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70 Commits

Author SHA1 Message Date
psychedelicious
0cfd713b93 fix(ui): typo 2025-03-06 08:52:10 +11:00
psychedelicious
45f5d7617a chore: bump version to v5.7.0 2025-03-06 08:38:59 +11:00
psychedelicious
f49df7d327 chore(ui): update whats new 2025-03-06 08:38:59 +11:00
Linos
87ed0ed48a translationBot(ui): update translation (Vietnamese)
Currently translated at 100.0% (1802 of 1802 strings)

Co-authored-by: Linos <linos.coding@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/vi/
Translation: InvokeAI/Web UI
2025-03-06 08:00:35 +11:00
Riccardo Giovanetti
d445c88e4c translationBot(ui): update translation (Italian)
Currently translated at 98.8% (1782 of 1802 strings)

translationBot(ui): update translation (Italian)

Currently translated at 98.8% (1782 of 1802 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
2025-03-06 08:00:35 +11:00
Riku
c15c43ed2a translationBot(ui): update translation (German)
Currently translated at 67.2% (1212 of 1802 strings)

Co-authored-by: Riku <riku.block@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/
Translation: InvokeAI/Web UI
2025-03-06 08:00:35 +11:00
psychedelicious
d2f8db9745 tidy: remove unused utils 2025-03-06 07:49:35 +11:00
psychedelicious
c1cf01a038 tests: use dangerously_run_function_in_subprocess to fix configure_torch_cuda_allocator tests 2025-03-06 07:49:35 +11:00
psychedelicious
2bfb4fc79c tests: add util to run a function in separate process
This allows our tests to run in an isolated environment. For tests taht implicitly depend on import behaviour, this can prevent side-effects.

The function should only be used for tests.
2025-03-06 07:49:35 +11:00
psychedelicious
d037d8f9aa tests: update tests for configure_torch_cuda_allocator 2025-03-06 07:49:35 +11:00
psychedelicious
d5401e8443 tests: add testing utils to set/unset env var 2025-03-06 07:49:35 +11:00
psychedelicious
d193e4f02a feat(app): log warning instead of raising if PYTORCH_CUDA_ALLOC_CONF is already set 2025-03-06 07:49:35 +11:00
psychedelicious
ec493e30ee feat(app): make logger a required arg in configure_torch_cuda_allocator 2025-03-06 07:49:35 +11:00
Jonathan
081b931edf Update util.py
Changed string to a literal
2025-03-05 14:39:17 +11:00
Jonathan
8cd7035494 Fixed validation of begin and end steps
Fixed logic to match the error message - begin should be <= end.
2025-03-05 14:39:17 +11:00
Eugene Brodsky
4de6fd3ae6 chore(docker): reduce size between docker builds (#7571)
by adding a layer with all the pytorch dependencies that don't change
most of the time.

## Summary

Every time the [`main` docker
images](https://github.com/invoke-ai/InvokeAI/pkgs/container/invokeai)
rebuild and I pull `main-cuda`, it gets another 3+ GB, which seems like
about a zillion times too much since most things don't change from one
commit on `main` to the next.

This is an attempt to follow the guidance in [Using uv in Docker:
Intermediate
Layers](https://docs.astral.sh/uv/guides/integration/docker/#intermediate-layers)
so there's one layer that installs all the dependencies—including
PyTorch with its bundled nvidia libraries—_before_ the project's own
frequently-changing files are copied in to the image.


## Related Issues / Discussions

- [Improved docker layer cache with
uv](https://discord.com/channels/1020123559063990373/1329975172022927370)
- [astral: Can `uv pip install` torch, but not `uv sync`
it](https://discord.com/channels/1039017663004942429/1329986610770612347)


## QA Instructions

Hopefully the CI system building the docker images is sufficient.

But there is one change to `pyproject.toml` related to xformers, so it'd
be worth checking that `python -m xformers.info` still says it has
triton on the platforms that expect it.


## Merge Plan

I don't expect this to be a disruptive merge.

(An earlier revision of this PR moved the venv, but I've reverted that
change at ebr's recommendation.)


## Checklist

- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
2025-03-04 20:42:28 -05:00
Eugene Brodsky
3feb1a6600 Merge branch 'main' into build/docker-dependency-layer 2025-03-04 20:33:24 -05:00
psychedelicious
ea2320c57b feat(ui): add button ref image layer empty state to pull bbox 2025-03-05 08:00:20 +11:00
psychedelicious
0ad0016c2d chore: bump version to v5.7.2rc2 2025-03-04 08:48:28 +11:00
psychedelicious
c2a3c66e49 feat(app): avoid nested cursors in workflow_records service 2025-03-04 08:33:42 +11:00
psychedelicious
c0a0d20935 feat(app): avoid nested cursors in style_preset_records service 2025-03-04 08:33:42 +11:00
psychedelicious
028d8d8ead feat(app): avoid nested cursors in model_records service 2025-03-04 08:33:42 +11:00
psychedelicious
657095d2e2 feat(app): avoid nested cursors in image_records service 2025-03-04 08:33:42 +11:00
psychedelicious
1c47dc997e feat(app): avoid nested cursors in board_records service 2025-03-04 08:33:42 +11:00
psychedelicious
a3de6b6165 feat(app): avoid nested cursors in board_image_records service 2025-03-04 08:33:42 +11:00
psychedelicious
e57f0ff055 experiment(app): avoid nested cursors in session_queue service
SQLite cursors are meant to be lightweight and not reused. For whatever reason, we reuse one per service for the entire app lifecycle.

This can cause issues where a cursor is used twice at the same time in different transactions.

This experiment makes the session queue use a fresh cursor for each method, hopefully fixing the issue.
2025-03-04 08:33:42 +11:00
Eugene Brodsky
0362bd5a06 Merge branch 'main' into build/docker-dependency-layer 2025-03-03 09:32:04 -05:00
Linos
feee4c49a2 translationBot(ui): update translation (Vietnamese)
Currently translated at 100.0% (1798 of 1798 strings)

Co-authored-by: Linos <linos.coding@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/vi/
Translation: InvokeAI/Web UI
2025-03-03 14:50:08 +11:00
Riccardo Giovanetti
42e052d6f2 translationBot(ui): update translation (Italian)
Currently translated at 98.8% (1777 of 1798 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
2025-03-03 14:50:08 +11:00
psychedelicious
b03e429b26 fix(ui): add missing builder translations 2025-03-03 14:43:23 +11:00
psychedelicious
7399909029 feat(app): use simpler syntax for enqueue_batch threaded execution 2025-03-03 14:40:48 +11:00
psychedelicious
c8aaf5e76b tidy(app): remove extraneous class attr type annotations 2025-03-03 14:40:48 +11:00
psychedelicious
0cdf7a7048 Revert "experiment(app): simulate very long enqueue operations (15s)"
This reverts commit eb6a323d0b70004732de493d6530e08eb5ca8acf.
2025-03-03 14:40:48 +11:00
psychedelicious
41985487d3 Revert "experiment(app): make socketio server ping every 1s"
This reverts commit ddf00bf260167092a3bc2afdce1244c6b116ebfb.
2025-03-03 14:40:48 +11:00
psychedelicious
41d5a17114 fix(ui): set RTKQ tag invalidationBehaviour to immediate
This allows tags to be invalidated while mutations are executing, resolving an issue in this situation:
- A long-running mutation starts.
- A tag is invalidated; for example, user edits a board name, and the boards list query tag is invalidated.
- The boards list query isn't fired, and the board name isn't updated.
- The long-running mutation finishes.
- Finally, the boards list query fires and the board name is updated.

This is the "delayed" behaviour. The "immediately" behaviour has the fires requests from tag invalidation immediately, without waiting for all mutations to finish.

It may cause extra network requests and stale data if we are mutating a lot of things very quickly. I don't think it will be an issue in practice and the improved responsiveness will be a net benefit.
2025-03-03 14:40:48 +11:00
psychedelicious
14f9d5b6bc experiment(app): remove db locking logic
Rely on WAL mode and the busy timeout.

Also changed:
- Remove extraneous rollbacks when we were only doing a `SELECT`
- Remove try/catch blocks that were made extraneous when removing the extraneous rollbacks
2025-03-03 14:40:48 +11:00
psychedelicious
eec4bdb038 experiment(app): enable WAL mode and set busy_timeout
This allows for read and write concurrency without using a global mutex. Operations may still fail they take longer than the busy timeout (5s).

If we get a database lock error after waiting 5s for an operation, we have a problem. So, I think it's actually better to use a busy timeout instead of a global mutex.

Alternatively, we could add a timeout to the global mutex.
2025-03-03 14:40:48 +11:00
psychedelicious
f3dd44044a experiment(app): run enqueue_batch async in a thread 2025-03-03 14:40:48 +11:00
psychedelicious
61a22eb8cb experiment(app): make socketio server ping every 1s 2025-03-03 14:40:48 +11:00
psychedelicious
03ca83fe13 experiment(app): simulate very long enqueue operations (15s) 2025-03-03 14:40:48 +11:00
psychedelicious
8f1e25c387 chore: bump version to v5.7.2rc1 2025-03-03 09:46:16 +11:00
Kevin Turner
29cf4bc002 feat: accept WebP uploads for assets 2025-03-02 08:50:38 -05:00
psychedelicious
9428642806 fix(ui): single or collection field rendering
Fixes an issue where fields like control weight on ControlNet nodes and image on IP Adapter nodes didn't render.

These are "single or collection" fields. They accept a single input object, or collection. They are supposed to render the UI input for a single object.

In a7a71ca935 a performance optimisation for a hot code-path inadvertently broke this.

The determination of which UI component to render for a given field was done using a type guard function for the field's template. Previously, this used a zod schema to parse the template. This is very slow, especially when the template was not the expected type.

The optimization changed the type guards to check the field name (aka its type, integer, image, etc) and cardinality directly, without any zod parsing.

It's much faster, but subtly changed the behaviour because it was a bit stricter. For some fields, it rejected "single or collection" cardinalities when it should have accepted them.

When these fields - like the aforementioned Control Weight and Image - were being rendered, none of the type guards passed and they rendered nothing.

The fix here updates the type guard functions to support multiple cardinalities. So now, when we go to render a "single or collection" field, we will render the "single" input component as it should be.
2025-03-01 10:54:31 +11:00
psychedelicious
8620572524 docs: update RELEASE.md 2025-02-28 18:43:52 -05:00
psychedelicious
f44c7e824d chore(ui): lint 2025-02-28 18:09:54 -05:00
psychedelicious
c5b8bde285 fix(ui): download button in workflow library downloads wrong workflow 2025-02-28 18:09:54 -05:00
Ryan Dick
4c86a7ecbf Update Low-VRAM docs guidance around max_cache_vram_gb. 2025-02-28 17:18:57 -05:00
Ryan Dick
b9f9d1c152 Increase the VAE decode memory estimates. to account for memory reserved by the memory allocator, but not allocated, and to generally be more conservative. 2025-02-28 17:18:57 -05:00
Ryan Dick
7567ee2adf Add pytorch_cuda_alloc_conf config to tune VRAM memory allocation (#7673)
## Summary

This PR adds a `pytorch_cuda_alloc_conf` config flag to control the
torch memory allocator behavior.

- `pytorch_cuda_alloc_conf` defaults to `None`, preserving the current
behavior.
- The configuration options are explained here:
https://pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf.
Tuning this configuration can reduce peak reserved VRAM and improve
performance.
- Setting `pytorch_cuda_alloc_conf: "backend:cudaMallocAsync"` in
`invokeai.yaml` is expected to work well on many systems. This is a good
first step for those looking to tune this config. (We may make this the
default in the future.)
- The optimal configuration seems to be dependent on a number of factors
such as device version, VRAM, CUDA kernel version, etc. For now, users
will have to experiment with this config to see if it hurts or helps on
their systems. In most cases, I expect it to help.

### Memory Tests

```
VAE decode memory usage comparison:

- SDXL, fp16, 1024x1024:
  - `cudaMallocAsync`: allocated=2593 MB, reserved=3200 MB
  - `native`:          allocated=2595 MB, reserved=4418 MB

- SDXL, fp32, 1024x1024:
  - `cudaMallocAsync`: allocated=3982 MB, reserved=5536 MB
  - `native`:          allocated=3982 MB, reserved=7276 MB

- SDXL, fp32, 1536x1536:
  - `cudaMallocAsync`: allocated=8643 MB, reserved=12032 MB
  - `native`:          allocated=8643 MB, reserved=15900 MB
```

## Related Issues / Discussions

N/A

## QA Instructions

- [x] Performance tests with `pytorch_cuda_alloc_conf` unset.
- [x] Performance tests with `pytorch_cuda_alloc_conf:
"backend:cudaMallocAsync"`.

## Merge Plan

- [x] Merge #7668 first and change target branch to `main`

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
2025-02-28 16:47:01 -05:00
Ryan Dick
0e632dbc5c (minor) typo 2025-02-28 21:39:09 +00:00
Ryan Dick
49191709a0 Mark test_configure_torch_cuda_allocator_raises_if_torch_is_already_imported() to only run if CUDA is available. 2025-02-28 21:39:09 +00:00
Ryan Dick
3af7fc26fa Update low-vram docs with info abhout . 2025-02-28 21:39:09 +00:00
Ryan Dick
a36a627f83 Switch from use_cuda_malloc flag to a general pytorch_cuda_alloc_conf config field that allows full customization of the CUDA allocator. 2025-02-28 21:39:09 +00:00
Ryan Dick
b31c71f302 Simplify is_torch_cuda_malloc_enabled() implementation and add unit tests. 2025-02-28 21:39:09 +00:00
Ryan Dick
5302d4890f Add use_cuda_malloc config option. 2025-02-28 21:39:09 +00:00
Ryan Dick
766b752572 Add utils for configuring the torch CUDA allocator. 2025-02-28 21:39:09 +00:00
Eugene Brodsky
7feae5e5ce do not cache image layers in CI docker build 2025-02-28 16:24:50 -05:00
Ryan Dick
26730ca702 Tidy app entrypoint (#7668)
## Summary

Prior to this PR, most of the app setup was being done in `api_app.py`
at import time. This PR cleans this up, by:
- Splitting app setup into more modular functions
- Narrower responsibility for the `api_app.py` file - it just
initializes the `FastAPI` app

The main motivation for this changes is to make it easier to support an
upcoming torch configuration feature that requires more careful ordering
of app initialization steps.

## Related Issues / Discussions

N/A

## QA Instructions

- [x] Launch the app via invokeai-web.py and smoke test it.
- [ ] Launch the app via the installer and smoke test it.
- [x] Test that generate_openapi_schema.py produces the same result
before and after the change.
- [x] No regression in unit tests that directly interact with the app.
(test_images.py)

## Merge Plan

- [x] Check to see if there are any commercial implications to modifying
the app entrypoint.

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
2025-02-28 16:07:30 -05:00
Ryan Dick
1e2c7c51b5 Move load_custom_nodes() to run_app() entrypoint. 2025-02-28 20:54:26 +00:00
Ryan Dick
da2b6815ac Make InvokeAILogger an inline import in startup_utils.py in response to review comment. 2025-02-28 20:10:24 +00:00
Ryan Dick
68d14de3ee Split run_app.py and api_app.py so that api_app.py is more narrowly responsible for just initializing the FastAPI app. This also gives clearer control over the order of the initialization steps, which will be important as we add planned torch configurations that must be applied before torch is imported. 2025-02-28 20:10:24 +00:00
Ryan Dick
38991ffc35 Add register_mime_types() startup util. 2025-02-28 20:10:24 +00:00
Ryan Dick
f345c0fabc Create an apply_monkeypatches() start util. 2025-02-28 20:10:24 +00:00
Ryan Dick
ca23b5337e Simplify port selection logic to avoid the need for a global port variable. 2025-02-28 20:10:19 +00:00
Ryan Dick
35910d3952 Move check_cudnn() and jurigged setup to startup_utils.py. 2025-02-28 20:08:53 +00:00
Ryan Dick
6f1dcf385b Move find_port() util to its own file. 2025-02-28 20:08:53 +00:00
Kevin Turner
80d38c0e47 chore(docker): include fewer files while installing dependencies
including just invokeai/version seems sufficient to appease uv sync here. including everything else would invalidate the cache we're trying to establish.
2025-02-16 12:31:14 -08:00
Kevin Turner
22362350dc chore(docker): revert to keeping venv in /opt/venv 2025-02-16 11:26:06 -08:00
Kevin Turner
275d891f48 Merge branch 'main' into build/docker-dependency-layer 2025-02-16 10:34:17 -08:00
Kevin Turner
3848e1926b chore(docker): reduce size between docker builds
by adding a layer with all the pytorch dependencies that don't change most of the time.
2025-01-18 09:10:54 -08:00
45 changed files with 1616 additions and 1375 deletions

View File

@@ -76,9 +76,6 @@ jobs:
latest=${{ matrix.gpu-driver == 'cuda' && github.ref == 'refs/heads/main' }}
suffix=-${{ matrix.gpu-driver }},onlatest=false
- name: Set up QEMU
uses: docker/setup-qemu-action@v3
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
@@ -103,7 +100,7 @@ jobs:
push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' || github.event.inputs.push-to-registry }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
cache-from: |
type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
type=gha,scope=main-${{ matrix.gpu-driver }}
cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
# cache-from: |
# type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
# type=gha,scope=main-${{ matrix.gpu-driver }}
# cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}

View File

@@ -13,48 +13,63 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
git
# Install `uv` for package management
COPY --from=ghcr.io/astral-sh/uv:0.5.5 /uv /uvx /bin/
COPY --from=ghcr.io/astral-sh/uv:0.6.0 /uv /uvx /bin/
ENV VIRTUAL_ENV=/opt/venv
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
ENV INVOKEAI_SRC=/opt/invokeai
ENV PYTHON_VERSION=3.11
ENV UV_PYTHON=3.11
ENV UV_COMPILE_BYTECODE=1
ENV UV_LINK_MODE=copy
ENV UV_PROJECT_ENVIRONMENT="$VIRTUAL_ENV"
ENV UV_INDEX="https://download.pytorch.org/whl/cu124"
ARG GPU_DRIVER=cuda
ARG TARGETPLATFORM="linux/amd64"
# unused but available
ARG BUILDPLATFORM
# Switch to the `ubuntu` user to work around dependency issues with uv-installed python
RUN mkdir -p ${VIRTUAL_ENV} && \
mkdir -p ${INVOKEAI_SRC} && \
chmod -R a+w /opt
chmod -R a+w /opt && \
mkdir ~ubuntu/.cache && chown ubuntu: ~ubuntu/.cache
USER ubuntu
# Install python and create the venv
RUN uv python install ${PYTHON_VERSION} && \
uv venv --relocatable --prompt "invoke" --python ${PYTHON_VERSION} ${VIRTUAL_ENV}
# Install python
RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
uv python install ${PYTHON_VERSION}
WORKDIR ${INVOKEAI_SRC}
COPY invokeai ./invokeai
COPY pyproject.toml ./
# Editable mode helps use the same image for development:
# the local working copy can be bind-mounted into the image
# at path defined by ${INVOKEAI_SRC}
# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
# bind-mount instead of copy to defer adding sources to the image until next layer.
#
# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
# x86_64/CUDA is the default
RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
--mount=type=bind,source=invokeai/version,target=invokeai/version \
if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
UV_INDEX="https://download.pytorch.org/whl/cpu"; \
elif [ "$GPU_DRIVER" = "rocm" ]; then \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm6.1"; \
else \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu124"; \
UV_INDEX="https://download.pytorch.org/whl/rocm6.1"; \
fi && \
uv pip install --python ${PYTHON_VERSION} $extra_index_url_arg -e "."
uv sync --no-install-project
# Now that the bulk of the dependencies have been installed, copy in the project files that change more frequently.
COPY invokeai invokeai
COPY pyproject.toml .
RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
UV_INDEX="https://download.pytorch.org/whl/cpu"; \
elif [ "$GPU_DRIVER" = "rocm" ]; then \
UV_INDEX="https://download.pytorch.org/whl/rocm6.1"; \
fi && \
uv sync
#### Build the Web UI ------------------------------------
@@ -98,6 +113,7 @@ RUN apt update && apt install -y --no-install-recommends \
ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv
ENV UV_PROJECT_ENVIRONMENT="$VIRTUAL_ENV"
ENV PYTHON_VERSION=3.11
ENV INVOKEAI_ROOT=/invokeai
ENV INVOKEAI_HOST=0.0.0.0
@@ -109,7 +125,7 @@ ENV CONTAINER_GID=${CONTAINER_GID:-1000}
# Install `uv` for package management
# and install python for the ubuntu user (expected to exist on ubuntu >=24.x)
# this is too tiny to optimize with multi-stage builds, but maybe we'll come back to it
COPY --from=ghcr.io/astral-sh/uv:0.5.5 /uv /uvx /bin/
COPY --from=ghcr.io/astral-sh/uv:0.6.0 /uv /uvx /bin/
USER ubuntu
RUN uv python install ${PYTHON_VERSION}
USER root

