Compare commits

..

34 Commits

Author SHA1 Message Date
0ca842ad87 Merge branch 'main' of github.com:invoke-ai/InvokeAI into main 2022-11-03 06:52:17 -04:00
12e7e6e8df move getpass_asterisk from conda dependency to pip dependency
- Addresses Issue #1354
2022-11-03 06:51:34 -04:00
152b7fc2bc fix up branch that 1-click installers will pull from 2022-11-03 00:32:49 -04:00
4bb6c8180e Update index.md 2022-11-03 00:19:57 -04:00
a338616ced final documentation fixes prior to release 2022-11-03 00:00:09 -04:00
65a99c47d3 add missing documentation image 2022-11-02 23:41:50 -04:00
e4bb49b4f0 update outpaint documentation 2022-11-02 23:41:16 -04:00
2ad489a1ef dream->invoke in inpainting docs 2022-11-02 23:18:30 -04:00
ecb904f8b7 update environment-mac.yml 2022-11-02 23:12:48 -04:00
61ead2c92d replace old fashined markdown templates with forms
this will help the readability of issues a lot 🤓
2022-11-02 22:26:01 -04:00
c5a8c499ab Raise exception instead of undefined internal state
Hi, please consider raising a proper exception here instead of an undefined internal state. This happens for example if the filepath to the model.ckpt is invalid on first load.
2022-11-02 22:26:00 -04:00
bd6278c361 Fixes indentation causing rendering issue with github.io page 2022-11-02 22:25:13 -04:00
e24d4dc15b Fix discord link
The discord badge has the correct link but the quick links did not
2022-11-02 22:25:12 -04:00
3d4c70604d update requirements to address #1149 2022-11-02 22:25:12 -04:00
d73aea43b7 update precision info 2022-11-02 22:17:50 -04:00
358f0af79a fix prompt in README.md 2022-11-02 22:17:50 -04:00
0650735f74 (re-) fix a lot in mkdocs 2022-11-02 22:17:50 -04:00
e469bbb89e fix links to point to invoke-ai.github.io 2022-11-02 22:17:14 -04:00
a46633a355 adding license using GitHub template
Did not attempt to add additional copyright information.
2022-11-02 22:17:14 -04:00
7275006c37 remove license files temporarily 2022-11-02 22:17:14 -04:00
e438d46314 remove additional copyrights from license file
Trying to get GitHub to recognize our MIT license. Perhaps the additional copyrights are confusing it.
2022-11-02 22:17:14 -04:00
bf8d6d8908 Second try at getting GitHub to register license 2022-11-02 22:17:13 -04:00
b9e1aeb2dd Fix broken links to CLI.md
* Looks like there was a bad paste
2022-11-02 22:16:28 -04:00
fd81a69b4d add current branch to push trigger 2022-11-02 22:16:13 -04:00
5a15ad3148 switch to default channel in environment-mac.yml 2022-11-02 22:15:45 -04:00
22a37ef714 use very short validation for Pull Requests 2022-11-02 22:12:15 -04:00
59c8024f0c remove pr trigger 2022-11-02 22:10:31 -04:00
22b3a59f16 squash merge update-gh-actions into fix-gh-actions
* fix mkdocs deployment

* update path to python bin

* add trigger for current branch

* change path to python_bin for mac as well

* try to use setup-python@v4 instead of setting env

* remove setup conda action

* try to use $CONDA

* remove overseen action

* change branch from master to main

* sort out if then else for faster syntax

* remove more if functions

* add updates to create-caches as well

* eliminate the rest of if functions

* try to unpin pytorch and torchvision

* restore pinned versions

* try switching from set-output to use env

* update test-invoke-conda as well

* fix env var creation

* quote variable

* add second equal to compare

* try another way to use outputs

* fix outputs

* pip install for mac before creating conda env

* fix output variable

* fix python bin path

* remove pip install for before creating conda env

* unpin streamlit version in conda mac env

* try to make git-workflows better readable

* remove 4gotten trigger

* Update-gh-actions (#6)

* fix mkdocs deployment

* update path to python bin

* add trigger for current branch

* change path to python_bin for mac as well

* try to use setup-python@v4 instead of setting env

* remove setup conda action

* try to use $CONDA

* remove overseen action

* change branch from master to main

* sort out if then else for faster syntax

* remove more if functions

* add updates to create-caches as well

* eliminate the rest of if functions

* try to unpin pytorch and torchvision

* restore pinned versions

* try switching from set-output to use env

* update test-invoke-conda as well

* fix env var creation

* quote variable

* add second equal to compare

* try another way to use outputs

* fix outputs

* pip install for mac before creating conda env

* fix output variable

* fix python bin path

* remove pip install for before creating conda env

* unpin streamlit version in conda mac env

* try to make git-workflows better readable

* use macos-latest

* try to update conda before creating mac env

* better conda update trial

* re-pin streamlit version

* re-added trigger to run workflow in current branch

* try to find out if conda mac env could be updated

* install cmake, protobuf and rust b4 conda

* add yes to conda update

* lets try anaconda3-2022.05

* try environment.yml for mac as well

* reenable conda mac env, add pip install
also fix gitignore by changing from dream to invoke

* remove
- unecesary virtualenv creation
- conda update

change != macos back to == linux

* remove cmake from brew install since pre-installed

* disable opencv-python pip requirement

* fixed commands to find latest package versions

* update requirements for mac env

* back to the roots - only install conda env
depending on runner_os with or without extra env variables

* check out macOS in azure-pipelines
since becoming kind of tired of the GitHub Runner which is broken as ...

* let's try to setup python and update conda env

* initialize conda before using it

* add trigger in azure-pipelines.yml

* And another go for update first ....

* update azure-pipelines.yml
- add caching
- add checkpoint download
- add paths to trigger
and more

* unquote checkpoint-url

* fix chekpoint-url variable

* mkdir before downloading model

* set pr trigger to main, rename anaconda cache

* unique cacheHitVariables

* try to use macos-latest instead of macos-12

* update test-invoke-conda.yml:
- remove unecesarry echo step
- use s-weigand/setup-conda@v1
- remove conda update from install deps step since updated with action

* update test-invoke-conda.yml:
- rename conda env cache from ldm to invokeai
- reorder steps:
  1. checkout sources
  2. setup python
  3. setup conda
  4. keep order after set platform variables

* change macos back to 12 since also fails with 11

* update condition in run the tests
make difference between main or not main

* fix path to cache invokeai conda env

* fix invokeai conda env cache path

* update mkdocs-flow.yml

* change conda-channel priority

* update create-caches

* update conda env also when cache was used

* os dependend conda env cache path

* use existing CONDA env pointing to conda root

* create CONDA_ROOT output from $CONDA

* use output variable to define test prompts

* use setup-python v4, get rid of PYTHON_BIN env

* add runner.os to result artifacts name

* update test-invoke-conda.yml:
- reuse macos-latest
- disable setup python 3.9
- setup conda with default python version
- create or update conda environment depending on cache success
- remove name parameter from conda update since name is set in env yml

* improve mkdocs-flow.yml

* disable cache-hugginface-torch
since preload_models.py downloads to more than one location

* update mkdocs-flow.yml with new name

* rename mkdocs action to mkdocs-material

* try to ignore error when creating conda env
maybe it would still be usable, lets see ;P

* remove bloat

* update environment-mac.yml
to match dependencies of invoke-ai/InvokeAI's main branch

* disable conda update, tweak prompt condition

* try to set some env vars for macOS to fix conda

* stop ignoring error, use env instead of outputs

* tweak `[[` connditions

* update python and pip dependencie
makes a difference of 1 sec per itteration compared to 3.9!!!
also I see no reason why using a old pip version would be beneficial

* remove unecesarry env for macOS
everything was pre-tested on my MacBook Air 2020 with M1

* update conda env in setup step

* activate conda env after installation

* update test-invoke-conda.yml
- set conda env dependent on matrix.os
- set CONDA_ENV_NAME to prevent breaking action when renaming conda env
- fix conda env activation

* fix activate conda env

* set bash -l as default shell

* use action to activate conda env

* add conda env file to env activation

* try to replace s-weigeand with conda-incubator

* remove azure-pipelines.yml
funniest part is that the macos runner is the same as the one on github!

