_width += 128 + padding # add pixels to account for mask variance
mesh_height += 128 + padding # add pixels to account for mask variance
crop_size = min(
max(mesh_width, mesh_height, 128), min_size
) # Choose the smaller of the two (given value or face mask size)
if crop_size > 128:
crop_size = (crop_size + 7) // 8 * 8 # Ensure crop side is multiple of 8
# Calculate the actual crop boundaries within the bounds of the original image.
x_min = int(center_x - crop_size / 2)
y_min = int(center_y - crop_size / 2)
x_max = int(center_x + crop_size / 2)
y_max = int(center_y + crop_size / 2)
# Adjust the crop boundaries to stay within the original image's dimensions
if x_min < 0:
context.services.logger.warning("FaceTools --> -X-axis padding reached image edge.")
x_max -= x_min
x_min = 0
elif x_max > mask.width:
context.services.logger.warning("FaceTools --> +X-axis padding reached image edge.")
x_min -= x_max - mask.width
x_max = mask.width
if y_min < 0:
context.services.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
y_max -= y_min
y_min = 0
elif y_max > mask.height:
context.services.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
y_min -= y_max - mask.height
y_max = mask.height
# Ensure the crop is square and adjust the boundaries if needed
if x_max - x_min != crop_size:
context.services.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
diff = crop_size - (x_max - x_min)
x_min -= diff // 2
x_max += diff - diff // 2
if y_max - y_min != crop_size:
context.services.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
diff = crop_size - (y_max - y_min)
y_min -= diff // 2
y_max += diff - diff // 2
context.services.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
# Crop the output image to the specified size with the center of the face mesh as the center.
mask = mask.crop((x_min, y_min, x_max, y_max))
bounded_image = image.crop((x_min, y_min, x_max, y_max))
# blur mask edge by small radius
mask = mask.filter(ImageFilter.GaussianBlur(radius=2))
return ExtractFaceData(
bounded_image=bounded_image,
bounded_mask=mask,
x_min=x_min,
y_min=y_min,
x_max=x_max,
y_max=y_max,
)
def get_faces_list(
context: InvocationContext,
image: ImageType,
should_chunk: bool,
minimum_confidence: float,
x_offset: float,
y_offset: float,
draw_mesh: bool = True,
) -> list[FaceResultDataWithId]:
result = []
# Generate the face box mask and get the center of the face.
if not should_chunk:
context.services.logger.info("FaceTools --> Attempting full image face detection.")
result = generate_face_box_mask(
context=context,
minimum_confidence=minimum_confidence,
x_offset=x_offset,
y_offset=y_offset,
pil_image=image,
chunk_x_offset=0,
chunk_y_offset=0,
draw_mesh=draw_mesh,
)
if should_chunk or len(result) == 0:
context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
width, height = image.size
image_chunks = []
x_offsets = []
y_offsets = []
result = []
# If width == height, there's nothing more we can do... otherwise...
if width > height:
# Landscape - slice the image horizontally
fx = 0.0
steps = int(width * 2 / height) + 1
increment = (width - height) / (steps - 1)
while fx <= (width - height):
x = int(fx)
image_chunks.append(image.crop((x, 0, x + height, height)))
x_offsets.append(x)
y_offsets.append(0)
fx += increment
context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}")
elif height > width:
# Portrait - slice the image vertically
fy = 0.0
steps = int(height * 2 / width) + 1
increment = (height - width) / (steps - 1)
while fy <= (height - width):
y = int(fy)
image_chunks.append(image.crop((0, y, width, y + width)))
x_offsets.append(0)
y_offsets.append(y)
fy += increment
context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}")
for idx in range(len(image_chunks)):
context.services.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
result = result + generate_face_box_mask(
context=context,
minimum_confidence=minimum_confidence,
x_offset=x_offset,
y_offset=y_offset,
pil_image=image_chunks[idx],
chunk_x_offset=x_offsets[idx],
chunk_y_offset=y_offsets[idx],
draw_mesh=draw_mesh,
)
if len(result) == 0:
# Give up
context.services.logger.warning(
"FaceTools --> No face detected in chunked input image. Passing through original image."
)
all_faces = prepare_faces_list(result)
return all_faces
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
class FaceOffInvocation(BaseInvocation):
"""Bound, extract, and mask a face from an image using MediaPipe detection"""
image: ImageField = InputField(description="Image for face detection")
face_id: int = InputField(
default=0,
ge=0,
description="The face ID to process, numbered from 0. Multiple faces not supported. Find a face's ID with FaceIdentifier node.",
)
minimum_confidence: float = InputField(
default=0.5, description="Minimum confidence for face detection (lower if detection is failing)"
)
x_offset: float = InputField(default=0.0, description="X-axis offset of the mask")
y_offset: float = InputField(default=0.0, description="Y-axis offset of the mask")
padding: int = InputField(default=0, description="All-axis padding around the mask in pixels")
chunk: bool = InputField(
default=False,
description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
)
def faceoff(self, context: InvocationContext, image: ImageType) -> Optional[ExtractFaceData]:
all_faces = get_faces_list(
context=context,
image=image,
should_chunk=self.chunk,
minimum_confidence=self.minimum_confidence,
x_offset=self.x_offset,
y_offset=self.y_offset,
draw_mesh=True,
)
if len(all_faces) == 0:
context.services.logger.warning("FaceOff --> No faces detected. Passing through original image.")
return None
if self.face_id > len(all_faces) - 1:
context.services.logger.warning(
f"FaceOff --> Face ID {self.face_id} is outside of the number of faces detected ({len(all_faces)}). Passing through original image."
)
return None
face_data = extract_face(context=context, image=image, face=all_faces[self.face_id], padding=self.padding)
# Convert the input image to RGBA mode to ensure it has an alpha channel.
face_data["bounded_image"] = face_data["bounded_image"].convert("RGBA")
return face_data
def invoke(self, context: InvocationContext) -> FaceOffOutput:
image = context.services.images.get_pil_image(self.image.image_name)
result = self.faceoff(context=context, image=image)
if result is None:
result_image = image
result_mask = create_white_image(*image.size)
x = 0
y = 0
else:
result_image = result["bounded_image"]
result_mask = result["bounded_mask"]
x = result["x_min"]
y = result["y_min"]
image_dto = context.services.images.create(
image=result_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
workflow=self.workflow,
)
mask_dto = context.services.images.create(
image=result_mask,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.MASK,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
output = FaceOffOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
mask=ImageField(image_name=mask_dto.image_name),
x=x,
y=y,
)
return output
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
class FaceMaskInvocation(BaseInvocation):
"""Face mask creation using mediapipe face detection"""
image: ImageField = InputField(description="Image to face detect")
face_ids: str = InputField(
default="",
description="Comma-separated list of face ids to mask eg '0,2,7'. Numbered from 0. Leave empty to mask all. Find face IDs with FaceIdentifier node.",
)
minimum_confidence: float = InputField(
default=0.5, description="Minimum confidence for face detection (lower if detection is failing)"
)
x_offset: float = InputField(default=0.0, description="Offset for the X-axis of the face mask")
y_offset: float = InputField(default=0.0, description="Offset for the Y-axis of the face mask")
chunk: bool = InputField(
default=False,
description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
)
invert_mask: bool = InputField(default=False, description="Toggle to invert the mask")
@field_validator("face_ids")
def validate_comma_separated_ints(cls, v) -> str:
comma_separated_ints_regex = re.compile(r"^\d*(,\d+)*$")
if comma_separated_ints_regex.match(v) is None:
raise ValueError('Face IDs must be a comma-separated list of integers (e.g. "1,2,3")')
return v
def facemask(self, context: InvocationContext, image: ImageType) -> FaceMaskResult:
all_faces = get_faces_list(
context=context,
image=image,
should_chunk=self.chunk,
minimum_confidence=self.minimum_confidence,
x_offset=self.x_offset,
y_offset=self.y_offset,
draw_mesh=True,
)
mask_pil = create_white_image(*image.size)
id_range = list(range(0, len(all_faces)))
ids_to_extract = id_range
if self.face_ids != "":
parsed_face_ids = [int(id) for id in self.face_ids.split(",")]
# get requested face_ids that are in range
intersected_face_ids = set(parsed_face_ids) & set(id_range)
if len(intersected_face_ids) == 0:
id_range_str = ",".join([str(id) for id in id_range])
context.services.logger.warning(
f"Face IDs must be in range of detected faces - requested {self.face_ids}, detected {id_range_str}. Passing through original image."
)
return FaceMaskResult(
image=image, # original image
mask=mask_pil, # white mask
)
ids_to_extract = list(intersected_face_ids)
for face_id in ids_to_extract:
face_data = extract_face(context=context, image=image, face=all_faces[face_id], padding=0)
face_mask_pil = face_data["bounded_mask"]
x_min = face_data["x_min"]
y_min = face_data["y_min"]
x_max = face_data["x_max"]
y_max = face_data["y_max"]
mask_pil.paste(
create_black_image(x_max - x_min, y_max - y_min),
box=(x_min, y_min),
mask=ImageOps.invert(face_mask_pil),
)
if self.invert_mask:
mask_pil = ImageOps.invert(mask_pil)
# Create an RGBA image with transparency
image = image.convert("RGBA")
return FaceMaskResult(
image=image,
mask=mask_pil,
)
def invoke(self, context: InvocationContext) -> FaceMaskOutput:
image = context.services.images.get_pil_image(self.image.image_name)
result = self.facemask(context=context, image=image)
image_dto = context.services.images.create(
image=result["image"],
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
workflow=self.workflow,
)
mask_dto = context.services.images.create(
image=result["mask"],
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.MASK,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
output = FaceMaskOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
mask=ImageField(image_name=mask_dto.image_name),
)
return output
@invocation(
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
)
class FaceIdentifierInvocation(BaseInvocation):
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
image: ImageField = InputField(description="Image to face detect")
minimum_confidence: float = InputField(
default=0.5, description="Minimum confidence for face detection (lower if detection is failing)"
)
chunk: bool = InputField(
default=False,
description="Whether to bypass full image face detection and default to image chunking. Chunking will occur if no faces are found in the full image.",
)
def faceidentifier(self, context: InvocationContext, image: ImageType) -> ImageType:
image = image.copy()
all_faces = get_faces_list(
context=context,
image=image,
should_chunk=self.chunk,
minimum_confidence=self.minimum_confidence,
x_offset=0,
y_offset=0,
draw_mesh=False,
)
# Note - font may be found either in the repo if running an editable install, or in the venv if running a package install
font_path = [x for x in [Path(y, "inter/Inter-Regular.ttf") for y in font_assets.__path__] if x.exists()]
font = ImageFont.truetype(font_path[0].as_posix(), FONT_SIZE)
# Paste face IDs on the output image
draw = ImageDraw.Draw(image)
for face in all_faces:
x_coord = face["x_center"]
y_coord = face["y_center"]
text = str(face["face_id"])
# get bbox of the text so we can center the id on the face
_, _, bbox_w, bbox_h = draw.textbbox(xy=(0, 0), text=text, font=font, stroke_width=FONT_STROKE_WIDTH)
x = x_coord - bbox_w / 2
y = y_coord - bbox_h / 2
draw.text(
xy=(x, y),
text=str(text),
fill=(255, 255, 255, 255),
font=font,
stroke_width=FONT_STROKE_WIDTH,
stroke_fill=(0, 0, 0, 255),
)
# Create an RGBA image with transparency
image = image.convert("RGBA")
return image
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
result_image = self.faceidentifier(context=context, image=image)
image_dto = context.services.images.create(
image=result_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
workflow=self.workflow,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)

View File

@ -8,12 +8,12 @@ import numpy
from PIL import Image, ImageChops, ImageFilter, ImageOps
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
@ -36,7 +36,13 @@ class ShowImageInvocation(BaseInvocation):
)
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
@invocation(
"blank_image",
title="Blank Image",
tags=["image"],
category="image",
version="1.0.0",
)
class BlankImageInvocation(BaseInvocation):
"""Creates a blank image and forwards it to the pipeline"""
@ -65,7 +71,13 @@ class BlankImageInvocation(BaseInvocation):
)
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
@invocation(
"img_crop",
title="Crop Image",
tags=["image", "crop"],
category="image",
version="1.0.0",
)
class ImageCropInvocation(BaseInvocation):
"""Crops an image to a specified box. The box can be outside of the image."""
@ -98,7 +110,13 @@ class ImageCropInvocation(BaseInvocation):
)
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
@invocation(
"img_paste",
title="Paste Image",
tags=["image", "paste"],
category="image",
version="1.0.1",
)
class ImagePasteInvocation(BaseInvocation):
"""Pastes an image into another image."""
@ -151,7 +169,13 @@ class ImagePasteInvocation(BaseInvocation):
)
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
@invocation(
"tomask",
title="Mask from Alpha",
tags=["image", "mask"],
category="image",
version="1.0.0",
)
class MaskFromAlphaInvocation(BaseInvocation):
"""Extracts the alpha channel of an image as a mask."""
@ -182,7 +206,13 @@ class MaskFromAlphaInvocation(BaseInvocation):
)
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
@invocation(
"img_mul",
title="Multiply Images",
tags=["image", "multiply"],
category="image",
version="1.0.0",
)
class ImageMultiplyInvocation(BaseInvocation):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
@ -215,7 +245,13 @@ class ImageMultiplyInvocation(BaseInvocation):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
@invocation(
"img_chan",
title="Extract Image Channel",
tags=["image", "channel"],
category="image",
version="1.0.0",
)
class ImageChannelInvocation(BaseInvocation):
"""Gets a channel from an image."""
@ -247,7 +283,13 @@ class ImageChannelInvocation(BaseInvocation):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
@invocation(
"img_conv",
title="Convert Image Mode",
tags=["image", "convert"],
category="image",
version="1.0.0",
)
class ImageConvertInvocation(BaseInvocation):
"""Converts an image to a different mode."""
@ -276,7 +318,13 @@ class ImageConvertInvocation(BaseInvocation):
)
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
@invocation(
"img_blur",
title="Blur Image",
tags=["image", "blur"],
category="image",
version="1.0.0",
)
class ImageBlurInvocation(BaseInvocation):
"""Blurs an image"""
@ -330,7 +378,13 @@ PIL_RESAMPLING_MAP = {
}
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
@invocation(
"img_resize",
title="Resize Image",
tags=["image", "resize"],
category="image",
version="1.0.0",
)
class ImageResizeInvocation(BaseInvocation):
"""Resizes an image to specific dimensions"""
@ -343,7 +397,7 @@ class ImageResizeInvocation(BaseInvocation):
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.get_image(self.image.image_name)
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
@ -352,25 +406,22 @@ class ImageResizeInvocation(BaseInvocation):
resample=resample_mode,
)
image_dto = context.services.images.create(
image=resize_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
workflow=self.workflow,
)
image_name = context.save_image(image=resize_image)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
image=ImageField(image_name=image_name),
width=resize_image.width,
height=resize_image.height,
)
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
@invocation(
"img_scale",
title="Scale Image",
tags=["image", "scale"],
category="image",
version="1.0.0",
)
class ImageScaleInvocation(BaseInvocation):
"""Scales an image by a factor"""
@ -411,7 +462,13 @@ class ImageScaleInvocation(BaseInvocation):
)
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
@invocation(
"img_lerp",
title="Lerp Image",
tags=["image", "lerp"],
category="image",
version="1.0.0",
)
class ImageLerpInvocation(BaseInvocation):
"""Linear interpolation of all pixels of an image"""
@ -444,7 +501,13 @@ class ImageLerpInvocation(BaseInvocation):
)
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
@invocation(
"img_ilerp",
title="Inverse Lerp Image",
tags=["image", "ilerp"],
category="image",
version="1.0.0",
)
class ImageInverseLerpInvocation(BaseInvocation):
"""Inverse linear interpolation of all pixels of an image"""
@ -456,7 +519,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
image = context.services.images.get_pil_image(self.image.image_name)
image_arr = numpy.asarray(image, dtype=numpy.float32)
image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255
image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255 # type: ignore [assignment]
ilerp_image = Image.fromarray(numpy.uint8(image_arr))
@ -477,7 +540,13 @@ class ImageInverseLerpInvocation(BaseInvocation):
)
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
@invocation(
"img_nsfw",
title="Blur NSFW Image",
tags=["image", "nsfw"],
category="image",
version="1.0.0",
)
class ImageNSFWBlurInvocation(BaseInvocation):
"""Add blur to NSFW-flagged images"""
@ -505,7 +574,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)
@ -515,7 +584,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
height=image_dto.height,
)
def _get_caution_img(self) -> Image:
def _get_caution_img(self) -> Image.Image:
import invokeai.app.assets.images as image_assets
caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
@ -523,7 +592,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
@invocation(
"img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image", version="1.0.0"
"img_watermark",
title="Add Invisible Watermark",
tags=["image", "watermark"],
category="image",
version="1.0.0",
)
class ImageWatermarkInvocation(BaseInvocation):
"""Add an invisible watermark to an image"""
@ -544,7 +617,7 @@ class ImageWatermarkInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)
@ -555,7 +628,13 @@ class ImageWatermarkInvocation(BaseInvocation):
)
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
@invocation(
"mask_edge",
title="Mask Edge",
tags=["image", "mask", "inpaint"],
category="image",
version="1.0.0",
)
class MaskEdgeInvocation(BaseInvocation):
"""Applies an edge mask to an image"""
@ -601,7 +680,11 @@ class MaskEdgeInvocation(BaseInvocation):
@invocation(
"mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image", version="1.0.0"
"mask_combine",
title="Combine Masks",
tags=["image", "mask", "multiply"],
category="image",
version="1.0.0",
)
class MaskCombineInvocation(BaseInvocation):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
@ -632,7 +715,13 @@ class MaskCombineInvocation(BaseInvocation):
)
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
@invocation(
"color_correct",
title="Color Correct",
tags=["image", "color"],
category="image",
version="1.0.0",
)
class ColorCorrectInvocation(BaseInvocation):
"""
Shifts the colors of a target image to match the reference image, optionally
@ -742,7 +831,13 @@ class ColorCorrectInvocation(BaseInvocation):
)
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
@invocation(
"img_hue_adjust",
title="Adjust Image Hue",
tags=["image", "hue"],
category="image",
version="1.0.0",
)
class ImageHueAdjustmentInvocation(BaseInvocation):
"""Adjusts the Hue of an image."""
@ -972,14 +1067,15 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
title="Save Image",
tags=["primitives", "image"],
category="primitives",
version="1.0.0",
version="1.0.1",
use_cache=False,
)
class SaveImageInvocation(BaseInvocation):
"""Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
image: ImageField = InputField(description="The image to load")
metadata: CoreMetadata = InputField(
image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
@ -992,10 +1088,11 @@ class SaveImageInvocation(BaseInvocation):
image=image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
board_id=self.board.board_id if self.board else None,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)

View File

@ -7,12 +7,12 @@ import numpy as np
from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
@ -269,7 +269,7 @@ class LaMaInfillInvocation(BaseInvocation):
)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
class CV2InfillInvocation(BaseInvocation):
"""Infills transparent areas of an image using OpenCV Inpainting"""

View File

@ -2,7 +2,7 @@ import os
from builtins import float
from typing import List, Union
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
@ -25,11 +25,15 @@ class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class CLIPVisionModelField(BaseModel):
model_name: str = Field(description="Name of the CLIP Vision image encoder model")
base_model: BaseModelType = Field(description="Base model (usually 'Any')")
model_config = ConfigDict(protected_namespaces=())
class IPAdapterField(BaseModel):
image: ImageField = Field(description="The IP-Adapter image prompt.")