View File

@@ -1,41 +1,50 @@
# Release Process
The app is published in twice, in different build formats.
The Invoke application is published as a python package on [PyPI]. This includes both a source distribution and built distribution (a wheel).
- A [PyPI] distribution. This includes both a source distribution and built distribution (a wheel). Users install with `pip install invokeai`. The updater uses this build.
- An installer on the [InvokeAI Releases Page]. This is a zip file with install scripts and a wheel. This is only used for new installs.
Most users install it with the [Launcher](https://github.com/invoke-ai/launcher/), others with `pip`.
The launcher uses GitHub as the source of truth for available releases.
## Broad Strokes
- Merge all changes and bump the version in the codebase.
- Tag the release commit.
- Wait for the release workflow to complete.
- Approve the PyPI publish jobs.
- Write GH release notes.
## General Prep
Make a developer call-out for PRs to merge. Merge and test things out.
While the release workflow does not include end-to-end tests, it does pause before publishing so you can download and test the final build.
Make a developer call-out for PRs to merge. Merge and test things out. Bump the version by editing `invokeai/version/invokeai_version.py`.
## Release Workflow
The `release.yml` workflow runs a number of jobs to handle code checks, tests, build and publish on PyPI.
It is triggered on **tag push**, when the tag matches `v*`. It doesn't matter if you've prepped a release branch like `release/v3.5.0` or are releasing from `main` - it works the same.
> Because commits are reference-counted, it is safe to create a release branch, tag it, let the workflow run, then delete the branch. So long as the tag exists, that commit will exist.
It is triggered on **tag push**, when the tag matches `v*`.
### Triggering the Workflow
Run `make tag-release` to tag the current commit and kick off the workflow.
Ensure all commits that should be in the release are merged, and you have pulled them locally.
The release may also be dispatched [manually].
Double-check that you have checked out the commit that will represent the release (typically the latest commit on `main`).
Run `make tag-release` to tag the current commit and kick off the workflow. You will be prompted to provide a message - use the version specifier.
If this version's tag already exists for some reason (maybe you had to make a last minute change), the script will overwrite it.
> In case you cannot use the Make target, the release may also be dispatched [manually] via GH.
### Workflow Jobs and Process
The workflow consists of a number of concurrently-run jobs, and two final publish jobs.
The workflow consists of a number of concurrently-run checks and tests, then two final publish jobs.
The publish jobs require manual approval and are only run if the other jobs succeed.
#### `check-version` Job
This job checks that the git ref matches the app version. It matches the ref against the `__version__` variable in `invokeai/version/invokeai_version.py`.
When the workflow is triggered by tag push, the ref is the tag. If the workflow is run manually, the ref is the target selected from the **Use workflow from** dropdown.
This job ensures that the `invokeai` python package version specifier matches the tag for the release. The version specifier is pulled from the `__version__` variable in `invokeai/version/invokeai_version.py`.
This job uses [samuelcolvin/check-python-version].
@@ -43,62 +52,52 @@ This job uses [samuelcolvin/check-python-version].
#### Check and Test Jobs
Next, these jobs run and must pass. They are the same jobs that are run for every PR.
- **`python-tests`**: runs `pytest` on matrix of platforms
- **`python-checks`**: runs `ruff` (format and lint)
- **`frontend-tests`**: runs `vitest`
- **`frontend-checks`**: runs `prettier` (format), `eslint` (lint), `dpdm` (circular refs), `tsc` (static type check) and `knip` (unused imports)
> **TODO** We should add `mypy` or `pyright` to the **`check-python`** job.
> **TODO** We should add an end-to-end test job that generates an image.
- **`typegen-checks`**: ensures the frontend and backend types are synced
#### `build-installer` Job
This sets up both python and frontend dependencies and builds the python package. Internally, this runs `installer/create_installer.sh` and uploads two artifacts:
- **`dist`**: the python distribution, to be published on PyPI
- **`InvokeAI-installer-${VERSION}.zip`**: the installer to be included in the GitHub release
- **`InvokeAI-installer-${VERSION}.zip`**: the legacy install scripts
You don't need to download either of these files.
> The legacy install scripts are no longer used, but we haven't updated the workflow to skip building them.
#### Sanity Check & Smoke Test
At this point, the release workflow pauses as the remaining publish jobs require approval. Time to test the installer.
At this point, the release workflow pauses as the remaining publish jobs require approval.
Because the installer pulls from PyPI, and we haven't published to PyPI yet, you will need to install from the wheel:
It's possible to test the python package before it gets published to PyPI. We've never had problems with it, so it's not necessary to do this.
- Download and unzip `dist.zip` and the installer from the **Summary** tab of the workflow
- Run the installer script using the `--wheel` CLI arg, pointing at the wheel:
But, if you want to be extra-super careful, here's how to test it:
```sh
./install.sh --wheel ../InvokeAI-4.0.0rc6-py3-none-any.whl
```
- Install to a temporary directory so you get the new user experience
- Download a model and generate
> The same wheel file is bundled in the installer and in the `dist` artifact, which is uploaded to PyPI. You should end up with the exactly the same installation as if the installer got the wheel from PyPI.
- Download the `dist.zip` build artifact from the `build-installer` job
- Unzip it and find the wheel file
- Create a fresh Invoke install by following the [manual install guide](https://invoke-ai.github.io/InvokeAI/installation/manual/) - but instead of installing from PyPI, install from the wheel
- Test the app
##### Something isn't right
If testing reveals any issues, no worries. Cancel the workflow, which will cancel the pending publish jobs (you didn't approve them prematurely, right?).
Now you can start from the top:
- Fix the issues and PR the fixes per usual
- Get the PR approved and merged per usual
- Switch to `main` and pull in the fixes
- Run `make tag-release` to move the tag to `HEAD` (which has the fixes) and kick off the release workflow again
- Re-do the sanity check
If testing reveals any issues, no worries. Cancel the workflow, which will cancel the pending publish jobs (you didn't approve them prematurely, right?) and start over.
#### PyPI Publish Jobs
The publish jobs will run if any of the previous jobs fail.
The publish jobs will not run if any of the previous jobs fail.
They use [GitHub environments], which are configured as [trusted publishers] on PyPI.
Both jobs require a maintainer to approve them from the workflow's **Summary** tab.
Both jobs require a @hipsterusername or @psychedelicious to approve them from the workflow's **Summary** tab.
- Click the **Review deployments** button
- Select the environment (either `testpypi` or `pypi`)
- Select the environment (either `testpypi` or `pypi` - typically you select both)
- Click **Approve and deploy**
> **If the version already exists on PyPI, the publish jobs will fail.** PyPI only allows a given version to be published once - you cannot change it. If version published on PyPI has a problem, you'll need to "fail forward" by bumping the app version and publishing a followup release.
@@ -113,46 +112,33 @@ If there are no incidents, contact @hipsterusername or @lstein, who have owner a
Publishes the distribution on the [Test PyPI] index, using the `testpypi` GitHub environment.
This job is not required for the production PyPI publish, but included just in case you want to test the PyPI release.
This job is not required for the production PyPI publish, but included just in case you want to test the PyPI release for some reason:
If approved and successful, you could try out the test release like this:
```sh
# Create a new virtual environment
python -m venv ~/.test-invokeai-dist --prompt test-invokeai-dist
# Install the distribution from Test PyPI
pip install --index-url https://test.pypi.org/simple/ invokeai
# Run and test the app
invokeai-web
# Cleanup
deactivate
rm -rf ~/.test-invokeai-dist
```
- Approve this publish job without approving the prod publish
- Let it finish
- Create a fresh Invoke install by following the [manual install guide](https://invoke-ai.github.io/InvokeAI/installation/manual/), making sure to use the Test PyPI index URL: `https://test.pypi.org/simple/`
- Test the app
#### `publish-pypi` Job
Publishes the distribution on the production PyPI index, using the `pypi` GitHub environment.
## Publish the GitHub Release with installer
It's a good idea to wait to approve and run this job until you have the release notes ready!
Once the release is published to PyPI, it's time to publish the GitHub release.
## Prep and publish the GitHub Release
1. [Draft a new release] on GitHub, choosing the tag that triggered the release.
1. Write the release notes, describing important changes. The **Generate release notes** button automatically inserts the changelog and new contributors, and you can copy/paste the intro from previous releases.
1. Use `scripts/get_external_contributions.py` to get a list of external contributions to shout out in the release notes.
1. Upload the zip file created in **`build`** job into the Assets section of the release notes.
1. Check **Set as a pre-release** if it's a pre-release.
1. Check **Create a discussion for this release**.
1. Publish the release.
1. Announce the release in Discord.
> **TODO** Workflows can create a GitHub release from a template and upload release assets. One popular action to handle this is [ncipollo/release-action]. A future enhancement to the release process could set this up.
## Manual Build
The `build installer` workflow can be dispatched manually. This is useful to test the installer for a given branch or tag.
No checks are run, it just builds.
2. The **Generate release notes** button automatically inserts the changelog and new contributors. Make sure to select the correct tags for this release and the last stable release. GH often selects the wrong tags - do this manually.
3. Write the release notes, describing important changes. Contributions from community members should be shouted out. Use the GH-generated changelog to see all contributors. If there are Weblate translation updates, open that PR and shout out every person who contributed a translation.
4. Check **Set as a pre-release** if it's a pre-release.
5. Approve and wait for the `publish-pypi` job to finish if you haven't already.
6. Publish the GH release.
7. Post the release in Discord in the [releases](https://discord.com/channels/1020123559063990373/1149260708098359327) channel with abbreviated notes. For example:
> Invoke v5.7.0 (stable): <https://github.com/invoke-ai/InvokeAI/releases/tag/v5.7.0>
>
> It's a pretty big one - Form Builder, Metadata Nodes (thanks @SkunkWorxDark!), and much more.
8. Right click the message in releases and copy the link to it. Then, post that link in the [new-release-discussion](https://discord.com/channels/1020123559063990373/1149506274971631688) channel. For example:
> Invoke v5.7.0 (stable): <https://discord.com/channels/1020123559063990373/1149260708098359327/1344521744916021248>
## Manual Release
@@ -160,12 +146,10 @@ The `release` workflow can be dispatched manually. You must dispatch the workflo
This functionality is available as a fallback in case something goes wonky. Typically, releases should be triggered via tag push as described above.
[InvokeAI Releases Page]: https://github.com/invoke-ai/InvokeAI/releases
[PyPI]: https://pypi.org/
[Draft a new release]: https://github.com/invoke-ai/InvokeAI/releases/new
[Test PyPI]: https://test.pypi.org/
[version specifier]: https://packaging.python.org/en/latest/specifications/version-specifiers/
[ncipollo/release-action]: https://github.com/ncipollo/release-action
[GitHub environments]: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment
[trusted publishers]: https://docs.pypi.org/trusted-publishers/
[samuelcolvin/check-python-version]: https://github.com/samuelcolvin/check-python-version

View File

@@ -31,6 +31,7 @@ It is possible to fine-tune the settings for best performance or if you still ge
Low-VRAM mode involves 4 features, each of which can be configured or fine-tuned:
- Partial model loading (`enable_partial_loading`)
- PyTorch CUDA allocator config (`pytorch_cuda_alloc_conf`)
- Dynamic RAM and VRAM cache sizes (`max_cache_ram_gb`, `max_cache_vram_gb`)
- Working memory (`device_working_mem_gb`)
- Keeping a RAM weight copy (`keep_ram_copy_of_weights`)
@@ -51,6 +52,16 @@ As described above, you can enable partial model loading by adding this line to
enable_partial_loading: true
```
### PyTorch CUDA allocator config
The PyTorch CUDA allocator's behavior can be configured using the `pytorch_cuda_alloc_conf` config. Tuning the allocator configuration can help to reduce the peak reserved VRAM. The optimal configuration is dependent on many factors (e.g. device type, VRAM, CUDA driver version, etc.), but switching from PyTorch's native allocator to using CUDA's built-in allocator works well on many systems. To try this, add the following line to your `invokeai.yaml` file:
```yaml
pytorch_cuda_alloc_conf: "backend:cudaMallocAsync"
```
A more complete explanation of the available configuration options is [here](https://pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf).
### Dynamic RAM and VRAM cache sizes
Loading models from disk is slow and can be a major bottleneck for performance. Invoke uses two model caches - RAM and VRAM - to reduce loading from disk to a minimum.
@@ -75,24 +86,26 @@ But, if your GPU has enough VRAM to hold models fully, you might get a perf boos
# As an example, if your system has 32GB of RAM and no other heavy processes, setting the `max_cache_ram_gb` to 28GB
# might be a good value to achieve aggressive model caching.
max_cache_ram_gb: 28
# The default max cache VRAM size is adjusted dynamically based on the amount of available VRAM (taking into
# consideration the VRAM used by other processes).
# You can override the default value by setting `max_cache_vram_gb`. Note that this value takes precedence over the
# `device_working_mem_gb`.
# It is recommended to set the VRAM cache size to be as large as possible while leaving enough room for the working
# memory of the tasks you will be doing. For example, on a 24GB GPU that will be running unquantized FLUX without any
# auxiliary models, 18GB might be a good value.
max_cache_vram_gb: 18
# You can override the default value by setting `max_cache_vram_gb`.
# CAUTION: Most users should not manually set this value. See warning below.
max_cache_vram_gb: 16
```
!!! tip "Max safe value for `max_cache_vram_gb`"
!!! warning "Max safe value for `max_cache_vram_gb`"
To determine the max safe value for `max_cache_vram_gb`, subtract `device_working_mem_gb` from your GPU's VRAM. As described below, the default for `device_working_mem_gb` is 3GB.
Most users should not manually configure the `max_cache_vram_gb`. This configuration value takes precedence over the `device_working_mem_gb` and any operations that explicitly reserve additional working memory (e.g. VAE decode). As such, manually configuring it increases the likelihood of encountering out-of-memory errors.
For users who wish to configure `max_cache_vram_gb`, the max safe value can be determined by subtracting `device_working_mem_gb` from your GPU's VRAM. As described below, the default for `device_working_mem_gb` is 3GB.
For example, if you have a 12GB GPU, the max safe value for `max_cache_vram_gb` is `12GB - 3GB = 9GB`.
If you had increased `device_working_mem_gb` to 4GB, then the max safe value for `max_cache_vram_gb` is `12GB - 4GB = 8GB`.
Most users who override `max_cache_vram_gb` are doing so because they wish to use significantly less VRAM, and should be setting `max_cache_vram_gb` to a value significantly less than the 'max safe value'.
### Working memory
Invoke cannot use _all_ of your VRAM for model caching and loading. It requires some VRAM to use as working memory for various operations.

View File

@@ -48,7 +48,9 @@ async def enqueue_batch(
) -> EnqueueBatchResult:
"""Processes a batch and enqueues the output graphs for execution."""
return ApiDependencies.invoker.services.session_queue.enqueue_batch(queue_id=queue_id, batch=batch, prepend=prepend)
return await ApiDependencies.invoker.services.session_queue.enqueue_batch(
queue_id=queue_id, batch=batch, prepend=prepend
)
@session_queue_router.get(

View File

@@ -1,12 +1,8 @@
import asyncio
import logging
import mimetypes
import socket
from contextlib import asynccontextmanager
from pathlib import Path
import torch
import uvicorn
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
@@ -15,11 +11,7 @@ from fastapi.responses import HTMLResponse, RedirectResponse
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint
from torch.backends.mps import is_available as is_mps_available
# for PyCharm:
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
import invokeai.frontend.web as web_dir
from invokeai.app.api.dependencies import ApiDependencies
from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
@@ -36,39 +28,15 @@ from invokeai.app.api.routers import (
workflows,
)
from invokeai.app.api.sockets import SocketIO
from invokeai.app.invocations.load_custom_nodes import load_custom_nodes
from invokeai.app.services.config.config_default import get_config
from invokeai.app.util.custom_openapi import get_openapi_func
from invokeai.backend.util.devices import TorchDevice
from invokeai.backend.util.logging import InvokeAILogger
app_config = get_config()
if is_mps_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
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
mimetypes.add_type("application/javascript", ".js")
mimetypes.add_type("text/css", ".css")
torch_device_name = TorchDevice.get_torch_device_name()
logger.info(f"Using torch device: {torch_device_name}")
loop = asyncio.new_event_loop()
# We may change the port if the default is in use, this global variable is used to store the port so that we can log
# the correct port when the server starts in the lifespan handler.
port = app_config.port
# Load custom nodes. This must be done after importing the Graph class, which itself imports all modules from the
# invocations module. The ordering here is implicit, but important - we want to load custom nodes after all the
# core nodes have been imported so that we can catch when a custom node clobbers a core node.
load_custom_nodes(custom_nodes_path=app_config.custom_nodes_path)
@asynccontextmanager
async def lifespan(app: FastAPI):
@@ -77,7 +45,7 @@ async def lifespan(app: FastAPI):
# Log the server address when it starts - in case the network log level is not high enough to see the startup log
proto = "https" if app_config.ssl_certfile else "http"
msg = f"Invoke running on {proto}://{app_config.host}:{port} (Press CTRL+C to quit)"
msg = f"Invoke running on {proto}://{app_config.host}:{app_config.port} (Press CTRL+C to quit)"
# Logging this way ignores the logger's log level and _always_ logs the message
record = logger.makeRecord(
@@ -192,73 +160,3 @@ except RuntimeError:
app.mount(
"/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
) # docs favicon is in here
def check_cudnn(logger: logging.Logger) -> None:
"""Check for cuDNN issues that could be causing degraded performance."""
if torch.backends.cudnn.is_available():
try:
# Note: At the time of writing (torch 2.2.1), torch.backends.cudnn.version() only raises an error the first
# time it is called. Subsequent calls will return the version number without complaining about a mismatch.
cudnn_version = torch.backends.cudnn.version()
logger.info(f"cuDNN version: {cudnn_version}")
except RuntimeError as e:
logger.warning(
"Encountered a cuDNN version issue. This may result in degraded performance. This issue is usually "
"caused by an incompatible cuDNN version installed in your python environment, or on the host "
f"system. Full error message:\n{e}"
)
def invoke_api() -> None:
def find_port(port: int) -> int:
"""Find a port not in use starting at given port"""
# Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
# https://github.com/WaylonWalker
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.settimeout(1)
if s.connect_ex(("localhost", port)) == 0:
return find_port(port=port + 1)
else:
return port
if app_config.dev_reload:
try:
import jurigged
except ImportError as e:
logger.error(
'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.',
exc_info=e,
)
else:
jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
global port
port = find_port(app_config.port)
if port != app_config.port:
logger.warn(f"Port {app_config.port} in use, using port {port}")
check_cudnn(logger)
config = uvicorn.Config(
app=app,
host=app_config.host,
port=port,
loop="asyncio",
log_level=app_config.log_level_network,
ssl_certfile=app_config.ssl_certfile,
ssl_keyfile=app_config.ssl_keyfile,
)
server = uvicorn.Server(config)
# replace uvicorn's loggers with InvokeAI's for consistent appearance
uvicorn_logger = InvokeAILogger.get_logger("uvicorn")
uvicorn_logger.handlers.clear()
for hdlr in logger.handlers:
uvicorn_logger.addHandler(hdlr)
loop.run_until_complete(server.serve())
if __name__ == "__main__":
invoke_api()