* include environment-file in matrix
- also disable auto-activate-base and auto-update-conda
- include macos-latest and macos-12 for debugging purpose
- set miniforge-version in matrix

* fix miniforge-variant, set fail-fast to false

* add step to setup miniconda
- make default shell a matrix variable
- remove bloat

* use a mac env yml without pinned versions

* unpin nomkl, pytorch and torchvision
also removed opencv-pyhton

* cache conda pkgs dir instead of conda env

* use python 3.10, exclude macos-12 from cache

* fix expression

* prepare for PR

* fix doubled id

* reuse pinned versions in mac conda env
- updated python pip version
- unpined pytorch and torchvision
- removed opencv-python
- updated versions to most recent (tested locally)

* fix classical copy/paste error

* remove unused env from shell-block comment

* fix hashFiles function to determine restore-keys

* reenable caching `~.cache`, update create-caches

* unpin all versions in mac conda env file
this was the only way I got it working in the action, also works locally
tested on MacBook Air 2020 M1
remove environment-mac-unpinned.yml

* prepare merge by removing this branch from trigger

* include pull_request trigger for main and dev

* remove pull_request trigger
2022-11-02 22:10:31 -04:00
48e21486cb remove pr trigger 2022-11-02 22:07:42 -04:00
a6fa882b7c squash merge update-gh-actions into fix-gh-actions
* fix mkdocs deployment

* update path to python bin

* add trigger for current branch

* change path to python_bin for mac as well

* try to use setup-python@v4 instead of setting env

* remove setup conda action

* try to use $CONDA

* remove overseen action

* change branch from master to main

* sort out if then else for faster syntax

* remove more if functions

* add updates to create-caches as well

* eliminate the rest of if functions

* try to unpin pytorch and torchvision

* restore pinned versions

* try switching from set-output to use env

* update test-invoke-conda as well

* fix env var creation

* quote variable

* add second equal to compare

* try another way to use outputs

* fix outputs

* pip install for mac before creating conda env

* fix output variable

* fix python bin path

* remove pip install for before creating conda env

* unpin streamlit version in conda mac env

* try to make git-workflows better readable

* remove 4gotten trigger

* Update-gh-actions (#6)

* fix mkdocs deployment

* update path to python bin

* add trigger for current branch

* change path to python_bin for mac as well

* try to use setup-python@v4 instead of setting env

* remove setup conda action

* try to use $CONDA

* remove overseen action

* change branch from master to main

* sort out if then else for faster syntax

* remove more if functions

* add updates to create-caches as well

* eliminate the rest of if functions

* try to unpin pytorch and torchvision

* restore pinned versions

* try switching from set-output to use env

* update test-invoke-conda as well

* fix env var creation

* quote variable

* add second equal to compare

* try another way to use outputs

* fix outputs

* pip install for mac before creating conda env

* fix output variable

* fix python bin path

* remove pip install for before creating conda env

* unpin streamlit version in conda mac env

* try to make git-workflows better readable

* use macos-latest

* try to update conda before creating mac env

* better conda update trial

* re-pin streamlit version

* re-added trigger to run workflow in current branch

* try to find out if conda mac env could be updated

* install cmake, protobuf and rust b4 conda

* add yes to conda update

* lets try anaconda3-2022.05

* try environment.yml for mac as well

* reenable conda mac env, add pip install
also fix gitignore by changing from dream to invoke

* remove
- unecesary virtualenv creation
- conda update

change != macos back to == linux

* remove cmake from brew install since pre-installed

* disable opencv-python pip requirement

* fixed commands to find latest package versions

* update requirements for mac env

* back to the roots - only install conda env
depending on runner_os with or without extra env variables

* check out macOS in azure-pipelines
since becoming kind of tired of the GitHub Runner which is broken as ...

* let's try to setup python and update conda env

* initialize conda before using it

* add trigger in azure-pipelines.yml

* And another go for update first ....

* update azure-pipelines.yml
- add caching
- add checkpoint download
- add paths to trigger
and more

* unquote checkpoint-url

* fix chekpoint-url variable

* mkdir before downloading model

* set pr trigger to main, rename anaconda cache

* unique cacheHitVariables

* try to use macos-latest instead of macos-12

* update test-invoke-conda.yml:
- remove unecesarry echo step
- use s-weigand/setup-conda@v1
- remove conda update from install deps step since updated with action

* update test-invoke-conda.yml:
- rename conda env cache from ldm to invokeai
- reorder steps:
  1. checkout sources
  2. setup python
  3. setup conda
  4. keep order after set platform variables

* change macos back to 12 since also fails with 11

* update condition in run the tests
make difference between main or not main

* fix path to cache invokeai conda env

* fix invokeai conda env cache path

* update mkdocs-flow.yml

* change conda-channel priority

* update create-caches

* update conda env also when cache was used

* os dependend conda env cache path

* use existing CONDA env pointing to conda root

* create CONDA_ROOT output from $CONDA

* use output variable to define test prompts

* use setup-python v4, get rid of PYTHON_BIN env

* add runner.os to result artifacts name

* update test-invoke-conda.yml:
- reuse macos-latest
- disable setup python 3.9
- setup conda with default python version
- create or update conda environment depending on cache success
- remove name parameter from conda update since name is set in env yml

* improve mkdocs-flow.yml

* disable cache-hugginface-torch
since preload_models.py downloads to more than one location

* update mkdocs-flow.yml with new name

* rename mkdocs action to mkdocs-material

* try to ignore error when creating conda env
maybe it would still be usable, lets see ;P

* remove bloat

* update environment-mac.yml
to match dependencies of invoke-ai/InvokeAI's main branch

* disable conda update, tweak prompt condition

* try to set some env vars for macOS to fix conda

* stop ignoring error, use env instead of outputs

* tweak `[[` connditions

* update python and pip dependencie
makes a difference of 1 sec per itteration compared to 3.9!!!
also I see no reason why using a old pip version would be beneficial

* remove unecesarry env for macOS
everything was pre-tested on my MacBook Air 2020 with M1

* update conda env in setup step

* activate conda env after installation

* update test-invoke-conda.yml
- set conda env dependent on matrix.os
- set CONDA_ENV_NAME to prevent breaking action when renaming conda env
- fix conda env activation

* fix activate conda env

* set bash -l as default shell

* use action to activate conda env

* add conda env file to env activation

* try to replace s-weigeand with conda-incubator

* remove azure-pipelines.yml
funniest part is that the macos runner is the same as the one on github!

* include environment-file in matrix
- also disable auto-activate-base and auto-update-conda
- include macos-latest and macos-12 for debugging purpose
- set miniforge-version in matrix

* fix miniforge-variant, set fail-fast to false

* add step to setup miniconda
- make default shell a matrix variable
- remove bloat

* use a mac env yml without pinned versions

* unpin nomkl, pytorch and torchvision
also removed opencv-pyhton

* cache conda pkgs dir instead of conda env

* use python 3.10, exclude macos-12 from cache

* fix expression

* prepare for PR

* fix doubled id

* reuse pinned versions in mac conda env
- updated python pip version
- unpined pytorch and torchvision
- removed opencv-python
- updated versions to most recent (tested locally)

* fix classical copy/paste error

* remove unused env from shell-block comment

* fix hashFiles function to determine restore-keys

* reenable caching `~.cache`, update create-caches

* unpin all versions in mac conda env file
this was the only way I got it working in the action, also works locally
tested on MacBook Air 2020 M1
remove environment-mac-unpinned.yml

* prepare merge by removing this branch from trigger

* include pull_request trigger for main and dev

* remove pull_request trigger
2022-11-02 22:07:42 -04:00
aa12adccf3 restore inline images
<div> around the inline images works great in gh-pages, but breaks plain old markdown in GitHub code display. This removes the <div>s, causing slight degradation in quality of gh-page appearance.
2022-11-02 22:02:17 -04:00
282a2f642b restore inline images
<div> seems to be messing with the ability of the plain-old markdown processor to display inline images. Slightly degrades appearance of gh-pages.
2022-11-02 22:02:16 -04:00
d211c34f7b Update 'ldm' env to 'invokeai' in troubleshooting steps 2022-11-02 22:01:48 -04:00
e995e97690 Update generate.py
Fixed spelling mistake (open source king)
2022-11-02 22:01:17 -04:00
2211 changed files with 71000 additions and 218557 deletions

View File

@ -20,13 +20,13 @@ def calc_images_mean_L1(image1_path, image2_path):
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("image1_path")
parser.add_argument("image2_path")
parser.add_argument('image1_path')
parser.add_argument('image2_path')
args = parser.parse_args()
return args
if __name__ == "__main__":
if __name__ == '__main__':
args = parse_args()
mean_L1 = calc_images_mean_L1(args.image1_path, args.image2_path)
print(mean_L1)

View File

@ -1,9 +1,3 @@
*
!invokeai
!pyproject.toml
!docker/docker-entrypoint.sh
!LICENSE
**/node_modules
**/__pycache__
**/*.egg-info
!environment*.yml
!docker-build

View File

@ -1,12 +0,0 @@
# All files
[*]
charset = utf-8
end_of_line = lf
indent_size = 2
indent_style = space
insert_final_newline = true
trim_trailing_whitespace = true
# Python
[*.py]
indent_size = 4

View File

@ -1,2 +0,0 @@
b3dccfaeb636599c02effc377cdd8a87d658256c
218b6d0546b990fc449c876fb99f44b50c4daa35