View File

@ -10,7 +10,7 @@ import torch
import torchvision.transforms as T
from diffusers import AutoencoderKL, AutoencoderTiny
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import UNet2DConditionModel
from diffusers.models.adapter import FullAdapterXL, T2IAdapter
from diffusers.models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
@ -19,7 +19,7 @@ from diffusers.models.attention_processor import (
)
from diffusers.schedulers import DPMSolverSDEScheduler
from diffusers.schedulers import SchedulerMixin as Scheduler
from pydantic import validator
from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.ip_adapter import IPAdapterField
@ -33,6 +33,8 @@ from invokeai.app.invocations.primitives import (
LatentsOutput,
build_latents_output,
)
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.controlnet_utils import prepare_control_image
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
@ -47,12 +49,12 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
ControlNetData,
IPAdapterData,
StableDiffusionGeneratorPipeline,
T2IAdapterData,
image_resized_to_grid_as_tensor,
)
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import choose_precision, choose_torch_device
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
@ -82,12 +84,20 @@ class SchedulerOutput(BaseInvocationOutput):
scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents", version="1.0.0")
@invocation(
"scheduler",
title="Scheduler",
tags=["scheduler"],
category="latents",
version="1.0.0",
)
class SchedulerInvocation(BaseInvocation):
"""Selects a scheduler."""
scheduler: SAMPLER_NAME_VALUES = InputField(
default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler
default="euler",
description=FieldDescriptions.scheduler,
ui_type=UIType.Scheduler,
)
def invoke(self, context: InvocationContext) -> SchedulerOutput:
@ -95,7 +105,11 @@ class SchedulerInvocation(BaseInvocation):
@invocation(
"create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents", version="1.0.0"
"create_denoise_mask",
title="Create Denoise Mask",
tags=["mask", "denoise"],
category="latents",
version="1.0.0",
)
class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
@ -104,7 +118,11 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32, ui_order=4)
fp32: bool = InputField(
default=DEFAULT_PRECISION == "float32",
description=FieldDescriptions.fp32,
ui_order=4,
)
def prep_mask_tensor(self, mask_image):
if mask_image.mode != "L":
@ -132,7 +150,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
if image is not None:
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
context=context,
)
@ -164,9 +182,8 @@ def get_scheduler(
seed: int,
) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.dict(),
context=context,
orig_scheduler_info = context.get_model(
**scheduler_info.model_dump(),
)
with orig_scheduler_info as orig_scheduler:
scheduler_config = orig_scheduler.config
@ -196,7 +213,7 @@ def get_scheduler(
title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents",
version="1.1.0",
version="1.3.0",
)
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
@ -207,31 +224,64 @@ class DenoiseLatentsInvocation(BaseInvocation):
negative_conditioning: ConditioningField = InputField(
description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1
)
noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection, ui_order=3)
noise: Optional[LatentsField] = InputField(
default=None,
description=FieldDescriptions.noise,
input=Input.Connection,
ui_order=3,
)
steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
cfg_scale: Union[float, List[float]] = InputField(
default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
)
denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start)
denoising_start: float = InputField(
default=0.0,
ge=0,
le=1,
description=FieldDescriptions.denoising_start,
)
denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
scheduler: SAMPLER_NAME_VALUES = InputField(
default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler
default="euler",
description=FieldDescriptions.scheduler,
ui_type=UIType.Scheduler,
)
unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2)
control: Union[ControlField, list[ControlField]] = InputField(
unet: UNetField = InputField(
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
ui_order=2,
)
control: Optional[Union[ControlField, list[ControlField]]] = InputField(
default=None,
input=Input.Connection,
ui_order=5,
)
ip_adapter: Optional[IPAdapterField] = InputField(
description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
description=FieldDescriptions.ip_adapter,
title="IP-Adapter",
default=None,
input=Input.Connection,
ui_order=6,
)
t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]] = InputField(
description=FieldDescriptions.t2i_adapter,
title="T2I-Adapter",
default=None,
input=Input.Connection,
ui_order=7,
)
latents: Optional[LatentsField] = InputField(
default=None, description=FieldDescriptions.latents, input=Input.Connection
)
latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
denoise_mask: Optional[DenoiseMaskField] = InputField(
default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=7
default=None,
description=FieldDescriptions.mask,
input=Input.Connection,
ui_order=8,
)
@validator("cfg_scale")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
@ -247,15 +297,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
def dispatch_progress(
self,
context: InvocationContext,
source_node_id: str,
intermediate_state: PipelineIntermediateState,
base_model: BaseModelType,
) -> None:
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
node=self.dict(),
source_node_id=source_node_id,
base_model=base_model,
)
@ -266,11 +313,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
unet,
seed,
) -> ConditioningData:
positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
positive_cond_data = context.get_conditioning(self.positive_conditioning.conditioning_name)
c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
extra_conditioning_info = c.extra_conditioning
negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
negative_cond_data = context.get_conditioning(self.negative_conditioning.conditioning_name)
uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
conditioning_data = ConditioningData(
@ -357,17 +404,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
controlnet_data = []
for control_info in control_list:
control_model = exit_stack.enter_context(
context.services.model_manager.get_model(
context.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)
input_image = context.get_image(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?
@ -404,52 +450,148 @@ class DenoiseLatentsInvocation(BaseInvocation):
def prep_ip_adapter_data(
self,
context: InvocationContext,
ip_adapter: Optional[IPAdapterField],
ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
conditioning_data: ConditioningData,
unet: UNet2DConditionModel,
exit_stack: ExitStack,
) -> Optional[IPAdapterData]:
) -> Optional[list[IPAdapterData]]:
"""If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
to the `conditioning_data` (in-place).
"""
if ip_adapter is None:
return None
image_encoder_model_info = context.services.model_manager.get_model(
model_name=ip_adapter.image_encoder_model.model_name,
model_type=ModelType.CLIPVision,
base_model=ip_adapter.image_encoder_model.base_model,
context=context,
)
# ip_adapter could be a list or a single IPAdapterField. Normalize to a list here.
if not isinstance(ip_adapter, list):
ip_adapter = [ip_adapter]
ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
context.services.model_manager.get_model(
model_name=ip_adapter.ip_adapter_model.model_name,
model_type=ModelType.IPAdapter,
base_model=ip_adapter.ip_adapter_model.base_model,
context=context,
)
)
if len(ip_adapter) == 0:
return None
input_image = context.services.images.get_pil_image(ip_adapter.image.image_name)
# TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
with image_encoder_model_info as image_encoder_model:
# Get image embeddings from CLIP and ImageProjModel.
image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
input_image, image_encoder_model
)
conditioning_data.ip_adapter_conditioning = IPAdapterConditioningInfo(
image_prompt_embeds, uncond_image_prompt_embeds
ip_adapter_data_list = []
conditioning_data.ip_adapter_conditioning = []
for single_ip_adapter in ip_adapter:
ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
context.get_model(
model_name=single_ip_adapter.ip_adapter_model.model_name,
model_type=ModelType.IPAdapter,
base_model=single_ip_adapter.ip_adapter_model.base_model,
)
)
return IPAdapterData(
ip_adapter_model=ip_adapter_model,
weight=ip_adapter.weight,
begin_step_percent=ip_adapter.begin_step_percent,
end_step_percent=ip_adapter.end_step_percent,
)
image_encoder_model_info = context.get_model(
model_name=single_ip_adapter.image_encoder_model.model_name,
model_type=ModelType.CLIPVision,
base_model=single_ip_adapter.image_encoder_model.base_model,
)
input_image = context.get_image(single_ip_adapter.image.image_name)
# TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
with image_encoder_model_info as image_encoder_model:
# Get image embeddings from CLIP and ImageProjModel.
(
image_prompt_embeds,
uncond_image_prompt_embeds,
) = ip_adapter_model.get_image_embeds(input_image, image_encoder_model)
conditioning_data.ip_adapter_conditioning.append(
IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
)
ip_adapter_data_list.append(
IPAdapterData(
ip_adapter_model=ip_adapter_model,
weight=single_ip_adapter.weight,
begin_step_percent=single_ip_adapter.begin_step_percent,
end_step_percent=single_ip_adapter.end_step_percent,
)
)
return ip_adapter_data_list
def run_t2i_adapters(
self,
context: InvocationContext,
t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
latents_shape: list[int],
do_classifier_free_guidance: bool,
) -> Optional[list[T2IAdapterData]]:
if t2i_adapter is None:
return None
# Handle the possibility that t2i_adapter could be a list or a single T2IAdapterField.
if isinstance(t2i_adapter, T2IAdapterField):
t2i_adapter = [t2i_adapter]
if len(t2i_adapter) == 0:
return None
t2i_adapter_data = []
for t2i_adapter_field in t2i_adapter:
t2i_adapter_model_info = context.get_model(
model_name=t2i_adapter_field.t2i_adapter_model.model_name,
model_type=ModelType.T2IAdapter,
base_model=t2i_adapter_field.t2i_adapter_model.base_model,
)
image = context.get_image(t2i_adapter_field.image.image_name)
# The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
max_unet_downscale = 8
elif t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusionXL:
max_unet_downscale = 4
else:
raise ValueError(
f"Unexpected T2I-Adapter base model type: '{t2i_adapter_field.t2i_adapter_model.base_model}'."
)
t2i_adapter_model: T2IAdapter
with t2i_adapter_model_info as t2i_adapter_model:
total_downscale_factor = t2i_adapter_model.total_downscale_factor
if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
# HACK(ryand): Work around a bug in FullAdapterXL. This is being addressed upstream in diffusers by
# this PR: https://github.com/huggingface/diffusers/pull/5134.
total_downscale_factor = total_downscale_factor // 2
# Resize the T2I-Adapter input image.
# We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the
# result will match the latent image's dimensions after max_unet_downscale is applied.
t2i_input_height = latents_shape[2] // max_unet_downscale * total_downscale_factor
t2i_input_width = latents_shape[3] // max_unet_downscale * total_downscale_factor
# Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
# a single image. If CFG is enabled, we will duplicate the resultant tensor after applying the
# T2I-Adapter model.
#
# Note: We re-use the `prepare_control_image(...)` from ControlNet for T2I-Adapter, because it has many
# of the same requirements (e.g. preserving binary masks during resize).
t2i_image = prepare_control_image(
image=image,
do_classifier_free_guidance=False,
width=t2i_input_width,
height=t2i_input_height,
num_channels=t2i_adapter_model.config.in_channels,
device=t2i_adapter_model.device,
dtype=t2i_adapter_model.dtype,
resize_mode=t2i_adapter_field.resize_mode,
)
adapter_state = t2i_adapter_model(t2i_image)
if do_classifier_free_guidance:
for idx, value in enumerate(adapter_state):
adapter_state[idx] = torch.cat([value] * 2, dim=0)
t2i_adapter_data.append(
T2IAdapterData(
adapter_state=adapter_state,
weight=t2i_adapter_field.weight,
begin_step_percent=t2i_adapter_field.begin_step_percent,
end_step_percent=t2i_adapter_field.end_step_percent,
)
)
return t2i_adapter_data
# original idea by https://github.com/AmericanPresidentJimmyCarter
# TODO: research more for second order schedulers timesteps
@ -501,11 +643,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
seed = None
noise = None
if self.noise is not None:
noise = context.services.latents.get(self.noise.latents_name)
noise = context.get_latents(self.noise.latents_name)
seed = self.noise.seed
if self.latents is not None:
latents = context.services.latents.get(self.latents.latents_name)
latents = context.get_latents(self.latents.latents_name)
if seed is None:
seed = self.latents.seed
@ -522,26 +664,29 @@ class DenoiseLatentsInvocation(BaseInvocation):
mask, masked_latents = self.prep_inpaint_mask(context, latents)
# 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)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
# TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
# below. Investigate whether this is appropriate.
t2i_adapter_data = self.run_t2i_adapters(
context,
self.t2i_adapter,
latents.shape,
do_classifier_free_guidance=True,
)
def step_callback(state: PipelineIntermediateState):
self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
self.dispatch_progress(context, state, self.unet.unet.base_model)
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}),
context=context,
lora_info = context.get_model(
**lora.model_dump(exclude={"weight"}),
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
context=context,
unet_info = context.get_model(
**self.unet.unet.model_dump(),
)
with (
ExitStack() as exit_stack,
@ -580,7 +725,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
context=context,
ip_adapter=self.ip_adapter,
conditioning_data=conditioning_data,
unet=unet,
exit_stack=exit_stack,
)
@ -592,7 +736,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
denoising_end=self.denoising_end,
)
result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
(
result_latents,
result_attention_map_saver,
) = pipeline.latents_from_embeddings(
latents=latents,
timesteps=timesteps,
init_timestep=init_timestep,
@ -602,8 +749,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
masked_latents=masked_latents,
num_inference_steps=num_inference_steps,
conditioning_data=conditioning_data,
control_data=controlnet_data, # list[ControlNetData],
ip_adapter_data=ip_adapter_data, # IPAdapterData,
control_data=controlnet_data,
ip_adapter_data=ip_adapter_data,
t2i_adapter_data=t2i_adapter_data,
callback=step_callback,
)
@ -613,13 +761,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
latents_name = context.save_latents(result_latents)
return build_latents_output(latents_name=latents_name, latents=result_latents, seed=seed)
@invocation(
"l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents", version="1.0.0"
"l2i",
title="Latents to Image",
tags=["latents", "image", "vae", "l2i"],
category="latents",
version="1.0.0",
)
class LatentsToImageInvocation(BaseInvocation):
"""Generates an image from latents."""
@ -634,7 +785,7 @@ class LatentsToImageInvocation(BaseInvocation):
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
metadata: CoreMetadata = InputField(
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
@ -642,11 +793,10 @@ class LatentsToImageInvocation(BaseInvocation):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput:
latents = context.services.latents.get(self.latents.latents_name)
latents = context.get_latents(self.latents.latents_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
context=context,
vae_info = context.get_model(
**self.vae.vae.model_dump(),
)
with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
@ -676,7 +826,7 @@ class LatentsToImageInvocation(BaseInvocation):
vae.to(dtype=torch.float16)
latents = latents.half()
if self.tiled or context.services.configuration.tiled_decode:
if self.tiled or context.config.tiled_decode:
vae.enable_tiling()
else:
vae.disable_tiling()
@ -700,28 +850,25 @@ class LatentsToImageInvocation(BaseInvocation):
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
image_dto = context.services.images.create(
image=image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
workflow=self.workflow,
)
image_name = context.save_image(image, category=context.categories.GENERAL)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
image=ImageField(image_name=image_name),
width=image.width,
height=image.height,
)
LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
@invocation(
"lresize",
title="Resize Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.0",
)
class ResizeLatentsInvocation(BaseInvocation):
"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@ -767,7 +914,13 @@ class ResizeLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
@invocation(
"lscale",
title="Scale Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.0",
)
class ScaleLatentsInvocation(BaseInvocation):
"""Scales latents by a given factor."""
@ -806,7 +959,11 @@ class ScaleLatentsInvocation(BaseInvocation):
@invocation(
"i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents", version="1.0.0"
"i2l",
title="Image to Latents",
tags=["latents", "image", "vae", "i2l"],
category="latents",
version="1.0.0",
)
class ImageToLatentsInvocation(BaseInvocation):
"""Encodes an image into latents."""
@ -870,7 +1027,7 @@ class ImageToLatentsInvocation(BaseInvocation):
image = context.services.images.get_pil_image(self.image.image_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
context=context,
)
@ -898,7 +1055,13 @@ class ImageToLatentsInvocation(BaseInvocation):
return vae.encode(image_tensor).latents
@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0")
@invocation(
"lblend",
title="Blend Latents",
tags=["latents", "blend"],
category="latents",
version="1.0.0",
)
class BlendLatentsInvocation(BaseInvocation):
"""Blend two latents using a given alpha. Latents must have same size."""