View File

@@ -41,16 +41,11 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoEncoder) -> int:
"""Estimate the working memory required by the invocation in bytes."""
# It was found experimentally that the peak working memory scales linearly with the number of pixels and the
# element size (precision).
out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
element_size = next(vae.parameters()).element_size()
scaling_constant = 1090 # Determined experimentally.
scaling_constant = 2200 # Determined experimentally.
working_memory = out_h * out_w * element_size * scaling_constant
# We add a 20% buffer to the working memory estimate to be safe.
working_memory = working_memory * 1.2
return int(working_memory)
def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:

View File

@@ -60,7 +60,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
# It was found experimentally that the peak working memory scales linearly with the number of pixels and the
# element size (precision). This estimate is accurate for both SD1 and SDXL.
element_size = 4 if self.fp32 else 2
scaling_constant = 960 # Determined experimentally.
scaling_constant = 2200 # Determined experimentally.
if use_tiling:
tile_size = self.tile_size
@@ -84,9 +84,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
# If we are running in FP32, then we should account for the likely increase in model size (~250MB).
working_memory += 250 * 2**20
# We add 20% to the working memory estimate to be safe.
working_memory = int(working_memory * 1.2)
return working_memory
return int(working_memory)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput:

View File

@@ -43,16 +43,11 @@ class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoencoderKL) -> int:
"""Estimate the working memory required by the invocation in bytes."""
# It was found experimentally that the peak working memory scales linearly with the number of pixels and the
# element size (precision).
out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
element_size = next(vae.parameters()).element_size()
scaling_constant = 1230 # Determined experimentally.
scaling_constant = 2200 # Determined experimentally.
working_memory = out_h * out_w * element_size * scaling_constant
# We add a 20% buffer to the working memory estimate to be safe.
working_memory = working_memory * 1.2
return int(working_memory)
@torch.no_grad()

View File

@@ -9,6 +9,6 @@ def validate_weights(weights: Union[float, list[float]]) -> None:
def validate_begin_end_step(begin_step_percent: float, end_step_percent: float) -> None:
"""Validate that begin_step_percent is less than end_step_percent"""
if begin_step_percent >= end_step_percent:
"""Validate that begin_step_percent is less than or equal to end_step_percent"""
if begin_step_percent > end_step_percent:
raise ValueError("Begin step percent must be less than or equal to end step percent")

View File

@@ -1,12 +1,82 @@
"""This is a wrapper around the main app entrypoint, to allow for CLI args to be parsed before running the app."""
import uvicorn
from invokeai.app.invocations.load_custom_nodes import load_custom_nodes
from invokeai.app.services.config.config_default import get_config
from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.cli.arg_parser import InvokeAIArgs
def get_app():
"""Import the app and event loop. We wrap this in a function to more explicitly control when it happens, because
importing from api_app does a bunch of stuff - it's more like calling a function than importing a module.
"""
from invokeai.app.api_app import app, loop
return app, loop
def run_app() -> None:
# Before doing _anything_, parse CLI args!
from invokeai.frontend.cli.arg_parser import InvokeAIArgs
"""The main entrypoint for the app."""
# Parse the CLI arguments.
InvokeAIArgs.parse_args()
from invokeai.app.api_app import invoke_api
# Load config.
app_config = get_config()
invoke_api()
logger = InvokeAILogger.get_logger(config=app_config)
# Configure the torch CUDA memory allocator.
# NOTE: It is important that this happens before torch is imported.
if app_config.pytorch_cuda_alloc_conf:
configure_torch_cuda_allocator(app_config.pytorch_cuda_alloc_conf, logger)
# Import from startup_utils here to avoid importing torch before configure_torch_cuda_allocator() is called.
from invokeai.app.util.startup_utils import (
apply_monkeypatches,
check_cudnn,
enable_dev_reload,
find_open_port,
register_mime_types,
)
# Find an open port, and modify the config accordingly.
orig_config_port = app_config.port
app_config.port = find_open_port(app_config.port)
if orig_config_port != app_config.port:
logger.warning(f"Port {orig_config_port} is already in use. Using port {app_config.port}.")
# Miscellaneous startup tasks.
apply_monkeypatches()
register_mime_types()
if app_config.dev_reload:
enable_dev_reload()
check_cudnn(logger)
# Initialize the app and event loop.
app, loop = get_app()
# Load custom nodes. This must be done after importing the Graph class, which itself imports all modules from the
# invocations module. The ordering here is implicit, but important - we want to load custom nodes after all the
# core nodes have been imported so that we can catch when a custom node clobbers a core node.
load_custom_nodes(custom_nodes_path=app_config.custom_nodes_path)
# Start the server.
config = uvicorn.Config(
app=app,
host=app_config.host,
port=app_config.port,
loop="asyncio",
log_level=app_config.log_level_network,
ssl_certfile=app_config.ssl_certfile,
ssl_keyfile=app_config.ssl_keyfile,
)
server = uvicorn.Server(config)
# replace uvicorn's loggers with InvokeAI's for consistent appearance
uvicorn_logger = InvokeAILogger.get_logger("uvicorn")
uvicorn_logger.handlers.clear()
for hdlr in logger.handlers:
uvicorn_logger.addHandler(hdlr)
loop.run_until_complete(server.serve())

View File

@@ -1,5 +1,4 @@
import sqlite3
import threading
from typing import Optional, cast
from invokeai.app.services.board_image_records.board_image_records_base import BoardImageRecordStorageBase
@@ -13,15 +12,9 @@ from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.RLock
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
def add_image_to_board(
self,
@@ -29,8 +22,8 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
image_name: str,
) -> None:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
INSERT INTO board_images (board_id, image_name)
VALUES (?, ?)
@@ -42,16 +35,14 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
def remove_image_from_board(
self,
image_name: str,
) -> None:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
DELETE FROM board_images
WHERE image_name = ?;
@@ -62,8 +53,6 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
def get_images_for_board(
self,
@@ -72,33 +61,27 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
limit: int = 10,
) -> OffsetPaginatedResults[ImageRecord]:
# TODO: this isn't paginated yet?
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT images.*
FROM board_images
INNER JOIN images ON board_images.image_name = images.image_name
WHERE board_images.board_id = ?
ORDER BY board_images.updated_at DESC;
""",
(board_id,),
)
result = cast(list[sqlite3.Row], self._cursor.fetchall())
images = [deserialize_image_record(dict(r)) for r in result]
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT images.*
FROM board_images
INNER JOIN images ON board_images.image_name = images.image_name
WHERE board_images.board_id = ?
ORDER BY board_images.updated_at DESC;
""",
(board_id,),
)
result = cast(list[sqlite3.Row], cursor.fetchall())
images = [deserialize_image_record(dict(r)) for r in result]
self._cursor.execute(
"""--sql
SELECT COUNT(*) FROM images WHERE 1=1;
"""
)
count = cast(int, self._cursor.fetchone()[0])
cursor.execute(
"""--sql
SELECT COUNT(*) FROM images WHERE 1=1;
"""
)
count = cast(int, cursor.fetchone()[0])
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
def get_all_board_image_names_for_board(
@@ -107,98 +90,79 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
categories: list[ImageCategory] | None,
is_intermediate: bool | None,
) -> list[str]:
try:
self._lock.acquire()
params: list[str | bool] = []
params: list[str | bool] = []
# Base query is a join between images and board_images
stmt = """
# Base query is a join between images and board_images
stmt = """
SELECT images.image_name
FROM images
LEFT JOIN board_images ON board_images.image_name = images.image_name
WHERE 1=1
AND board_images.board_id = ?
"""
params.append(board_id)
params.append(board_id)
# Add the category filter
if categories is not None:
# Convert the enum values to unique list of strings
category_strings = [c.value for c in set(categories)]
# Create the correct length of placeholders
placeholders = ",".join("?" * len(category_strings))
stmt += f"""--sql
# Add the category filter
if categories is not None:
# Convert the enum values to unique list of strings
category_strings = [c.value for c in set(categories)]
# Create the correct length of placeholders
placeholders = ",".join("?" * len(category_strings))
stmt += f"""--sql
AND images.image_category IN ( {placeholders} )
"""
# Unpack the included categories into the query params
for c in category_strings:
params.append(c)
# Unpack the included categories into the query params
for c in category_strings:
params.append(c)
# Add the is_intermediate filter
if is_intermediate is not None:
stmt += """--sql
# Add the is_intermediate filter
if is_intermediate is not None:
stmt += """--sql
AND images.is_intermediate = ?
"""
params.append(is_intermediate)
params.append(is_intermediate)
# Put a ring on it
stmt += ";"
# Put a ring on it
stmt += ";"
# Execute the query
self._cursor.execute(stmt, params)
# Execute the query
cursor = self._conn.cursor()
cursor.execute(stmt, params)
result = cast(list[sqlite3.Row], self._cursor.fetchall())
image_names = [r[0] for r in result]
return image_names
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
result = cast(list[sqlite3.Row], cursor.fetchall())
image_names = [r[0] for r in result]
return image_names
def get_board_for_image(
self,
image_name: str,
) -> Optional[str]:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT board_id
FROM board_images
WHERE image_name = ?;
""",
(image_name,),
)
result = self._cursor.fetchone()
if result is None:
return None
return cast(str, result[0])
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
(image_name,),
)
result = cursor.fetchone()
if result is None:
return None
return cast(str, result[0])
def get_image_count_for_board(self, board_id: str) -> int:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT COUNT(*)
FROM board_images
INNER JOIN images ON board_images.image_name = images.image_name
WHERE images.is_intermediate = FALSE
AND board_images.board_id = ?;
""",
(board_id,),
)
count = cast(int, self._cursor.fetchone()[0])
return count
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
(board_id,),
)
count = cast(int, cursor.fetchone()[0])
return count

View File

@@ -1,5 +1,4 @@
import sqlite3
import threading
from typing import Union, cast
from invokeai.app.services.board_records.board_records_base import BoardRecordStorageBase
@@ -19,20 +18,14 @@ from invokeai.app.util.misc import uuid_string
class SqliteBoardRecordStorage(BoardRecordStorageBase):
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.RLock
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
def delete(self, board_id: str) -> None:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
DELETE FROM boards
WHERE board_id = ?;
@@ -40,14 +33,9 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
(board_id,),
)
self._conn.commit()
except sqlite3.Error as e:
self._conn.rollback()
raise BoardRecordDeleteException from e
except Exception as e:
self._conn.rollback()
raise BoardRecordDeleteException from e
finally:
self._lock.release()
def save(
self,
@@ -55,8 +43,8 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
) -> BoardRecord:
try:
board_id = uuid_string()
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
INSERT OR IGNORE INTO boards (board_id, board_name)
VALUES (?, ?);
@@ -67,8 +55,6 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
except sqlite3.Error as e:
self._conn.rollback()
raise BoardRecordSaveException from e
finally:
self._lock.release()
return self.get(board_id)
def get(
@@ -76,8 +62,8 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
board_id: str,
) -> BoardRecord:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT *
FROM boards
@@ -86,12 +72,9 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
(board_id,),
)
result = cast(Union[sqlite3.Row, None], self._cursor.fetchone())
result = cast(Union[sqlite3.Row, None], cursor.fetchone())
except sqlite3.Error as e:
self._conn.rollback()
raise BoardRecordNotFoundException from e
finally:
self._lock.release()
if result is None:
raise BoardRecordNotFoundException
return BoardRecord(**dict(result))
@@ -102,11 +85,10 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
changes: BoardChanges,
) -> BoardRecord:
try:
self._lock.acquire()
cursor = self._conn.cursor()
# Change the name of a board
if changes.board_name is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE boards
SET board_name = ?
@@ -117,7 +99,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
# Change the cover image of a board
if changes.cover_image_name is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE boards
SET cover_image_name = ?
@@ -128,7 +110,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
# Change the archived status of a board
if changes.archived is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE boards
SET archived = ?
@@ -141,8 +123,6 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
except sqlite3.Error as e:
self._conn.rollback()
raise BoardRecordSaveException from e
finally:
self._lock.release()
return self.get(board_id)
def get_many(
@@ -153,11 +133,10 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
limit: int = 10,
include_archived: bool = False,
) -> OffsetPaginatedResults[BoardRecord]:
try:
self._lock.acquire()
cursor = self._conn.cursor()
# Build base query
base_query = """
# Build base query
base_query = """
SELECT *
FROM boards
{archived_filter}
@@ -165,81 +144,67 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
LIMIT ? OFFSET ?;
"""
# Determine archived filter condition
archived_filter = "" if include_archived else "WHERE archived = 0"
# Determine archived filter condition
archived_filter = "" if include_archived else "WHERE archived = 0"
final_query = base_query.format(
archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
)
final_query = base_query.format(
archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
)
# Execute query to fetch boards
self._cursor.execute(final_query, (limit, offset))
# Execute query to fetch boards
cursor.execute(final_query, (limit, offset))
result = cast(list[sqlite3.Row], self._cursor.fetchall())
boards = [deserialize_board_record(dict(r)) for r in result]
result = cast(list[sqlite3.Row], cursor.fetchall())
boards = [deserialize_board_record(dict(r)) for r in result]
# Determine count query
if include_archived:
count_query = """
# Determine count query
if include_archived:
count_query = """
SELECT COUNT(*)
FROM boards;
"""
else:
count_query = """
else:
count_query = """
SELECT COUNT(*)
FROM boards
WHERE archived = 0;
"""
# Execute count query
self._cursor.execute(count_query)
# Execute count query
cursor.execute(count_query)
count = cast(int, self._cursor.fetchone()[0])
count = cast(int, cursor.fetchone()[0])
return OffsetPaginatedResults[BoardRecord](items=boards, offset=offset, limit=limit, total=count)
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
return OffsetPaginatedResults[BoardRecord](items=boards, offset=offset, limit=limit, total=count)
def get_all(
self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
) -> list[BoardRecord]:
try:
self._lock.acquire()
if order_by == BoardRecordOrderBy.Name:
base_query = """
cursor = self._conn.cursor()
if order_by == BoardRecordOrderBy.Name:
base_query = """
SELECT *
FROM boards
{archived_filter}
ORDER BY LOWER(board_name) {direction}
"""
else:
base_query = """
else:
base_query = """
SELECT *
FROM boards
{archived_filter}
ORDER BY {order_by} {direction}
"""
archived_filter = "" if include_archived else "WHERE archived = 0"
archived_filter = "" if include_archived else "WHERE archived = 0"
final_query = base_query.format(
archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
)
final_query = base_query.format(
archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
)
self._cursor.execute(final_query)
cursor.execute(final_query)
result = cast(list[sqlite3.Row], self._cursor.fetchall())
boards = [deserialize_board_record(dict(r)) for r in result]
result = cast(list[sqlite3.Row], cursor.fetchall())
boards = [deserialize_board_record(dict(r)) for r in result]
return boards
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
return boards

View File

@@ -91,6 +91,7 @@ class InvokeAIAppConfig(BaseSettings):
ram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_ram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
vram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
lazy_offload: DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.
pytorch_cuda_alloc_conf: Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to "backend:cudaMallocAsync" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.
device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
@@ -169,6 +170,9 @@ class InvokeAIAppConfig(BaseSettings):
vram: Optional[float] = Field(default=None, ge=0, description="DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.")
lazy_offload: bool = Field(default=True, description="DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.")
# PyTorch Memory Allocator
pytorch_cuda_alloc_conf: Optional[str] = Field(default=None, description="Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to \"backend:cudaMallocAsync\" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.")
# DEVICE
device: DEVICE = Field(default="auto", description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.")
precision: PRECISION = Field(default="auto", description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.")