3
.gitattributes vendored
View File

@ -1,5 +1,4 @@
# Auto normalizes line endings on commit so devs don't need to change local settings.
# Only affects text files and ignores other file types.
# Only affects text files and ignores other file types.
# For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
* text=auto
docker/** text eol=lf

36
.github/CODEOWNERS vendored
View File

@ -1,32 +1,4 @@
# continuous integration
/.github/workflows/ @lstein @blessedcoolant @hipsterusername @ebr
# documentation
/docs/ @lstein @blessedcoolant @hipsterusername @Millu
/mkdocs.yml @lstein @blessedcoolant @hipsterusername @Millu
# nodes
/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername
# installation and configuration
/pyproject.toml @lstein @blessedcoolant @hipsterusername
/docker/ @lstein @blessedcoolant @hipsterusername @ebr
/scripts/ @ebr @lstein @hipsterusername
/installer/ @lstein @ebr @hipsterusername
/invokeai/assets @lstein @ebr @hipsterusername
/invokeai/configs @lstein @hipsterusername
/invokeai/version @lstein @blessedcoolant @hipsterusername
# web ui
/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
# generation, model management, postprocessing
/invokeai/backend @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername
# front ends
/invokeai/frontend/CLI @lstein @hipsterusername
/invokeai/frontend/install @lstein @ebr @hipsterusername
/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername
ldm/invoke/pngwriter.py @CapableWeb
ldm/invoke/server_legacy.py @CapableWeb
scripts/legacy_api.py @CapableWeb
tests/legacy_tests.sh @CapableWeb

View File

@ -6,6 +6,10 @@ title: '[bug]: '
labels: ['bug']
# assignees:
# - moderator_bot
# - lstein
body:
- type: markdown
attributes:
@ -14,9 +18,10 @@ body:
- type: checkboxes
attributes:
label: Is there an existing issue for this problem?
label: Is there an existing issue for this?
description: |
Please [search](https://github.com/invoke-ai/InvokeAI/issues) first to see if an issue already exists for the problem.
Please use the [search function](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
irst to see if an issue already exists for the bug you encountered.
options:
- label: I have searched the existing issues
required: true
@ -28,119 +33,70 @@ body:
- type: dropdown
id: os_dropdown
attributes:
label: Operating system
description: Your computer's operating system.
label: OS
description: Which operating System did you use when the bug occured
multiple: false
options:
- 'Linux'
- 'Windows'
- 'macOS'
- 'other'
validations:
required: true
- type: dropdown
id: gpu_dropdown
attributes:
label: GPU vendor
description: Your GPU's vendor.
label: GPU
description: Which kind of Graphic-Adapter is your System using
multiple: false
options:
- 'Nvidia (CUDA)'
- 'AMD (ROCm)'
- 'Apple Silicon (MPS)'
- 'None (CPU)'
- 'cuda'
- 'amd'
- 'mps'
- 'cpu'
validations:
required: true
- type: input
id: gpu_model
attributes:
label: GPU model
description: Your GPU's model. If on Apple Silicon, this is your Mac's chip. Leave blank if on CPU.
placeholder: ex. RTX 2080 Ti, Mac M1 Pro
validations:
required: false
- type: input
id: vram
attributes:
label: GPU VRAM
description: Your GPU's VRAM. If on Apple Silicon, this is your Mac's unified memory. Leave blank if on CPU.
label: VRAM
description: Size of the VRAM if known
placeholder: 8GB
validations:
required: false
- type: input
id: version-number
attributes:
label: Version number
description: |
The version of Invoke you have installed. If it is not the latest version, please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
placeholder: ex. 3.6.1
validations:
required: true
- type: input
id: browser-version
attributes:
label: Browser
description: Your web browser and version.
placeholder: ex. Firefox 123.0b3
validations:
required: true
- type: textarea
id: python-deps
attributes:
label: Python dependencies
description: |
If the problem occurred during image generation, click the gear icon at the bottom left corner, click "About", click the copy button and then paste here.
validations:
required: false
- type: textarea
id: what-happened
attributes:
label: What happened
label: What happened?
description: |
Describe what happened. Include any relevant error messages, stack traces and screenshots here.
placeholder: I clicked button X and then Y happened.
Briefly describe what happened, what you expected to happen and how to reproduce this bug.
placeholder: When using the webinterface and right-clicking on button X instead of the popup-menu there error Y appears
validations:
required: true
- type: textarea
id: what-you-expected
attributes:
label: What you expected to happen
description: Describe what you expected to happen.
placeholder: I expected Z to happen.
validations:
required: true
- type: textarea
id: how-to-repro
attributes:
label: How to reproduce the problem
description: List steps to reproduce the problem.
placeholder: Start the app, generate an image with these settings, then click button X.
label: Screenshots
description: If applicable, add screenshots to help explain your problem
placeholder: this is what the result looked like <screenshot>
validations:
required: false
- type: textarea
id: additional-context
attributes:
label: Additional context
description: Any other context that might help us to understand the problem.
description: Add any other context about the problem here
placeholder: Only happens when there is full moon and Friday the 13th on Christmas Eve 🎅🏻
validations:
required: false
- type: input
id: discord-username
id: contact
attributes:
label: Discord username
description: If you are on the Invoke discord and would prefer to be contacted there, please provide your username.
placeholder: supercoolusername123
label: Contact Details
description: __OPTIONAL__ How can we get in touch with you if we need more info (besides this issue)?
placeholder: ex. email@example.com, discordname, twitter, ...
validations:
required: false

View File

@ -1,5 +1,5 @@
name: Feature Request
description: Contribute a idea or request a new feature
description: Commit a idea or Request a new feature
title: '[enhancement]: '
labels: ['enhancement']
# assignees:
@ -9,14 +9,14 @@ body:
- type: markdown
attributes:
value: |
Thanks for taking the time to fill out this feature request!
Thanks for taking the time to fill out this Feature request!
- type: checkboxes
attributes:
label: Is there an existing issue for this?
description: |
Please make use of the [search function](https://github.com/invoke-ai/InvokeAI/labels/enhancement)
to see if a similar issue already exists for the feature you want to request
to see if a simmilar issue already exists for the feature you want to request
options:
- label: I have searched the existing issues
required: true
@ -34,9 +34,12 @@ body:
id: whatisexpected
attributes:
label: What should this feature add?
description: Explain the functionality this feature should add. Feature requests should be for single features. Please create multiple requests if you want to request multiple features.
description: Please try to explain the functionality this feature should add
placeholder: |
I'd like a button that creates an image of banana sushi every time I press it. Each image should be different. There should be a toggle next to the button that enables strawberry mode, in which the images are of strawberry sushi instead.
Instead of one huge textfield, it would be nice to have forms for bug-reports, feature-requests, ...
Great benefits with automatic labeling, assigning and other functionalitys not available in that form
via old-fashioned markdown-templates. I would also love to see the use of a moderator bot 🤖 like
https://github.com/marketplace/actions/issue-moderator-with-commands to auto close old issues and other things
validations:
required: true
@ -48,6 +51,6 @@ body:
- type: textarea
attributes:
label: Additional Content
label: Aditional Content
description: Add any other context or screenshots about the feature request here.
placeholder: This is a mockup of the design how I imagine it <screenshot>
placeholder: This is a Mockup of the design how I imagine it <screenshot>

View File

@ -1,33 +0,0 @@
name: install frontend dependencies
description: Installs frontend dependencies with pnpm, with caching
runs:
using: 'composite'
steps:
- name: setup node 18
uses: actions/setup-node@v4
with:
node-version: '18'
- name: setup pnpm
uses: pnpm/action-setup@v4
with:
version: 8.15.6
run_install: false
- name: get pnpm store directory
shell: bash
run: |
echo "STORE_PATH=$(pnpm store path --silent)" >> $GITHUB_ENV
- name: setup cache
uses: actions/cache@v4
with:
path: ${{ env.STORE_PATH }}
key: ${{ runner.os }}-pnpm-store-${{ hashFiles('**/pnpm-lock.yaml') }}
restore-keys: |
${{ runner.os }}-pnpm-store-
- name: install frontend dependencies
run: pnpm install --prefer-frozen-lockfile
shell: bash
working-directory: invokeai/frontend/web