View File

@ -3,7 +3,7 @@
from typing import Literal
import numpy as np
from pydantic import validator
from pydantic import field_validator
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
@ -65,13 +65,34 @@ class DivideInvocation(BaseInvocation):
class RandomIntInvocation(BaseInvocation):
"""Outputs a single random integer."""
low: int = InputField(default=0, description="The inclusive low value")
high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
low: int = InputField(default=0, description=FieldDescriptions.inclusive_low)
high: int = InputField(default=np.iinfo(np.int32).max, description=FieldDescriptions.exclusive_high)
def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=np.random.randint(self.low, self.high))
@invocation(
"rand_float",
title="Random Float",
tags=["math", "float", "random"],
category="math",
version="1.0.1",
use_cache=False,
)
class RandomFloatInvocation(BaseInvocation):
"""Outputs a single random float"""
low: float = InputField(default=0.0, description=FieldDescriptions.inclusive_low)
high: float = InputField(default=1.0, description=FieldDescriptions.exclusive_high)
decimals: int = InputField(default=2, description=FieldDescriptions.decimal_places)
def invoke(self, context: InvocationContext) -> FloatOutput:
random_float = np.random.uniform(self.low, self.high)
rounded_float = round(random_float, self.decimals)
return FloatOutput(value=rounded_float)
@invocation(
"float_to_int",
title="Float To Integer",
@ -164,7 +185,7 @@ class IntegerMathInvocation(BaseInvocation):
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
@validator("b")
@field_validator("b")
def no_unrepresentable_results(cls, v, values):
if values["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero")
@ -238,7 +259,7 @@ class FloatMathInvocation(BaseInvocation):
a: float = InputField(default=0, description=FieldDescriptions.num_1)
b: float = InputField(default=0, description=FieldDescriptions.num_2)
@validator("b")
@field_validator("b")
def no_unrepresentable_results(cls, v, values):
if values["operation"] == "DIV" and v == 0:
raise ValueError("Cannot divide by zero")

View File

@ -12,7 +12,10 @@ from invokeai.app.invocations.baseinvocation import (
invocation_output,
)
from invokeai.app.invocations.controlnet_image_processors import ControlField
from invokeai.app.invocations.ip_adapter import IPAdapterModelField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
from ...version import __version__
@ -25,30 +28,47 @@ class LoRAMetadataField(BaseModelExcludeNull):
weight: float = Field(description="The weight of the LoRA model")
class IPAdapterMetadataField(BaseModelExcludeNull):
image: ImageField = Field(description="The IP-Adapter image prompt.")
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
weight: float = Field(description="The weight of the IP-Adapter model")
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
)
class CoreMetadata(BaseModelExcludeNull):
"""Core generation metadata for an image generated in InvokeAI."""
app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
generation_mode: str = Field(
generation_mode: Optional[str] = Field(
default=None,
description="The generation mode that output this image",
)
created_by: Optional[str] = Field(description="The name of the creator of the image")
positive_prompt: str = Field(description="The positive prompt parameter")
negative_prompt: str = Field(description="The negative prompt parameter")
width: int = Field(description="The width parameter")
height: int = Field(description="The height parameter")
seed: int = Field(description="The seed used for noise generation")
rand_device: str = Field(description="The device used for random number generation")
cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
steps: int = Field(description="The number of steps used for inference")
scheduler: str = Field(description="The scheduler used for inference")
positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
width: Optional[int] = Field(default=None, description="The width parameter")
height: Optional[int] = Field(default=None, description="The height parameter")
seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
clip_skip: Optional[int] = Field(
default=None,
description="The number of skipped CLIP layers",
)
model: MainModelField = Field(description="The main model used for inference")
controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
default=None, description="The IP Adapters used for inference"
)
t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
loras: Optional[list[LoRAMetadataField]] = Field(default=None, description="The LoRAs used for inference")
vae: Optional[VAEModelField] = Field(
default=None,
description="The VAE used for decoding, if the main model's default was not used",
@ -105,25 +125,34 @@ class MetadataAccumulatorOutput(BaseInvocationOutput):
class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object"""
generation_mode: str = InputField(
generation_mode: Optional[str] = InputField(
default=None,
description="The generation mode that output this image",
)
positive_prompt: str = InputField(description="The positive prompt parameter")
negative_prompt: str = InputField(description="The negative prompt parameter")
width: int = InputField(description="The width parameter")
height: int = InputField(description="The height parameter")
seed: int = InputField(description="The seed used for noise generation")
rand_device: str = InputField(description="The device used for random number generation")
cfg_scale: float = InputField(description="The classifier-free guidance scale parameter")
steps: int = InputField(description="The number of steps used for inference")
scheduler: str = InputField(description="The scheduler used for inference")
clip_skip: Optional[int] = Field(
positive_prompt: Optional[str] = InputField(default=None, description="The positive prompt parameter")
negative_prompt: Optional[str] = InputField(default=None, description="The negative prompt parameter")
width: Optional[int] = InputField(default=None, description="The width parameter")
height: Optional[int] = InputField(default=None, description="The height parameter")
seed: Optional[int] = InputField(default=None, description="The seed used for noise generation")
rand_device: Optional[str] = InputField(default=None, description="The device used for random number generation")
cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
clip_skip: Optional[int] = InputField(
default=None,
description="The number of skipped CLIP layers",
)
model: MainModelField = InputField(description="The main model used for inference")
controlnets: list[ControlField] = InputField(description="The ControlNets used for inference")
loras: list[LoRAMetadataField] = InputField(description="The LoRAs used for inference")
model: Optional[MainModelField] = InputField(default=None, description="The main model used for inference")
controlnets: Optional[list[ControlField]] = InputField(
default=None, description="The ControlNets used for inference"
)
ipAdapters: Optional[list[IPAdapterMetadataField]] = InputField(
default=None, description="The IP Adapters used for inference"
)
t2iAdapters: Optional[list[T2IAdapterField]] = InputField(
default=None, description="The IP Adapters used for inference"
)
loras: Optional[list[LoRAMetadataField]] = InputField(default=None, description="The LoRAs used for inference")
strength: Optional[float] = InputField(
default=None,
description="The strength used for latents-to-latents",
@ -137,6 +166,20 @@ class MetadataAccumulatorInvocation(BaseInvocation):
description="The VAE used for decoding, if the main model's default was not used",
)
# High resolution fix metadata.
hrf_width: Optional[int] = InputField(
default=None,
description="The high resolution fix height and width multipler.",
)
hrf_height: Optional[int] = InputField(
default=None,
description="The high resolution fix height and width multipler.",
)
hrf_strength: Optional[float] = InputField(
default=None,
description="The high resolution fix img2img strength used in the upscale pass.",
)
# SDXL
positive_style_prompt: Optional[str] = InputField(
default=None,
@ -180,4 +223,4 @@ class MetadataAccumulatorInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
"""Collects and outputs a CoreMetadata object"""
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict()))
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))

View File

@ -1,7 +1,7 @@
import copy
from typing import List, Optional
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
from ...backend.model_management import BaseModelType, ModelType, SubModelType
from .baseinvocation import (
@ -24,6 +24,8 @@ class ModelInfo(BaseModel):
model_type: ModelType = Field(description="Info to load submodel")
submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
model_config = ConfigDict(protected_namespaces=())
class LoraInfo(ModelInfo):
weight: float = Field(description="Lora's weight which to use when apply to model")
@ -65,6 +67,8 @@ class MainModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Model Type")
model_config = ConfigDict(protected_namespaces=())
class LoRAModelField(BaseModel):
"""LoRA model field"""
@ -72,8 +76,16 @@ class LoRAModelField(BaseModel):
model_name: str = Field(description="Name of the LoRA model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
@invocation("main_model_loader", title="Main Model", tags=["model"], category="model", version="1.0.0")
@invocation(
"main_model_loader",
title="Main Model",
tags=["model"],
category="model",
version="1.0.0",
)
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
@ -86,7 +98,7 @@ class MainModelLoaderInvocation(BaseInvocation):
model_type = ModelType.Main
# TODO: not found exceptions
if not context.services.model_manager.model_exists(
if not context.model_exists(
model_name=model_name,
base_model=base_model,
model_type=model_type,
@ -180,10 +192,16 @@ class LoraLoaderInvocation(BaseInvocation):
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
)
clip: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP"
default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP",
)
def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
@ -244,20 +262,35 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model", version="1.0.0")
@invocation(
"sdxl_lora_loader",
title="SDXL LoRA",
tags=["lora", "model"],
category="model",
version="1.0.0",
)
class SDXLLoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
)
clip: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1"
default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP 1",
)
clip2: Optional[ClipField] = InputField(
default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2"
default=None,
description=FieldDescriptions.clip,
input=Input.Connection,
title="CLIP 2",
)
def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
@ -330,6 +363,8 @@ class VAEModelField(BaseModel):
model_name: str = Field(description="Name of the model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
@invocation_output("vae_loader_output")
class VaeLoaderOutput(BaseInvocationOutput):
@ -343,7 +378,10 @@ class VaeLoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput"""
vae_model: VAEModelField = InputField(
description=FieldDescriptions.vae_model, input=Input.Direct, ui_type=UIType.VaeModel, title="VAE"
description=FieldDescriptions.vae_model,
input=Input.Direct,
ui_type=UIType.VaeModel,
title="VAE",
)
def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
@ -372,19 +410,31 @@ class VaeLoaderInvocation(BaseInvocation):
class SeamlessModeOutput(BaseInvocationOutput):
"""Modified Seamless Model output"""
unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet")
vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model", version="1.0.0")
@invocation(
"seamless",
title="Seamless",
tags=["seamless", "model"],
category="model",
version="1.0.0",
)
class SeamlessModeInvocation(BaseInvocation):
"""Applies the seamless transformation to the Model UNet and VAE."""
unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
default=None,
description=FieldDescriptions.unet,
input=Input.Connection,
title="UNet",
)
vae: Optional[VaeField] = InputField(
default=None, description=FieldDescriptions.vae_model, input=Input.Connection, title="VAE"
default=None,
description=FieldDescriptions.vae_model,
input=Input.Connection,
title="VAE",
)
seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")

View File

@ -2,7 +2,7 @@
import torch
from pydantic import validator
from pydantic import field_validator
from invokeai.app.invocations.latent import LatentsField
from invokeai.app.util.misc import SEED_MAX, get_random_seed
@ -65,7 +65,7 @@ Nodes
class NoiseOutput(BaseInvocationOutput):
"""Invocation noise output"""
noise: LatentsField = OutputField(default=None, description=FieldDescriptions.noise)
noise: LatentsField = OutputField(description=FieldDescriptions.noise)
width: int = OutputField(description=FieldDescriptions.width)
height: int = OutputField(description=FieldDescriptions.height)
@ -78,7 +78,13 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
)
@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents", version="1.0.0")
@invocation(
"noise",
title="Noise",
tags=["latents", "noise"],
category="latents",
version="1.0.0",
)
class NoiseInvocation(BaseInvocation):
"""Generates latent noise."""
@ -105,7 +111,7 @@ class NoiseInvocation(BaseInvocation):
description="Use CPU for noise generation (for reproducible results across platforms)",
)
@validator("seed", pre=True)
@field_validator("seed", mode="before")
def modulo_seed(cls, v):
"""Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
return v % (SEED_MAX + 1)
@ -118,6 +124,5 @@ class NoiseInvocation(BaseInvocation):
seed=self.seed,
use_cpu=self.use_cpu,
)
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, noise)
return build_noise_output(latents_name=name, latents=noise, seed=self.seed)
latents_name = context.save_latents(noise)
return build_noise_output(latents_name=latents_name, latents=noise, seed=self.seed)

View File

@ -9,18 +9,18 @@ from typing import List, Literal, Optional, Union
import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from pydantic import BaseModel, Field, validator
from pydantic import BaseModel, ConfigDict, Field, field_validator
from tqdm import tqdm
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend import BaseModelType, ModelType, SubModelType
from ...backend.model_management import ONNXModelPatcher
from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.util import choose_torch_device
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
@ -63,14 +63,17 @@ class ONNXPromptInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(),
**self.clip.tokenizer.model_dump(),
)
text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(),
**self.clip.text_encoder.model_dump(),
)
with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder: # , ExitStack() as stack:
loras = [
(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)
(
context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
lora.weight,
)
for lora in self.clip.loras
]
@ -175,14 +178,14 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
description=FieldDescriptions.unet,
input=Input.Connection,
)
control: Optional[Union[ControlField, list[ControlField]]] = InputField(
control: Union[ControlField, list[ControlField]] = InputField(
default=None,
description=FieldDescriptions.control,
)
# seamless: bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'")
@validator("cfg_scale")
@field_validator("cfg_scale")
def ge_one(cls, v):
"""validate that all cfg_scale values are >= 1"""
if isinstance(v, list):
@ -241,7 +244,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
stable_diffusion_step_callback(
context=context,
intermediate_state=intermediate_state,
node=self.dict(),
node=self.model_dump(),
source_node_id=source_node_id,
)
@ -254,12 +257,15 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
eta=0.0,
)
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict())
unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump())
with unet_info as unet: # , ExitStack() as stack:
# loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
loras = [
(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight)
(
context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
lora.weight,
)
for lora in self.unet.loras
]
@ -346,7 +352,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
**self.vae.vae.model_dump(),
)
# clear memory as vae decode can request a lot
@ -375,7 +381,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
metadata=self.metadata.model_dump() if self.metadata else None,
workflow=self.workflow,
)
@ -403,6 +409,8 @@ class OnnxModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Model Type")
model_config = ConfigDict(protected_namespaces=())
@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0")
class OnnxModelLoaderInvocation(BaseInvocation):

View File

@ -44,13 +44,22 @@ from invokeai.app.invocations.primitives import FloatCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@invocation("float_range", title="Float Range", tags=["math", "range"], category="math", version="1.0.0")
@invocation(
"float_range",
title="Float Range",
tags=["math", "range"],
category="math",
version="1.0.0",
)
class FloatLinearRangeInvocation(BaseInvocation):
"""Creates a range"""
start: float = InputField(default=5, description="The first value of the range")
stop: float = InputField(default=10, description="The last value of the range")
steps: int = InputField(default=30, description="number of values to interpolate over (including start and stop)")
steps: int = InputField(
default=30,
description="number of values to interpolate over (including start and stop)",
)
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
param_list = list(np.linspace(self.start, self.stop, self.steps))
@ -95,7 +104,13 @@ EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
# actually I think for now could just use CollectionOutput (which is list[Any]
@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step", version="1.0.0")
@invocation(
"step_param_easing",
title="Step Param Easing",
tags=["step", "easing"],
category="step",
version="1.0.0",
)
class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps"""
@ -159,7 +174,9 @@ class StepParamEasingInvocation(BaseInvocation):
context.services.logger.debug("base easing duration: " + str(base_easing_duration))
even_num_steps = num_easing_steps % 2 == 0 # even number of steps
easing_function = easing_class(
start=self.start_value, end=self.end_value, duration=base_easing_duration - 1
start=self.start_value,
end=self.end_value,
duration=base_easing_duration - 1,
)
base_easing_vals = list()
for step_index in range(base_easing_duration):
@ -199,7 +216,11 @@ class StepParamEasingInvocation(BaseInvocation):
#
else: # no mirroring (default)
easing_function = easing_class(start=self.start_value, end=self.end_value, duration=num_easing_steps - 1)
easing_function = easing_class(
start=self.start_value,
end=self.end_value,
duration=num_easing_steps - 1,
)
for step_index in range(num_easing_steps):
step_val = easing_function.ease(step_index)
easing_list.append(step_val)

View File

@ -226,6 +226,12 @@ class ImageField(BaseModel):
image_name: str = Field(description="The name of the image")
class BoardField(BaseModel):
"""A board primitive field"""
board_id: str = Field(description="The id of the board")
@invocation_output("image_output")
class ImageOutput(BaseInvocationOutput):
"""Base class for nodes that output a single image"""

View File

@ -3,7 +3,7 @@ from typing import Optional, Union
import numpy as np
from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
from pydantic import validator
from pydantic import field_validator
from invokeai.app.invocations.primitives import StringCollectionOutput
@ -21,7 +21,10 @@ from .baseinvocation import BaseInvocation, InputField, InvocationContext, UICom
class DynamicPromptInvocation(BaseInvocation):
"""Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""
prompt: str = InputField(description="The prompt to parse with dynamicprompts", ui_component=UIComponent.Textarea)
prompt: str = InputField(
description="The prompt to parse with dynamicprompts",
ui_component=UIComponent.Textarea,
)
max_prompts: int = InputField(default=1, description="The number of prompts to generate")
combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator")
@ -36,21 +39,31 @@ class DynamicPromptInvocation(BaseInvocation):
return StringCollectionOutput(collection=prompts)
@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt", version="1.0.0")
@invocation(
"prompt_from_file",
title="Prompts from File",
tags=["prompt", "file"],
category="prompt",
version="1.0.0",
)
class PromptsFromFileInvocation(BaseInvocation):
"""Loads prompts from a text file"""
file_path: str = InputField(description="Path to prompt text file")
pre_prompt: Optional[str] = InputField(
default=None, description="String to prepend to each prompt", ui_component=UIComponent.Textarea
default=None,
description="String to prepend to each prompt",
ui_component=UIComponent.Textarea,
)
post_prompt: Optional[str] = InputField(
default=None, description="String to append to each prompt", ui_component=UIComponent.Textarea
default=None,
description="String to append to each prompt",
ui_component=UIComponent.Textarea,
)
start_line: int = InputField(default=1, ge=1, description="Line in the file to start start from")
max_prompts: int = InputField(default=1, ge=0, description="Max lines to read from file (0=all)")
@validator("file_path")
@field_validator("file_path")
def file_path_exists(cls, v):
if not exists(v):
raise ValueError(FileNotFoundError)
@ -79,6 +92,10 @@ class PromptsFromFileInvocation(BaseInvocation):
def invoke(self, context: InvocationContext) -> StringCollectionOutput:
prompts = self.promptsFromFile(
self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts
self.file_path,
self.pre_prompt,
self.post_prompt,
self.start_line,
self.max_prompts,
)
return StringCollectionOutput(collection=prompts)