View File

@@ -1,5 +1,4 @@
import sqlite3
import threading
from datetime import datetime
from typing import Optional, Union, cast
@@ -22,21 +21,14 @@ from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
class SqliteImageRecordStorage(ImageRecordStorageBase):
_conn: sqlite3.Connection
_cursor: sqlite3.Cursor
_lock: threading.RLock
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
def get(self, image_name: str) -> ImageRecord:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
f"""--sql
SELECT {IMAGE_DTO_COLS} FROM images
WHERE image_name = ?;
@@ -44,12 +36,9 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
(image_name,),
)
result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
result = cast(Optional[sqlite3.Row], cursor.fetchone())
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordNotFoundException from e
finally:
self._lock.release()
if not result:
raise ImageRecordNotFoundException
@@ -58,9 +47,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT metadata FROM images
WHERE image_name = ?;
@@ -68,7 +56,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
(image_name,),
)
result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
result = cast(Optional[sqlite3.Row], cursor.fetchone())
if not result:
raise ImageRecordNotFoundException
@@ -77,10 +65,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
metadata_raw = cast(Optional[str], as_dict.get("metadata", None))
return MetadataFieldValidator.validate_json(metadata_raw) if metadata_raw is not None else None
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordNotFoundException from e
finally:
self._lock.release()
def update(
self,
@@ -88,10 +73,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
changes: ImageRecordChanges,
) -> None:
try:
self._lock.acquire()
cursor = self._conn.cursor()
# Change the category of the image
if changes.image_category is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE images
SET image_category = ?
@@ -102,7 +87,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
# Change the session associated with the image
if changes.session_id is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE images
SET session_id = ?
@@ -113,7 +98,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
# Change the image's `is_intermediate`` flag
if changes.is_intermediate is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE images
SET is_intermediate = ?
@@ -124,7 +109,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
# Change the image's `starred`` state
if changes.starred is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE images
SET starred = ?
@@ -137,8 +122,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordSaveException from e
finally:
self._lock.release()
def get_many(
self,
@@ -152,110 +135,104 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
board_id: Optional[str] = None,
search_term: Optional[str] = None,
) -> OffsetPaginatedResults[ImageRecord]:
try:
self._lock.acquire()
cursor = self._conn.cursor()
# Manually build two queries - one for the count, one for the records
count_query = """--sql
SELECT COUNT(*)
FROM images
LEFT JOIN board_images ON board_images.image_name = images.image_name
WHERE 1=1
# Manually build two queries - one for the count, one for the records
count_query = """--sql
SELECT COUNT(*)
FROM images
LEFT JOIN board_images ON board_images.image_name = images.image_name
WHERE 1=1
"""
images_query = f"""--sql
SELECT {IMAGE_DTO_COLS}
FROM images
LEFT JOIN board_images ON board_images.image_name = images.image_name
WHERE 1=1
"""
query_conditions = ""
query_params: list[Union[int, str, bool]] = []
if image_origin is not None:
query_conditions += """--sql
AND images.image_origin = ?
"""
query_params.append(image_origin.value)
if categories is not None:
# Convert the enum values to unique list of strings
category_strings = [c.value for c in set(categories)]
# Create the correct length of placeholders
placeholders = ",".join("?" * len(category_strings))
query_conditions += f"""--sql
AND images.image_category IN ( {placeholders} )
"""
images_query = f"""--sql
SELECT {IMAGE_DTO_COLS}
FROM images
LEFT JOIN board_images ON board_images.image_name = images.image_name
WHERE 1=1
# Unpack the included categories into the query params
for c in category_strings:
query_params.append(c)
if is_intermediate is not None:
query_conditions += """--sql
AND images.is_intermediate = ?
"""
query_conditions = ""
query_params: list[Union[int, str, bool]] = []
query_params.append(is_intermediate)
if image_origin is not None:
query_conditions += """--sql
AND images.image_origin = ?
"""
query_params.append(image_origin.value)
# board_id of "none" is reserved for images without a board
if board_id == "none":
query_conditions += """--sql
AND board_images.board_id IS NULL
"""
elif board_id is not None:
query_conditions += """--sql
AND board_images.board_id = ?
"""
query_params.append(board_id)
if categories is not None:
# Convert the enum values to unique list of strings
category_strings = [c.value for c in set(categories)]
# Create the correct length of placeholders
placeholders = ",".join("?" * len(category_strings))
# Search term condition
if search_term:
query_conditions += """--sql
AND images.metadata LIKE ?
"""
query_params.append(f"%{search_term.lower()}%")
query_conditions += f"""--sql
AND images.image_category IN ( {placeholders} )
"""
if starred_first:
query_pagination = f"""--sql
ORDER BY images.starred DESC, images.created_at {order_dir.value} LIMIT ? OFFSET ?
"""
else:
query_pagination = f"""--sql
ORDER BY images.created_at {order_dir.value} LIMIT ? OFFSET ?
"""
# Unpack the included categories into the query params
for c in category_strings:
query_params.append(c)
# Final images query with pagination
images_query += query_conditions + query_pagination + ";"
# Add all the parameters
images_params = query_params.copy()
# Add the pagination parameters
images_params.extend([limit, offset])
if is_intermediate is not None:
query_conditions += """--sql
AND images.is_intermediate = ?
"""
# Build the list of images, deserializing each row
cursor.execute(images_query, images_params)
result = cast(list[sqlite3.Row], cursor.fetchall())
images = [deserialize_image_record(dict(r)) for r in result]
query_params.append(is_intermediate)
# board_id of "none" is reserved for images without a board
if board_id == "none":
query_conditions += """--sql
AND board_images.board_id IS NULL
"""
elif board_id is not None:
query_conditions += """--sql
AND board_images.board_id = ?
"""
query_params.append(board_id)
# Search term condition
if search_term:
query_conditions += """--sql
AND images.metadata LIKE ?
"""
query_params.append(f"%{search_term.lower()}%")
if starred_first:
query_pagination = f"""--sql
ORDER BY images.starred DESC, images.created_at {order_dir.value} LIMIT ? OFFSET ?
"""
else:
query_pagination = f"""--sql
ORDER BY images.created_at {order_dir.value} LIMIT ? OFFSET ?
"""
# Final images query with pagination
images_query += query_conditions + query_pagination + ";"
# Add all the parameters
images_params = query_params.copy()
# Add the pagination parameters
images_params.extend([limit, offset])
# Build the list of images, deserializing each row
self._cursor.execute(images_query, images_params)
result = cast(list[sqlite3.Row], self._cursor.fetchall())
images = [deserialize_image_record(dict(r)) for r in result]
# Set up and execute the count query, without pagination
count_query += query_conditions + ";"
count_params = query_params.copy()
self._cursor.execute(count_query, count_params)
count = cast(int, self._cursor.fetchone()[0])
except sqlite3.Error as e:
self._conn.rollback()
raise e
finally:
self._lock.release()
# Set up and execute the count query, without pagination
count_query += query_conditions + ";"
count_params = query_params.copy()
cursor.execute(count_query, count_params)
count = cast(int, cursor.fetchone()[0])
return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
def delete(self, image_name: str) -> None:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
DELETE FROM images
WHERE image_name = ?;
@@ -266,58 +243,48 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordDeleteException from e
finally:
self._lock.release()
def delete_many(self, image_names: list[str]) -> None:
try:
placeholders = ",".join("?" for _ in image_names)
cursor = self._conn.cursor()
self._lock.acquire()
placeholders = ",".join("?" for _ in image_names)
# Construct the SQLite query with the placeholders
query = f"DELETE FROM images WHERE image_name IN ({placeholders})"
# Execute the query with the list of IDs as parameters
self._cursor.execute(query, image_names)
cursor.execute(query, image_names)
self._conn.commit()
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordDeleteException from e
finally:
self._lock.release()
def get_intermediates_count(self) -> int:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT COUNT(*) FROM images
WHERE is_intermediate = TRUE;
"""
)
count = cast(int, self._cursor.fetchone()[0])
self._conn.commit()
return count
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordDeleteException from e
finally:
self._lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT COUNT(*) FROM images
WHERE is_intermediate = TRUE;
"""
)
count = cast(int, cursor.fetchone()[0])
self._conn.commit()
return count
def delete_intermediates(self) -> list[str]:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT image_name FROM images
WHERE is_intermediate = TRUE;
"""
)
result = cast(list[sqlite3.Row], self._cursor.fetchall())
result = cast(list[sqlite3.Row], cursor.fetchall())
image_names = [r[0] for r in result]
self._cursor.execute(
cursor.execute(
"""--sql
DELETE FROM images
WHERE is_intermediate = TRUE;
@@ -328,8 +295,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordDeleteException from e
finally:
self._lock.release()
def save(
self,
@@ -346,8 +311,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
metadata: Optional[str] = None,
) -> datetime:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
INSERT OR IGNORE INTO images (
image_name,
@@ -380,7 +345,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
)
self._conn.commit()
self._cursor.execute(
cursor.execute(
"""--sql
SELECT created_at
FROM images
@@ -389,34 +354,30 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
(image_name,),
)
created_at = datetime.fromisoformat(self._cursor.fetchone()[0])
created_at = datetime.fromisoformat(cursor.fetchone()[0])
return created_at
except sqlite3.Error as e:
self._conn.rollback()
raise ImageRecordSaveException from e
finally:
self._lock.release()
def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT images.*
FROM images
JOIN board_images ON images.image_name = board_images.image_name
WHERE board_images.board_id = ?
AND images.is_intermediate = FALSE
ORDER BY images.starred DESC, images.created_at DESC
LIMIT 1;
""",
(board_id,),
)
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT images.*
FROM images
JOIN board_images ON images.image_name = board_images.image_name
WHERE board_images.board_id = ?
AND images.is_intermediate = FALSE
ORDER BY images.starred DESC, images.created_at DESC
LIMIT 1;
""",
(board_id,),
)
result = cast(Optional[sqlite3.Row], cursor.fetchone())
result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
finally:
self._lock.release()
if result is None:
return None

View File

@@ -78,7 +78,6 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
"""
super().__init__()
self._db = db
self._cursor = db.conn.cursor()
self._logger = logger
@property
@@ -96,38 +95,38 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
Can raise DuplicateModelException and InvalidModelConfigException exceptions.
"""
with self._db.lock:
try:
self._cursor.execute(
"""--sql
INSERT INTO models (
id,
config
)
VALUES (?,?);
""",
(
config.key,
config.model_dump_json(),
),
)
self._db.conn.commit()
try:
cursor = self._db.conn.cursor()
cursor.execute(
"""--sql
INSERT INTO models (
id,
config
)
VALUES (?,?);
""",
(
config.key,
config.model_dump_json(),
),
)
self._db.conn.commit()
except sqlite3.IntegrityError as e:
self._db.conn.rollback()
if "UNIQUE constraint failed" in str(e):
if "models.path" in str(e):
msg = f"A model with path '{config.path}' is already installed"
elif "models.name" in str(e):
msg = f"A model with name='{config.name}', type='{config.type}', base='{config.base}' is already installed"
else:
msg = f"A model with key '{config.key}' is already installed"
raise DuplicateModelException(msg) from e
except sqlite3.IntegrityError as e:
self._db.conn.rollback()
if "UNIQUE constraint failed" in str(e):
if "models.path" in str(e):
msg = f"A model with path '{config.path}' is already installed"
elif "models.name" in str(e):
msg = f"A model with name='{config.name}', type='{config.type}', base='{config.base}' is already installed"
else:
raise e
except sqlite3.Error as e:
self._db.conn.rollback()
msg = f"A model with key '{config.key}' is already installed"
raise DuplicateModelException(msg) from e
else:
raise e
except sqlite3.Error as e:
self._db.conn.rollback()
raise e
return self.get_model(config.key)
@@ -139,21 +138,21 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
Can raise an UnknownModelException
"""
with self._db.lock:
try:
self._cursor.execute(
"""--sql
DELETE FROM models
WHERE id=?;
""",
(key,),
)
if self._cursor.rowcount == 0:
raise UnknownModelException("model not found")
self._db.conn.commit()
except sqlite3.Error as e:
self._db.conn.rollback()
raise e
try:
cursor = self._db.conn.cursor()
cursor.execute(
"""--sql
DELETE FROM models
WHERE id=?;
""",
(key,),
)
if cursor.rowcount == 0:
raise UnknownModelException("model not found")
self._db.conn.commit()
except sqlite3.Error as e:
self._db.conn.rollback()
raise e
def update_model(self, key: str, changes: ModelRecordChanges) -> AnyModelConfig:
record = self.get_model(key)
@@ -164,23 +163,23 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
json_serialized = record.model_dump_json()
with self._db.lock:
try:
self._cursor.execute(
"""--sql
UPDATE models
SET
config=?
WHERE id=?;
""",
(json_serialized, key),
)
if self._cursor.rowcount == 0:
raise UnknownModelException("model not found")
self._db.conn.commit()
except sqlite3.Error as e:
self._db.conn.rollback()
raise e
try:
cursor = self._db.conn.cursor()
cursor.execute(
"""--sql
UPDATE models
SET
config=?
WHERE id=?;
""",
(json_serialized, key),
)
if cursor.rowcount == 0:
raise UnknownModelException("model not found")
self._db.conn.commit()
except sqlite3.Error as e:
self._db.conn.rollback()
raise e
return self.get_model(key)
@@ -192,33 +191,33 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
Exceptions: UnknownModelException
"""
with self._db.lock:
self._cursor.execute(
"""--sql
SELECT config, strftime('%s',updated_at) FROM models
WHERE id=?;
""",
(key,),
)
rows = self._cursor.fetchone()
if not rows:
raise UnknownModelException("model not found")
model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
cursor = self._db.conn.cursor()
cursor.execute(
"""--sql
SELECT config, strftime('%s',updated_at) FROM models
WHERE id=?;
""",
(key,),
)
rows = cursor.fetchone()
if not rows:
raise UnknownModelException("model not found")
model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
return model
def get_model_by_hash(self, hash: str) -> AnyModelConfig:
with self._db.lock:
self._cursor.execute(
"""--sql
SELECT config, strftime('%s',updated_at) FROM models
WHERE hash=?;
""",
(hash,),
)
rows = self._cursor.fetchone()
if not rows:
raise UnknownModelException("model not found")
model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
cursor = self._db.conn.cursor()
cursor.execute(
"""--sql
SELECT config, strftime('%s',updated_at) FROM models
WHERE hash=?;
""",
(hash,),
)
rows = cursor.fetchone()
if not rows:
raise UnknownModelException("model not found")
model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
return model
def exists(self, key: str) -> bool:
@@ -227,16 +226,15 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
:param key: Unique key for the model to be deleted
"""
count = 0
with self._db.lock:
self._cursor.execute(
"""--sql
select count(*) FROM models
WHERE id=?;
""",
(key,),
)
count = self._cursor.fetchone()[0]
cursor = self._db.conn.cursor()
cursor.execute(
"""--sql
select count(*) FROM models
WHERE id=?;
""",
(key,),
)
count = cursor.fetchone()[0]
return count > 0
def search_by_attr(
@@ -284,17 +282,18 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
where_clause.append("format=?")
bindings.append(model_format)
where = f"WHERE {' AND '.join(where_clause)}" if where_clause else ""
with self._db.lock:
self._cursor.execute(
f"""--sql
SELECT config, strftime('%s',updated_at)
FROM models
{where}
ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason;
""",
tuple(bindings),
)
result = self._cursor.fetchall()
cursor = self._db.conn.cursor()
cursor.execute(
f"""--sql
SELECT config, strftime('%s',updated_at)
FROM models
{where}
ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason;
""",
tuple(bindings),
)
result = cursor.fetchall()
# Parse the model configs.
results: list[AnyModelConfig] = []
@@ -313,34 +312,28 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
def search_by_path(self, path: Union[str, Path]) -> List[AnyModelConfig]:
"""Return models with the indicated path."""
results = []
with self._db.lock:
self._cursor.execute(
"""--sql
SELECT config, strftime('%s',updated_at) FROM models
WHERE path=?;
""",
(str(path),),
)
results = [
ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in self._cursor.fetchall()
]
cursor = self._db.conn.cursor()
cursor.execute(
"""--sql
SELECT config, strftime('%s',updated_at) FROM models
WHERE path=?;
""",
(str(path),),
)
results = [ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in cursor.fetchall()]
return results
def search_by_hash(self, hash: str) -> List[AnyModelConfig]:
"""Return models with the indicated hash."""
results = []
with self._db.lock:
self._cursor.execute(
"""--sql
SELECT config, strftime('%s',updated_at) FROM models
WHERE hash=?;
""",
(hash,),
)
results = [
ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in self._cursor.fetchall()
]
cursor = self._db.conn.cursor()
cursor.execute(
"""--sql
SELECT config, strftime('%s',updated_at) FROM models
WHERE hash=?;
""",
(hash,),
)
results = [ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in cursor.fetchall()]
return results
def list_models(
@@ -356,33 +349,32 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
ModelRecordOrderBy.Format: "format",
}
# Lock so that the database isn't updated while we're doing the two queries.
with self._db.lock:
# query1: get the total number of model configs
self._cursor.execute(
"""--sql
select count(*) from models;
""",
(),
)
total = int(self._cursor.fetchone()[0])
cursor = self._db.conn.cursor()
# query2: fetch key fields
self._cursor.execute(
f"""--sql
SELECT config
FROM models
ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason
LIMIT ?
OFFSET ?;
""",
(
per_page,
page * per_page,
),
)
rows = self._cursor.fetchall()
items = [ModelSummary.model_validate(dict(x)) for x in rows]
return PaginatedResults(
page=page, pages=ceil(total / per_page), per_page=per_page, total=total, items=items
)
# Lock so that the database isn't updated while we're doing the two queries.
# query1: get the total number of model configs
cursor.execute(
"""--sql
select count(*) from models;
""",
(),
)
total = int(cursor.fetchone()[0])
# query2: fetch key fields
cursor.execute(
f"""--sql
SELECT config
FROM models
ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason
LIMIT ?
OFFSET ?;
""",
(
per_page,
page * per_page,
),
)
rows = cursor.fetchall()
items = [ModelSummary.model_validate(dict(x)) for x in rows]
return PaginatedResults(page=page, pages=ceil(total / per_page), per_page=per_page, total=total, items=items)

View File

@@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
from typing import Optional
from typing import Any, Coroutine, Optional
from invokeai.app.services.session_queue.session_queue_common import (
QUEUE_ITEM_STATUS,
@@ -33,7 +33,7 @@ class SessionQueueBase(ABC):
pass
@abstractmethod
def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> Coroutine[Any, Any, EnqueueBatchResult]:
"""Enqueues all permutations of a batch for execution."""
pass