59
.github/pr_labels.yml vendored
View File

@ -1,59 +0,0 @@
root:
- changed-files:
- any-glob-to-any-file: '*'
python-deps:
- changed-files:
- any-glob-to-any-file: 'pyproject.toml'
python:
- changed-files:
- all-globs-to-any-file:
- 'invokeai/**'
- '!invokeai/frontend/web/**'
python-tests:
- changed-files:
- any-glob-to-any-file: 'tests/**'
ci-cd:
- changed-files:
- any-glob-to-any-file: .github/**
docker:
- changed-files:
- any-glob-to-any-file: docker/**
installer:
- changed-files:
- any-glob-to-any-file: installer/**
docs:
- changed-files:
- any-glob-to-any-file: docs/**
invocations:
- changed-files:
- any-glob-to-any-file: 'invokeai/app/invocations/**'
backend:
- changed-files:
- any-glob-to-any-file: 'invokeai/backend/**'
api:
- changed-files:
- any-glob-to-any-file: 'invokeai/app/api/**'
services:
- changed-files:
- any-glob-to-any-file: 'invokeai/app/services/**'
frontend-deps:
- changed-files:
- any-glob-to-any-file:
- '**/*/package.json'
- '**/*/pnpm-lock.yaml'
frontend:
- changed-files:
- any-glob-to-any-file: 'invokeai/frontend/web/**'

View File

@ -1,21 +0,0 @@
## Summary
<!--A description of the changes in this PR. Include the kind of change (fix, feature, docs, etc), the "why" and the "how". Screenshots or videos are useful for frontend changes.-->
## Related Issues / Discussions
<!--WHEN APPLICABLE: List any related issues or discussions on github or discord. If this PR closes an issue, please use the "Closes #1234" format, so that the issue will be automatically closed when the PR merges.-->
## QA Instructions
<!--WHEN APPLICABLE: Describe how you have tested the changes in this PR. Provide enough detail that a reviewer can reproduce your tests.-->
## Merge Plan
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like DB schemas, may need some care when merging. For example, a careful rebase by the change author, timing to not interfere with a pending release, or a message to contributors on discord after merging.-->
## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_

19
.github/stale.yaml vendored
View File

@ -1,19 +0,0 @@
# Number of days of inactivity before an issue becomes stale
daysUntilStale: 28
# Number of days of inactivity before a stale issue is closed
daysUntilClose: 14
# Issues with these labels will never be considered stale
exemptLabels:
- pinned
- security
# Label to use when marking an issue as stale
staleLabel: stale
# Comment to post when marking an issue as stale. Set to `false` to disable
markComment: >
This issue has been automatically marked as stale because it has not had
recent activity. It will be closed if no further activity occurs. Please
update the ticket if this is still a problem on the latest release.
# Comment to post when closing a stale issue. Set to `false` to disable
closeComment: >
Due to inactivity, this issue has been automatically closed. If this is
still a problem on the latest release, please recreate the issue.

View File

@ -1,120 +1,42 @@
# Building the Image without pushing to confirm it is still buildable
# confirum functionality would unfortunately need way more resources
name: build container image
on:
push:
branches:
- 'main'
paths:
- 'pyproject.toml'
- '.dockerignore'
- 'invokeai/**'
- 'docker/Dockerfile'
- 'docker/docker-entrypoint.sh'
- 'workflows/build-container.yml'
tags:
- 'v*.*.*'
workflow_dispatch:
permissions:
contents: write
packages: write
- 'development'
pull_request:
branches:
- 'main'
- 'development'
jobs:
docker:
if: github.event.pull_request.draft == false
strategy:
fail-fast: false
matrix:
gpu-driver:
- cuda
- cpu
- rocm
runs-on: ubuntu-latest
name: ${{ matrix.gpu-driver }}
env:
# torch/arm64 does not support GPU currently, so arm64 builds
# would not be GPU-accelerated.
# re-enable arm64 if there is sufficient demand.
# PLATFORMS: 'linux/amd64,linux/arm64'
PLATFORMS: 'linux/amd64'
steps:
- name: Free up more disk space on the runner
# https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
run: |
echo "----- Free space before cleanup"
df -h
sudo rm -rf /usr/share/dotnet
sudo rm -rf "$AGENT_TOOLSDIRECTORY"
sudo swapoff /mnt/swapfile
sudo rm -rf /mnt/swapfile
echo "----- Free space after cleanup"
df -h
- name: prepare docker-tag
env:
repository: ${{ github.repository }}
run: echo "dockertag=${repository,,}" >> $GITHUB_ENV
- name: Checkout
uses: actions/checkout@v3
- name: Docker meta
id: meta
uses: docker/metadata-action@v4
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
images: |
ghcr.io/${{ github.repository }}
${{ env.DOCKERHUB_REPOSITORY }}
tags: |
type=ref,event=branch
type=ref,event=tag
type=pep440,pattern={{version}}
type=pep440,pattern={{major}}.{{minor}}
type=pep440,pattern={{major}}
type=sha,enable=true,prefix=sha-,format=short
flavor: |
latest=${{ matrix.gpu-driver == 'cuda' && github.ref == 'refs/heads/main' }}
suffix=-${{ matrix.gpu-driver }},onlatest=false
- name: Set up QEMU
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
- name: Cache Docker layers
uses: actions/cache@v2
with:
platforms: ${{ env.PLATFORMS }}
- name: Login to GitHub Container Registry
if: github.event_name != 'pull_request'
uses: docker/login-action@v2
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
# - name: Login to Docker Hub
# if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
# uses: docker/login-action@v2
# with:
# username: ${{ secrets.DOCKERHUB_USERNAME }}
# password: ${{ secrets.DOCKERHUB_TOKEN }}
path: /tmp/.buildx-cache
key: buildx-${{ hashFiles('docker-build/Dockerfile') }}
- name: Build container
timeout-minutes: 40
id: docker_build
uses: docker/build-push-action@v4
uses: docker/build-push-action@v3
with:
context: .
file: docker/Dockerfile
platforms: ${{ env.PLATFORMS }}
push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
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 }}
# - name: Docker Hub Description
# if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
# uses: peter-evans/dockerhub-description@v3
# with:
# username: ${{ secrets.DOCKERHUB_USERNAME }}
# password: ${{ secrets.DOCKERHUB_TOKEN }}
# repository: ${{ vars.DOCKERHUB_REPOSITORY }}
# short-description: ${{ github.event.repository.description }}
file: docker-build/Dockerfile
platforms: linux/amd64
push: false
tags: ${{ env.dockertag }}:latest
cache-from: type=local,src=/tmp/.buildx-cache
cache-to: type=local,dest=/tmp/.buildx-cache

View File

@ -1,45 +0,0 @@
# Builds and uploads the installer and python build artifacts.
name: build installer
on:
workflow_dispatch:
workflow_call:
jobs:
build-installer:
runs-on: ubuntu-latest
timeout-minutes: 5 # expected run time: <2 min
steps:
- name: checkout
uses: actions/checkout@v4
- name: setup python
uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: pip
cache-dependency-path: pyproject.toml
- name: install pypa/build
run: pip install --upgrade build
- name: setup frontend
uses: ./.github/actions/install-frontend-deps
- name: create installer
id: create_installer
run: ./create_installer.sh
working-directory: installer
- name: upload python distribution artifact
uses: actions/upload-artifact@v4
with:
name: dist
path: ${{ steps.create_installer.outputs.DIST_PATH }}
- name: upload installer artifact
uses: actions/upload-artifact@v4
with:
name: installer
path: ${{ steps.create_installer.outputs.INSTALLER_PATH }}

View File

@ -1,34 +0,0 @@
name: cleanup caches by a branch
on:
pull_request:
types:
- closed
workflow_dispatch:
jobs:
cleanup:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v3
- name: Cleanup
run: |
gh extension install actions/gh-actions-cache
REPO=${{ github.repository }}
BRANCH=${{ github.ref }}
echo "Fetching list of cache key"
cacheKeysForPR=$(gh actions-cache list -R $REPO -B $BRANCH | cut -f 1 )
## Setting this to not fail the workflow while deleting cache keys.
set +e
echo "Deleting caches..."
for cacheKey in $cacheKeysForPR
do
gh actions-cache delete $cacheKey -R $REPO -B $BRANCH --confirm
done
echo "Done"
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}

View File

@ -1,28 +0,0 @@
name: Close inactive issues
on:
schedule:
- cron: "00 4 * * *"
env:
DAYS_BEFORE_ISSUE_STALE: 30
DAYS_BEFORE_ISSUE_CLOSE: 14
jobs:
close-issues:
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
steps:
- uses: actions/stale@v8
with:
days-before-issue-stale: ${{ env.DAYS_BEFORE_ISSUE_STALE }}
days-before-issue-close: ${{ env.DAYS_BEFORE_ISSUE_CLOSE }}
stale-issue-label: "Inactive Issue"
stale-issue-message: "There has been no activity in this issue for ${{ env.DAYS_BEFORE_ISSUE_STALE }} days. If this issue is still being experienced, please reply with an updated confirmation that the issue is still being experienced with the latest release."
close-issue-message: "Due to inactivity, this issue was automatically closed. If you are still experiencing the issue, please recreate the issue."
days-before-pr-stale: -1
days-before-pr-close: -1
exempt-issue-labels: "Active Issue"
repo-token: ${{ secrets.GITHUB_TOKEN }}
operations-per-run: 500