View File

@ -0,0 +1,85 @@
from typing import Union
from pydantic import BaseModel, ConfigDict, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
FieldDescriptions,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
from invokeai.app.invocations.primitives import ImageField
from invokeai.backend.model_management.models.base import BaseModelType
class T2IAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the T2I-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class T2IAdapterField(BaseModel):
image: ImageField = Field(description="The T2I-Adapter image prompt.")
t2i_adapter_model: T2IAdapterModelField = Field(description="The T2I-Adapter model to use.")
weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
)
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@invocation_output("t2i_adapter_output")
class T2IAdapterOutput(BaseInvocationOutput):
t2i_adapter: T2IAdapterField = OutputField(description=FieldDescriptions.t2i_adapter, title="T2I Adapter")
@invocation(
"t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.0"
)
class T2IAdapterInvocation(BaseInvocation):
"""Collects T2I-Adapter info to pass to other nodes."""
# Inputs
image: ImageField = InputField(description="The IP-Adapter image prompt.")
t2i_adapter_model: T2IAdapterModelField = InputField(
description="The T2I-Adapter model.",
title="T2I-Adapter Model",
input=Input.Direct,
ui_order=-1,
)
weight: Union[float, list[float]] = InputField(
default=1, ge=0, description="The weight given to the T2I-Adapter", ui_type=UIType.Float, title="Weight"
)
begin_step_percent: float = InputField(
default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"
)
end_step_percent: float = InputField(
default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
)
resize_mode: CONTROLNET_RESIZE_VALUES = InputField(
default="just_resize",
description="The resize mode applied to the T2I-Adapter input image so that it matches the target output size.",
)
def invoke(self, context: InvocationContext) -> T2IAdapterOutput:
return T2IAdapterOutput(
t2i_adapter=T2IAdapterField(
image=self.image,
t2i_adapter_model=self.t2i_adapter_model,
weight=self.weight,
begin_step_percent=self.begin_step_percent,
end_step_percent=self.end_step_percent,
resize_mode=self.resize_mode,
)
)

View File

@ -4,12 +4,15 @@ from typing import Literal
import cv2 as cv
import numpy as np
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from PIL import Image
from pydantic import ConfigDict
from realesrgan import RealESRGANer
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.util.devices import choose_torch_device
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@ -22,13 +25,21 @@ ESRGAN_MODELS = Literal[
"RealESRGAN_x2plus.pth",
]
if choose_torch_device() == torch.device("mps"):
from torch import mps
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.0.0")
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
class ESRGANInvocation(BaseInvocation):
"""Upscales an image using RealESRGAN."""
image: ImageField = InputField(description="The input image")
model_name: ESRGAN_MODELS = InputField(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
tile_size: int = InputField(
default=400, ge=0, description="Tile size for tiled ESRGAN upscaling (0=tiling disabled)"
)
model_config = ConfigDict(protected_namespaces=())
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
@ -86,9 +97,11 @@ class ESRGANInvocation(BaseInvocation):
model_path=str(models_path / esrgan_model_path),
model=rrdbnet_model,
half=False,
tile=self.tile_size,
)
# prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
# TODO: This strips the alpha... is that okay?
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
@ -99,6 +112,10 @@ class ESRGANInvocation(BaseInvocation):
# back to PIL
pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
torch.cuda.empty_cache()
if choose_torch_device() == torch.device("mps"):
mps.empty_cache()
image_dto = context.services.images.create(
image=pil_image,
image_origin=ResourceOrigin.INTERNAL,

View File

@ -1,4 +0,0 @@
class CanceledException(Exception):
"""Execution canceled by user."""
pass

View File

@ -1,71 +0,0 @@
from enum import Enum
from pydantic import BaseModel, Field
from invokeai.app.util.metaenum import MetaEnum
class ProgressImage(BaseModel):
"""The progress image sent intermittently during processing"""
width: int = Field(description="The effective width of the image in pixels")
height: int = Field(description="The effective height of the image in pixels")
dataURL: str = Field(description="The image data as a b64 data URL")
class ResourceOrigin(str, Enum, metaclass=MetaEnum):
"""The origin of a resource (eg image).
- INTERNAL: The resource was created by the application.
- EXTERNAL: The resource was not created by the application.
This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
"""
INTERNAL = "internal"
"""The resource was created by the application."""
EXTERNAL = "external"
"""The resource was not created by the application.
This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
"""
class InvalidOriginException(ValueError):
"""Raised when a provided value is not a valid ResourceOrigin.
Subclasses `ValueError`.
"""
def __init__(self, message="Invalid resource origin."):
super().__init__(message)
class ImageCategory(str, Enum, metaclass=MetaEnum):
"""The category of an image.
- GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose.
- MASK: The image is a mask image.
- CONTROL: The image is a ControlNet control image.
- USER: The image is a user-provide image.
- OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes.
"""
GENERAL = "general"
"""GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose."""
MASK = "mask"
"""MASK: The image is a mask image."""
CONTROL = "control"
"""CONTROL: The image is a ControlNet control image."""
USER = "user"
"""USER: The image is a user-provide image."""
OTHER = "other"
"""OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
class InvalidImageCategoryException(ValueError):
"""Raised when a provided value is not a valid ImageCategory.
Subclasses `ValueError`.
"""
def __init__(self, message="Invalid image category."):
super().__init__(message)

View File

@ -0,0 +1,47 @@
from abc import ABC, abstractmethod
from typing import Optional
class BoardImageRecordStorageBase(ABC):
"""Abstract base class for the one-to-many board-image relationship record storage."""
@abstractmethod
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
"""Adds an image to a board."""
pass
@abstractmethod
def remove_image_from_board(
self,
image_name: str,
) -> None:
"""Removes an image from a board."""
pass
@abstractmethod
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
"""Gets all board images for a board, as a list of the image names."""
pass
@abstractmethod
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's board id, if it has one."""
pass
@abstractmethod
def get_image_count_for_board(
self,
board_id: str,
) -> int:
"""Gets the number of images for a board."""
pass

View File

@ -1,69 +1,24 @@
import sqlite3
import threading
from abc import ABC, abstractmethod
from typing import Optional, cast
from invokeai.app.services.image_record_storage import OffsetPaginatedResults
from invokeai.app.services.models.image_record import ImageRecord, deserialize_image_record
from invokeai.app.services.image_records.image_records_common import ImageRecord, deserialize_image_record
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
class BoardImageRecordStorageBase(ABC):
"""Abstract base class for the one-to-many board-image relationship record storage."""
@abstractmethod
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
"""Adds an image to a board."""
pass
@abstractmethod
def remove_image_from_board(
self,
image_name: str,
) -> None:
"""Removes an image from a board."""
pass
@abstractmethod
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
"""Gets all board images for a board, as a list of the image names."""
pass
@abstractmethod
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's board id, if it has one."""
pass
@abstractmethod
def get_image_count_for_board(
self,
board_id: str,
) -> int:
"""Gets the number of images for a board."""
pass
from .board_image_records_base import BoardImageRecordStorageBase
class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.Lock
_lock: threading.RLock
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._conn = conn
# Enable row factory to get rows as dictionaries (must be done before making the cursor!)
self._conn.row_factory = sqlite3.Row
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
self._lock = lock
try:
self._lock.acquire()

View File

@ -1,112 +0,0 @@
from abc import ABC, abstractmethod
from logging import Logger
from typing import Optional
from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
from invokeai.app.services.board_record_storage import BoardRecord, BoardRecordStorageBase
from invokeai.app.services.image_record_storage import ImageRecordStorageBase
from invokeai.app.services.models.board_record import BoardDTO
from invokeai.app.services.urls import UrlServiceBase
class BoardImagesServiceABC(ABC):
"""High-level service for board-image relationship management."""
@abstractmethod
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
"""Adds an image to a board."""
pass
@abstractmethod
def remove_image_from_board(
self,
image_name: str,
) -> None:
"""Removes an image from a board."""
pass
@abstractmethod
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
"""Gets all board images for a board, as a list of the image names."""
pass
@abstractmethod
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's board id, if it has one."""
pass
class BoardImagesServiceDependencies:
"""Service dependencies for the BoardImagesService."""
board_image_records: BoardImageRecordStorageBase
board_records: BoardRecordStorageBase
image_records: ImageRecordStorageBase
urls: UrlServiceBase
logger: Logger
def __init__(
self,
board_image_record_storage: BoardImageRecordStorageBase,
image_record_storage: ImageRecordStorageBase,
board_record_storage: BoardRecordStorageBase,
url: UrlServiceBase,
logger: Logger,
):
self.board_image_records = board_image_record_storage
self.image_records = image_record_storage
self.board_records = board_record_storage
self.urls = url
self.logger = logger
class BoardImagesService(BoardImagesServiceABC):
_services: BoardImagesServiceDependencies
def __init__(self, services: BoardImagesServiceDependencies):
self._services = services
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
self._services.board_image_records.add_image_to_board(board_id, image_name)
def remove_image_from_board(
self,
image_name: str,
) -> None:
self._services.board_image_records.remove_image_from_board(image_name)
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
return self._services.board_image_records.get_all_board_image_names_for_board(board_id)
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
board_id = self._services.board_image_records.get_board_for_image(image_name)
return board_id
def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
"""Converts a board record to a board DTO."""
return BoardDTO(
**board_record.dict(exclude={"cover_image_name"}),
cover_image_name=cover_image_name,
image_count=image_count,
)

View File

@ -0,0 +1,39 @@
from abc import ABC, abstractmethod
from typing import Optional
class BoardImagesServiceABC(ABC):
"""High-level service for board-image relationship management."""
@abstractmethod
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
"""Adds an image to a board."""
pass
@abstractmethod
def remove_image_from_board(
self,
image_name: str,
) -> None:
"""Removes an image from a board."""
pass
@abstractmethod
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
"""Gets all board images for a board, as a list of the image names."""
pass
@abstractmethod
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
"""Gets an image's board id, if it has one."""
pass

View File

@ -0,0 +1,38 @@
from typing import Optional
from invokeai.app.services.invoker import Invoker
from .board_images_base import BoardImagesServiceABC
class BoardImagesService(BoardImagesServiceABC):
__invoker: Invoker
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
def add_image_to_board(
self,
board_id: str,
image_name: str,
) -> None:
self.__invoker.services.board_image_records.add_image_to_board(board_id, image_name)
def remove_image_from_board(
self,
image_name: str,
) -> None:
self.__invoker.services.board_image_records.remove_image_from_board(image_name)
def get_all_board_image_names_for_board(
self,
board_id: str,
) -> list[str]:
return self.__invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
board_id = self.__invoker.services.board_image_records.get_board_for_image(image_name)
return board_id

View File

@ -0,0 +1,55 @@
from abc import ABC, abstractmethod
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .board_records_common import BoardChanges, BoardRecord
class BoardRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the board record store."""
@abstractmethod
def delete(self, board_id: str) -> None:
"""Deletes a board record."""
pass
@abstractmethod
def save(
self,
board_name: str,
) -> BoardRecord:
"""Saves a board record."""
pass
@abstractmethod
def get(
self,
board_id: str,
) -> BoardRecord:
"""Gets a board record."""
pass
@abstractmethod
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardRecord:
"""Updates a board record."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
) -> OffsetPaginatedResults[BoardRecord]:
"""Gets many board records."""
pass
@abstractmethod
def get_all(
self,
) -> list[BoardRecord]:
"""Gets all board records."""
pass

View File

@ -1,7 +1,7 @@
from datetime import datetime
from typing import Optional, Union
from pydantic import Field
from pydantic import BaseModel, Field
from invokeai.app.util.misc import get_iso_timestamp
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
@ -18,21 +18,12 @@ class BoardRecord(BaseModelExcludeNull):
"""The created timestamp of the image."""
updated_at: Union[datetime, str] = Field(description="The updated timestamp of the board.")
"""The updated timestamp of the image."""
deleted_at: Union[datetime, str, None] = Field(description="The deleted timestamp of the board.")
deleted_at: Optional[Union[datetime, str]] = Field(default=None, description="The deleted timestamp of the board.")
"""The updated timestamp of the image."""
cover_image_name: Optional[str] = Field(description="The name of the cover image of the board.")
cover_image_name: Optional[str] = Field(default=None, description="The name of the cover image of the board.")
"""The name of the cover image of the board."""
class BoardDTO(BoardRecord):
"""Deserialized board record with cover image URL and image count."""
cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
"""The URL of the thumbnail of the most recent image in the board."""
image_count: int = Field(description="The number of images in the board.")
"""The number of images in the board."""
def deserialize_board_record(board_dict: dict) -> BoardRecord:
"""Deserializes a board record."""
@ -53,3 +44,29 @@ def deserialize_board_record(board_dict: dict) -> BoardRecord:
updated_at=updated_at,
deleted_at=deleted_at,
)
class BoardChanges(BaseModel, extra="forbid"):
board_name: Optional[str] = Field(default=None, description="The board's new name.")
cover_image_name: Optional[str] = Field(default=None, description="The name of the board's new cover image.")
class BoardRecordNotFoundException(Exception):
"""Raised when an board record is not found."""
def __init__(self, message="Board record not found"):
super().__init__(message)
class BoardRecordSaveException(Exception):
"""Raised when an board record cannot be saved."""
def __init__(self, message="Board record not saved"):
super().__init__(message)
class BoardRecordDeleteException(Exception):
"""Raised when an board record cannot be deleted."""
def __init__(self, message="Board record not deleted"):
super().__init__(message)

View File

@ -1,103 +1,32 @@
import sqlite3
import threading
from abc import ABC, abstractmethod
from typing import Optional, Union, cast
from typing import Union, cast
from pydantic import BaseModel, Extra, Field
from invokeai.app.services.image_record_storage import OffsetPaginatedResults
from invokeai.app.services.models.board_record import BoardRecord, deserialize_board_record
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
from invokeai.app.util.misc import uuid_string
class BoardChanges(BaseModel, extra=Extra.forbid):
board_name: Optional[str] = Field(description="The board's new name.")
cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.")
class BoardRecordNotFoundException(Exception):
"""Raised when an board record is not found."""
def __init__(self, message="Board record not found"):
super().__init__(message)
class BoardRecordSaveException(Exception):
"""Raised when an board record cannot be saved."""
def __init__(self, message="Board record not saved"):
super().__init__(message)
class BoardRecordDeleteException(Exception):
"""Raised when an board record cannot be deleted."""
def __init__(self, message="Board record not deleted"):
super().__init__(message)
class BoardRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the board record store."""
@abstractmethod
def delete(self, board_id: str) -> None:
"""Deletes a board record."""
pass
@abstractmethod
def save(
self,
board_name: str,
) -> BoardRecord:
"""Saves a board record."""
pass
@abstractmethod
def get(
self,
board_id: str,
) -> BoardRecord:
"""Gets a board record."""
pass
@abstractmethod
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardRecord:
"""Updates a board record."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
) -> OffsetPaginatedResults[BoardRecord]:
"""Gets many board records."""
pass
@abstractmethod
def get_all(
self,
) -> list[BoardRecord]:
"""Gets all board records."""
pass
from .board_records_base import BoardRecordStorageBase
from .board_records_common import (
BoardChanges,
BoardRecord,
BoardRecordDeleteException,
BoardRecordNotFoundException,
BoardRecordSaveException,
deserialize_board_record,
)
class SqliteBoardRecordStorage(BoardRecordStorageBase):
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.Lock
_lock: threading.RLock
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._conn = conn
# Enable row factory to get rows as dictionaries (must be done before making the cursor!)
self._conn.row_factory = sqlite3.Row
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
self._lock = lock
try:
self._lock.acquire()

View File

@ -1,158 +0,0 @@
from abc import ABC, abstractmethod
from logging import Logger
from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
from invokeai.app.services.board_images import board_record_to_dto
from invokeai.app.services.board_record_storage import BoardChanges, BoardRecordStorageBase
from invokeai.app.services.image_record_storage import ImageRecordStorageBase, OffsetPaginatedResults
from invokeai.app.services.models.board_record import BoardDTO
from invokeai.app.services.urls import UrlServiceBase
class BoardServiceABC(ABC):
"""High-level service for board management."""
@abstractmethod
def create(
self,
board_name: str,
) -> BoardDTO:
"""Creates a board."""
pass
@abstractmethod
def get_dto(
self,
board_id: str,
) -> BoardDTO:
"""Gets a board."""
pass
@abstractmethod
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardDTO:
"""Updates a board."""
pass
@abstractmethod
def delete(
self,
board_id: str,
) -> None:
"""Deletes a board."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
) -> OffsetPaginatedResults[BoardDTO]:
"""Gets many boards."""
pass
@abstractmethod
def get_all(
self,
) -> list[BoardDTO]:
"""Gets all boards."""
pass
class BoardServiceDependencies:
"""Service dependencies for the BoardService."""
board_image_records: BoardImageRecordStorageBase
board_records: BoardRecordStorageBase
image_records: ImageRecordStorageBase
urls: UrlServiceBase
logger: Logger
def __init__(
self,
board_image_record_storage: BoardImageRecordStorageBase,
image_record_storage: ImageRecordStorageBase,
board_record_storage: BoardRecordStorageBase,
url: UrlServiceBase,
logger: Logger,
):
self.board_image_records = board_image_record_storage
self.image_records = image_record_storage
self.board_records = board_record_storage
self.urls = url
self.logger = logger
class BoardService(BoardServiceABC):
_services: BoardServiceDependencies
def __init__(self, services: BoardServiceDependencies):
self._services = services
def create(
self,
board_name: str,
) -> BoardDTO:
board_record = self._services.board_records.save(board_name)
return board_record_to_dto(board_record, None, 0)
def get_dto(self, board_id: str) -> BoardDTO:
board_record = self._services.board_records.get(board_id)
cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardDTO:
board_record = self._services.board_records.update(board_id, changes)
cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def delete(self, board_id: str) -> None:
self._services.board_records.delete(board_id)
def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
board_records = self._services.board_records.get_many(offset, limit)
board_dtos = []
for r in board_records.items:
cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
def get_all(self) -> list[BoardDTO]:
board_records = self._services.board_records.get_all()
board_dtos = []
for r in board_records:
cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return board_dtos