View File

@@ -1,6 +1,6 @@
import asyncio
import json
import sqlite3
import threading
from typing import Optional, Union, cast
from pydantic_core import to_jsonable_python
@@ -37,9 +37,6 @@ from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
class SqliteSessionQueue(SessionQueueBase):
__invoker: Invoker
__conn: sqlite3.Connection
__cursor: sqlite3.Cursor
__lock: threading.RLock
def start(self, invoker: Invoker) -> None:
self.__invoker = invoker
@@ -55,9 +52,7 @@ class SqliteSessionQueue(SessionQueueBase):
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self.__lock = db.lock
self.__conn = db.conn
self.__cursor = self.__conn.cursor()
self._conn = db.conn
def _set_in_progress_to_canceled(self) -> None:
"""
@@ -65,8 +60,8 @@ class SqliteSessionQueue(SessionQueueBase):
This is necessary because the invoker may have been killed while processing a queue item.
"""
try:
self.__lock.acquire()
self.__cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
UPDATE session_queue
SET status = 'canceled'
@@ -74,14 +69,13 @@ class SqliteSessionQueue(SessionQueueBase):
"""
)
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
def _get_current_queue_size(self, queue_id: str) -> int:
"""Gets the current number of pending queue items"""
self.__cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT count(*)
FROM session_queue
@@ -91,11 +85,12 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(queue_id,),
)
return cast(int, self.__cursor.fetchone()[0])
return cast(int, cursor.fetchone()[0])
def _get_highest_priority(self, queue_id: str) -> int:
"""Gets the highest priority value in the queue"""
self.__cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT MAX(priority)
FROM session_queue
@@ -105,12 +100,14 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(queue_id,),
)
return cast(Union[int, None], self.__cursor.fetchone()[0]) or 0
return cast(Union[int, None], cursor.fetchone()[0]) or 0
def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
async def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
return await asyncio.to_thread(self._enqueue_batch, queue_id, batch, prepend)
def _enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
try:
self.__lock.acquire()
cursor = self._conn.cursor()
# TODO: how does this work in a multi-user scenario?
current_queue_size = self._get_current_queue_size(queue_id)
max_queue_size = self.__invoker.services.configuration.max_queue_size
@@ -132,19 +129,17 @@ class SqliteSessionQueue(SessionQueueBase):
if requested_count > enqueued_count:
values_to_insert = values_to_insert[:max_new_queue_items]
self.__cursor.executemany(
cursor.executemany(
"""--sql
INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination, retried_from_item_id)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
values_to_insert,
)
self.__conn.commit()
self._conn.commit()
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
enqueue_result = EnqueueBatchResult(
queue_id=queue_id,
requested=requested_count,
@@ -156,25 +151,19 @@ class SqliteSessionQueue(SessionQueueBase):
return enqueue_result
def dequeue(self) -> Optional[SessionQueueItem]:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT *
FROM session_queue
WHERE status = 'pending'
ORDER BY
priority DESC,
item_id ASC
LIMIT 1
"""
)
result = cast(Union[sqlite3.Row, None], self.__cursor.fetchone())
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT *
FROM session_queue
WHERE status = 'pending'
ORDER BY
priority DESC,
item_id ASC
LIMIT 1
"""
)
result = cast(Union[sqlite3.Row, None], cursor.fetchone())
if result is None:
return None
queue_item = SessionQueueItem.queue_item_from_dict(dict(result))
@@ -182,52 +171,40 @@ class SqliteSessionQueue(SessionQueueBase):
return queue_item
def get_next(self, queue_id: str) -> Optional[SessionQueueItem]:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT *
FROM session_queue
WHERE
queue_id = ?
AND status = 'pending'
ORDER BY
priority DESC,
created_at ASC
LIMIT 1
""",
(queue_id,),
)
result = cast(Union[sqlite3.Row, None], self.__cursor.fetchone())
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT *
FROM session_queue
WHERE
queue_id = ?
AND status = 'pending'
ORDER BY
priority DESC,
created_at ASC
LIMIT 1
""",
(queue_id,),
)
result = cast(Union[sqlite3.Row, None], cursor.fetchone())
if result is None:
return None
return SessionQueueItem.queue_item_from_dict(dict(result))
def get_current(self, queue_id: str) -> Optional[SessionQueueItem]:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT *
FROM session_queue
WHERE
queue_id = ?
AND status = 'in_progress'
LIMIT 1
""",
(queue_id,),
)
result = cast(Union[sqlite3.Row, None], self.__cursor.fetchone())
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT *
FROM session_queue
WHERE
queue_id = ?
AND status = 'in_progress'
LIMIT 1
""",
(queue_id,),
)
result = cast(Union[sqlite3.Row, None], cursor.fetchone())
if result is None:
return None
return SessionQueueItem.queue_item_from_dict(dict(result))
@@ -241,8 +218,8 @@ class SqliteSessionQueue(SessionQueueBase):
error_traceback: Optional[str] = None,
) -> SessionQueueItem:
try:
self.__lock.acquire()
self.__cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
UPDATE session_queue
SET status = ?, error_type = ?, error_message = ?, error_traceback = ?
@@ -250,12 +227,10 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(status, error_type, error_message, error_traceback, item_id),
)
self.__conn.commit()
self._conn.commit()
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
queue_item = self.get_queue_item(item_id)
batch_status = self.get_batch_status(queue_id=queue_item.queue_id, batch_id=queue_item.batch_id)
queue_status = self.get_queue_status(queue_id=queue_item.queue_id)
@@ -263,48 +238,36 @@ class SqliteSessionQueue(SessionQueueBase):
return queue_item
def is_empty(self, queue_id: str) -> IsEmptyResult:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT count(*)
FROM session_queue
WHERE queue_id = ?
""",
(queue_id,),
)
is_empty = cast(int, self.__cursor.fetchone()[0]) == 0
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT count(*)
FROM session_queue
WHERE queue_id = ?
""",
(queue_id,),
)
is_empty = cast(int, cursor.fetchone()[0]) == 0
return IsEmptyResult(is_empty=is_empty)
def is_full(self, queue_id: str) -> IsFullResult:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT count(*)
FROM session_queue
WHERE queue_id = ?
""",
(queue_id,),
)
max_queue_size = self.__invoker.services.configuration.max_queue_size
is_full = cast(int, self.__cursor.fetchone()[0]) >= max_queue_size
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT count(*)
FROM session_queue
WHERE queue_id = ?
""",
(queue_id,),
)
max_queue_size = self.__invoker.services.configuration.max_queue_size
is_full = cast(int, cursor.fetchone()[0]) >= max_queue_size
return IsFullResult(is_full=is_full)
def clear(self, queue_id: str) -> ClearResult:
try:
self.__lock.acquire()
self.__cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT COUNT(*)
FROM session_queue
@@ -312,8 +275,8 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(queue_id,),
)
count = self.__cursor.fetchone()[0]
self.__cursor.execute(
count = cursor.fetchone()[0]
cursor.execute(
"""--sql
DELETE
FROM session_queue
@@ -321,17 +284,16 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(queue_id,),
)
self.__conn.commit()
self._conn.commit()
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
self.__invoker.services.events.emit_queue_cleared(queue_id)
return ClearResult(deleted=count)
def prune(self, queue_id: str) -> PruneResult:
try:
cursor = self._conn.cursor()
where = """--sql
WHERE
queue_id = ?
@@ -341,8 +303,7 @@ class SqliteSessionQueue(SessionQueueBase):
OR status = 'canceled'
)
"""
self.__lock.acquire()
self.__cursor.execute(
cursor.execute(
f"""--sql
SELECT COUNT(*)
FROM session_queue
@@ -350,8 +311,8 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(queue_id,),
)
count = self.__cursor.fetchone()[0]
self.__cursor.execute(
count = cursor.fetchone()[0]
cursor.execute(
f"""--sql
DELETE
FROM session_queue
@@ -359,12 +320,10 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(queue_id,),
)
self.__conn.commit()
self._conn.commit()
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
return PruneResult(deleted=count)
def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
@@ -393,8 +352,8 @@ class SqliteSessionQueue(SessionQueueBase):
def cancel_by_batch_ids(self, queue_id: str, batch_ids: list[str]) -> CancelByBatchIDsResult:
try:
cursor = self._conn.cursor()
current_queue_item = self.get_current(queue_id)
self.__lock.acquire()
placeholders = ", ".join(["?" for _ in batch_ids])
where = f"""--sql
WHERE
@@ -405,7 +364,7 @@ class SqliteSessionQueue(SessionQueueBase):
AND status != 'failed'
"""
params = [queue_id] + batch_ids
self.__cursor.execute(
cursor.execute(
f"""--sql
SELECT COUNT(*)
FROM session_queue
@@ -413,8 +372,8 @@ class SqliteSessionQueue(SessionQueueBase):
""",
tuple(params),
)
count = self.__cursor.fetchone()[0]
self.__cursor.execute(
count = cursor.fetchone()[0]
cursor.execute(
f"""--sql
UPDATE session_queue
SET status = 'canceled'
@@ -422,20 +381,18 @@ class SqliteSessionQueue(SessionQueueBase):
""",
tuple(params),
)
self.__conn.commit()
self._conn.commit()
if current_queue_item is not None and current_queue_item.batch_id in batch_ids:
self._set_queue_item_status(current_queue_item.item_id, "canceled")
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
return CancelByBatchIDsResult(canceled=count)
def cancel_by_destination(self, queue_id: str, destination: str) -> CancelByDestinationResult:
try:
cursor = self._conn.cursor()
current_queue_item = self.get_current(queue_id)
self.__lock.acquire()
where = """--sql
WHERE
queue_id == ?
@@ -445,7 +402,7 @@ class SqliteSessionQueue(SessionQueueBase):
AND status != 'failed'
"""
params = (queue_id, destination)
self.__cursor.execute(
cursor.execute(
f"""--sql
SELECT COUNT(*)
FROM session_queue
@@ -453,8 +410,8 @@ class SqliteSessionQueue(SessionQueueBase):
""",
params,
)
count = self.__cursor.fetchone()[0]
self.__cursor.execute(
count = cursor.fetchone()[0]
cursor.execute(
f"""--sql
UPDATE session_queue
SET status = 'canceled'
@@ -462,20 +419,18 @@ class SqliteSessionQueue(SessionQueueBase):
""",
params,
)
self.__conn.commit()
self._conn.commit()
if current_queue_item is not None and current_queue_item.destination == destination:
self._set_queue_item_status(current_queue_item.item_id, "canceled")
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
return CancelByDestinationResult(canceled=count)
def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
try:
cursor = self._conn.cursor()
current_queue_item = self.get_current(queue_id)
self.__lock.acquire()
where = """--sql
WHERE
queue_id is ?
@@ -484,7 +439,7 @@ class SqliteSessionQueue(SessionQueueBase):
AND status != 'failed'
"""
params = [queue_id]
self.__cursor.execute(
cursor.execute(
f"""--sql
SELECT COUNT(*)
FROM session_queue
@@ -492,8 +447,8 @@ class SqliteSessionQueue(SessionQueueBase):
""",
tuple(params),
)
count = self.__cursor.fetchone()[0]
self.__cursor.execute(
count = cursor.fetchone()[0]
cursor.execute(
f"""--sql
UPDATE session_queue
SET status = 'canceled'
@@ -501,7 +456,7 @@ class SqliteSessionQueue(SessionQueueBase):
""",
tuple(params),
)
self.__conn.commit()
self._conn.commit()
if current_queue_item is not None and current_queue_item.queue_id == queue_id:
batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
queue_status = self.get_queue_status(queue_id=queue_id)
@@ -509,21 +464,19 @@ class SqliteSessionQueue(SessionQueueBase):
current_queue_item, batch_status, queue_status
)
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
return CancelByQueueIDResult(canceled=count)
def cancel_all_except_current(self, queue_id: str) -> CancelAllExceptCurrentResult:
try:
cursor = self._conn.cursor()
where = """--sql
WHERE
queue_id == ?
AND status == 'pending'
"""
self.__lock.acquire()
self.__cursor.execute(
cursor.execute(
f"""--sql
SELECT COUNT(*)
FROM session_queue
@@ -531,8 +484,8 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(queue_id,),
)
count = self.__cursor.fetchone()[0]
self.__cursor.execute(
count = cursor.fetchone()[0]
cursor.execute(
f"""--sql
UPDATE session_queue
SET status = 'canceled'
@@ -540,43 +493,35 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(queue_id,),
)
self.__conn.commit()
self._conn.commit()
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
return CancelAllExceptCurrentResult(canceled=count)
def get_queue_item(self, item_id: int) -> SessionQueueItem:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT * FROM session_queue
WHERE
item_id = ?
""",
(item_id,),
)
result = cast(Union[sqlite3.Row, None], self.__cursor.fetchone())
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT * FROM session_queue
WHERE
item_id = ?
""",
(item_id,),
)
result = cast(Union[sqlite3.Row, None], cursor.fetchone())
if result is None:
raise SessionQueueItemNotFoundError(f"No queue item with id {item_id}")
return SessionQueueItem.queue_item_from_dict(dict(result))
def set_queue_item_session(self, item_id: int, session: GraphExecutionState) -> SessionQueueItem:
try:
cursor = self._conn.cursor()
# Use exclude_none so we don't end up with a bunch of nulls in the graph - this can cause validation errors
# when the graph is loaded. Graph execution occurs purely in memory - the session saved here is not referenced
# during execution.
session_json = session.model_dump_json(warnings=False, exclude_none=True)
self.__lock.acquire()
self.__cursor.execute(
cursor.execute(
"""--sql
UPDATE session_queue
SET session = ?
@@ -584,12 +529,10 @@ class SqliteSessionQueue(SessionQueueBase):
""",
(session_json, item_id),
)
self.__conn.commit()
self._conn.commit()
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
return self.get_queue_item(item_id)
def list_queue_items(
@@ -600,83 +543,71 @@ class SqliteSessionQueue(SessionQueueBase):
cursor: Optional[int] = None,
status: Optional[QUEUE_ITEM_STATUS] = None,
) -> CursorPaginatedResults[SessionQueueItemDTO]:
try:
item_id = cursor
self.__lock.acquire()
query = """--sql
SELECT item_id,
status,
priority,
field_values,
error_type,
error_message,
error_traceback,
created_at,
updated_at,
completed_at,
started_at,
session_id,
batch_id,
queue_id,
origin,
destination
FROM session_queue
WHERE queue_id = ?
"""
params: list[Union[str, int]] = [queue_id]
if status is not None:
query += """--sql
AND status = ?
"""
params.append(status)
if item_id is not None:
query += """--sql
AND (priority < ?) OR (priority = ? AND item_id > ?)
"""
params.extend([priority, priority, item_id])
cursor_ = self._conn.cursor()
item_id = cursor
query = """--sql
SELECT item_id,
status,
priority,
field_values,
error_type,
error_message,
error_traceback,
created_at,
updated_at,
completed_at,
started_at,
session_id,
batch_id,
queue_id,
origin,
destination
FROM session_queue
WHERE queue_id = ?
"""
params: list[Union[str, int]] = [queue_id]
if status is not None:
query += """--sql
ORDER BY
priority DESC,
item_id ASC
LIMIT ?
AND status = ?
"""
params.append(limit + 1)
self.__cursor.execute(query, params)
results = cast(list[sqlite3.Row], self.__cursor.fetchall())
items = [SessionQueueItemDTO.queue_item_dto_from_dict(dict(result)) for result in results]
has_more = False
if len(items) > limit:
# remove the extra item
items.pop()
has_more = True
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
params.append(status)
if item_id is not None:
query += """--sql
AND (priority < ?) OR (priority = ? AND item_id > ?)
"""
params.extend([priority, priority, item_id])
query += """--sql
ORDER BY
priority DESC,
item_id ASC
LIMIT ?
"""
params.append(limit + 1)
cursor_.execute(query, params)
results = cast(list[sqlite3.Row], cursor_.fetchall())
items = [SessionQueueItemDTO.queue_item_dto_from_dict(dict(result)) for result in results]
has_more = False
if len(items) > limit:
# remove the extra item
items.pop()
has_more = True
return CursorPaginatedResults(items=items, limit=limit, has_more=has_more)
def get_queue_status(self, queue_id: str) -> SessionQueueStatus:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT status, count(*)
FROM session_queue
WHERE queue_id = ?
GROUP BY status
""",
(queue_id,),
)
counts_result = cast(list[sqlite3.Row], self.__cursor.fetchall())
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT status, count(*)
FROM session_queue
WHERE queue_id = ?
GROUP BY status
""",
(queue_id,),
)
counts_result = cast(list[sqlite3.Row], cursor.fetchall())
current_item = self.get_current(queue_id=queue_id)
total = sum(row[1] for row in counts_result)
@@ -695,29 +626,23 @@ class SqliteSessionQueue(SessionQueueBase):
)
def get_batch_status(self, queue_id: str, batch_id: str) -> BatchStatus:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT status, count(*), origin, destination
FROM session_queue
WHERE
queue_id = ?
AND batch_id = ?
GROUP BY status
""",
(queue_id, batch_id),
)
result = cast(list[sqlite3.Row], self.__cursor.fetchall())
total = sum(row[1] for row in result)
counts: dict[str, int] = {row[0]: row[1] for row in result}
origin = result[0]["origin"] if result else None
destination = result[0]["destination"] if result else None
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT status, count(*), origin, destination
FROM session_queue
WHERE
queue_id = ?
AND batch_id = ?
GROUP BY status
""",
(queue_id, batch_id),
)
result = cast(list[sqlite3.Row], cursor.fetchall())
total = sum(row[1] for row in result)
counts: dict[str, int] = {row[0]: row[1] for row in result}
origin = result[0]["origin"] if result else None
destination = result[0]["destination"] if result else None
return BatchStatus(
batch_id=batch_id,
@@ -733,24 +658,18 @@ class SqliteSessionQueue(SessionQueueBase):
)
def get_counts_by_destination(self, queue_id: str, destination: str) -> SessionQueueCountsByDestination:
try:
self.__lock.acquire()
self.__cursor.execute(
"""--sql
SELECT status, count(*)
FROM session_queue
WHERE queue_id = ?
AND destination = ?
GROUP BY status
""",
(queue_id, destination),
)
counts_result = cast(list[sqlite3.Row], self.__cursor.fetchall())
except Exception:
self.__conn.rollback()
raise
finally:
self.__lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT status, count(*)
FROM session_queue
WHERE queue_id = ?
AND destination = ?
GROUP BY status
""",
(queue_id, destination),
)
counts_result = cast(list[sqlite3.Row], cursor.fetchall())
total = sum(row[1] for row in counts_result)
counts: dict[str, int] = {row[0]: row[1] for row in counts_result}
@@ -769,8 +688,7 @@ class SqliteSessionQueue(SessionQueueBase):
def retry_items_by_id(self, queue_id: str, item_ids: list[int]) -> RetryItemsResult:
"""Retries the given queue items"""
try:
self.__lock.acquire()
cursor = self._conn.cursor()
values_to_insert: list[tuple] = []
retried_item_ids: list[int] = []
@@ -813,7 +731,7 @@ class SqliteSessionQueue(SessionQueueBase):
# TODO(psyche): Handle max queue size?
self.__cursor.executemany(
cursor.executemany(
"""--sql
INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination, retried_from_item_id)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
@@ -821,12 +739,10 @@ class SqliteSessionQueue(SessionQueueBase):
values_to_insert,
)
self.__conn.commit()
self._conn.commit()
except Exception:
self.__conn.rollback()
self._conn.rollback()
raise
finally:
self.__lock.release()
retry_result = RetryItemsResult(
queue_id=queue_id,
retried_item_ids=retried_item_ids,

View File

@@ -1,5 +1,4 @@
import sqlite3
import threading
from logging import Logger
from pathlib import Path
@@ -38,14 +37,20 @@ class SqliteDatabase:
self.logger.info(f"Initializing database at {self.db_path}")
self.conn = sqlite3.connect(database=self.db_path or sqlite_memory, check_same_thread=False)
self.lock = threading.RLock()
self.conn.row_factory = sqlite3.Row
if self.verbose:
self.conn.set_trace_callback(self.logger.debug)
# Enable foreign key constraints
self.conn.execute("PRAGMA foreign_keys = ON;")
# Enable Write-Ahead Logging (WAL) mode for better concurrency
self.conn.execute("PRAGMA journal_mode = WAL;")
# Set a busy timeout to prevent database lockups during writes
self.conn.execute("PRAGMA busy_timeout = 5000;") # 5 seconds
def clean(self) -> None:
"""
Cleans the database by running the VACUUM command, reporting on the freed space.
@@ -53,15 +58,14 @@ class SqliteDatabase:
# No need to clean in-memory database
if not self.db_path:
return
with self.lock:
try:
initial_db_size = Path(self.db_path).stat().st_size
self.conn.execute("VACUUM;")
self.conn.commit()
final_db_size = Path(self.db_path).stat().st_size
freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
if freed_space_in_mb > 0:
self.logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
except Exception as e:
self.logger.error(f"Error cleaning database: {e}")
raise
try:
initial_db_size = Path(self.db_path).stat().st_size
self.conn.execute("VACUUM;")
self.conn.commit()
final_db_size = Path(self.db_path).stat().st_size
freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
if freed_space_in_mb > 0:
self.logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
except Exception as e:
self.logger.error(f"Error cleaning database: {e}")
raise

View File

@@ -43,46 +43,45 @@ class SqliteMigrator:
def run_migrations(self) -> bool:
"""Migrates the database to the latest version."""
with self._db.lock:
# This throws if there is a problem.
self._migration_set.validate_migration_chain()
cursor = self._db.conn.cursor()
self._create_migrations_table(cursor=cursor)
# This throws if there is a problem.
self._migration_set.validate_migration_chain()
cursor = self._db.conn.cursor()
self._create_migrations_table(cursor=cursor)
if self._migration_set.count == 0:
self._logger.debug("No migrations registered")
return False
if self._migration_set.count == 0:
self._logger.debug("No migrations registered")
return False
if self._get_current_version(cursor=cursor) == self._migration_set.latest_version:
self._logger.debug("Database is up to date, no migrations to run")
return False
if self._get_current_version(cursor=cursor) == self._migration_set.latest_version:
self._logger.debug("Database is up to date, no migrations to run")
return False
self._logger.info("Database update needed")
self._logger.info("Database update needed")
# Make a backup of the db if it needs to be updated and is a file db
if self._db.db_path is not None:
timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
self._backup_path = self._db.db_path.parent / f"{self._db.db_path.stem}_backup_{timestamp}.db"
self._logger.info(f"Backing up database to {str(self._backup_path)}")
# Use SQLite to do the backup
with closing(sqlite3.connect(self._backup_path)) as backup_conn:
self._db.conn.backup(backup_conn)
else:
self._logger.info("Using in-memory database, no backup needed")
# Make a backup of the db if it needs to be updated and is a file db
if self._db.db_path is not None:
timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
self._backup_path = self._db.db_path.parent / f"{self._db.db_path.stem}_backup_{timestamp}.db"
self._logger.info(f"Backing up database to {str(self._backup_path)}")
# Use SQLite to do the backup
with closing(sqlite3.connect(self._backup_path)) as backup_conn:
self._db.conn.backup(backup_conn)
else:
self._logger.info("Using in-memory database, no backup needed")
next_migration = self._migration_set.get(from_version=self._get_current_version(cursor))
while next_migration is not None:
self._run_migration(next_migration)
next_migration = self._migration_set.get(self._get_current_version(cursor))
self._logger.info("Database updated successfully")
return True
next_migration = self._migration_set.get(from_version=self._get_current_version(cursor))
while next_migration is not None:
self._run_migration(next_migration)
next_migration = self._migration_set.get(self._get_current_version(cursor))
self._logger.info("Database updated successfully")
return True
def _run_migration(self, migration: Migration) -> None:
"""Runs a single migration."""
try:
# Using sqlite3.Connection as a context manager commits a the transaction on exit, or rolls it back if an
# exception is raised.
with self._db.lock, self._db.conn as conn:
with self._db.conn as conn:
cursor = conn.cursor()
if self._get_current_version(cursor) != migration.from_version:
raise MigrationError(
@@ -108,27 +107,26 @@ class SqliteMigrator:
def _create_migrations_table(self, cursor: sqlite3.Cursor) -> None:
"""Creates the migrations table for the database, if one does not already exist."""
with self._db.lock:
try:
cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='migrations';")
if cursor.fetchone() is not None:
return
cursor.execute(
"""--sql
CREATE TABLE migrations (
version INTEGER PRIMARY KEY,
migrated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW'))
);
"""
)
cursor.execute("INSERT INTO migrations (version) VALUES (0);")
cursor.connection.commit()
self._logger.debug("Created migrations table")
except sqlite3.Error as e:
msg = f"Problem creating migrations table: {e}"
self._logger.error(msg)
cursor.connection.rollback()
raise MigrationError(msg) from e
try:
cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='migrations';")
if cursor.fetchone() is not None:
return
cursor.execute(
"""--sql
CREATE TABLE migrations (
version INTEGER PRIMARY KEY,
migrated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW'))
);
"""
)
cursor.execute("INSERT INTO migrations (version) VALUES (0);")
cursor.connection.commit()
self._logger.debug("Created migrations table")
except sqlite3.Error as e:
msg = f"Problem creating migrations table: {e}"
self._logger.error(msg)
cursor.connection.rollback()
raise MigrationError(msg) from e
@classmethod
def _get_current_version(cls, cursor: sqlite3.Cursor) -> int:

View File

@@ -17,9 +17,7 @@ from invokeai.app.util.misc import uuid_string
class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
@@ -27,31 +25,25 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
def get(self, style_preset_id: str) -> StylePresetRecordDTO:
"""Gets a style preset by ID."""
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
SELECT *
FROM style_presets
WHERE id = ?;
""",
(style_preset_id,),
)
row = self._cursor.fetchone()
if row is None:
raise StylePresetNotFoundError(f"Style preset with id {style_preset_id} not found")
return StylePresetRecordDTO.from_dict(dict(row))
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
SELECT *
FROM style_presets
WHERE id = ?;
""",
(style_preset_id,),
)
row = cursor.fetchone()
if row is None:
raise StylePresetNotFoundError(f"Style preset with id {style_preset_id} not found")
return StylePresetRecordDTO.from_dict(dict(row))
def create(self, style_preset: StylePresetWithoutId) -> StylePresetRecordDTO:
style_preset_id = uuid_string()
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
INSERT OR IGNORE INTO style_presets (
id,
@@ -72,18 +64,16 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return self.get(style_preset_id)
def create_many(self, style_presets: list[StylePresetWithoutId]) -> None:
style_preset_ids = []
try:
self._lock.acquire()
cursor = self._conn.cursor()
for style_preset in style_presets:
style_preset_id = uuid_string()
style_preset_ids.append(style_preset_id)
self._cursor.execute(
cursor.execute(
"""--sql
INSERT OR IGNORE INTO style_presets (
id,
@@ -104,17 +94,15 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return None
def update(self, style_preset_id: str, changes: StylePresetChanges) -> StylePresetRecordDTO:
try:
self._lock.acquire()
cursor = self._conn.cursor()
# Change the name of a style preset
if changes.name is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE style_presets
SET name = ?
@@ -125,7 +113,7 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
# Change the preset data for a style preset
if changes.preset_data is not None:
self._cursor.execute(
cursor.execute(
"""--sql
UPDATE style_presets
SET preset_data = ?
@@ -138,14 +126,12 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return self.get(style_preset_id)
def delete(self, style_preset_id: str) -> None:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
DELETE from style_presets
WHERE id = ?;
@@ -156,46 +142,38 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return None
def get_many(self, type: PresetType | None = None) -> list[StylePresetRecordDTO]:
try:
self._lock.acquire()
main_query = """
SELECT
*
FROM style_presets
"""
main_query = """
SELECT
*
FROM style_presets
"""
if type is not None:
main_query += "WHERE type = ? "
if type is not None:
main_query += "WHERE type = ? "
main_query += "ORDER BY LOWER(name) ASC"
main_query += "ORDER BY LOWER(name) ASC"
if type is not None:
self._cursor.execute(main_query, (type,))
else:
self._cursor.execute(main_query)
cursor = self._conn.cursor()
if type is not None:
cursor.execute(main_query, (type,))
else:
cursor.execute(main_query)
rows = self._cursor.fetchall()
style_presets = [StylePresetRecordDTO.from_dict(dict(row)) for row in rows]
rows = cursor.fetchall()
style_presets = [StylePresetRecordDTO.from_dict(dict(row)) for row in rows]
return style_presets
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return style_presets
def _sync_default_style_presets(self) -> None:
"""Syncs default style presets to the database. Internal use only."""
# First delete all existing default style presets
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
DELETE FROM style_presets
WHERE type = "default";
@@ -205,10 +183,8 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
# Next, parse and create the default style presets
with self._lock, open(Path(__file__).parent / Path("default_style_presets.json"), "r") as file:
with open(Path(__file__).parent / Path("default_style_presets.json"), "r") as file:
presets = json.load(file)
for preset in presets:
style_preset = StylePresetWithoutId.model_validate(preset)

View File

@@ -23,9 +23,7 @@ from invokeai.app.util.misc import uuid_string
class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
def __init__(self, db: SqliteDatabase) -> None:
super().__init__()
self._lock = db.lock
self._conn = db.conn
self._cursor = self._conn.cursor()
def start(self, invoker: Invoker) -> None:
self._invoker = invoker
@@ -33,42 +31,36 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
def get(self, workflow_id: str) -> WorkflowRecordDTO:
"""Gets a workflow by ID. Updates the opened_at column."""
try:
self._lock.acquire()
self._cursor.execute(
"""--sql
UPDATE workflow_library
SET opened_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
WHERE workflow_id = ?;
""",
(workflow_id,),
)
self._conn.commit()
self._cursor.execute(
"""--sql
SELECT workflow_id, workflow, name, created_at, updated_at, opened_at
FROM workflow_library
WHERE workflow_id = ?;
""",
(workflow_id,),
)
row = self._cursor.fetchone()
if row is None:
raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found")
return WorkflowRecordDTO.from_dict(dict(row))
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
cursor = self._conn.cursor()
cursor.execute(
"""--sql
UPDATE workflow_library
SET opened_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
WHERE workflow_id = ?;
""",
(workflow_id,),
)
self._conn.commit()
cursor.execute(
"""--sql
SELECT workflow_id, workflow, name, created_at, updated_at, opened_at
FROM workflow_library
WHERE workflow_id = ?;
""",
(workflow_id,),
)
row = cursor.fetchone()
if row is None:
raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found")
return WorkflowRecordDTO.from_dict(dict(row))
def create(self, workflow: WorkflowWithoutID) -> WorkflowRecordDTO:
try:
# Only user workflows may be created by this method
assert workflow.meta.category is WorkflowCategory.User
workflow_with_id = Workflow(**workflow.model_dump(), id=uuid_string())
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
INSERT OR IGNORE INTO workflow_library (
workflow_id,
@@ -82,14 +74,12 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return self.get(workflow_with_id.id)
def update(self, workflow: Workflow) -> WorkflowRecordDTO:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
UPDATE workflow_library
SET workflow = ?
@@ -101,14 +91,12 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return self.get(workflow.id)
def delete(self, workflow_id: str) -> None:
try:
self._lock.acquire()
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
DELETE from workflow_library
WHERE workflow_id = ? AND category = 'user';
@@ -119,8 +107,6 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return None
def get_many(
@@ -132,66 +118,60 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
per_page: Optional[int] = None,
query: Optional[str] = None,
) -> PaginatedResults[WorkflowRecordListItemDTO]:
try:
self._lock.acquire()
# sanitize!
assert order_by in WorkflowRecordOrderBy
assert direction in SQLiteDirection
assert category in WorkflowCategory
count_query = "SELECT COUNT(*) FROM workflow_library WHERE category = ?"
main_query = """
SELECT
workflow_id,
category,
name,
description,
created_at,
updated_at,
opened_at
FROM workflow_library
WHERE category = ?
"""
main_params: list[int | str] = [category.value]
count_params: list[int | str] = [category.value]
# sanitize!
assert order_by in WorkflowRecordOrderBy
assert direction in SQLiteDirection
assert category in WorkflowCategory
count_query = "SELECT COUNT(*) FROM workflow_library WHERE category = ?"
main_query = """
SELECT
workflow_id,
category,
name,
description,
created_at,
updated_at,
opened_at
FROM workflow_library
WHERE category = ?
"""
main_params: list[int | str] = [category.value]
count_params: list[int | str] = [category.value]
stripped_query = query.strip() if query else None
if stripped_query:
wildcard_query = "%" + stripped_query + "%"
main_query += " AND name LIKE ? OR description LIKE ? "
count_query += " AND name LIKE ? OR description LIKE ?;"
main_params.extend([wildcard_query, wildcard_query])
count_params.extend([wildcard_query, wildcard_query])
stripped_query = query.strip() if query else None
if stripped_query:
wildcard_query = "%" + stripped_query + "%"
main_query += " AND name LIKE ? OR description LIKE ? "
count_query += " AND name LIKE ? OR description LIKE ?;"
main_params.extend([wildcard_query, wildcard_query])
count_params.extend([wildcard_query, wildcard_query])
main_query += f" ORDER BY {order_by.value} {direction.value}"
main_query += f" ORDER BY {order_by.value} {direction.value}"
if per_page:
main_query += " LIMIT ? OFFSET ?"
main_params.extend([per_page, page * per_page])
if per_page:
main_query += " LIMIT ? OFFSET ?"
main_params.extend([per_page, page * per_page])
self._cursor.execute(main_query, main_params)
rows = self._cursor.fetchall()
workflows = [WorkflowRecordListItemDTOValidator.validate_python(dict(row)) for row in rows]
cursor = self._conn.cursor()
cursor.execute(main_query, main_params)
rows = cursor.fetchall()
workflows = [WorkflowRecordListItemDTOValidator.validate_python(dict(row)) for row in rows]
self._cursor.execute(count_query, count_params)
total = self._cursor.fetchone()[0]
cursor.execute(count_query, count_params)
total = cursor.fetchone()[0]
if per_page:
pages = total // per_page + (total % per_page > 0)
else:
pages = 1 # If no pagination, there is only one page
if per_page:
pages = total // per_page + (total % per_page > 0)
else:
pages = 1 # If no pagination, there is only one page
return PaginatedResults(
items=workflows,
page=page,
per_page=per_page if per_page else total,
pages=pages,
total=total,
)
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()
return PaginatedResults(
items=workflows,
page=page,
per_page=per_page if per_page else total,
pages=pages,
total=total,
)
def _sync_default_workflows(self) -> None:
"""Syncs default workflows to the database. Internal use only."""
@@ -207,7 +187,6 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
"""
try:
self._lock.acquire()
workflows: list[Workflow] = []
workflows_dir = Path(__file__).parent / Path("default_workflows")
workflow_paths = workflows_dir.glob("*.json")
@@ -218,14 +197,15 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
workflows.append(workflow)
# Only default workflows may be managed by this method
assert all(w.meta.category is WorkflowCategory.Default for w in workflows)
self._cursor.execute(
cursor = self._conn.cursor()
cursor.execute(
"""--sql
DELETE FROM workflow_library
WHERE category = 'default';
"""
)
for w in workflows:
self._cursor.execute(
cursor.execute(
"""--sql
INSERT OR REPLACE INTO workflow_library (
workflow_id,
@@ -239,5 +219,3 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
except Exception:
self._conn.rollback()
raise
finally:
self._lock.release()

View File

@@ -0,0 +1,64 @@
import logging
import mimetypes
import socket
import torch
def find_open_port(port: int) -> int:
"""Find a port not in use starting at given port"""
# Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
# https://github.com/WaylonWalker
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.settimeout(1)
if s.connect_ex(("localhost", port)) == 0:
return find_open_port(port=port + 1)
else:
return port
def check_cudnn(logger: logging.Logger) -> None:
"""Check for cuDNN issues that could be causing degraded performance."""
if torch.backends.cudnn.is_available():
try:
# Note: At the time of writing (torch 2.2.1), torch.backends.cudnn.version() only raises an error the first
# time it is called. Subsequent calls will return the version number without complaining about a mismatch.
cudnn_version = torch.backends.cudnn.version()
logger.info(f"cuDNN version: {cudnn_version}")
except RuntimeError as e:
logger.warning(
"Encountered a cuDNN version issue. This may result in degraded performance. This issue is usually "
"caused by an incompatible cuDNN version installed in your python environment, or on the host "
f"system. Full error message:\n{e}"
)
def enable_dev_reload() -> None:
"""Enable hot reloading on python file changes during development."""
from invokeai.backend.util.logging import InvokeAILogger
try:
import jurigged
except ImportError as e:
raise RuntimeError(
'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.'
) from e
else:
jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
def apply_monkeypatches() -> None:
"""Apply monkeypatches to fix issues with third-party libraries."""
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
def register_mime_types() -> None:
"""Register additional mime types for windows."""
# Fix for windows mimetypes registry entries being borked.
# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
mimetypes.add_type("application/javascript", ".js")
mimetypes.add_type("text/css", ".css")

View File

@@ -0,0 +1,52 @@
import logging
import os
import sys
def configure_torch_cuda_allocator(pytorch_cuda_alloc_conf: str, logger: logging.Logger):
"""Configure the PyTorch CUDA memory allocator. See
https://pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf for supported
configurations.
"""
if "torch" in sys.modules:
raise RuntimeError("configure_torch_cuda_allocator() must be called before importing torch.")
# Log a warning if the PYTORCH_CUDA_ALLOC_CONF environment variable is already set.
prev_cuda_alloc_conf = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", None)
if prev_cuda_alloc_conf is not None:
if prev_cuda_alloc_conf == pytorch_cuda_alloc_conf:
logger.info(
f"PYTORCH_CUDA_ALLOC_CONF is already set to '{pytorch_cuda_alloc_conf}'. Skipping configuration."
)
return
else:
logger.warning(
f"Attempted to configure the PyTorch CUDA memory allocator with '{pytorch_cuda_alloc_conf}', but PYTORCH_CUDA_ALLOC_CONF is already set to "
f"'{prev_cuda_alloc_conf}'. Skipping configuration."
)
return
# Configure the PyTorch CUDA memory allocator.
# NOTE: It is important that this happens before torch is imported.
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = pytorch_cuda_alloc_conf
import torch
# Relevant docs: https://pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf
if not torch.cuda.is_available():
raise RuntimeError(
"Attempted to configure the PyTorch CUDA memory allocator, but no CUDA devices are available."
)
# Verify that the torch allocator was properly configured.
allocator_backend = torch.cuda.get_allocator_backend()
expected_backend = "cudaMallocAsync" if "cudaMallocAsync" in pytorch_cuda_alloc_conf else "native"
if allocator_backend != expected_backend:
raise RuntimeError(
f"Failed to configure the PyTorch CUDA memory allocator. Expected backend: '{expected_backend}', but got "
f"'{allocator_backend}'. Verify that 1) the pytorch_cuda_alloc_conf is set correctly, and 2) that torch is "
"not imported before calling configure_torch_cuda_allocator()."
)
logger.info(f"PyTorch CUDA memory allocator: {torch.cuda.get_allocator_backend()}")

View File

@@ -107,7 +107,13 @@
"min": "Min",
"max": "Max",
"resetToDefaults": "Auf Standard zurücksetzen",
"seed": "Seed"
"seed": "Seed",
"row": "Reihe",
"column": "Spalte",
"end": "Ende",
"layout": "Layout",
"board": "Ordner",
"combinatorial": "Kombinatorisch"
},
"gallery": {
"galleryImageSize": "Bildgröße",
@@ -616,7 +622,9 @@
"hfTokenUnableToVerify": "HF-Token kann nicht überprüft werden",
"hfTokenUnableToVerifyErrorMessage": "HuggingFace-Token kann nicht überprüft werden. Dies ist wahrscheinlich auf einen Netzwerkfehler zurückzuführen. Bitte versuchen Sie es später erneut.",
"hfTokenSaved": "HF-Token gespeichert",
"hfTokenRequired": "Sie versuchen, ein Modell herunterzuladen, für das ein gültiges HuggingFace-Token erforderlich ist."
"hfTokenRequired": "Sie versuchen, ein Modell herunterzuladen, für das ein gültiges HuggingFace-Token erforderlich ist.",
"urlUnauthorizedErrorMessage2": "Hier erfahren wie.",
"urlForbidden": "Sie haben keinen Zugriff auf dieses Modell"
},
"parameters": {
"images": "Bilder",
@@ -683,7 +691,8 @@
"iterations": "Iterationen",
"guidance": "Führung",
"coherenceMode": "Modus",
"recallMetadata": "Metadaten abrufen"
"recallMetadata": "Metadaten abrufen",
"gaussianBlur": "Gaußsche Unschärfe"
},
"settings": {
"displayInProgress": "Zwischenbilder anzeigen",
@@ -883,7 +892,8 @@
"canvas": "Leinwand",
"prompts_one": "Prompt",
"prompts_other": "Prompts",
"batchSize": "Stapelgröße"
"batchSize": "Stapelgröße",
"confirm": "Bestätigen"
},
"metadata": {
"negativePrompt": "Negativ Beschreibung",
@@ -1298,7 +1308,13 @@
"noBatchGroup": "keine Gruppe",
"generatorNoValues": "leer",
"generatorLoading": "wird geladen",
"generatorLoadFromFile": "Aus Datei laden"
"generatorLoadFromFile": "Aus Datei laden",
"showEdgeLabels": "Kantenbeschriftungen anzeigen",
"downloadWorkflowError": "Fehler beim Herunterladen des Arbeitsablaufs",
"nodeName": "Knotenname",
"description": "Beschreibung",
"loadWorkflowDesc": "Arbeitsablauf laden?",
"loadWorkflowDesc2": "Ihr aktueller Arbeitsablauf enthält nicht gespeicherte Änderungen."
},
"hrf": {
"enableHrf": "Korrektur für hohe Auflösungen",

View File

@@ -921,6 +921,7 @@
"currentImage": "Current Image",
"currentImageDescription": "Displays the current image in the Node Editor",
"downloadWorkflow": "Download Workflow JSON",
"downloadWorkflowError": "Error downloading workflow",
"edge": "Edge",
"edit": "Edit",
"editMode": "Edit in Workflow Editor",
@@ -1725,6 +1726,10 @@
"layout": "Layout",
"row": "Row",
"column": "Column",
"container": "Container",
"heading": "Heading",
"text": "Text",
"divider": "Divider",
"nodeField": "Node Field",
"zoomToNode": "Zoom to Node",
"nodeFieldTooltip": "To add a node field, click the small plus sign button on the field in the Workflow Editor, or drag the field by its name into the form.",
@@ -1906,7 +1911,7 @@
"resetGenerationSettings": "Reset Generation Settings",
"replaceCurrent": "Replace Current",
"controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, or draw on the canvas to get started.",
"referenceImageEmptyState": "<UploadButton>Upload an image</UploadButton> or drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer to get started.",
"referenceImageEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, or <PullBboxButton>pull the bounding box into this layer</PullBboxButton> to get started.",
"warnings": {
"problemsFound": "Problems found",
"unsupportedModel": "layer not supported for selected base model",
@@ -2301,8 +2306,8 @@
"whatsNew": {
"whatsNewInInvoke": "What's New in Invoke",
"items": [
"Workflow Editor: New drag-and-drop form builder for easier workflow creation.",
"Other improvements: Faster batch queuing, better upscaling, improved color picker, and metadata nodes."
"Memory Management: New setting for users with Nvidia GPUs to reduce VRAM usage.",
"Performance: Continued improvements to overall application performance and responsiveness."
],
"readReleaseNotes": "Read Release Notes",
"watchRecentReleaseVideos": "Watch Recent Release Videos",