80
.github/workflows/create-caches.yml vendored Normal file
View File

@ -0,0 +1,80 @@
name: Create Caches
on: workflow_dispatch
jobs:
os_matrix:
strategy:
matrix:
os: [ubuntu-latest, macos-latest]
include:
- os: ubuntu-latest
environment-file: environment.yml
default-shell: bash -l {0}
- os: macos-latest
environment-file: environment-mac.yml
default-shell: bash -l {0}
name: Test invoke.py on ${{ matrix.os }} with conda
runs-on: ${{ matrix.os }}
defaults:
run:
shell: ${{ matrix.default-shell }}
steps:
- name: Checkout sources
uses: actions/checkout@v3
- name: setup miniconda
uses: conda-incubator/setup-miniconda@v2
with:
auto-activate-base: false
auto-update-conda: false
miniconda-version: latest
- name: set environment
run: |
[[ "$GITHUB_REF" == 'refs/heads/main' ]] \
&& echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV \
|| echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
echo "CONDA_ROOT=$CONDA" >> $GITHUB_ENV
echo "CONDA_ENV_NAME=invokeai" >> $GITHUB_ENV
- name: Use Cached Stable Diffusion v1.4 Model
id: cache-sd-v1-4
uses: actions/cache@v3
env:
cache-name: cache-sd-v1-4
with:
path: models/ldm/stable-diffusion-v1/model.ckpt
key: ${{ env.cache-name }}
restore-keys: ${{ env.cache-name }}
- name: Download Stable Diffusion v1.4 Model
if: ${{ steps.cache-sd-v1-4.outputs.cache-hit != 'true' }}
run: |
[[ -d models/ldm/stable-diffusion-v1 ]] \
|| mkdir -p models/ldm/stable-diffusion-v1
[[ -r models/ldm/stable-diffusion-v1/model.ckpt ]] \
|| curl \
-H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
-o models/ldm/stable-diffusion-v1/model.ckpt \
-L https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
- name: Activate Conda Env
uses: conda-incubator/setup-miniconda@v2
with:
activate-environment: ${{ env.CONDA_ENV_NAME }}
environment-file: ${{ matrix.environment-file }}
- name: Use Cached Huggingface and Torch models
id: cache-hugginface-torch
uses: actions/cache@v3
env:
cache-name: cache-hugginface-torch
with:
path: ~/.cache
key: ${{ env.cache-name }}
restore-keys: |
${{ env.cache-name }}-${{ hashFiles('scripts/preload_models.py') }}
- name: run preload_models.py
run: python scripts/preload_models.py

View File

@ -1,80 +0,0 @@
# Runs frontend code quality checks.
#
# Checks for changes to frontend files before running the checks.
# If always_run is true, always runs the checks.
name: 'frontend checks'
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
workflow_dispatch:
inputs:
always_run:
description: 'Always run the checks'
required: true
type: boolean
default: true
workflow_call:
inputs:
always_run:
description: 'Always run the checks'
required: true
type: boolean
default: true
defaults:
run:
working-directory: invokeai/frontend/web
jobs:
frontend-checks:
runs-on: ubuntu-latest
timeout-minutes: 10 # expected run time: <2 min
steps:
- uses: actions/checkout@v4
- name: check for changed frontend files
if: ${{ inputs.always_run != true }}
id: changed-files
uses: tj-actions/changed-files@v42
with:
files_yaml: |
frontend:
- 'invokeai/frontend/web/**'
- name: install dependencies
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
uses: ./.github/actions/install-frontend-deps
- name: tsc
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
run: 'pnpm lint:tsc'
shell: bash
- name: dpdm
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
run: 'pnpm lint:dpdm'
shell: bash
- name: eslint
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
run: 'pnpm lint:eslint'
shell: bash
- name: prettier
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
run: 'pnpm lint:prettier'
shell: bash
- name: knip
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
run: 'pnpm lint:knip'
shell: bash

View File

@ -1,60 +0,0 @@
# Runs frontend tests.
#
# Checks for changes to frontend files before running the tests.
# If always_run is true, always runs the tests.
name: 'frontend tests'
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
workflow_dispatch:
inputs:
always_run:
description: 'Always run the tests'
required: true
type: boolean
default: true
workflow_call:
inputs:
always_run:
description: 'Always run the tests'
required: true
type: boolean
default: true
defaults:
run:
working-directory: invokeai/frontend/web
jobs:
frontend-tests:
runs-on: ubuntu-latest
timeout-minutes: 10 # expected run time: <2 min
steps:
- uses: actions/checkout@v4
- name: check for changed frontend files
if: ${{ inputs.always_run != true }}
id: changed-files
uses: tj-actions/changed-files@v42
with:
files_yaml: |
frontend:
- 'invokeai/frontend/web/**'
- name: install dependencies
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
uses: ./.github/actions/install-frontend-deps
- name: vitest
if: ${{ steps.changed-files.outputs.frontend_any_changed == 'true' || inputs.always_run == true }}
run: 'pnpm test:no-watch'
shell: bash

View File

@ -1,18 +0,0 @@
name: 'label PRs'
on:
- pull_request_target
jobs:
labeler:
permissions:
contents: read
pull-requests: write
runs-on: ubuntu-latest
steps:
- name: checkout
uses: actions/checkout@v4
- name: label PRs
uses: actions/labeler@v5
with:
configuration-path: .github/pr_labels.yml

View File

@ -1,49 +1,40 @@
# This is a mostly a copy-paste from https://github.com/squidfunk/mkdocs-material/blob/master/docs/publishing-your-site.md
name: mkdocs
name: mkdocs-material
on:
push:
branches:
- main
workflow_dispatch:
permissions:
contents: write
- 'main'
- 'development'
jobs:
deploy:
if: github.event.pull_request.draft == false
mkdocs-material:
runs-on: ubuntu-latest
env:
REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
REPO_NAME: '${{ github.repository }}'
SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
steps:
- name: checkout
uses: actions/checkout@v4
- name: checkout sources
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: setup python
uses: actions/setup-python@v5
uses: actions/setup-python@v4
with:
python-version: '3.10'
cache: pip
cache-dependency-path: pyproject.toml
- name: set cache id
run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
- name: install requirements
run: |
python -m \
pip install -r requirements-mkdocs.txt
- name: use cache
uses: actions/cache@v4
with:
key: mkdocs-material-${{ env.cache_id }}
path: .cache
restore-keys: |
mkdocs-material-
- name: confirm buildability
run: |
python -m \
mkdocs build \
--clean \
--verbose
- name: install dependencies
run: python -m pip install ".[docs]"
- name: build & deploy
run: mkdocs gh-deploy --force
- name: deploy to gh-pages
if: ${{ github.ref == 'refs/heads/main' }}
run: |
python -m \
mkdocs gh-deploy \
--clean \
--force

View File

@ -1,76 +0,0 @@
# Runs python code quality checks.
#
# Checks for changes to python files before running the checks.
# If always_run is true, always runs the checks.
#
# TODO: Add mypy or pyright to the checks.
name: 'python checks'
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
workflow_dispatch:
inputs:
always_run:
description: 'Always run the checks'
required: true
type: boolean
default: true
workflow_call:
inputs:
always_run:
description: 'Always run the checks'
required: true
type: boolean
default: true
jobs:
python-checks:
runs-on: ubuntu-latest
timeout-minutes: 5 # expected run time: <1 min
steps:
- name: checkout
uses: actions/checkout@v4
- name: check for changed python files
if: ${{ inputs.always_run != true }}
id: changed-files
uses: tj-actions/changed-files@v42
with:
files_yaml: |
python:
- 'pyproject.toml'
- 'invokeai/**'
- '!invokeai/frontend/web/**'
- 'tests/**'
- name: setup python
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: pip
cache-dependency-path: pyproject.toml
- name: install ruff
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
run: pip install ruff
shell: bash
- name: ruff check
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
run: ruff check --output-format=github .
shell: bash
- name: ruff format
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
run: ruff format --check .
shell: bash