View File

View File

@ -0,0 +1,59 @@
from abc import ABC, abstractmethod
from invokeai.app.services.board_records.board_records_common import BoardChanges
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .boards_common import BoardDTO
class BoardServiceABC(ABC):
"""High-level service for board management."""
@abstractmethod
def create(
self,
board_name: str,
) -> BoardDTO:
"""Creates a board."""
pass
@abstractmethod
def get_dto(
self,
board_id: str,
) -> BoardDTO:
"""Gets a board."""
pass
@abstractmethod
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardDTO:
"""Updates a board."""
pass
@abstractmethod
def delete(
self,
board_id: str,
) -> None:
"""Deletes a board."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
) -> OffsetPaginatedResults[BoardDTO]:
"""Gets many boards."""
pass
@abstractmethod
def get_all(
self,
) -> list[BoardDTO]:
"""Gets all boards."""
pass

View File

@ -0,0 +1,23 @@
from typing import Optional
from pydantic import Field
from ..board_records.board_records_common import BoardRecord
class BoardDTO(BoardRecord):
"""Deserialized board record with cover image URL and image count."""
cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
"""The URL of the thumbnail of the most recent image in the board."""
image_count: int = Field(description="The number of images in the board.")
"""The number of images in the board."""
def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
"""Converts a board record to a board DTO."""
return BoardDTO(
**board_record.model_dump(exclude={"cover_image_name"}),
cover_image_name=cover_image_name,
image_count=image_count,
)

View File

@ -0,0 +1,79 @@
from invokeai.app.services.board_records.board_records_common import BoardChanges
from invokeai.app.services.boards.boards_common import BoardDTO
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .boards_base import BoardServiceABC
from .boards_common import board_record_to_dto
class BoardService(BoardServiceABC):
__invoker: Invoker
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
def create(
self,
board_name: str,
) -> BoardDTO:
board_record = self.__invoker.services.board_records.save(board_name)
return board_record_to_dto(board_record, None, 0)
def get_dto(self, board_id: str) -> BoardDTO:
board_record = self.__invoker.services.board_records.get(board_id)
cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def update(
self,
board_id: str,
changes: BoardChanges,
) -> BoardDTO:
board_record = self.__invoker.services.board_records.update(board_id, changes)
cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def delete(self, board_id: str) -> None:
self.__invoker.services.board_records.delete(board_id)
def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
board_records = self.__invoker.services.board_records.get_many(offset, limit)
board_dtos = []
for r in board_records.items:
cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
def get_all(self) -> list[BoardDTO]:
board_records = self.__invoker.services.board_records.get_all()
board_dtos = []
for r in board_records:
cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return board_dtos

View File

@ -2,5 +2,5 @@
Init file for InvokeAI configure package
"""
from .base import PagingArgumentParser # noqa F401
from .invokeai_config import InvokeAIAppConfig, get_invokeai_config # noqa F401
from .config_base import PagingArgumentParser # noqa F401
from .config_default import InvokeAIAppConfig, get_invokeai_config # noqa F401

View File

@ -12,25 +12,15 @@ from __future__ import annotations
import argparse
import os
import pydoc
import sys
from argparse import ArgumentParser
from pathlib import Path
from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
from omegaconf import DictConfig, ListConfig, OmegaConf
from pydantic import BaseSettings
from pydantic_settings import BaseSettings, SettingsConfigDict
class PagingArgumentParser(argparse.ArgumentParser):
"""
A custom ArgumentParser that uses pydoc to page its output.
It also supports reading defaults from an init file.
"""
def print_help(self, file=None):
text = self.format_help()
pydoc.pager(text)
from invokeai.app.services.config.config_common import PagingArgumentParser, int_or_float_or_str
class InvokeAISettings(BaseSettings):
@ -42,12 +32,14 @@ class InvokeAISettings(BaseSettings):
initconf: ClassVar[Optional[DictConfig]] = None
argparse_groups: ClassVar[Dict] = {}
model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
def parse_args(self, argv: Optional[list] = sys.argv[1:]):
parser = self.get_parser()
opt, unknown_opts = parser.parse_known_args(argv)
if len(unknown_opts) > 0:
print("Unknown args:", unknown_opts)
for name in self.__fields__:
for name in self.model_fields:
if name not in self._excluded():
value = getattr(opt, name)
if isinstance(value, ListConfig):
@ -64,10 +56,12 @@ class InvokeAISettings(BaseSettings):
cls = self.__class__
type = get_args(get_type_hints(cls)["type"])[0]
field_dict = dict({type: dict()})
for name, field in self.__fields__.items():
for name, field in self.model_fields.items():
if name in cls._excluded_from_yaml():
continue
category = field.field_info.extra.get("category") or "Uncategorized"
category = (
field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
)
value = getattr(self, name)
if category not in field_dict[type]:
field_dict[type][category] = dict()
@ -83,7 +77,7 @@ class InvokeAISettings(BaseSettings):
else:
settings_stanza = "Uncategorized"
env_prefix = getattr(cls.Config, "env_prefix", None)
env_prefix = getattr(cls.model_config, "env_prefix", None)
env_prefix = env_prefix if env_prefix is not None else settings_stanza.upper()
initconf = (
@ -99,14 +93,18 @@ class InvokeAISettings(BaseSettings):
for key, value in os.environ.items():
upcase_environ[key.upper()] = value
fields = cls.__fields__
fields = cls.model_fields
cls.argparse_groups = {}
for name, field in fields.items():
if name not in cls._excluded():
current_default = field.default
category = field.field_info.extra.get("category", "Uncategorized")
category = (
field.json_schema_extra.get("category", "Uncategorized")
if field.json_schema_extra
else "Uncategorized"
)
env_name = env_prefix + "_" + name
if category in initconf and name in initconf.get(category):
field.default = initconf.get(category).get(name)
@ -156,11 +154,6 @@ class InvokeAISettings(BaseSettings):
"tiled_decode",
]
class Config:
env_file_encoding = "utf-8"
arbitrary_types_allowed = True
case_sensitive = True
@classmethod
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
field_type = get_type_hints(cls).get(name)
@ -171,7 +164,7 @@ class InvokeAISettings(BaseSettings):
if field.default_factory is None
else field.default_factory()
)
if category := field.field_info.extra.get("category"):
if category := (field.json_schema_extra.get("category", None) if field.json_schema_extra else None):
if category not in cls.argparse_groups:
cls.argparse_groups[category] = command_parser.add_argument_group(category)
argparse_group = cls.argparse_groups[category]
@ -179,7 +172,7 @@ class InvokeAISettings(BaseSettings):
argparse_group = command_parser
if get_origin(field_type) == Literal:
allowed_values = get_args(field.type_)
allowed_values = get_args(field.annotation)
allowed_types = set()
for val in allowed_values:
allowed_types.add(type(val))
@ -192,7 +185,7 @@ class InvokeAISettings(BaseSettings):
type=field_type,
default=default,
choices=allowed_values,
help=field.field_info.description,
help=field.description,
)
elif get_origin(field_type) == Union:
@ -201,7 +194,7 @@ class InvokeAISettings(BaseSettings):
dest=name,
type=int_or_float_or_str,
default=default,
help=field.field_info.description,
help=field.description,
)
elif get_origin(field_type) == list:
@ -209,32 +202,17 @@ class InvokeAISettings(BaseSettings):
f"--{name}",
dest=name,
nargs="*",
type=field.type_,
type=field.annotation,
default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
help=field.description,
)
else:
argparse_group.add_argument(
f"--{name}",
dest=name,
type=field.type_,
type=field.annotation,
default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
help=field.description,
)
def int_or_float_or_str(value: str) -> Union[int, float, str]:
"""
Workaround for argparse type checking.
"""
try:
return int(value)
except Exception as e: # noqa F841
pass
try:
return float(value)
except Exception as e: # noqa F841
pass
return str(value)

View File

@ -0,0 +1,41 @@
# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
"""
Base class for the InvokeAI configuration system.
It defines a type of pydantic BaseSettings object that
is able to read and write from an omegaconf-based config file,
with overriding of settings from environment variables and/or
the command line.
"""
from __future__ import annotations
import argparse
import pydoc
from typing import Union
class PagingArgumentParser(argparse.ArgumentParser):
"""
A custom ArgumentParser that uses pydoc to page its output.
It also supports reading defaults from an init file.
"""
def print_help(self, file=None):
text = self.format_help()
pydoc.pager(text)
def int_or_float_or_str(value: str) -> Union[int, float, str]:
"""
Workaround for argparse type checking.
"""
try:
return int(value)
except Exception as e: # noqa F841
pass
try:
return float(value)
except Exception as e: # noqa F841
pass
return str(value)

View File

@ -144,8 +144,8 @@ which is set to the desired top-level name. For example, to create a
class InvokeBatch(InvokeAISettings):
type: Literal["InvokeBatch"] = "InvokeBatch"
node_count : int = Field(default=1, description="Number of nodes to run on", category='Resources')
cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", category='Resources')
node_count : int = Field(default=1, description="Number of nodes to run on", json_schema_extra=dict(category='Resources'))
cpu_count : int = Field(default=8, description="Number of GPUs to run on per node", json_schema_extra=dict(category='Resources'))
This will now read and write from the "InvokeBatch" section of the
config file, look for environment variables named INVOKEBATCH_*, and
@ -175,9 +175,10 @@ from pathlib import Path
from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hints
from omegaconf import DictConfig, OmegaConf
from pydantic import Field, parse_obj_as
from pydantic import Field, TypeAdapter
from pydantic_settings import SettingsConfigDict
from .base import InvokeAISettings
from .config_base import InvokeAISettings
INIT_FILE = Path("invokeai.yaml")
DB_FILE = Path("invokeai.db")
@ -185,6 +186,21 @@ LEGACY_INIT_FILE = Path("invokeai.init")
DEFAULT_MAX_VRAM = 0.5
class Categories(object):
WebServer = dict(category="Web Server")
Features = dict(category="Features")
Paths = dict(category="Paths")
Logging = dict(category="Logging")
Development = dict(category="Development")
Other = dict(category="Other")
ModelCache = dict(category="Model Cache")
Device = dict(category="Device")
Generation = dict(category="Generation")
Queue = dict(category="Queue")
Nodes = dict(category="Nodes")
MemoryPerformance = dict(category="Memory/Performance")
class InvokeAIAppConfig(InvokeAISettings):
"""
Generate images using Stable Diffusion. Use "invokeai" to launch
@ -201,85 +217,88 @@ class InvokeAIAppConfig(InvokeAISettings):
type: Literal["InvokeAI"] = "InvokeAI"
# WEB
host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
port : int = Field(default=9090, description="Port to bind to", category='Web Server')
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server')
allow_credentials : bool = Field(default=True, description="Allow CORS credentials", category='Web Server')
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server')
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server')
host : str = Field(default="127.0.0.1", description="IP address to bind to", json_schema_extra=Categories.WebServer)
port : int = Field(default=9090, description="Port to bind to", json_schema_extra=Categories.WebServer)
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", json_schema_extra=Categories.WebServer)
allow_credentials : bool = Field(default=True, description="Allow CORS credentials", json_schema_extra=Categories.WebServer)
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", json_schema_extra=Categories.WebServer)
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", json_schema_extra=Categories.WebServer)
# FEATURES
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", json_schema_extra=Categories.Features)
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", json_schema_extra=Categories.Features)
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", json_schema_extra=Categories.Features)
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", json_schema_extra=Categories.Features)
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', json_schema_extra=Categories.Features)
# PATHS
root : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths')
autoimport_dir : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
lora_dir : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
embedding_dir : Path = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='Paths')
controlnet_dir : Path = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', category='Paths')
conf_path : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
models_dir : Path = Field(default='models', description='Path to the models directory', category='Paths')
legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
db_dir : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths')
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
root : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
autoimport_dir : Optional[Path] = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
lora_dir : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
embedding_dir : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
controlnet_dir : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
conf_path : Optional[Path] = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
models_dir : Optional[Path] = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
legacy_conf_dir : Optional[Path] = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
outdir : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths)
# LOGGING
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', json_schema_extra=Categories.Logging)
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging")
log_sql : bool = Field(default=False, description="Log SQL queries", category="Logging")
log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', json_schema_extra=Categories.Logging)
log_level : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher", json_schema_extra=Categories.Logging)
log_sql : bool = Field(default=False, description="Log SQL queries", json_schema_extra=Categories.Logging)
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", category="Development")
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", json_schema_extra=Categories.Development)
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
version : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
# CACHE
ram : Union[float, Literal["auto"]] = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number or 'auto')", category="Model Cache", )
vram : Union[float, Literal["auto"]] = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number or 'auto')", category="Model Cache", )
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", )
ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
vram : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
# DEVICE
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", category="Device", )
precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", category="Device", )
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
precision : Literal["auto", "float16", "float32", "autocast"] = Field(default="auto", description="Floating point precision", json_schema_extra=Categories.Device)
# GENERATION
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category="Generation", )
attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", category="Generation", )
attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", json_schema_extra=Categories.Generation)
attention_type : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type", json_schema_extra=Categories.Generation)
attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', json_schema_extra=Categories.Generation)
force_tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Generation)
png_compress_level : int = Field(default=6, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize", json_schema_extra=Categories.Generation)
# QUEUE
max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", )
max_queue_size : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", json_schema_extra=Categories.Queue)
# NODES
allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", category="Nodes")
deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", category="Nodes")
node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", category="Nodes", )
allow_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", json_schema_extra=Categories.Nodes)
deny_nodes : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", json_schema_extra=Categories.Nodes)
node_cache_size : int = Field(default=512, description="How many cached nodes to keep in memory", json_schema_extra=Categories.Nodes)
# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.MemoryPerformance)
max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.MemoryPerformance)
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.MemoryPerformance)
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.MemoryPerformance)
# See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
# fmt: on
class Config:
validate_assignment = True
env_prefix = "INVOKEAI"
model_config = SettingsConfigDict(validate_assignment=True, env_prefix="INVOKEAI")
def parse_args(self, argv: Optional[list[str]] = None, conf: Optional[DictConfig] = None, clobber=False):
def parse_args(
self,
argv: Optional[list[str]] = None,
conf: Optional[DictConfig] = None,
clobber=False,
):
"""
Update settings with contents of init file, environment, and
command-line settings.
@ -307,7 +326,11 @@ class InvokeAIAppConfig(InvokeAISettings):
if self.singleton_init and not clobber:
hints = get_type_hints(self.__class__)
for k in self.singleton_init:
setattr(self, k, parse_obj_as(hints[k], self.singleton_init[k]))
setattr(
self,
k,
TypeAdapter(hints[k]).validate_python(self.singleton_init[k]),
)
@classmethod
def get_config(cls, **kwargs) -> InvokeAIAppConfig:

View File

View File

@ -2,10 +2,16 @@
from typing import Any, Optional
from invokeai.app.models.image import ProgressImage
from invokeai.app.services.model_manager_service import BaseModelType, ModelInfo, ModelType, SubModelType
from invokeai.app.services.session_queue.session_queue_common import EnqueueBatchResult, SessionQueueItem
from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
from invokeai.app.services.session_queue.session_queue_common import (
BatchStatus,
EnqueueBatchResult,
SessionQueueItem,
SessionQueueStatus,
)
from invokeai.app.util.misc import get_timestamp
from invokeai.backend.model_management.model_manager import ModelInfo
from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
class EventServiceBase:
@ -49,7 +55,7 @@ class EventServiceBase:
graph_execution_state_id=graph_execution_state_id,
node_id=node.get("id"),
source_node_id=source_node_id,
progress_image=progress_image.dict() if progress_image is not None else None,
progress_image=progress_image.model_dump() if progress_image is not None else None,
step=step,
order=order,
total_steps=total_steps,
@ -262,21 +268,31 @@ class EventServiceBase:
),
)
def emit_queue_item_status_changed(self, session_queue_item: SessionQueueItem) -> None:
def emit_queue_item_status_changed(
self,
session_queue_item: SessionQueueItem,
batch_status: BatchStatus,
queue_status: SessionQueueStatus,
) -> None:
"""Emitted when a queue item's status changes"""
self.__emit_queue_event(
event_name="queue_item_status_changed",
payload=dict(
queue_id=session_queue_item.queue_id,
queue_item_id=session_queue_item.item_id,
status=session_queue_item.status,
batch_id=session_queue_item.batch_id,
session_id=session_queue_item.session_id,
error=session_queue_item.error,
created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
queue_id=queue_status.queue_id,
queue_item=dict(
queue_id=session_queue_item.queue_id,
item_id=session_queue_item.item_id,
status=session_queue_item.status,
batch_id=session_queue_item.batch_id,
session_id=session_queue_item.session_id,
error=session_queue_item.error,
created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
),
batch_status=batch_status.model_dump(),
queue_status=queue_status.model_dump(),
),
)