View File

@@ -105,7 +105,11 @@
"resetToDefaults": "Ripristina le impostazioni predefinite",
"seed": "Seme",
"combinatorial": "Combinatorio",
"count": "Quantità"
"count": "Quantità",
"board": "Bacheca",
"layout": "Schema",
"row": "Riga",
"column": "Colonna"
},
"gallery": {
"galleryImageSize": "Dimensione dell'immagine",
@@ -173,7 +177,8 @@
"assetsTab": "File che hai caricato per usarli nei tuoi progetti.",
"boardsSettings": "Impostazioni Bacheche",
"imagesSettings": "Impostazioni Immagini Galleria",
"assets": "Risorse"
"assets": "Risorse",
"images": "Immagini"
},
"hotkeys": {
"searchHotkeys": "Cerca tasti di scelta rapida",
@@ -703,7 +708,8 @@
"collectionNumberNotMultipleOf": "{{value}} non è multiplo di {{multipleOf}}",
"collectionNumberLTMin": "{{value}} < {{minimum}} (incr min)",
"collectionNumberGTExclusiveMax": "{{value}} >= {{exclusiveMaximum}} (excl max)",
"collectionNumberLTExclusiveMin": "{{value}} <= {{exclusiveMinimum}} (excl min)"
"collectionNumberLTExclusiveMin": "{{value}} <= {{exclusiveMinimum}} (excl min)",
"collectionEmpty": "raccolta vuota"
},
"useCpuNoise": "Usa la CPU per generare rumore",
"iterations": "Iterazioni",
@@ -920,8 +926,8 @@
"boolean": "Booleani",
"node": "Nodo",
"collection": "Raccolta",
"cannotConnectInputToInput": "Impossibile collegare Input a Input",
"cannotConnectOutputToOutput": "Impossibile collegare Output ad Output",
"cannotConnectInputToInput": "Impossibile collegare ingresso a ingresso",
"cannotConnectOutputToOutput": "Impossibile collegare uscita ad uscita",
"cannotConnectToSelf": "Impossibile connettersi a se stesso",
"mismatchedVersion": "Nodo non valido: il nodo {{node}} di tipo {{type}} ha una versione non corrispondente (provare ad aggiornare?)",
"loadingNodes": "Caricamento nodi...",
@@ -1014,7 +1020,21 @@
"generatorNRandomValues_one": "{{count}} valore casuale",
"generatorNRandomValues_many": "{{count}} valori casuali",
"generatorNRandomValues_other": "{{count}} valori casuali",
"arithmeticSequence": "Sequenza aritmetica"
"arithmeticSequence": "Sequenza aritmetica",
"nodeName": "Nome del nodo",
"loadWorkflowDesc": "Caricare il flusso di lavoro?",
"loadWorkflowDesc2": "Il flusso di lavoro corrente presenta modifiche non salvate.",
"downloadWorkflowError": "Errore durante lo scaricamento del flusso di lavoro",
"deletedMissingNodeFieldFormElement": "Campo modulo mancante eliminato: nodo {{nodeId}} campo {{fieldName}}",
"loadingTemplates": "Caricamento {{name}}",
"unableToUpdateNode": "Aggiornamento del nodo non riuscito: nodo {{node}} di tipo {{type}} (potrebbe essere necessario eliminarlo e ricrearlo)",
"description": "Descrizione",
"generatorImagesCategory": "Categoria",
"generatorImages_one": "{{count}} immagine",
"generatorImages_many": "{{count}} immagini",
"generatorImages_other": "{{count}} immagini",
"generatorImagesFromBoard": "Immagini dalla Bacheca",
"missingSourceOrTargetNode": "Nodo sorgente o di destinazione mancante"
},
"boards": {
"autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1140,7 +1160,10 @@
"cancelAllExceptCurrentQueueItemAlertDialog2": "Vuoi davvero annullare tutti gli elementi in coda in sospeso?",
"confirm": "Conferma",
"cancelAllExceptCurrentQueueItemAlertDialog": "L'annullamento di tutti gli elementi della coda, eccetto quello corrente, interromperà gli elementi in sospeso ma consentirà il completamento di quello in corso.",
"cancelAllExceptCurrentTooltip": "Annulla tutto tranne l'elemento corrente"
"cancelAllExceptCurrentTooltip": "Annulla tutto tranne l'elemento corrente",
"retrySucceeded": "Elemento rieseguito",
"retryItem": "Riesegui elemento",
"retryFailed": "Problema riesecuzione elemento"
},
"models": {
"noMatchingModels": "Nessun modello corrispondente",
@@ -1718,7 +1741,38 @@
"download": "Scarica",
"copyShareLink": "Copia Condividi Link",
"copyShareLinkForWorkflow": "Copia Condividi Link del Flusso di lavoro",
"delete": "Elimina"
"delete": "Elimina",
"openLibrary": "Apri la libreria",
"builder": {
"resetAllNodeFields": "Reimposta tutti i campi del nodo",
"row": "Riga",
"nodeField": "Campo del nodo",
"slider": "Cursore",
"emptyRootPlaceholderEditMode": "Per iniziare, trascina qui un elemento del modulo o un campo nodo.",
"containerPlaceholder": "Contenitore vuoto",
"headingPlaceholder": "Titolo vuoto",
"column": "Colonna",
"nodeFieldTooltip": "Per aggiungere un campo nodo, fare clic sul piccolo pulsante con il segno più sul campo nell'editor del flusso di lavoro, oppure trascinare il campo in base al suo nome nel modulo.",
"label": "Etichetta",
"deleteAllElements": "Elimina tutti gli elementi del modulo",
"addToForm": "Aggiungi al Modulo",
"layout": "Schema",
"builder": "Generatore Modulo",
"zoomToNode": "Zoom sul nodo",
"component": "Componente",
"showDescription": "Mostra Descrizione",
"singleLine": "Linea singola",
"multiLine": "Linea Multipla",
"both": "Entrambi",
"emptyRootPlaceholderViewMode": "Fare clic su Modifica per iniziare a creare un modulo per questo flusso di lavoro.",
"textPlaceholder": "Testo vuoto",
"workflowBuilderAlphaWarning": "Il generatore di flussi di lavoro è attualmente in versione alpha. Potrebbero esserci cambiamenti radicali prima della versione stabile.",
"heading": "Intestazione",
"divider": "Divisore",
"container": "Contenitore",
"text": "Testo",
"numberInput": "Ingresso numerico"
}
},
"accordions": {
"compositing": {
@@ -2140,7 +2194,7 @@
"controlLayerEmptyState": "<UploadButton>Carica un'immagine</UploadButton>, trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello oppure disegna sulla tela per iniziare.",
"useImage": "Usa immagine",
"resetGenerationSettings": "Ripristina impostazioni di generazione",
"referenceImageEmptyState": "Per iniziare, <UploadButton>carica un'immagine</UploadButton> oppure trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello.",
"referenceImageEmptyState": "Per iniziare, <UploadButton>carica un'immagine</UploadButton>, trascina un'immagine dalla <GalleryButton>galleria</GalleryButton>, oppure <PullBboxButton>trascina il riquadro di delimitazione in questo livello</PullBboxButton> su questo livello.",
"asRasterLayer": "Come $t(controlLayers.rasterLayer)",
"asRasterLayerResize": "Come $t(controlLayers.rasterLayer) (Ridimensiona)",
"asControlLayer": "Come $t(controlLayers.controlLayer)",
@@ -2276,8 +2330,8 @@
"watchRecentReleaseVideos": "Guarda i video su questa versione",
"watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
"items": [
"Impostazioni predefinite VRAM migliorate",
"Cancellazione della cache del modello su richiesta"
"Editor del flusso di lavoro: nuovo generatore di moduli trascina-e-rilascia per una creazione più facile del flusso di lavoro.",
"Altri miglioramenti: messa in coda dei lotti più rapida, migliore ampliamento, selettore colore migliorato e nodi metadati."
]
},
"system": {

View File

@@ -118,7 +118,8 @@
"compareHelp2": "Nhấn <Kbd>M</Kbd> để tuần hoàn trong chế độ so sánh.",
"boardsSettings": "Thiết Lập Bảng",
"imagesSettings": "Cài Đặt Ảnh Trong Thư Viện Ảnh",
"assets": "Tài Nguyên"
"assets": "Tài Nguyên",
"images": "Hình Ảnh"
},
"common": {
"ipAdapter": "IP Adapter",
@@ -233,7 +234,8 @@
"combinatorial": "Tổ Hợp",
"column": "Cột",
"layout": "Bố Cục",
"row": "Hàng"
"row": "Hàng",
"board": "Bảng"
},
"prompt": {
"addPromptTrigger": "Thêm Prompt Trigger",
@@ -873,7 +875,7 @@
"removeLinearView": "Xoá Khỏi Chế Độ Xem Tuyến Tính",
"unknownErrorValidatingWorkflow": "Lỗi không rõ khi xác thực workflow",
"unableToLoadWorkflow": "Không Thể Tải Workflow",
"workflowSettings": "Cài Đặt Trình Biên Tập Viên Workflow",
"workflowSettings": "Cài Đặt Biên Tập Workflow",
"workflowVersion": "Phiên Bản",
"unableToGetWorkflowVersion": "Không thể tìm phiên bản của lược đồ workflow",
"collection": "Đa tài nguyên",
@@ -1010,7 +1012,11 @@
"loadWorkflowDesc2": "Workflow hiện tại của bạn có những điều chỉnh chưa được lưu.",
"loadingTemplates": "Đang Tải {{name}}",
"nodeName": "Tên Node",
"unableToUpdateNode": "Cập nhật node thất bại: node {{node}} thuộc dạng {{type}} (có thể cần xóa và tạo lại)"
"unableToUpdateNode": "Cập nhật node thất bại: node {{node}} thuộc dạng {{type}} (có thể cần xóa và tạo lại)",
"downloadWorkflowError": "Lỗi tải xuống workflow",
"generatorImagesFromBoard": "Ảnh Từ Bảng",
"generatorImagesCategory": "Phân Loại",
"generatorImages_other": "{{count}} ảnh"
},
"popovers": {
"paramCFGRescaleMultiplier": {
@@ -1494,7 +1500,9 @@
"batchNodeCollectionSizeMismatch": "Kích cỡ tài nguyên không phù hợp với Lô {{batchGroupId}}",
"emptyBatches": "lô trống",
"batchNodeNotConnected": "Node Hàng Loạt chưa được kết nối: {{label}}",
"batchNodeEmptyCollection": "Một vài node hàng loạt có tài nguyên rỗng"
"batchNodeEmptyCollection": "Một vài node hàng loạt có tài nguyên rỗng",
"collectionEmpty": "tài nguyên trống",
"batchNodeCollectionSizeMismatchNoGroupId": "tài nguyên theo nhóm có kích thước sai lệch"
},
"cfgScale": "Thang CFG",
"useSeed": "Dùng Hạt Giống",
@@ -2159,7 +2167,7 @@
"parameterSetDesc": "Gợi lại {{parameter}}",
"loadedWithWarnings": "Đã Tải Workflow Với Cảnh Báo",
"outOfMemoryErrorDesc": "Thiết lập tạo sinh hiện tại đã vượt mức cho phép của thiết bị. Hãy điều chỉnh thiết lập và thử lại.",
"setNodeField": "Đặt làm vùng cho node",
"setNodeField": "Đặt làm vùng node",
"problemRetrievingWorkflow": "Có Vấn Đề Khi Lấy Lại Workflow",
"somethingWentWrong": "Có Vấn Đề Phát Sinh",
"problemDeletingWorkflow": "Có Vấn Đề Khi Xoá Workflow",
@@ -2247,11 +2255,11 @@
"workflowLibrary": "Thư Viện",
"opened": "Ngày Mở",
"deleteWorkflow": "Xoá Workflow",
"workflowEditorMenu": "Menu Biên Tập Viên Workflow",
"workflowEditorMenu": "Menu Biên Tập Workflow",
"uploadAndSaveWorkflow": "Tải Lên Thư Viện",
"openLibrary": "Mở Thư Viện",
"builder": {
"resetAllNodeFields": "Khởi Động Lại Tất Cả Vùng Cho Node",
"resetAllNodeFields": "Tải Lại Các Vùng Node",
"builder": "Trình Tạo Vùng Nhập",
"layout": "Bố Cục",
"row": "Hàng",
@@ -2266,15 +2274,19 @@
"slider": "Thanh Trượt",
"both": "Cả Hai",
"emptyRootPlaceholderViewMode": "Chọn Chỉnh Sửa để bắt đầu tạo nên một vùng nhập cho workflow này.",
"emptyRootPlaceholderEditMode": "Kéo thành phần vùng nhập hoặc vùng cho node vào đây để bắt đầu.",
"emptyRootPlaceholderEditMode": "Kéo thành phần vùng nhập hoặc vùng node vào đây để bắt đầu.",
"containerPlaceholder": "Hộp Chứa Trống",
"headingPlaceholder": "Đầu Dòng Trống",
"textPlaceholder": "Mô Tả Trống",
"textPlaceholder": "Văn Bản Trống",
"column": "Cột",
"deleteAllElements": "Xóa Tất Cả Thành Phần Vùng Nhập",
"nodeField": "Vùng Cho Node",
"nodeFieldTooltip": "Để thêm vùng cho node, bấm vào dấu cộng nhỏ trên vùng trong Vùng Biên Tập Workflow, hoặc kéo vùng theo tên của nó vào vùng nhập.",
"workflowBuilderAlphaWarning": "Trình tạo workflow đang trong giai đoạn alpha. Nó có thể xuất hiện những thay đổi đột ngột trước khi chính thức được phát hành."
"deleteAllElements": "Xóa Tất Cả Thành Phần",
"nodeField": "Vùng Node",
"nodeFieldTooltip": "Để thêm vùng node, bấm vào dấu cộng nhỏ trên vùng trong Trình Biên Tập Workflow, hoặc kéo vùng theo tên của nó vào vùng nhập.",
"workflowBuilderAlphaWarning": "Trình tạo vùng nhập đang trong giai đoạn alpha. Nó có thể xuất hiện những thay đổi đột ngột trước khi chính thức được phát hành.",
"container": "Hộp Chứa",
"heading": "Đầu Dòng",
"text": "Văn Bản",
"divider": "Gạch Chia"
}
},
"upscaling": {
@@ -2310,8 +2322,8 @@
"watchRecentReleaseVideos": "Xem Video Phát Hành Mới Nhất",
"watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
"items": [
"Cải thiện các thiết lập mặc định của VRAM",
"Xoá bộ nhớ đệm của model theo yêu cầu"
"Trình Biên Tập Workflow: trình tạo vùng nhập dưới dạng kéo thả nhằm tạo dựng workflow dễ dàng hơn.",
"Các nâng cấp khác: Xếp hàng tạo sinh theo nhóm nhanh hơn, upscale tốt hơn, trình chọn màu được cải thiện, và node chứa metadata."
]
},
"upsell": {

View File

@@ -128,7 +128,11 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
getInputProps: getUploadInputProps,
open: openUploader,
} = useDropzone({
accept: { 'image/png': ['.png'], 'image/jpeg': ['.jpg', '.jpeg', '.png'] },
accept: {
'image/png': ['.png'],
'image/jpeg': ['.jpg', '.jpeg', '.png'],
'image/webp': ['.webp'],
},
onDropAccepted,
onDropRejected,
disabled: isDisabled,

View File

@@ -2,6 +2,7 @@ import { Button, Flex, Text } from '@invoke-ai/ui-library';
import { useAppDispatch } from 'app/store/storeHooks';
import { useImageUploadButton } from 'common/hooks/useImageUploadButton';
import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
import { usePullBboxIntoGlobalReferenceImage } from 'features/controlLayers/hooks/saveCanvasHooks';
import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
import type { SetGlobalReferenceImageDndTargetData } from 'features/dnd/dnd';
import { setGlobalReferenceImageDndTarget } from 'features/dnd/dnd';
@@ -27,6 +28,7 @@ export const IPAdapterSettingsEmptyState = memo(() => {
const onClickGalleryButton = useCallback(() => {
dispatch(activeTabCanvasRightPanelChanged('gallery'));
}, [dispatch]);
const pullBboxIntoIPAdapter = usePullBboxIntoGlobalReferenceImage(entityIdentifier);
const dndTargetData = useMemo<SetGlobalReferenceImageDndTargetData>(
() => setGlobalReferenceImageDndTarget.getData({ entityIdentifier }),
@@ -41,8 +43,11 @@ export const IPAdapterSettingsEmptyState = memo(() => {
GalleryButton: (
<Button onClick={onClickGalleryButton} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
),
PullBboxButton: (
<Button onClick={pullBboxIntoIPAdapter} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
),
}),
[isBusy, onClickGalleryButton, uploadApi]
[isBusy, onClickGalleryButton, pullBboxIntoIPAdapter, uploadApi]
);
return (

View File

@@ -2,6 +2,7 @@ import { Button, Flex, IconButton, Spacer, Text } from '@invoke-ai/ui-library';
import { useAppDispatch } from 'app/store/storeHooks';
import { useImageUploadButton } from 'common/hooks/useImageUploadButton';
import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
import { usePullBboxIntoRegionalGuidanceReferenceImage } from 'features/controlLayers/hooks/saveCanvasHooks';
import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
import { rgIPAdapterDeleted } from 'features/controlLayers/store/canvasSlice';
import type { SetRegionalGuidanceReferenceImageDndTargetData } from 'features/dnd/dnd';
@@ -36,6 +37,7 @@ export const RegionalGuidanceIPAdapterSettingsEmptyState = memo(({ referenceImag
const onDeleteIPAdapter = useCallback(() => {
dispatch(rgIPAdapterDeleted({ entityIdentifier, referenceImageId }));
}, [dispatch, entityIdentifier, referenceImageId]);
const pullBboxIntoIPAdapter = usePullBboxIntoRegionalGuidanceReferenceImage(entityIdentifier, referenceImageId);
const dndTargetData = useMemo<SetRegionalGuidanceReferenceImageDndTargetData>(
() =>
@@ -46,6 +48,21 @@ export const RegionalGuidanceIPAdapterSettingsEmptyState = memo(({ referenceImag
[entityIdentifier, referenceImageId]
);
const components = useMemo(
() => ({
UploadButton: (
<Button isDisabled={isBusy} size="sm" variant="link" color="base.300" {...uploadApi.getUploadButtonProps()} />
),
GalleryButton: (
<Button onClick={onClickGalleryButton} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
),
PullBboxButton: (
<Button onClick={pullBboxIntoIPAdapter} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
),
}),
[isBusy, onClickGalleryButton, pullBboxIntoIPAdapter, uploadApi]
);
return (
<Flex flexDir="column" gap={2} position="relative" w="full">
<Flex alignItems="center" gap={2}>
@@ -66,23 +83,7 @@ export const RegionalGuidanceIPAdapterSettingsEmptyState = memo(({ referenceImag
</Flex>
<Flex alignItems="center" gap={2} p={4}>
<Text textAlign="center" color="base.300">
<Trans
i18nKey="controlLayers.referenceImageEmptyState"
components={{
UploadButton: (
<Button
isDisabled={isBusy}
size="sm"
variant="link"
color="base.300"
{...uploadApi.getUploadButtonProps()}
/>
),
GalleryButton: (
<Button onClick={onClickGalleryButton} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
),
}}
/>
<Trans i18nKey="controlLayers.referenceImageEmptyState" components={components} />
</Text>
</Flex>
<input {...uploadApi.getUploadInputProps()} />

View File

@@ -22,8 +22,8 @@ import { useBoardName } from 'services/api/hooks/useBoardName';
import type { UploadImageArg } from 'services/api/types';
import { z } from 'zod';
const ACCEPTED_IMAGE_TYPES = ['image/png', 'image/jpg', 'image/jpeg'];
const ACCEPTED_FILE_EXTENSIONS = ['.png', '.jpg', '.jpeg'];
const ACCEPTED_IMAGE_TYPES = ['image/png', 'image/jpg', 'image/jpeg', 'image/webp'];
const ACCEPTED_FILE_EXTENSIONS = ['.png', '.jpg', '.jpeg', '.webp'];
// const MAX_IMAGE_SIZE = 4; //In MegaBytes
// const sizeInMB = (sizeInBytes: number, decimalsNum = 2) => {