View File

@ -1,106 +0,0 @@
# Runs python tests on a matrix of python versions and platforms.
#
# Checks for changes to python files before running the tests.
# If always_run is true, always runs the tests.
name: 'python tests'
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
workflow_dispatch:
inputs:
always_run:
description: 'Always run the tests'
required: true
type: boolean
default: true
workflow_call:
inputs:
always_run:
description: 'Always run the tests'
required: true
type: boolean
default: true
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
cancel-in-progress: true
jobs:
matrix:
strategy:
matrix:
python-version:
- '3.10'
- '3.11'
platform:
- linux-cuda-11_7
- linux-rocm-5_2
- linux-cpu
- macos-default
- windows-cpu
include:
- platform: linux-cuda-11_7
os: ubuntu-22.04
github-env: $GITHUB_ENV
- platform: linux-rocm-5_2
os: ubuntu-22.04
extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
github-env: $GITHUB_ENV
- platform: linux-cpu
os: ubuntu-22.04
extra-index-url: 'https://download.pytorch.org/whl/cpu'
github-env: $GITHUB_ENV
- platform: macos-default
os: macOS-12
github-env: $GITHUB_ENV
- platform: windows-cpu
os: windows-2022
github-env: $env:GITHUB_ENV
name: 'py${{ matrix.python-version }}: ${{ matrix.platform }}'
runs-on: ${{ matrix.os }}
timeout-minutes: 15 # expected run time: 2-6 min, depending on platform
env:
PIP_USE_PEP517: '1'
steps:
- name: checkout
uses: actions/checkout@v4
- name: check for changed python files
if: ${{ inputs.always_run != true }}
id: changed-files
uses: tj-actions/changed-files@v42
with:
files_yaml: |
python:
- 'pyproject.toml'
- 'invokeai/**'
- '!invokeai/frontend/web/**'
- 'tests/**'
- name: setup python
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
cache: pip
cache-dependency-path: pyproject.toml
- name: install dependencies
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
env:
PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
run: >
pip3 install --editable=".[test]"
- name: run pytest
if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
run: pytest

View File

@ -1,108 +0,0 @@
# Main release workflow. Triggered on tag push or manual trigger.
#
# - Runs all code checks and tests
# - Verifies the app version matches the tag version.
# - Builds the installer and build, uploading them as artifacts.
# - Publishes to TestPyPI and PyPI. Both are conditional on the previous steps passing and require a manual approval.
#
# See docs/RELEASE.md for more information on the release process.
name: release
on:
push:
tags:
- 'v*'
workflow_dispatch:
jobs:
check-version:
runs-on: ubuntu-latest
steps:
- name: checkout
uses: actions/checkout@v4
- name: check python version
uses: samuelcolvin/check-python-version@v4
id: check-python-version
with:
version_file_path: invokeai/version/invokeai_version.py
frontend-checks:
uses: ./.github/workflows/frontend-checks.yml
with:
always_run: true
frontend-tests:
uses: ./.github/workflows/frontend-tests.yml
with:
always_run: true
python-checks:
uses: ./.github/workflows/python-checks.yml
with:
always_run: true
python-tests:
uses: ./.github/workflows/python-tests.yml
with:
always_run: true
build:
uses: ./.github/workflows/build-installer.yml
publish-testpypi:
runs-on: ubuntu-latest
timeout-minutes: 5 # expected run time: <1 min
needs:
[
check-version,
frontend-checks,
frontend-tests,
python-checks,
python-tests,
build,
]
environment:
name: testpypi
url: https://test.pypi.org/p/invokeai
permissions:
id-token: write
steps:
- name: download distribution from build job
uses: actions/download-artifact@v4
with:
name: dist
path: dist/
- name: publish distribution to TestPyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
repository-url: https://test.pypi.org/legacy/
publish-pypi:
runs-on: ubuntu-latest
timeout-minutes: 5 # expected run time: <1 min
needs:
[
check-version,
frontend-checks,
frontend-tests,
python-checks,
python-tests,
build,
]
environment:
name: pypi
url: https://pypi.org/p/invokeai
permissions:
id-token: write
steps:
- name: download distribution from build job
uses: actions/download-artifact@v4
with:
name: dist
path: dist/
- name: publish distribution to PyPI
uses: pypa/gh-action-pypi-publish@release/v1

113
.github/workflows/test-invoke-conda.yml vendored Normal file
View File

@ -0,0 +1,113 @@
name: Test invoke.py
on:
push:
branches:
- 'main'
- 'development'
- 'fix-gh-actions-fork'
pull_request:
branches:
- 'main'
- 'development'
jobs:
matrix:
strategy:
fail-fast: false
matrix:
stable-diffusion-model:
# - 'https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt'
- 'https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt'
os:
- ubuntu-latest
- macOS-12
include:
- os: ubuntu-latest
environment-file: environment.yml
default-shell: bash -l {0}
- os: macOS-12
environment-file: environment-mac.yml
default-shell: bash -l {0}
# - stable-diffusion-model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
# stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
# stable-diffusion-model-switch: stable-diffusion-1.4
- stable-diffusion-model: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
stable-diffusion-model-switch: stable-diffusion-1.5
name: ${{ matrix.os }} with ${{ matrix.stable-diffusion-model-switch }}
runs-on: ${{ matrix.os }}
env:
CONDA_ENV_NAME: invokeai
defaults:
run:
shell: ${{ matrix.default-shell }}
steps:
- name: Checkout sources
id: checkout-sources
uses: actions/checkout@v3
- name: create models.yaml from example
run: cp configs/models.yaml.example configs/models.yaml
- name: Use cached conda packages
id: use-cached-conda-packages
uses: actions/cache@v3
with:
path: ~/conda_pkgs_dir
key: conda-pkgs-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles(matrix.environment-file) }}
- name: Activate Conda Env
id: activate-conda-env
uses: conda-incubator/setup-miniconda@v2
with:
activate-environment: ${{ env.CONDA_ENV_NAME }}
environment-file: ${{ matrix.environment-file }}
miniconda-version: latest
- name: set test prompt to main branch validation
if: ${{ github.ref == 'refs/heads/main' }}
run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV
- name: set test prompt to development branch validation
if: ${{ github.ref == 'refs/heads/development' }}
run: echo "TEST_PROMPTS=tests/dev_prompts.txt" >> $GITHUB_ENV
- name: set test prompt to Pull Request validation
if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
- name: Download ${{ matrix.stable-diffusion-model-switch }}
id: download-stable-diffusion-model
run: |
[[ -d models/ldm/stable-diffusion-v1 ]] \
|| mkdir -p models/ldm/stable-diffusion-v1
curl \
-H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
-o ${{ matrix.stable-diffusion-model-dl-path }} \
-L ${{ matrix.stable-diffusion-model }}
- name: run preload_models.py
id: run-preload-models
run: |
python scripts/preload_models.py \
--no-interactive
- name: Run the tests
id: run-tests
run: |
time python scripts/invoke.py \
--model ${{ matrix.stable-diffusion-model-switch }} \
--from_file ${{ env.TEST_PROMPTS }}
- name: export conda env
id: export-conda-env
run: |
mkdir -p outputs/img-samples
conda env export --name ${{ env.CONDA_ENV_NAME }} > outputs/img-samples/environment-${{ runner.os }}-${{ runner.arch }}.yml
- name: Archive results
id: archive-results
uses: actions/upload-artifact@v3
with:
name: results_${{ matrix.os }}_${{ matrix.stable-diffusion-model-switch }}
path: outputs/img-samples

50
.gitignore vendored
View File

@ -1,4 +1,16 @@
.idea/
# ignore default image save location and model symbolic link
outputs/
models/ldm/stable-diffusion-v1/model.ckpt
ldm/invoke/restoration/codeformer/weights
# ignore user models config
configs/models.user.yaml
config/models.user.yml
# ignore the Anaconda/Miniconda installer used while building Docker image
anaconda.sh
# ignore a directory which serves as a place for initial images
inputs/
# Byte-compiled / optimized / DLL files
__pycache__/
@ -20,6 +32,7 @@ dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
@ -46,21 +59,16 @@ pip-delete-this-directory.txt
htmlcov/
.tox/
.nox/
.coveragerc
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
cov.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
.pytest.ini
cover/
junit/
notes/
# Translations
*.mo
@ -133,10 +141,12 @@ celerybeat.pid
# Environments
.env
.venv*
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
@ -169,22 +179,32 @@ cython_debug/
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
src
**/__pycache__/
outputs
# Logs and associated folders
# created from generated embeddings.
logs
testtube
checkpoints
# If it's a Mac
.DS_Store
# Let the frontend manage its own gitignore
!invokeai/frontend/web/*
!frontend/*
# Scratch folder
.scratch/
.vscode/
gfpgan/
models/ldm/stable-diffusion-v1/*.sha256
# source installer files
installer/*zip
installer/install.bat
installer/install.sh
installer/update.bat
installer/update.sh
installer/InvokeAI-Installer/
# GFPGAN model files
gfpgan/
# config file (will be created by installer)
configs/models.yaml
# weights (will be created by installer)
models/ldm/stable-diffusion-v1/*.ckpt

View File

@ -1,24 +0,0 @@
# See https://pre-commit.com/ for usage and config
repos:
- repo: local
hooks:
- id: black
name: black
stages: [commit]
language: system
entry: black
types: [python]
- id: flake8
name: flake8
stages: [commit]
language: system
entry: flake8
types: [python]
- id: isort
name: isort
stages: [commit]
language: system
entry: isort
types: [python]

View File

@ -7,7 +7,7 @@ embeddedLanguageFormatting: auto
overrides:
- files: '*.md'
options:
proseWrap: preserve
proseWrap: always
printWidth: 80
parser: markdown
cursorOffset: -1