View File

@ -0,0 +1,43 @@
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Optional
from PIL.Image import Image as PILImageType
class ImageFileStorageBase(ABC):
"""Low-level service responsible for storing and retrieving image files."""
@abstractmethod
def get(self, image_name: str) -> PILImageType:
"""Retrieves an image as PIL Image."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> Path:
"""Gets the internal path to an image or thumbnail."""
pass
# TODO: We need to validate paths before starlette makes the FileResponse, else we get a
# 500 internal server error. I don't like having this method on the service.
@abstractmethod
def validate_path(self, path: str) -> bool:
"""Validates the path given for an image or thumbnail."""
pass
@abstractmethod
def save(
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
thumbnail_size: int = 256,
) -> None:
"""Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
pass
@abstractmethod
def delete(self, image_name: str) -> None:
"""Deletes an image and its thumbnail (if one exists)."""
pass

View File

@ -0,0 +1,20 @@
# TODO: Should these excpetions subclass existing python exceptions?
class ImageFileNotFoundException(Exception):
"""Raised when an image file is not found in storage."""
def __init__(self, message="Image file not found"):
super().__init__(message)
class ImageFileSaveException(Exception):
"""Raised when an image cannot be saved."""
def __init__(self, message="Image file not saved"):
super().__init__(message)
class ImageFileDeleteException(Exception):
"""Raised when an image cannot be deleted."""
def __init__(self, message="Image file not deleted"):
super().__init__(message)

View File

@ -1,6 +1,5 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
import json
from abc import ABC, abstractmethod
from pathlib import Path
from queue import Queue
from typing import Dict, Optional, Union
@ -9,67 +8,11 @@ from PIL import Image, PngImagePlugin
from PIL.Image import Image as PILImageType
from send2trash import send2trash
from invokeai.app.services.invoker import Invoker
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
# TODO: Should these excpetions subclass existing python exceptions?
class ImageFileNotFoundException(Exception):
"""Raised when an image file is not found in storage."""
def __init__(self, message="Image file not found"):
super().__init__(message)
class ImageFileSaveException(Exception):
"""Raised when an image cannot be saved."""
def __init__(self, message="Image file not saved"):
super().__init__(message)
class ImageFileDeleteException(Exception):
"""Raised when an image cannot be deleted."""
def __init__(self, message="Image file not deleted"):
super().__init__(message)
class ImageFileStorageBase(ABC):
"""Low-level service responsible for storing and retrieving image files."""
@abstractmethod
def get(self, image_name: str) -> PILImageType:
"""Retrieves an image as PIL Image."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets the internal path to an image or thumbnail."""
pass
# TODO: We need to validate paths before starlette makes the FileResponse, else we get a
# 500 internal server error. I don't like having this method on the service.
@abstractmethod
def validate_path(self, path: str) -> bool:
"""Validates the path given for an image or thumbnail."""
pass
@abstractmethod
def save(
self,
image: PILImageType,
image_name: str,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
thumbnail_size: int = 256,
) -> None:
"""Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
pass
@abstractmethod
def delete(self, image_name: str) -> None:
"""Deletes an image and its thumbnail (if one exists)."""
pass
from .image_files_base import ImageFileStorageBase
from .image_files_common import ImageFileDeleteException, ImageFileNotFoundException, ImageFileSaveException
class DiskImageFileStorage(ImageFileStorageBase):
@ -79,6 +22,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
__cache_ids: Queue # TODO: this is an incredibly naive cache
__cache: Dict[Path, PILImageType]
__max_cache_size: int
__invoker: Invoker
def __init__(self, output_folder: Union[str, Path]):
self.__cache = dict()
@ -87,10 +31,12 @@ class DiskImageFileStorage(ImageFileStorageBase):
self.__output_folder: Path = output_folder if isinstance(output_folder, Path) else Path(output_folder)
self.__thumbnails_folder = self.__output_folder / "thumbnails"
# Validate required output folders at launch
self.__validate_storage_folders()
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
def get(self, image_name: str) -> PILImageType:
try:
image_path = self.get_path(image_name)
@ -134,7 +80,12 @@ class DiskImageFileStorage(ImageFileStorageBase):
if original_workflow is not None:
pnginfo.add_text("invokeai_workflow", original_workflow)
image.save(image_path, "PNG", pnginfo=pnginfo)
image.save(
image_path,
"PNG",
pnginfo=pnginfo,
compress_level=self.__invoker.services.configuration.png_compress_level,
)
thumbnail_name = get_thumbnail_name(image_name)
thumbnail_path = self.get_path(thumbnail_name, thumbnail=True)

View File

@ -0,0 +1,84 @@
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Optional
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
class ImageRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the image record store."""
# TODO: Implement an `update()` method
@abstractmethod
def get(self, image_name: str) -> ImageRecord:
"""Gets an image record."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]:
"""Gets an image's metadata'."""
pass
@abstractmethod
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> None:
"""Updates an image record."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageRecord]:
"""Gets a page of image records."""
pass
# TODO: The database has a nullable `deleted_at` column, currently unused.
# Should we implement soft deletes? Would need coordination with ImageFileStorage.
@abstractmethod
def delete(self, image_name: str) -> None:
"""Deletes an image record."""
pass
@abstractmethod
def delete_many(self, image_names: list[str]) -> None:
"""Deletes many image records."""
pass
@abstractmethod
def delete_intermediates(self) -> list[str]:
"""Deletes all intermediate image records, returning a list of deleted image names."""
pass
@abstractmethod
def save(
self,
image_name: str,
image_origin: ResourceOrigin,
image_category: ImageCategory,
width: int,
height: int,
is_intermediate: Optional[bool] = False,
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
) -> datetime:
"""Saves an image record."""
pass
@abstractmethod
def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
"""Gets the most recent image for a board."""
pass

View File

@ -1,13 +1,117 @@
# TODO: Should these excpetions subclass existing python exceptions?
import datetime
from enum import Enum
from typing import Optional, Union
from pydantic import Extra, Field, StrictBool, StrictStr
from pydantic import Field, StrictBool, StrictStr
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from invokeai.app.util.metaenum import MetaEnum
from invokeai.app.util.misc import get_iso_timestamp
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
class ResourceOrigin(str, Enum, metaclass=MetaEnum):
"""The origin of a resource (eg image).
- INTERNAL: The resource was created by the application.
- EXTERNAL: The resource was not created by the application.
This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
"""
INTERNAL = "internal"
"""The resource was created by the application."""
EXTERNAL = "external"
"""The resource was not created by the application.
This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
"""
class InvalidOriginException(ValueError):
"""Raised when a provided value is not a valid ResourceOrigin.
Subclasses `ValueError`.
"""
def __init__(self, message="Invalid resource origin."):
super().__init__(message)
class ImageCategory(str, Enum, metaclass=MetaEnum):
"""The category of an image.
- GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose.
- MASK: The image is a mask image.
- CONTROL: The image is a ControlNet control image.
- USER: The image is a user-provide image.
- OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes.
"""
GENERAL = "general"
"""GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose."""
MASK = "mask"
"""MASK: The image is a mask image."""
CONTROL = "control"
"""CONTROL: The image is a ControlNet control image."""
USER = "user"
"""USER: The image is a user-provide image."""
OTHER = "other"
"""OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
class InvalidImageCategoryException(ValueError):
"""Raised when a provided value is not a valid ImageCategory.
Subclasses `ValueError`.
"""
def __init__(self, message="Invalid image category."):
super().__init__(message)
class ImageRecordNotFoundException(Exception):
"""Raised when an image record is not found."""
def __init__(self, message="Image record not found"):
super().__init__(message)
class ImageRecordSaveException(Exception):
"""Raised when an image record cannot be saved."""
def __init__(self, message="Image record not saved"):
super().__init__(message)
class ImageRecordDeleteException(Exception):
"""Raised when an image record cannot be deleted."""
def __init__(self, message="Image record not deleted"):
super().__init__(message)
IMAGE_DTO_COLS = ", ".join(
list(
map(
lambda c: "images." + c,
[
"image_name",
"image_origin",
"image_category",
"width",
"height",
"session_id",
"node_id",
"is_intermediate",
"created_at",
"updated_at",
"deleted_at",
"starred",
],
)
)
)
class ImageRecord(BaseModelExcludeNull):
"""Deserialized image record without metadata."""
@ -25,7 +129,9 @@ class ImageRecord(BaseModelExcludeNull):
"""The created timestamp of the image."""
updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the image.")
"""The updated timestamp of the image."""
deleted_at: Union[datetime.datetime, str, None] = Field(description="The deleted timestamp of the image.")
deleted_at: Optional[Union[datetime.datetime, str]] = Field(
default=None, description="The deleted timestamp of the image."
)
"""The deleted timestamp of the image."""
is_intermediate: bool = Field(description="Whether this is an intermediate image.")
"""Whether this is an intermediate image."""
@ -43,7 +149,7 @@ class ImageRecord(BaseModelExcludeNull):
"""Whether this image is starred."""
class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid):
class ImageRecordChanges(BaseModelExcludeNull, extra="allow"):
"""A set of changes to apply to an image record.
Only limited changes are valid:
@ -66,41 +172,6 @@ class ImageRecordChanges(BaseModelExcludeNull, extra=Extra.forbid):
"""The image's new `starred` state."""
class ImageUrlsDTO(BaseModelExcludeNull):
"""The URLs for an image and its thumbnail."""
image_name: str = Field(description="The unique name of the image.")
"""The unique name of the image."""
image_url: str = Field(description="The URL of the image.")
"""The URL of the image."""
thumbnail_url: str = Field(description="The URL of the image's thumbnail.")
"""The URL of the image's thumbnail."""
class ImageDTO(ImageRecord, ImageUrlsDTO):
"""Deserialized image record, enriched for the frontend."""
board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.")
"""The id of the board the image belongs to, if one exists."""
pass
def image_record_to_dto(
image_record: ImageRecord,
image_url: str,
thumbnail_url: str,
board_id: Optional[str],
) -> ImageDTO:
"""Converts an image record to an image DTO."""
return ImageDTO(
**image_record.dict(),
image_url=image_url,
thumbnail_url=thumbnail_url,
board_id=board_id,
)
def deserialize_image_record(image_dict: dict) -> ImageRecord:
"""Deserializes an image record."""

View File

@ -1,164 +1,36 @@
import json
import sqlite3
import threading
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Generic, Optional, TypeVar, cast
from typing import Optional, Union, cast
from pydantic import BaseModel, Field
from pydantic.generics import GenericModel
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.services.shared.sqlite import SqliteDatabase
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from invokeai.app.services.models.image_record import ImageRecord, ImageRecordChanges, deserialize_image_record
T = TypeVar("T", bound=BaseModel)
class OffsetPaginatedResults(GenericModel, Generic[T]):
"""Offset-paginated results"""
# fmt: off
items: list[T] = Field(description="Items")
offset: int = Field(description="Offset from which to retrieve items")
limit: int = Field(description="Limit of items to get")
total: int = Field(description="Total number of items in result")
# fmt: on
# TODO: Should these excpetions subclass existing python exceptions?
class ImageRecordNotFoundException(Exception):
"""Raised when an image record is not found."""
def __init__(self, message="Image record not found"):
super().__init__(message)
class ImageRecordSaveException(Exception):
"""Raised when an image record cannot be saved."""
def __init__(self, message="Image record not saved"):
super().__init__(message)
class ImageRecordDeleteException(Exception):
"""Raised when an image record cannot be deleted."""
def __init__(self, message="Image record not deleted"):
super().__init__(message)
IMAGE_DTO_COLS = ", ".join(
list(
map(
lambda c: "images." + c,
[
"image_name",
"image_origin",
"image_category",
"width",
"height",
"session_id",
"node_id",
"is_intermediate",
"created_at",
"updated_at",
"deleted_at",
"starred",
],
)
)
from .image_records_base import ImageRecordStorageBase
from .image_records_common import (
IMAGE_DTO_COLS,
ImageCategory,
ImageRecord,
ImageRecordChanges,
ImageRecordDeleteException,
ImageRecordNotFoundException,
ImageRecordSaveException,
ResourceOrigin,
deserialize_image_record,
)
class ImageRecordStorageBase(ABC):
"""Low-level service responsible for interfacing with the image record store."""
# TODO: Implement an `update()` method
@abstractmethod
def get(self, image_name: str) -> ImageRecord:
"""Gets an image record."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> Optional[dict]:
"""Gets an image's metadata'."""
pass
@abstractmethod
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> None:
"""Updates an image record."""
pass
@abstractmethod
def get_many(
self,
offset: Optional[int] = None,
limit: Optional[int] = None,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageRecord]:
"""Gets a page of image records."""
pass
# TODO: The database has a nullable `deleted_at` column, currently unused.
# Should we implement soft deletes? Would need coordination with ImageFileStorage.
@abstractmethod
def delete(self, image_name: str) -> None:
"""Deletes an image record."""
pass
@abstractmethod
def delete_many(self, image_names: list[str]) -> None:
"""Deletes many image records."""
pass
@abstractmethod
def delete_intermediates(self) -> list[str]:
"""Deletes all intermediate image records, returning a list of deleted image names."""
pass
@abstractmethod
def save(
self,
image_name: str,
image_origin: ResourceOrigin,
image_category: ImageCategory,
width: int,
height: int,
session_id: Optional[str],
node_id: Optional[str],
metadata: Optional[dict],
is_intermediate: bool = False,
starred: bool = False,
) -> datetime:
"""Saves an image record."""
pass
@abstractmethod
def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
"""Gets the most recent image for a board."""
pass
class SqliteImageRecordStorage(ImageRecordStorageBase):
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.Lock
_lock: threading.RLock
def __init__(self, conn: sqlite3.Connection, lock: threading.Lock) -> None:
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._conn = conn
# Enable row factory to get rows as dictionaries (must be done before making the cursor!)
self._conn.row_factory = sqlite3.Row
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
self._lock = lock
try:
self._lock.acquire()
@ -245,7 +117,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
"""
)
def get(self, image_name: str) -> Optional[ImageRecord]:
def get(self, image_name: str) -> ImageRecord:
try:
self._lock.acquire()
@ -351,8 +223,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
def get_many(
self,
offset: Optional[int] = None,
limit: Optional[int] = None,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
@ -377,7 +249,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
"""
query_conditions = ""
query_params = []
query_params: list[Union[int, str, bool]] = []
if image_origin is not None:
query_conditions += """--sql
@ -515,13 +387,13 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
image_name: str,
image_origin: ResourceOrigin,
image_category: ImageCategory,
session_id: Optional[str],
width: int,
height: int,
node_id: Optional[str],
metadata: Optional[dict],
is_intermediate: bool = False,
starred: bool = False,
is_intermediate: Optional[bool] = False,
starred: Optional[bool] = False,
session_id: Optional[str] = None,
node_id: Optional[str] = None,
metadata: Optional[dict] = None,
) -> datetime:
try:
metadata_json = None if metadata is None else json.dumps(metadata)
@ -584,7 +456,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
FROM images
JOIN board_images ON images.image_name = board_images.image_name
WHERE board_images.board_id = ?
ORDER BY images.created_at DESC
ORDER BY images.starred DESC, images.created_at DESC
LIMIT 1;
""",
(board_id,),