View File

@@ -72,7 +72,11 @@ const ModelImageUpload = ({ model_key, model_image }: Props) => {
}, [model_key, t, deleteModelImage]);
const { getInputProps, getRootProps } = useDropzone({
accept: { 'image/png': ['.png'], 'image/jpeg': ['.jpg', '.jpeg', '.png'] },
accept: {
'image/png': ['.png'],
'image/jpeg': ['.jpg', '.jpeg', '.png'],
'image/webp': ['.webp'],
},
onDropAccepted,
noDrag: true,
multiple: false,

View File

@@ -6,7 +6,7 @@ import dateFormat, { masks } from 'dateformat';
import { selectWorkflowId } from 'features/nodes/store/workflowSlice';
import { useDeleteWorkflow } from 'features/workflowLibrary/components/DeleteLibraryWorkflowConfirmationAlertDialog';
import { useLoadWorkflow } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
import { useDownloadWorkflow } from 'features/workflowLibrary/hooks/useDownloadWorkflow';
import { useDownloadWorkflowById } from 'features/workflowLibrary/hooks/useDownloadWorkflowById';
import type { MouseEvent } from 'react';
import { useCallback, useMemo, useState } from 'react';
import { useTranslation } from 'react-i18next';
@@ -30,7 +30,7 @@ export const WorkflowListItem = ({ workflow }: { workflow: WorkflowRecordListIte
}, []);
const workflowId = useAppSelector(selectWorkflowId);
const downloadWorkflow = useDownloadWorkflow();
const { downloadWorkflow, isLoading: isLoadingDownloadWorkflow } = useDownloadWorkflowById();
const shareWorkflow = useShareWorkflow();
const deleteWorkflow = useDeleteWorkflow();
const loadWorkflow = useLoadWorkflow();
@@ -71,9 +71,9 @@ export const WorkflowListItem = ({ workflow }: { workflow: WorkflowRecordListIte
(e: MouseEvent<HTMLButtonElement>) => {
e.stopPropagation();
setIsHovered(false);
downloadWorkflow();
downloadWorkflow(workflow.workflow_id);
},
[downloadWorkflow]
[downloadWorkflow, workflow.workflow_id]
);
return (
@@ -144,6 +144,7 @@ export const WorkflowListItem = ({ workflow }: { workflow: WorkflowRecordListIte
aria-label={t('workflows.download')}
onClick={handleClickDownload}
icon={<PiDownloadSimpleBold />}
isLoading={isLoadingDownloadWorkflow}
/>
</Tooltip>
{!!projectUrl && workflow.workflow_id && workflow.category !== 'user' && (

View File

@@ -14,8 +14,7 @@ import { $hasTemplates } from 'features/nodes/store/nodesSlice';
import { selectIsFormEmpty } from 'features/nodes/store/workflowSlice';
import type { FormElement } from 'features/nodes/types/workflow';
import { buildContainer, buildDivider, buildHeading, buildText } from 'features/nodes/types/workflow';
import { startCase } from 'lodash-es';
import type { RefObject } from 'react';
import type { PropsWithChildren, RefObject } from 'react';
import { memo, useEffect, useRef, useState } from 'react';
import { useTranslation } from 'react-i18next';
import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
@@ -39,10 +38,10 @@ export const WorkflowBuilder = memo(() => {
<Flex justifyContent="center" w="full" h="full">
<Flex flexDir="column" w="full" maxW="768px" gap={2}>
<Flex w="full" alignItems="center" gap={2} pt={3}>
<AddFormElementDndButton type="container" />
<AddFormElementDndButton type="divider" />
<AddFormElementDndButton type="heading" />
<AddFormElementDndButton type="text" />
<AddFormElementDndButton type="container">{t('workflows.builder.container')}</AddFormElementDndButton>
<AddFormElementDndButton type="divider">{t('workflows.builder.divider')}</AddFormElementDndButton>
<AddFormElementDndButton type="heading">{t('workflows.builder.heading')}</AddFormElementDndButton>
<AddFormElementDndButton type="text">{t('workflows.builder.text')}</AddFormElementDndButton>
<Button size="sm" variant="ghost" tooltip={t('workflows.builder.nodeFieldTooltip')}>
{t('workflows.builder.nodeField')}
</Button>
@@ -130,14 +129,17 @@ const addFormElementButtonSx: SystemStyleObject = {
_disabled: { borderStyle: 'dashed', opacity: 0.5 },
};
const AddFormElementDndButton = ({ type }: { type: Parameters<typeof useAddFormElementDnd>[0] }) => {
const AddFormElementDndButton = ({
type,
children,
}: PropsWithChildren<{ type: Parameters<typeof useAddFormElementDnd>[0] }>) => {
const draggableRef = useRef<HTMLDivElement>(null);
const isDragging = useAddFormElementDnd(type, draggableRef);
return (
// Must be as div for draggable to work correctly
<Button as="div" ref={draggableRef} size="sm" isDisabled={isDragging} variant="outline" sx={addFormElementButtonSx}>
{startCase(type)}
{children}
</Button>
);
};

View File

@@ -331,14 +331,25 @@ const buildInstanceTypeGuard = <T extends z.ZodTypeAny>(schema: T) => {
return (val: unknown): val is z.infer<T> => schema.safeParse(val).success;
};
/**
* Builds a type guard for a specific field input template type.
*
* The output type guards are primarily used for determining which input component to render for fields in the
* <InputFieldRenderer/> component.
*
* @param name The name of the field type.
* @param cardinalities The allowed cardinalities for the field type. If omitted, all cardinalities are allowed.
*
* @returns A type guard for the specified field type.
*/
const buildTemplateTypeGuard =
<T extends FieldInputTemplate>(name: string, cardinality?: 'SINGLE' | 'COLLECTION' | 'SINGLE_OR_COLLECTION') =>
<T extends FieldInputTemplate>(name: string, cardinalities?: FieldType['cardinality'][]) =>
(template: FieldInputTemplate): template is T => {
if (template.type.name !== name) {
return false;
}
if (cardinality) {
return template.type.cardinality === cardinality;
if (cardinalities) {
return cardinalities.includes(template.type.cardinality);
}
return true;
};
@@ -366,7 +377,10 @@ export type IntegerFieldValue = z.infer<typeof zIntegerFieldValue>;
export type IntegerFieldInputInstance = z.infer<typeof zIntegerFieldInputInstance>;
export type IntegerFieldInputTemplate = z.infer<typeof zIntegerFieldInputTemplate>;
export const isIntegerFieldInputInstance = buildInstanceTypeGuard(zIntegerFieldInputInstance);
export const isIntegerFieldInputTemplate = buildTemplateTypeGuard<IntegerFieldInputTemplate>('IntegerField', 'SINGLE');
export const isIntegerFieldInputTemplate = buildTemplateTypeGuard<IntegerFieldInputTemplate>('IntegerField', [
'SINGLE',
'SINGLE_OR_COLLECTION',
]);
// #endregion
// #region IntegerField Collection
@@ -406,7 +420,7 @@ export type IntegerFieldCollectionInputTemplate = z.infer<typeof zIntegerFieldCo
export const isIntegerFieldCollectionInputInstance = buildInstanceTypeGuard(zIntegerFieldCollectionInputInstance);
export const isIntegerFieldCollectionInputTemplate = buildTemplateTypeGuard<IntegerFieldCollectionInputTemplate>(
'IntegerField',
'COLLECTION'
['COLLECTION']
);
// #endregion
@@ -432,7 +446,10 @@ export type FloatFieldValue = z.infer<typeof zFloatFieldValue>;
export type FloatFieldInputInstance = z.infer<typeof zFloatFieldInputInstance>;
export type FloatFieldInputTemplate = z.infer<typeof zFloatFieldInputTemplate>;
export const isFloatFieldInputInstance = buildInstanceTypeGuard(zFloatFieldInputInstance);
export const isFloatFieldInputTemplate = buildTemplateTypeGuard<FloatFieldInputTemplate>('FloatField', 'SINGLE');
export const isFloatFieldInputTemplate = buildTemplateTypeGuard<FloatFieldInputTemplate>('FloatField', [
'SINGLE',
'SINGLE_OR_COLLECTION',
]);
// #endregion
// #region FloatField Collection
@@ -471,7 +488,7 @@ export type FloatFieldCollectionInputTemplate = z.infer<typeof zFloatFieldCollec
export const isFloatFieldCollectionInputInstance = buildInstanceTypeGuard(zFloatFieldCollectionInputInstance);
export const isFloatFieldCollectionInputTemplate = buildTemplateTypeGuard<FloatFieldCollectionInputTemplate>(
'FloatField',
'COLLECTION'
['COLLECTION']
);
// #endregion
@@ -504,7 +521,10 @@ export type StringFieldValue = z.infer<typeof zStringFieldValue>;
export type StringFieldInputInstance = z.infer<typeof zStringFieldInputInstance>;
export type StringFieldInputTemplate = z.infer<typeof zStringFieldInputTemplate>;
export const isStringFieldInputInstance = buildInstanceTypeGuard(zStringFieldInputInstance);
export const isStringFieldInputTemplate = buildTemplateTypeGuard<StringFieldInputTemplate>('StringField', 'SINGLE');
export const isStringFieldInputTemplate = buildTemplateTypeGuard<StringFieldInputTemplate>('StringField', [
'SINGLE',
'SINGLE_OR_COLLECTION',
]);
// #endregion
// #region StringField Collection
@@ -550,7 +570,7 @@ export type StringFieldCollectionInputTemplate = z.infer<typeof zStringFieldColl
export const isStringFieldCollectionInputInstance = buildInstanceTypeGuard(zStringFieldCollectionInputInstance);
export const isStringFieldCollectionInputTemplate = buildTemplateTypeGuard<StringFieldCollectionInputTemplate>(
'StringField',
'COLLECTION'
['COLLECTION']
);
// #endregion
@@ -613,7 +633,10 @@ export type ImageFieldValue = z.infer<typeof zImageFieldValue>;
export type ImageFieldInputInstance = z.infer<typeof zImageFieldInputInstance>;
export type ImageFieldInputTemplate = z.infer<typeof zImageFieldInputTemplate>;
export const isImageFieldInputInstance = buildInstanceTypeGuard(zImageFieldInputInstance);
export const isImageFieldInputTemplate = buildTemplateTypeGuard<ImageFieldInputTemplate>('ImageField', 'SINGLE');
export const isImageFieldInputTemplate = buildTemplateTypeGuard<ImageFieldInputTemplate>('ImageField', [
'SINGLE',
'SINGLE_OR_COLLECTION',
]);
// #endregion
// #region ImageField Collection
@@ -648,7 +671,7 @@ export type ImageFieldCollectionInputTemplate = z.infer<typeof zImageFieldCollec
export const isImageFieldCollectionInputInstance = buildInstanceTypeGuard(zImageFieldCollectionInputInstance);
export const isImageFieldCollectionInputTemplate = buildTemplateTypeGuard<ImageFieldCollectionInputTemplate>(
'ImageField',
'COLLECTION'
['COLLECTION']
);
// #endregion

View File

@@ -1,12 +1,12 @@
import { MenuItem } from '@invoke-ai/ui-library';
import { useDownloadWorkflow } from 'features/workflowLibrary/hooks/useDownloadWorkflow';
import { useDownloadCurrentlyLoadedWorkflow } from 'features/workflowLibrary/hooks/useDownloadCurrentlyLoadedWorkflow';
import { memo } from 'react';
import { useTranslation } from 'react-i18next';
import { PiDownloadSimpleBold } from 'react-icons/pi';
const DownloadWorkflowMenuItem = () => {
const { t } = useTranslation();
const downloadWorkflow = useDownloadWorkflow();
const downloadWorkflow = useDownloadCurrentlyLoadedWorkflow();
return (
<MenuItem as="button" icon={<PiDownloadSimpleBold />} onClick={downloadWorkflow}>

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@@ -3,7 +3,7 @@ import { $builtWorkflow } from 'features/nodes/hooks/useWorkflowWatcher';
import { workflowDownloaded } from 'features/workflowLibrary/store/actions';
import { useCallback } from 'react';
export const useDownloadWorkflow = () => {
export const useDownloadCurrentlyLoadedWorkflow = () => {
const dispatch = useAppDispatch();
const downloadWorkflow = useCallback(() => {

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@@ -0,0 +1,42 @@
import { useAppDispatch } from 'app/store/storeHooks';
import { toast } from 'features/toast/toast';
import { workflowDownloaded } from 'features/workflowLibrary/store/actions';
import { useCallback } from 'react';
import { useTranslation } from 'react-i18next';
import { useLazyGetWorkflowQuery } from 'services/api/endpoints/workflows';
export const useDownloadWorkflowById = () => {
const { t } = useTranslation();
const dispatch = useAppDispatch();
const [trigger, query] = useLazyGetWorkflowQuery();
const toastError = useCallback(() => {
toast({ status: 'error', description: t('nodes.downloadWorkflowError') });
}, [t]);
const downloadWorkflow = useCallback(
async (workflowId: string) => {
try {
const { data } = await trigger(workflowId);
if (!data) {
toastError();
return;
}
const { workflow } = data;
const blob = new Blob([JSON.stringify(workflow, null, 2)]);
const a = document.createElement('a');
a.href = URL.createObjectURL(blob);
a.download = `${workflow.name || 'My Workflow'}.json`;
document.body.appendChild(a);
a.click();
a.remove();
dispatch(workflowDownloaded());
} catch {
toastError();
}
},
[dispatch, toastError, trigger]
);
return { downloadWorkflow, isLoading: query.isLoading };
};

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@@ -103,6 +103,7 @@ export const api = customCreateApi({
reducerPath: 'api',
tagTypes,
endpoints: () => ({}),
invalidationBehavior: 'immediately',
});
function getCircularReplacer() {

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@@ -1 +1 @@
__version__ = "5.7.1"
__version__ = "5.7.2"

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@@ -101,8 +101,7 @@ dependencies = [
"xformers" = [
# Core generation dependencies, pinned for reproducible builds.
"xformers>=0.0.28.post1; sys_platform!='darwin'",
# Auxiliary dependencies, pinned only if necessary.
"triton; sys_platform=='linux'",
# torch 2.4+cu carries its own triton dependency
]
"onnx" = ["onnxruntime"]
"onnx-cuda" = ["onnxruntime-gpu"]

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@@ -0,0 +1,127 @@
import pytest
import torch
from tests.dangerously_run_function_in_subprocess import dangerously_run_function_in_subprocess
# These tests are a bit fiddly, because the depend on the import behaviour of torch. They use subprocesses to isolate
# the import behaviour of torch, and then check that the function behaves as expected. We have to hack in some logging
# to check that the tested function is behaving as expected.
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires CUDA device.")
def test_configure_torch_cuda_allocator_configures_backend():
"""Test that configure_torch_cuda_allocator() raises a RuntimeError if the configured backend does not match the
expected backend."""
def test_func():
import os
# Unset the environment variable if it is set so that we can test setting it
try:
del os.environ["PYTORCH_CUDA_ALLOC_CONF"]
except KeyError:
pass
from unittest.mock import MagicMock
from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
mock_logger = MagicMock()
# Set the PyTorch CUDA memory allocator to cudaMallocAsync
configure_torch_cuda_allocator("backend:cudaMallocAsync", logger=mock_logger)
# Verify that the PyTorch CUDA memory allocator was configured correctly
import torch
assert torch.cuda.get_allocator_backend() == "cudaMallocAsync"
# Verify that the logger was called with the correct message
mock_logger.info.assert_called_once()
args, _kwargs = mock_logger.info.call_args
logged_message = args[0]
print(logged_message)
stdout, _stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode == 0
assert "PyTorch CUDA memory allocator: cudaMallocAsync" in stdout
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires CUDA device.")
def test_configure_torch_cuda_allocator_raises_if_torch_already_imported():
"""Test that configure_torch_cuda_allocator() raises a RuntimeError if torch was already imported."""
def test_func():
from unittest.mock import MagicMock
# Import torch before calling configure_torch_cuda_allocator()
import torch # noqa: F401
from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
try:
configure_torch_cuda_allocator("backend:cudaMallocAsync", logger=MagicMock())
except RuntimeError as e:
print(e)
stdout, _stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode == 0
assert "configure_torch_cuda_allocator() must be called before importing torch." in stdout
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires CUDA device.")
def test_configure_torch_cuda_allocator_warns_if_env_var_is_set_differently():
"""Test that configure_torch_cuda_allocator() logs at WARNING level if PYTORCH_CUDA_ALLOC_CONF is set and doesn't
match the requested configuration."""
def test_func():
import os
# Explicitly set the environment variable
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "backend:native"
from unittest.mock import MagicMock
from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
mock_logger = MagicMock()
# Set the PyTorch CUDA memory allocator a different configuration
configure_torch_cuda_allocator("backend:cudaMallocAsync", logger=mock_logger)
# Verify that the logger was called with the correct message
mock_logger.warning.assert_called_once()
args, _kwargs = mock_logger.warning.call_args
logged_message = args[0]
print(logged_message)
stdout, _stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode == 0
assert "Attempted to configure the PyTorch CUDA memory allocator with 'backend:cudaMallocAsync'" in stdout
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Requires CUDA device.")
def test_configure_torch_cuda_allocator_logs_if_env_var_is_already_set_correctly():
"""Test that configure_torch_cuda_allocator() logs at INFO level if PYTORCH_CUDA_ALLOC_CONF is set and matches the
requested configuration."""
def test_func():
import os
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "backend:native"
from unittest.mock import MagicMock
from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
mock_logger = MagicMock()
configure_torch_cuda_allocator("backend:native", logger=mock_logger)
mock_logger.info.assert_called_once()
args, _kwargs = mock_logger.info.call_args
logged_message = args[0]
print(logged_message)
stdout, _stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode == 0
assert "PYTORCH_CUDA_ALLOC_CONF is already set to 'backend:native'" in stdout

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@@ -0,0 +1,46 @@
import inspect
import subprocess
import sys
import textwrap
from typing import Any, Callable
def dangerously_run_function_in_subprocess(func: Callable[[], Any]) -> tuple[str, str, int]:
"""**Use with caution! This should _only_ be used with trusted code!**
Extracts a function's source and runs it in a separate subprocess. Returns stdout, stderr, and return code
from the subprocess.
This is useful for tests where an isolated environment is required.
The function to be called must not have any arguments and must not have any closures over the scope in which is was
defined.
Any modules that the function depends on must be imported inside the function.
"""
source_code = inspect.getsource(func)
# Must dedent the source code to avoid indentation errors
dedented_source_code = textwrap.dedent(source_code)
# Get the function name so we can call it in the subprocess
func_name = func.__name__
# Create a script that calls the function
script = f"""
import sys
{dedented_source_code}
if __name__ == "__main__":
{func_name}()
"""
result = subprocess.run(
[sys.executable, "-c", textwrap.dedent(script)], # Run the script in a subprocess
capture_output=True, # Capture stdout and stderr
text=True,
)
return result.stdout, result.stderr, result.returncode

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@@ -0,0 +1,57 @@
from tests.dangerously_run_function_in_subprocess import dangerously_run_function_in_subprocess
def test_simple_function():
def test_func():
print("Hello, Test!")
stdout, stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode == 0
assert stdout.strip() == "Hello, Test!"
assert stderr == ""
def test_function_with_error():
def test_func():
raise ValueError("This is an error")
_stdout, stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode != 0 # Should fail
assert "ValueError: This is an error" in stderr
def test_function_with_imports():
def test_func():
import math
print(math.sqrt(4))
stdout, stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode == 0
assert stdout.strip() == "2.0"
assert stderr == ""
def test_function_with_sys_exit():
def test_func():
import sys
sys.exit(42)
_stdout, _stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode == 42 # Should return the custom exit code
def test_function_with_closure():
foo = "bar"
def test_func():
print(foo)
_stdout, _stderr, returncode = dangerously_run_function_in_subprocess(test_func)
assert returncode == 1 # Should fail because of closure