View File

@ -1,84 +0,0 @@
<img src="docs/assets/invoke_ai_banner.png" align="center">
Invoke-AI is a community of software developers, researchers, and user
interface experts who have come together on a voluntary basis to build
software tools which support cutting edge AI text-to-image
applications. This community is open to anyone who wishes to
contribute to the effort and has the skill and time to do so.
# Our Values
The InvokeAI team is a diverse community which includes individuals
from various parts of the world and many walks of life. Despite our
differences, we share a number of core values which we ask prospective
contributors to understand and respect. We believe:
1. That Open Source Software is a positive force in the world. We
create software that can be used, reused, and redistributed, without
restrictions, under a straightforward Open Source license (MIT). We
believe that Open Source benefits society as a whole by increasing the
availability of high quality software to all.
2. That those who create software should receive proper attribution
for their creative work. While we support the exchange and reuse of
Open Source Software, we feel strongly that the original authors of a
piece of code should receive credit for their contribution, and we
endeavor to do so whenever possible.
3. That there is moral ambiguity surrounding AI-assisted art. We are
aware of the moral and ethical issues surrounding the release of the
Stable Diffusion model and similar products. We are aware that, due to
the composition of their training sets, current AI-generated image
models are biased against certain ethnic groups, cultural concepts of
beauty, ethnic stereotypes, and gender roles.
1. We recognize the potential for harm to these groups that these biases
represent and trust that future AI models will take steps towards
reducing or eliminating the biases noted above, respect and give due
credit to the artists whose work is sourced, and call on developers
and users to favor these models over the older ones as they become
available.
4. We are deeply committed to ensuring that this technology benefits
everyone, including artists. We see AI art not as a replacement for
the artist, but rather as a tool to empower them. With that
in mind, we are constantly debating how to build systems that put
artists needs first: tools which can be readily integrated into an
artists existing workflows and practices, enhancing their work and
helping them to push it further. Every decision we take as a team,
which includes several artists, aims to build towards that goal.
5. That artificial intelligence can be a force for good in the world,
but must be used responsibly. Artificial intelligence technologies
have the potential to improve society, in everything from cancer care,
to customer service, to creative writing.
1. While we do not believe that software should arbitrarily limit what
users can do with it, we recognize that when used irresponsibly, AI
has the potential to do much harm. Our Discord server is actively
moderated in order to minimize the potential of harm from
user-contributed images. In addition, we ask users of our software to
refrain from using it in any way that would cause mental, emotional or
physical harm to individuals and vulnerable populations including (but
not limited to) women; minors; ethnic minorities; religious groups;
members of LGBTQIA communities; and people with disabilities or
impairments.
2. Note that some of the image generation AI models which the Invoke-AI
toolkit supports carry licensing agreements which impose restrictions
on how the model is used. We ask that our users read and agree to
these terms if they wish to make use of these models. These agreements
are distinct from the MIT license which applies to the InvokeAI
software and source code.
6. That mutual respect is key to a healthy software development
community. Members of the InvokeAI community are expected to treat
each other with respect, beneficence, and empathy. Each of us has a
different background and a unique set of skills. We strive to help
each other grow and gain new skills, and we apportion expectations in
a way that balances the members' time, skillset, and interest
area. Disputes are resolved by open and honest communication.
## Signature
This document has been collectively crafted and approved by the current InvokeAI team members, as of 28 Nov 2022: **lstein** (Lincoln Stein), **blessedcoolant**, **hipsterusername** (Kent Keirsey), **Kyle0654** (Kyle Schouviller), **damian0815**, **mauwii** (Matthias Wild), **Netsvetaev** (Artur Netsvetaev), **psychedelicious**, **tildebyte**, **keturn**, and **ebr** (Eugene Brodsky). Although individuals within the group may hold differing views on particular details and/or their implications, we are all in agreement about its fundamental statements, as well as their significance and importance to this project moving forward.

189
LICENSE
View File

@ -1,176 +1,21 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
MIT License
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
Copyright (c) 2022 InvokeAI Team
1. Definitions.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
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"Contribution" shall mean any work of authorship, including
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to that Work or Derivative Works thereof, that is intentionally
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communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
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designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
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You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
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the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
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7. Disclaimer of Warranty. Unless required by applicable law or
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8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
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9. Accepting Warranty or Additional Liability. While redistributing
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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SOFTWARE.

View File

@ -1,290 +0,0 @@
Copyright (c) 2023 Stability AI
CreativeML Open RAIL++-M License dated July 26, 2023
Section I: PREAMBLE
Multimodal generative models are being widely adopted and used, and
have the potential to transform the way artists, among other
individuals, conceive and benefit from AI or ML technologies as a tool
for content creation.
Notwithstanding the current and potential benefits that these
artifacts can bring to society at large, there are also concerns about
potential misuses of them, either due to their technical limitations
or ethical considerations.
In short, this license strives for both the open and responsible
downstream use of the accompanying model. When it comes to the open
character, we took inspiration from open source permissive licenses
regarding the grant of IP rights. Referring to the downstream
responsible use, we added use-based restrictions not permitting the
use of the model in very specific scenarios, in order for the licensor
to be able to enforce the license in case potential misuses of the
Model may occur. At the same time, we strive to promote open and
responsible research on generative models for art and content
generation.
Even though downstream derivative versions of the model could be
released under different licensing terms, the latter will always have
to include - at minimum - the same use-based restrictions as the ones
in the original license (this license). We believe in the intersection
between open and responsible AI development; thus, this agreement aims
to strike a balance between both in order to enable responsible
open-science in the field of AI.
This CreativeML Open RAIL++-M License governs the use of the model
(and its derivatives) and is informed by the model card associated
with the model.
NOW THEREFORE, You and Licensor agree as follows:
Definitions
"License" means the terms and conditions for use, reproduction, and
Distribution as defined in this document.
"Data" means a collection of information and/or content extracted from
the dataset used with the Model, including to train, pretrain, or
otherwise evaluate the Model. The Data is not licensed under this
License.
"Output" means the results of operating a Model as embodied in
informational content resulting therefrom.
"Model" means any accompanying machine-learning based assemblies
(including checkpoints), consisting of learnt weights, parameters
(including optimizer states), corresponding to the model architecture
as embodied in the Complementary Material, that have been trained or
tuned, in whole or in part on the Data, using the Complementary
Material.
"Derivatives of the Model" means all modifications to the Model, works
based on the Model, or any other model which is created or initialized
by transfer of patterns of the weights, parameters, activations or
output of the Model, to the other model, in order to cause the other
model to perform similarly to the Model, including - but not limited
to - distillation methods entailing the use of intermediate data
representations or methods based on the generation of synthetic data
by the Model for training the other model.
"Complementary Material" means the accompanying source code and
scripts used to define, run, load, benchmark or evaluate the Model,
and used to prepare data for training or evaluation, if any. This
includes any accompanying documentation, tutorials, examples, etc, if
any.
"Distribution" means any transmission, reproduction, publication or
other sharing of the Model or Derivatives of the Model to a third
party, including providing the Model as a hosted service made
available by electronic or other remote means - e.g. API-based or web
access.
"Licensor" means the copyright owner or entity authorized by the
copyright owner that is granting the License, including the persons or
entities that may have rights in the Model and/or distributing the
Model.
"You" (or "Your") means an individual or Legal Entity exercising
permissions granted by this License and/or making use of the Model for
whichever purpose and in any field of use, including usage of the
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Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
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the use of Complementary Material. You must give any Third Party
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Model. You may add Your own copyright statement to Your modifications
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respecting paragraph 4.a. - for use, reproduction, or Distribution of
Your modifications, or for any such Derivatives of the Model as a
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Use-based restrictions. The restrictions set forth in Attachment A are
considered Use-based restrictions. Therefore You cannot use the Model
and the Derivatives of the Model for the specified restricted
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include creating any content with, finetuning, updating, running,
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require all of Your users who use the Model or a Derivative of the
Model to comply with the terms of this paragraph (paragraph 5).
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no rights in the Output You generate using the Model. You are
accountable for the Output you generate and its subsequent uses. No
use of the output can contravene any provision as stated in the
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Section IV: OTHER PROVISIONS
Updates and Runtime Restrictions. To the maximum extent permitted by
law, Licensor reserves the right to restrict (remotely or otherwise)
usage of the Model in violation of this License.
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use of Licensors trademarks, trade names, logos or to otherwise
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If any provision of this License is held to be invalid, illegal or
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END OF TERMS AND CONDITIONS
Attachment A
Use Restrictions
You agree not to use the Model or Derivatives of the Model:
* In any way that violates any applicable national, federal, state,
local or international law or regulation;
* For the purpose of exploiting, harming or attempting to exploit or
harm minors in any way;
* To generate or disseminate verifiably false information and/or
content with the purpose of harming others;
* To generate or disseminate personal identifiable information that
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* To defame, disparage or otherwise harass others;
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* For any use intended to or which has the effect of discriminating
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for administration of justice, law enforcement, immigration or
asylum processes, such as predicting an individual will commit
fraud/crime commitment (e.g. by text profiling, drawing causal
relationships between assertions made in documents, indiscriminate
and arbitrarily-targeted use).