View File

@ -1,449 +0,0 @@
from abc import ABC, abstractmethod
from logging import Logger
from typing import TYPE_CHECKING, Callable, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.models.image import (
ImageCategory,
InvalidImageCategoryException,
InvalidOriginException,
ResourceOrigin,
)
from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
from invokeai.app.services.image_file_storage import (
ImageFileDeleteException,
ImageFileNotFoundException,
ImageFileSaveException,
ImageFileStorageBase,
)
from invokeai.app.services.image_record_storage import (
ImageRecordDeleteException,
ImageRecordNotFoundException,
ImageRecordSaveException,
ImageRecordStorageBase,
OffsetPaginatedResults,
)
from invokeai.app.services.item_storage import ItemStorageABC
from invokeai.app.services.models.image_record import ImageDTO, ImageRecord, ImageRecordChanges, image_record_to_dto
from invokeai.app.services.resource_name import NameServiceBase
from invokeai.app.services.urls import UrlServiceBase
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
if TYPE_CHECKING:
from invokeai.app.services.graph import GraphExecutionState
class ImageServiceABC(ABC):
"""High-level service for image management."""
_on_changed_callbacks: list[Callable[[ImageDTO], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = list()
self._on_deleted_callbacks = list()
def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
"""Register a callback for when an image is changed"""
self._on_changed_callbacks.append(on_changed)
def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
"""Register a callback for when an image is deleted"""
self._on_deleted_callbacks.append(on_deleted)
def _on_changed(self, item: ImageDTO) -> None:
for callback in self._on_changed_callbacks:
callback(item)
def _on_deleted(self, item_id: str) -> None:
for callback in self._on_deleted_callbacks:
callback(item_id)
@abstractmethod
def create(
self,
image: PILImageType,
image_origin: ResourceOrigin,
image_category: ImageCategory,
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: bool = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
"""Creates an image, storing the file and its metadata."""
pass
@abstractmethod
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> ImageDTO:
"""Updates an image."""
pass
@abstractmethod
def get_pil_image(self, image_name: str) -> PILImageType:
"""Gets an image as a PIL image."""
pass
@abstractmethod
def get_record(self, image_name: str) -> ImageRecord:
"""Gets an image record."""
pass
@abstractmethod
def get_dto(self, image_name: str) -> ImageDTO:
"""Gets an image DTO."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata:
"""Gets an image's metadata."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's path."""
pass
@abstractmethod
def validate_path(self, path: str) -> bool:
"""Validates an image's path."""
pass
@abstractmethod
def get_url(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's or thumbnail's URL."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageDTO]:
"""Gets a paginated list of image DTOs."""
pass
@abstractmethod
def delete(self, image_name: str):
"""Deletes an image."""
pass
@abstractmethod
def delete_intermediates(self) -> int:
"""Deletes all intermediate images."""
pass
@abstractmethod
def delete_images_on_board(self, board_id: str):
"""Deletes all images on a board."""
pass
class ImageServiceDependencies:
"""Service dependencies for the ImageService."""
image_records: ImageRecordStorageBase
image_files: ImageFileStorageBase
board_image_records: BoardImageRecordStorageBase
urls: UrlServiceBase
logger: Logger
names: NameServiceBase
graph_execution_manager: ItemStorageABC["GraphExecutionState"]
def __init__(
self,
image_record_storage: ImageRecordStorageBase,
image_file_storage: ImageFileStorageBase,
board_image_record_storage: BoardImageRecordStorageBase,
url: UrlServiceBase,
logger: Logger,
names: NameServiceBase,
graph_execution_manager: ItemStorageABC["GraphExecutionState"],
):
self.image_records = image_record_storage
self.image_files = image_file_storage
self.board_image_records = board_image_record_storage
self.urls = url
self.logger = logger
self.names = names
self.graph_execution_manager = graph_execution_manager
class ImageService(ImageServiceABC):
_services: ImageServiceDependencies
def __init__(self, services: ImageServiceDependencies):
super().__init__()
self._services = services
def create(
self,
image: PILImageType,
image_origin: ResourceOrigin,
image_category: ImageCategory,
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: bool = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
if image_origin not in ResourceOrigin:
raise InvalidOriginException
if image_category not in ImageCategory:
raise InvalidImageCategoryException
image_name = self._services.names.create_image_name()
# TODO: Do we want to store the graph in the image at all? I don't think so...
# graph = None
# if session_id is not None:
# session_raw = self._services.graph_execution_manager.get_raw(session_id)
# if session_raw is not None:
# try:
# graph = get_metadata_graph_from_raw_session(session_raw)
# except Exception as e:
# self._services.logger.warn(f"Failed to parse session graph: {e}")
# graph = None
(width, height) = image.size
try:
# TODO: Consider using a transaction here to ensure consistency between storage and database
self._services.image_records.save(
# Non-nullable fields
image_name=image_name,
image_origin=image_origin,
image_category=image_category,
width=width,
height=height,
# Meta fields
is_intermediate=is_intermediate,
# Nullable fields
node_id=node_id,
metadata=metadata,
session_id=session_id,
)
if board_id is not None:
self._services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
self._services.image_files.save(image_name=image_name, image=image, metadata=metadata, workflow=workflow)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self._services.logger.error("Failed to save image record")
raise
except ImageFileSaveException:
self._services.logger.error("Failed to save image file")
raise
except Exception as e:
self._services.logger.error(f"Problem saving image record and file: {str(e)}")
raise e
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> ImageDTO:
try:
self._services.image_records.update(image_name, changes)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self._services.logger.error("Failed to update image record")
raise
except Exception as e:
self._services.logger.error("Problem updating image record")
raise e
def get_pil_image(self, image_name: str) -> PILImageType:
try:
return self._services.image_files.get(image_name)
except ImageFileNotFoundException:
self._services.logger.error("Failed to get image file")
raise
except Exception as e:
self._services.logger.error("Problem getting image file")
raise e
def get_record(self, image_name: str) -> ImageRecord:
try:
return self._services.image_records.get(image_name)
except ImageRecordNotFoundException:
self._services.logger.error("Image record not found")
raise
except Exception as e:
self._services.logger.error("Problem getting image record")
raise e
def get_dto(self, image_name: str) -> ImageDTO:
try:
image_record = self._services.image_records.get(image_name)
image_dto = image_record_to_dto(
image_record,
self._services.urls.get_image_url(image_name),
self._services.urls.get_image_url(image_name, True),
self._services.board_image_records.get_board_for_image(image_name),
)
return image_dto
except ImageRecordNotFoundException:
self._services.logger.error("Image record not found")
raise
except Exception as e:
self._services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
try:
image_record = self._services.image_records.get(image_name)
metadata = self._services.image_records.get_metadata(image_name)
if not image_record.session_id:
return ImageMetadata(metadata=metadata)
session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id)
graph = None
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self._services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
return ImageMetadata(graph=graph, metadata=metadata)
except ImageRecordNotFoundException:
self._services.logger.error("Image record not found")
raise
except Exception as e:
self._services.logger.error("Problem getting image DTO")
raise e
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
try:
return self._services.image_files.get_path(image_name, thumbnail)
except Exception as e:
self._services.logger.error("Problem getting image path")
raise e
def validate_path(self, path: str) -> bool:
try:
return self._services.image_files.validate_path(path)
except Exception as e:
self._services.logger.error("Problem validating image path")
raise e
def get_url(self, image_name: str, thumbnail: bool = False) -> str:
try:
return self._services.urls.get_image_url(image_name, thumbnail)
except Exception as e:
self._services.logger.error("Problem getting image path")
raise e
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageDTO]:
try:
results = self._services.image_records.get_many(
offset,
limit,
image_origin,
categories,
is_intermediate,
board_id,
)
image_dtos = list(
map(
lambda r: image_record_to_dto(
r,
self._services.urls.get_image_url(r.image_name),
self._services.urls.get_image_url(r.image_name, True),
self._services.board_image_records.get_board_for_image(r.image_name),
),
results.items,
)
)
return OffsetPaginatedResults[ImageDTO](
items=image_dtos,
offset=results.offset,
limit=results.limit,
total=results.total,
)
except Exception as e:
self._services.logger.error("Problem getting paginated image DTOs")
raise e
def delete(self, image_name: str):
try:
self._services.image_files.delete(image_name)
self._services.image_records.delete(image_name)
self._on_deleted(image_name)
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image record")
raise
except ImageFileDeleteException:
self._services.logger.error("Failed to delete image file")
raise
except Exception as e:
self._services.logger.error("Problem deleting image record and file")
raise e
def delete_images_on_board(self, board_id: str):
try:
image_names = self._services.board_image_records.get_all_board_image_names_for_board(board_id)
for image_name in image_names:
self._services.image_files.delete(image_name)
self._services.image_records.delete_many(image_names)
for image_name in image_names:
self._on_deleted(image_name)
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image records")
raise
except ImageFileDeleteException:
self._services.logger.error("Failed to delete image files")
raise
except Exception as e:
self._services.logger.error("Problem deleting image records and files")
raise e
def delete_intermediates(self) -> int:
try:
image_names = self._services.image_records.delete_intermediates()
count = len(image_names)
for image_name in image_names:
self._services.image_files.delete(image_name)
self._on_deleted(image_name)
return count
except ImageRecordDeleteException:
self._services.logger.error("Failed to delete image records")
raise
except ImageFileDeleteException:
self._services.logger.error("Failed to delete image files")
raise
except Exception as e:
self._services.logger.error("Problem deleting image records and files")
raise e

View File

View File

@ -0,0 +1,129 @@
from abc import ABC, abstractmethod
from typing import Callable, Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.services.image_records.image_records_common import (
ImageCategory,
ImageRecord,
ImageRecordChanges,
ResourceOrigin,
)
from invokeai.app.services.images.images_common import ImageDTO
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
class ImageServiceABC(ABC):
"""High-level service for image management."""
_on_changed_callbacks: list[Callable[[ImageDTO], None]]
_on_deleted_callbacks: list[Callable[[str], None]]
def __init__(self) -> None:
self._on_changed_callbacks = list()
self._on_deleted_callbacks = list()
def on_changed(self, on_changed: Callable[[ImageDTO], None]) -> None:
"""Register a callback for when an image is changed"""
self._on_changed_callbacks.append(on_changed)
def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
"""Register a callback for when an image is deleted"""
self._on_deleted_callbacks.append(on_deleted)
def _on_changed(self, item: ImageDTO) -> None:
for callback in self._on_changed_callbacks:
callback(item)
def _on_deleted(self, item_id: str) -> None:
for callback in self._on_deleted_callbacks:
callback(item_id)
@abstractmethod
def create(
self,
image: PILImageType,
image_origin: ResourceOrigin,
image_category: ImageCategory,
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
"""Creates an image, storing the file and its metadata."""
pass
@abstractmethod
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> ImageDTO:
"""Updates an image."""
pass
@abstractmethod
def get_pil_image(self, image_name: str) -> PILImageType:
"""Gets an image as a PIL image."""
pass
@abstractmethod
def get_record(self, image_name: str) -> ImageRecord:
"""Gets an image record."""
pass
@abstractmethod
def get_dto(self, image_name: str) -> ImageDTO:
"""Gets an image DTO."""
pass
@abstractmethod
def get_metadata(self, image_name: str) -> ImageMetadata:
"""Gets an image's metadata."""
pass
@abstractmethod
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's path."""
pass
@abstractmethod
def validate_path(self, path: str) -> bool:
"""Validates an image's path."""
pass
@abstractmethod
def get_url(self, image_name: str, thumbnail: bool = False) -> str:
"""Gets an image's or thumbnail's URL."""
pass
@abstractmethod
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageDTO]:
"""Gets a paginated list of image DTOs."""
pass
@abstractmethod
def delete(self, image_name: str):
"""Deletes an image."""
pass
@abstractmethod
def delete_intermediates(self) -> int:
"""Deletes all intermediate images."""
pass
@abstractmethod
def delete_images_on_board(self, board_id: str):
"""Deletes all images on a board."""
pass

View File

@ -0,0 +1,43 @@
from typing import Optional
from pydantic import Field
from invokeai.app.services.image_records.image_records_common import ImageRecord
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
class ImageUrlsDTO(BaseModelExcludeNull):
"""The URLs for an image and its thumbnail."""
image_name: str = Field(description="The unique name of the image.")
"""The unique name of the image."""
image_url: str = Field(description="The URL of the image.")
"""The URL of the image."""
thumbnail_url: str = Field(description="The URL of the image's thumbnail.")
"""The URL of the image's thumbnail."""
class ImageDTO(ImageRecord, ImageUrlsDTO):
"""Deserialized image record, enriched for the frontend."""
board_id: Optional[str] = Field(
default=None, description="The id of the board the image belongs to, if one exists."
)
"""The id of the board the image belongs to, if one exists."""
pass
def image_record_to_dto(
image_record: ImageRecord,
image_url: str,
thumbnail_url: str,
board_id: Optional[str],
) -> ImageDTO:
"""Converts an image record to an image DTO."""
return ImageDTO(
**image_record.model_dump(),
image_url=image_url,
thumbnail_url=thumbnail_url,
board_id=board_id,
)

View File

@ -0,0 +1,286 @@
from typing import Optional
from PIL.Image import Image as PILImageType
from invokeai.app.invocations.metadata import ImageMetadata
from invokeai.app.services.invoker import Invoker
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
from ..image_files.image_files_common import (
ImageFileDeleteException,
ImageFileNotFoundException,
ImageFileSaveException,
)
from ..image_records.image_records_common import (
ImageCategory,
ImageRecord,
ImageRecordChanges,
ImageRecordDeleteException,
ImageRecordNotFoundException,
ImageRecordSaveException,
InvalidImageCategoryException,
InvalidOriginException,
ResourceOrigin,
)
from .images_base import ImageServiceABC
from .images_common import ImageDTO, image_record_to_dto
class ImageService(ImageServiceABC):
__invoker: Invoker
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
def create(
self,
image: PILImageType,
image_origin: ResourceOrigin,
image_category: ImageCategory,
node_id: Optional[str] = None,
session_id: Optional[str] = None,
board_id: Optional[str] = None,
is_intermediate: Optional[bool] = False,
metadata: Optional[dict] = None,
workflow: Optional[str] = None,
) -> ImageDTO:
if image_origin not in ResourceOrigin:
raise InvalidOriginException
if image_category not in ImageCategory:
raise InvalidImageCategoryException
image_name = self.__invoker.services.names.create_image_name()
(width, height) = image.size
try:
# TODO: Consider using a transaction here to ensure consistency between storage and database
self.__invoker.services.image_records.save(
# Non-nullable fields
image_name=image_name,
image_origin=image_origin,
image_category=image_category,
width=width,
height=height,
# Meta fields
is_intermediate=is_intermediate,
# Nullable fields
node_id=node_id,
metadata=metadata,
session_id=session_id,
)
if board_id is not None:
self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
self.__invoker.services.image_files.save(
image_name=image_name, image=image, metadata=metadata, workflow=workflow
)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self.__invoker.services.logger.error("Failed to save image record")
raise
except ImageFileSaveException:
self.__invoker.services.logger.error("Failed to save image file")
raise
except Exception as e:
self.__invoker.services.logger.error(f"Problem saving image record and file: {str(e)}")
raise e
def update(
self,
image_name: str,
changes: ImageRecordChanges,
) -> ImageDTO:
try:
self.__invoker.services.image_records.update(image_name, changes)
image_dto = self.get_dto(image_name)
self._on_changed(image_dto)
return image_dto
except ImageRecordSaveException:
self.__invoker.services.logger.error("Failed to update image record")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem updating image record")
raise e
def get_pil_image(self, image_name: str) -> PILImageType:
try:
return self.__invoker.services.image_files.get(image_name)
except ImageFileNotFoundException:
self.__invoker.services.logger.error("Failed to get image file")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image file")
raise e
def get_record(self, image_name: str) -> ImageRecord:
try:
return self.__invoker.services.image_records.get(image_name)
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image record")
raise e
def get_dto(self, image_name: str) -> ImageDTO:
try:
image_record = self.__invoker.services.image_records.get(image_name)
image_dto = image_record_to_dto(
image_record,
self.__invoker.services.urls.get_image_url(image_name),
self.__invoker.services.urls.get_image_url(image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(image_name),
)
return image_dto
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
def get_metadata(self, image_name: str) -> ImageMetadata:
try:
image_record = self.__invoker.services.image_records.get(image_name)
metadata = self.__invoker.services.image_records.get_metadata(image_name)
if not image_record.session_id:
return ImageMetadata(metadata=metadata)
session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id)
graph = None
if session_raw:
try:
graph = get_metadata_graph_from_raw_session(session_raw)
except Exception as e:
self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}")
graph = None
return ImageMetadata(graph=graph, metadata=metadata)
except ImageRecordNotFoundException:
self.__invoker.services.logger.error("Image record not found")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem getting image DTO")
raise e
def get_path(self, image_name: str, thumbnail: bool = False) -> str:
try:
return str(self.__invoker.services.image_files.get_path(image_name, thumbnail))
except Exception as e:
self.__invoker.services.logger.error("Problem getting image path")
raise e
def validate_path(self, path: str) -> bool:
try:
return self.__invoker.services.image_files.validate_path(path)
except Exception as e:
self.__invoker.services.logger.error("Problem validating image path")
raise e
def get_url(self, image_name: str, thumbnail: bool = False) -> str:
try:
return self.__invoker.services.urls.get_image_url(image_name, thumbnail)
except Exception as e:
self.__invoker.services.logger.error("Problem getting image path")
raise e
def get_many(
self,
offset: int = 0,
limit: int = 10,
image_origin: Optional[ResourceOrigin] = None,
categories: Optional[list[ImageCategory]] = None,
is_intermediate: Optional[bool] = None,
board_id: Optional[str] = None,
) -> OffsetPaginatedResults[ImageDTO]:
try:
results = self.__invoker.services.image_records.get_many(
offset,
limit,
image_origin,
categories,
is_intermediate,
board_id,
)
image_dtos = list(
map(
lambda r: image_record_to_dto(
r,
self.__invoker.services.urls.get_image_url(r.image_name),
self.__invoker.services.urls.get_image_url(r.image_name, True),
self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
),
results.items,
)
)
return OffsetPaginatedResults[ImageDTO](
items=image_dtos,
offset=results.offset,
limit=results.limit,
total=results.total,
)
except Exception as e:
self.__invoker.services.logger.error("Problem getting paginated image DTOs")
raise e
def delete(self, image_name: str):
try:
self.__invoker.services.image_files.delete(image_name)
self.__invoker.services.image_records.delete(image_name)
self._on_deleted(image_name)
except ImageRecordDeleteException:
self.__invoker.services.logger.error("Failed to delete image record")
raise
except ImageFileDeleteException:
self.__invoker.services.logger.error("Failed to delete image file")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem deleting image record and file")
raise e
def delete_images_on_board(self, board_id: str):
try:
image_names = self.__invoker.services.board_image_records.get_all_board_image_names_for_board(board_id)
for image_name in image_names:
self.__invoker.services.image_files.delete(image_name)
self.__invoker.services.image_records.delete_many(image_names)
for image_name in image_names:
self._on_deleted(image_name)
except ImageRecordDeleteException:
self.__invoker.services.logger.error("Failed to delete image records")
raise
except ImageFileDeleteException:
self.__invoker.services.logger.error("Failed to delete image files")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem deleting image records and files")
raise e
def delete_intermediates(self) -> int:
try:
image_names = self.__invoker.services.image_records.delete_intermediates()
count = len(image_names)
for image_name in image_names:
self.__invoker.services.image_files.delete(image_name)
self._on_deleted(image_name)
return count
except ImageRecordDeleteException:
self.__invoker.services.logger.error("Failed to delete image records")
raise
except ImageFileDeleteException:
self.__invoker.services.logger.error("Failed to delete image files")
raise
except Exception as e:
self.__invoker.services.logger.error("Problem deleting image records and files")
raise e

View File

@ -1,4 +1,6 @@
from queue import Queue
from collections import OrderedDict
from dataclasses import dataclass, field
from threading import Lock
from typing import Optional, Union
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
@ -7,105 +9,123 @@ from invokeai.app.services.invocation_cache.invocation_cache_common import Invoc
from invokeai.app.services.invoker import Invoker
@dataclass(order=True)
class CachedItem:
invocation_output: BaseInvocationOutput = field(compare=False)
invocation_output_json: str = field(compare=False)
class MemoryInvocationCache(InvocationCacheBase):
__cache: dict[Union[int, str], tuple[BaseInvocationOutput, str]]
__max_cache_size: int
__disabled: bool
__hits: int
__misses: int
__cache_ids: Queue
__invoker: Invoker
_cache: OrderedDict[Union[int, str], CachedItem]
_max_cache_size: int
_disabled: bool
_hits: int
_misses: int
_invoker: Invoker
_lock: Lock
def __init__(self, max_cache_size: int = 0) -> None:
self.__cache = dict()
self.__max_cache_size = max_cache_size
self.__disabled = False
self.__hits = 0
self.__misses = 0
self.__cache_ids = Queue()
self._cache = OrderedDict()
self._max_cache_size = max_cache_size
self._disabled = False
self._hits = 0
self._misses = 0
self._lock = Lock()
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
if self.__max_cache_size == 0:
self._invoker = invoker
if self._max_cache_size == 0:
return
self.__invoker.services.images.on_deleted(self._delete_by_match)
self.__invoker.services.latents.on_deleted(self._delete_by_match)
self._invoker.services.images.on_deleted(self._delete_by_match)
self._invoker.services.latents.on_deleted(self._delete_by_match)
def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
if self.__max_cache_size == 0 or self.__disabled:
return
item = self.__cache.get(key, None)
if item is not None:
self.__hits += 1
return item[0]
self.__misses += 1
with self._lock:
if self._max_cache_size == 0 or self._disabled:
return None
item = self._cache.get(key, None)
if item is not None:
self._hits += 1
self._cache.move_to_end(key)
return item.invocation_output
self._misses += 1
return None
def save(self, key: Union[int, str], invocation_output: BaseInvocationOutput) -> None:
if self.__max_cache_size == 0 or self.__disabled:
return
with self._lock:
if self._max_cache_size == 0 or self._disabled or key in self._cache:
return
# If the cache is full, we need to remove the least used
number_to_delete = len(self._cache) + 1 - self._max_cache_size
self._delete_oldest_access(number_to_delete)
self._cache[key] = CachedItem(
invocation_output,
invocation_output.model_dump_json(
warnings=False, exclude_defaults=True, exclude_unset=True, include={"type"}
),
)
if key not in self.__cache:
self.__cache[key] = (invocation_output, invocation_output.json())
self.__cache_ids.put(key)
if self.__cache_ids.qsize() > self.__max_cache_size:
try:
self.__cache.pop(self.__cache_ids.get())
except KeyError:
# this means the cache_ids are somehow out of sync w/ the cache
pass
def _delete_oldest_access(self, number_to_delete: int) -> None:
number_to_delete = min(number_to_delete, len(self._cache))
for _ in range(number_to_delete):
self._cache.popitem(last=False)
def _delete(self, key: Union[int, str]) -> None:
if self._max_cache_size == 0:
return
if key in self._cache:
del self._cache[key]
def delete(self, key: Union[int, str]) -> None:
if self.__max_cache_size == 0 or self.__disabled:
return
if key in self.__cache:
del self.__cache[key]
with self._lock:
return self._delete(key)
def clear(self, *args, **kwargs) -> None:
if self.__max_cache_size == 0 or self.__disabled:
return
with self._lock:
if self._max_cache_size == 0:
return
self._cache.clear()
self._misses = 0
self._hits = 0
self.__cache.clear()
self.__cache_ids = Queue()
self.__misses = 0
self.__hits = 0
def create_key(self, invocation: BaseInvocation) -> int:
return hash(invocation.json(exclude={"id"}))
@staticmethod
def create_key(invocation: BaseInvocation) -> int:
return hash(invocation.model_dump_json(exclude={"id"}, warnings=False))
def disable(self) -> None:
if self.__max_cache_size == 0:
return
self.__disabled = True
with self._lock:
if self._max_cache_size == 0:
return
self._disabled = True
def enable(self) -> None:
if self.__max_cache_size == 0:
return
self.__disabled = False
with self._lock:
if self._max_cache_size == 0:
return
self._disabled = False
def get_status(self) -> InvocationCacheStatus:
return InvocationCacheStatus(
hits=self.__hits,
misses=self.__misses,
enabled=not self.__disabled and self.__max_cache_size > 0,
size=len(self.__cache),
max_size=self.__max_cache_size,
)
with self._lock:
return InvocationCacheStatus(
hits=self._hits,
misses=self._misses,
enabled=not self._disabled and self._max_cache_size > 0,
size=len(self._cache),
max_size=self._max_cache_size,
)
def _delete_by_match(self, to_match: str) -> None:
if self.__max_cache_size == 0 or self.__disabled:
return
keys_to_delete = set()
for key, value_tuple in self.__cache.items():
if to_match in value_tuple[1]:
keys_to_delete.add(key)
if not keys_to_delete:
return
for key in keys_to_delete:
self.delete(key)
self.__invoker.services.logger.debug(f"Deleted {len(keys_to_delete)} cached invocation outputs for {to_match}")
with self._lock:
if self._max_cache_size == 0:
return
keys_to_delete = set()
for key, cached_item in self._cache.items():
if to_match in cached_item.invocation_output_json:
keys_to_delete.add(key)
if not keys_to_delete:
return
for key in keys_to_delete:
self._delete(key)
self._invoker.services.logger.debug(
f"Deleted {len(keys_to_delete)} cached invocation outputs for {to_match}"
)

View File

@ -0,0 +1,5 @@
from abc import ABC
class InvocationProcessorABC(ABC):
pass

View File

@ -0,0 +1,15 @@
from pydantic import BaseModel, Field
class ProgressImage(BaseModel):
"""The progress image sent intermittently during processing"""
width: int = Field(description="The effective width of the image in pixels")
height: int = Field(description="The effective height of the image in pixels")
dataURL: str = Field(description="The image data as a b64 data URL")
class CanceledException(Exception):
"""Execution canceled by user."""
pass

View File

@ -4,12 +4,12 @@ from threading import BoundedSemaphore, Event, Thread
from typing import Optional
import invokeai.backend.util.logging as logger
from invokeai.app.invocations.baseinvocation import AppInvocationContext
from invokeai.app.services.invocation_queue.invocation_queue_common import InvocationQueueItem
from ..invocations.baseinvocation import InvocationContext
from ..models.exceptions import CanceledException
from .invocation_queue import InvocationQueueItem
from .invocation_stats import InvocationStatsServiceBase
from .invoker import InvocationProcessorABC, Invoker
from ..invoker import Invoker
from .invocation_processor_base import InvocationProcessorABC
from .invocation_processor_common import CanceledException
class DefaultInvocationProcessor(InvocationProcessorABC):
@ -37,7 +37,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
def __process(self, stop_event: Event):
try:
self.__threadLimit.acquire()
statistics: InvocationStatsServiceBase = self.__invoker.services.performance_statistics
queue_item: Optional[InvocationQueueItem] = None
while not stop_event.is_set():
@ -90,26 +89,28 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
node=invocation.model_dump(),
source_node_id=source_node_id,
)
# Invoke
try:
graph_id = graph_execution_state.id
model_manager = self.__invoker.services.model_manager
with statistics.collect_stats(invocation, graph_id, model_manager):
source_node_id = graph_execution_state.prepared_source_mapping[invocation.id]
with self.__invoker.services.performance_statistics.collect_stats(invocation, graph_id):
# use the internal invoke_internal(), which wraps the node's invoke() method,
# which handles a few things:
# - nodes that require a value, but get it only from a connection
# - referencing the invocation cache instead of executing the node
outputs = invocation.invoke_internal(
InvocationContext(
AppInvocationContext(
services=self.__invoker.services,
graph_execution_state_id=graph_execution_state.id,
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
queue_batch_id=queue_item.session_queue_batch_id,
source_node_id=source_node_id,
)
)
@ -129,17 +130,17 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
node=invocation.model_dump(),
source_node_id=source_node_id,
result=outputs.dict(),
result=outputs.model_dump(),
)
statistics.log_stats()
self.__invoker.services.performance_statistics.log_stats()
except KeyboardInterrupt:
pass
except CanceledException:
statistics.reset_stats(graph_execution_state.id)
self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
pass
except Exception as e:
@ -159,12 +160,12 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
node=invocation.model_dump(),
source_node_id=source_node_id,
error_type=e.__class__.__name__,
error=error,
)
statistics.reset_stats(graph_execution_state.id)
self.__invoker.services.performance_statistics.reset_stats(graph_execution_state.id)
pass
# Check queue to see if this is canceled, and skip if so
@ -189,7 +190,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
queue_item_id=queue_item.session_queue_item_id,
queue_id=queue_item.session_queue_id,
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
node=invocation.model_dump(),
source_node_id=source_node_id,
error_type=e.__class__.__name__,
error=traceback.format_exc(),

View File

@ -0,0 +1,26 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from abc import ABC, abstractmethod
from typing import Optional
from .invocation_queue_common import InvocationQueueItem
class InvocationQueueABC(ABC):
"""Abstract base class for all invocation queues"""
@abstractmethod
def get(self) -> InvocationQueueItem:
pass
@abstractmethod
def put(self, item: Optional[InvocationQueueItem]) -> None:
pass
@abstractmethod
def cancel(self, graph_execution_state_id: str) -> None:
pass
@abstractmethod
def is_canceled(self, graph_execution_state_id: str) -> bool:
pass

View File

@ -0,0 +1,19 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import time
from pydantic import BaseModel, Field
class InvocationQueueItem(BaseModel):
graph_execution_state_id: str = Field(description="The ID of the graph execution state")
invocation_id: str = Field(description="The ID of the node being invoked")
session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
session_queue_item_id: int = Field(
description="The ID of session queue item from which this invocation queue item came"
)
session_queue_batch_id: str = Field(
description="The ID of the session batch from which this invocation queue item came"
)
invoke_all: bool = Field(default=False)
timestamp: float = Field(default_factory=time.time)

View File

@ -1,45 +1,11 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
import time
from abc import ABC, abstractmethod
from queue import Queue
from typing import Optional
from pydantic import BaseModel, Field
class InvocationQueueItem(BaseModel):
graph_execution_state_id: str = Field(description="The ID of the graph execution state")
invocation_id: str = Field(description="The ID of the node being invoked")
session_queue_id: str = Field(description="The ID of the session queue from which this invocation queue item came")
session_queue_item_id: int = Field(
description="The ID of session queue item from which this invocation queue item came"
)
session_queue_batch_id: str = Field(
description="The ID of the session batch from which this invocation queue item came"
)
invoke_all: bool = Field(default=False)
timestamp: float = Field(default_factory=time.time)
class InvocationQueueABC(ABC):
"""Abstract base class for all invocation queues"""
@abstractmethod
def get(self) -> InvocationQueueItem:
pass
@abstractmethod
def put(self, item: Optional[InvocationQueueItem]) -> None:
pass
@abstractmethod
def cancel(self, graph_execution_state_id: str) -> None:
pass
@abstractmethod
def is_canceled(self, graph_execution_state_id: str) -> bool:
pass
from .invocation_queue_base import InvocationQueueABC
from .invocation_queue_common import InvocationQueueItem
class MemoryInvocationQueue(InvocationQueueABC):

View File

@ -6,21 +6,27 @@ from typing import TYPE_CHECKING
if TYPE_CHECKING:
from logging import Logger
from invokeai.app.services.board_images import BoardImagesServiceABC
from invokeai.app.services.boards import BoardServiceABC
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.events import EventServiceBase
from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
from invokeai.app.services.images import ImageServiceABC
from invokeai.app.services.invocation_cache.invocation_cache_base import InvocationCacheBase
from invokeai.app.services.invocation_queue import InvocationQueueABC
from invokeai.app.services.invocation_stats import InvocationStatsServiceBase
from invokeai.app.services.invoker import InvocationProcessorABC
from invokeai.app.services.item_storage import ItemStorageABC
from invokeai.app.services.latent_storage import LatentsStorageBase
from invokeai.app.services.model_manager_service import ModelManagerServiceBase
from invokeai.app.services.session_processor.session_processor_base import SessionProcessorBase
from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
from .board_image_records.board_image_records_base import BoardImageRecordStorageBase
from .board_images.board_images_base import BoardImagesServiceABC
from .board_records.board_records_base import BoardRecordStorageBase
from .boards.boards_base import BoardServiceABC
from .config import InvokeAIAppConfig
from .events.events_base import EventServiceBase
from .image_files.image_files_base import ImageFileStorageBase
from .image_records.image_records_base import ImageRecordStorageBase
from .images.images_base import ImageServiceABC
from .invocation_cache.invocation_cache_base import InvocationCacheBase
from .invocation_processor.invocation_processor_base import InvocationProcessorABC
from .invocation_queue.invocation_queue_base import InvocationQueueABC
from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
from .item_storage.item_storage_base import ItemStorageABC
from .latents_storage.latents_storage_base import LatentsStorageBase
from .model_manager.model_manager_base import ModelManagerServiceBase
from .names.names_base import NameServiceBase
from .session_processor.session_processor_base import SessionProcessorBase
from .session_queue.session_queue_base import SessionQueueBase
from .shared.graph import GraphExecutionState, LibraryGraph
from .urls.urls_base import UrlServiceBase
class InvocationServices:
@ -28,12 +34,16 @@ class InvocationServices:
# TODO: Just forward-declared everything due to circular dependencies. Fix structure.
board_images: "BoardImagesServiceABC"
board_image_record_storage: "BoardImageRecordStorageBase"
boards: "BoardServiceABC"
board_records: "BoardRecordStorageBase"
configuration: "InvokeAIAppConfig"
events: "EventServiceBase"
graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
graph_library: "ItemStorageABC[LibraryGraph]"
images: "ImageServiceABC"
image_records: "ImageRecordStorageBase"
image_files: "ImageFileStorageBase"
latents: "LatentsStorageBase"
logger: "Logger"
model_manager: "ModelManagerServiceBase"
@ -43,16 +53,22 @@ class InvocationServices:
session_queue: "SessionQueueBase"
session_processor: "SessionProcessorBase"
invocation_cache: "InvocationCacheBase"
names: "NameServiceBase"
urls: "UrlServiceBase"
def __init__(
self,
board_images: "BoardImagesServiceABC",
board_image_records: "BoardImageRecordStorageBase",
boards: "BoardServiceABC",
board_records: "BoardRecordStorageBase",
configuration: "InvokeAIAppConfig",
events: "EventServiceBase",
graph_execution_manager: "ItemStorageABC[GraphExecutionState]",
graph_library: "ItemStorageABC[LibraryGraph]",
images: "ImageServiceABC",
image_files: "ImageFileStorageBase",
image_records: "ImageRecordStorageBase",
latents: "LatentsStorageBase",
logger: "Logger",
model_manager: "ModelManagerServiceBase",
@ -62,14 +78,20 @@ class InvocationServices:
session_queue: "SessionQueueBase",
session_processor: "SessionProcessorBase",
invocation_cache: "InvocationCacheBase",
names: "NameServiceBase",
urls: "UrlServiceBase",
):
self.board_images = board_images
self.board_image_records = board_image_records
self.boards = boards
self.board_records = board_records
self.configuration = configuration
self.events = events
self.graph_execution_manager = graph_execution_manager
self.graph_library = graph_library
self.images = images
self.image_files = image_files
self.image_records = image_records
self.latents = latents
self.logger = logger
self.model_manager = model_manager
@ -79,3 +101,5 @@ class InvocationServices:
self.session_queue = session_queue
self.session_processor = session_processor
self.invocation_cache = invocation_cache
self.names = names
self.urls = urls

View File

@ -0,0 +1,121 @@
# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
"""Utility to collect execution time and GPU usage stats on invocations in flight
Usage:
statistics = InvocationStatsService(graph_execution_manager)
with statistics.collect_stats(invocation, graph_execution_state.id):
... execute graphs...
statistics.log_stats()
Typical output:
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used
[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader 1 0.005s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip 1 0.004s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel 2 0.512s 0.26G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate 1 0.001s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise 1 0.002s 0.01G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l 1 3.541s 1.93G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i 1 0.679s 0.58G
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 4.749s
[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
writes to the system log is stored in InvocationServices.performance_statistics.
"""
from abc import ABC, abstractmethod
from contextlib import AbstractContextManager
from typing import Dict
from invokeai.app.invocations.baseinvocation import BaseInvocation
from invokeai.backend.model_management.model_cache import CacheStats
from .invocation_stats_common import NodeLog
class InvocationStatsServiceBase(ABC):
"Abstract base class for recording node memory/time performance statistics"
# {graph_id => NodeLog}
_stats: Dict[str, NodeLog]
_cache_stats: Dict[str, CacheStats]
ram_used: float
ram_changed: float
@abstractmethod
def __init__(self):
"""
Initialize the InvocationStatsService and reset counters to zero
"""
pass
@abstractmethod
def collect_stats(
self,
invocation: BaseInvocation,
graph_execution_state_id: str,
) -> AbstractContextManager:
"""
Return a context object that will capture the statistics on the execution
of invocaation. Use with: to place around the part of the code that executes the invocation.
:param invocation: BaseInvocation object from the current graph.
:param graph_execution_state_id: The id of the current session.
"""
pass
@abstractmethod
def reset_stats(self, graph_execution_state_id: str):
"""
Reset all statistics for the indicated graph
:param graph_execution_state_id
"""
pass
@abstractmethod
def reset_all_stats(self):
"""Zero all statistics"""
pass
@abstractmethod
def update_invocation_stats(
self,
graph_id: str,
invocation_type: str,
time_used: float,
vram_used: float,
):
"""
Add timing information on execution of a node. Usually
used internally.
:param graph_id: ID of the graph that is currently executing
:param invocation_type: String literal type of the node
:param time_used: Time used by node's exection (sec)
:param vram_used: Maximum VRAM used during exection (GB)
"""
pass
@abstractmethod
def log_stats(self):
"""
Write out the accumulated statistics to the log or somewhere else.
"""
pass
@abstractmethod
def update_mem_stats(
self,
ram_used: float,
ram_changed: float,
):
"""
Update the collector with RAM memory usage info.
:param ram_used: How much RAM is currently in use.
:param ram_changed: How much RAM changed since last generation.
"""
pass

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