View File

@ -1,76 +0,0 @@
# simple Makefile with scripts that are otherwise hard to remember
# to use, run from the repo root `make <command>`
default: help
help:
@echo Developer commands:
@echo
@echo "ruff Run ruff, fixing any safely-fixable errors and formatting"
@echo "ruff-unsafe Run ruff, fixing all fixable errors and formatting"
@echo "mypy Run mypy using the config in pyproject.toml to identify type mismatches and other coding errors"
@echo "mypy-all Run mypy ignoring the config in pyproject.tom but still ignoring missing imports"
@echo "test Run the unit tests."
@echo "update-config-docstring Update the app's config docstring so mkdocs can autogenerate it correctly."
@echo "frontend-install Install the pnpm modules needed for the front end"
@echo "frontend-build Build the frontend in order to run on localhost:9090"
@echo "frontend-dev Run the frontend in developer mode on localhost:5173"
@echo "frontend-typegen Generate types for the frontend from the OpenAPI schema"
@echo "installer-zip Build the installer .zip file for the current version"
@echo "tag-release Tag the GitHub repository with the current version (use at release time only!)"
@echo "openapi Generate the OpenAPI schema for the app, outputting to stdout"
# Runs ruff, fixing any safely-fixable errors and formatting
ruff:
ruff check . --fix
ruff format .
# Runs ruff, fixing all errors it can fix and formatting
ruff-unsafe:
ruff check . --fix --unsafe-fixes
ruff format .
# Runs mypy, using the config in pyproject.toml
mypy:
mypy scripts/invokeai-web.py
# Runs mypy, ignoring the config in pyproject.toml but still ignoring missing (untyped) imports
# (many files are ignored by the config, so this is useful for checking all files)
mypy-all:
mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports
# Run the unit tests
test:
pytest ./tests
# Update config docstring
update-config-docstring:
python scripts/update_config_docstring.py
# Install the pnpm modules needed for the front end
frontend-install:
rm -rf invokeai/frontend/web/node_modules
cd invokeai/frontend/web && pnpm install
# Build the frontend
frontend-build:
cd invokeai/frontend/web && pnpm build
# Run the frontend in dev mode
frontend-dev:
cd invokeai/frontend/web && pnpm dev
frontend-typegen:
cd invokeai/frontend/web && python ../../../scripts/generate_openapi_schema.py | pnpm typegen
# Installer zip file
installer-zip:
cd installer && ./create_installer.sh
# Tag the release
tag-release:
cd installer && ./tag_release.sh
# Generate the OpenAPI Schema for the app
openapi:
python scripts/generate_openapi_schema.py

332
README.md
View File

@ -1,142 +1,23 @@
<div align="center">
![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/6e3728c7-e90e-4711-905c-3b55844ff5be)
# InvokeAI: A Stable Diffusion Toolkit
# Invoke - Professional Creative AI Tools for Visual Media
_Formerly known as lstein/stable-diffusion_
#### To learn more about Invoke, or implement our Business solutions, visit [invoke.com]
![project logo](docs/assets/logo.png)
[![discord badge]][discord link] [![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link] [![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link] [![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
[![discord badge]][discord link]
</div>
[![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]
Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
[![CI checks on main badge]][CI checks on main link] [![CI checks on dev badge]][CI checks on dev link] [![latest commit to dev badge]][latest commit to dev link]
Invoke is available in two editions:
[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
| **Community Edition** | **Professional Edition** |
|----------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------|
| **For users looking for a locally installed, self-hosted and self-managed service** | **For users or teams looking for a cloud-hosted, fully managed service** |
| - Free to use under a commercially-friendly license | - Monthly subscription fee with three different plan levels |
| - Download and install on compatible hardware | - Offers additional benefits, including multi-user support, improved model training, and more |
| - Includes all core studio features: generate, refine, iterate on images, and build workflows | - Hosted in the cloud for easy, secure model access and scalability |
| Quick Start -> [Installation and Updates][installation docs] | More Information -> [www.invoke.com/pricing](https://www.invoke.com/pricing) |
![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
# Documentation
| **Quick Links** |
|----------------------------------------------------------------------------------------------------------------------------|
| [Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs] |
</div>
## Quick Start
1. Download and unzip the installer from the bottom of the [latest release][latest release link].
2. Run the installer script.
- **Windows**: Double-click on the `install.bat` script.
- **macOS**: Open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press enter.
- **Linux**: Run `install.sh`.
3. When prompted, enter a location for the install and select your GPU type.
4. Once the install finishes, find the directory you selected during install. The default location is `C:\Users\Username\invokeai` for Windows or `~/invokeai` for Linux/macOS.
5. Run the launcher script (`invoke.bat` for Windows, `invoke.sh` for macOS and Linux) the same way you ran the installer script in step 2.
6. Select option 1 to start the application. Once it starts up, open your browser and go to <http://localhost:9090>.
7. Open the model manager tab to install a starter model and then you'll be ready to generate.
More detail, including hardware requirements and manual install instructions, are available in the [installation documentation][installation docs].
## Docker Container
We publish official container images in Github Container Registry: https://github.com/invoke-ai/InvokeAI/pkgs/container/invokeai. Both CUDA and ROCm images are available. Check the above link for relevant tags.
> [!IMPORTANT]
> Ensure that Docker is set up to use the GPU. Refer to [NVIDIA][nvidia docker docs] or [AMD][amd docker docs] documentation.
### Generate!
Run the container, modifying the command as necessary:
```bash
docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
```
Then open `http://localhost:9090` and install some models using the Model Manager tab to begin generating.
For ROCm, add `--device /dev/kfd --device /dev/dri` to the `docker run` command.
### Persist your data
You will likely want to persist your workspace outside of the container. Use the `--volume /home/myuser/invokeai:/invokeai` flag to mount some local directory (using its **absolute** path) to the `/invokeai` path inside the container. Your generated images and models will reside there. You can use this directory with other InvokeAI installations, or switch between runtime directories as needed.
### DIY
Build your own image and customize the environment to match your needs using our `docker-compose` stack. See [README.md](./docker/README.md) in the [docker](./docker) directory.
## Troubleshooting, FAQ and Support
Please review our [FAQ][faq] for solutions to common installation problems and other issues.
For more help, please join our [Discord][discord link].
## Features
Full details on features can be found in [our documentation][features docs].
### Web Server & UI
Invoke runs a locally hosted web server & React UI with an industry-leading user experience.
### Unified Canvas
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/out-painting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
### Workflows & Nodes
Invoke offers a fully featured workflow management solution, enabling users to combine the power of node-based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
### Board & Gallery Management
Invoke features an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow.
### Other features
- Support for both ckpt and diffusers models
- SD1.5, SD2.0, and SDXL support
- Upscaling Tools
- Embedding Manager & Support
- Model Manager & Support
- Workflow creation & management
- Node-Based Architecture
## Contributing
Anyone who wishes to contribute to this project - whether documentation, features, bug fixes, code cleanup, testing, or code reviews - is very much encouraged to do so.
Get started with contributing by reading our [contribution documentation][contributing docs], joining the [#dev-chat] or the GitHub discussion board.
We hope you enjoy using Invoke as much as we enjoy creating it, and we hope you will elect to become part of our community.
## Thanks
Invoke is a combined effort of [passionate and talented people from across the world][contributors]. We thank them for their time, hard work and effort.
Original portions of the software are Copyright © 2024 by respective contributors.
[features docs]: https://invoke-ai.github.io/InvokeAI/features/
[faq]: https://invoke-ai.github.io/InvokeAI/help/FAQ/
[contributors]: https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/
[invoke.com]: https://www.invoke.com/about
[github issues]: https://github.com/invoke-ai/InvokeAI/issues
[docs home]: https://invoke-ai.github.io/InvokeAI
[installation docs]: https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/
[#dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
[contributing docs]: https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/
[CI checks on dev badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
[CI checks on dev link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
[CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
[discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
[discord link]: https://discord.gg/ZmtBAhwWhy
[github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
@ -147,11 +28,190 @@ Original portions of the software are Copyright © 2024 by respective contributo
[github open prs link]: https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
[github stars badge]: https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
[github stars link]: https://github.com/invoke-ai/InvokeAI/stargazers
[latest commit to main badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/main?icon=github&color=yellow&label=last%20dev%20commit&cache=900
[latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
[latest commit to dev badge]: https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
[latest commit to dev link]: https://github.com/invoke-ai/InvokeAI/commits/development
[latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
[latest release link]: https://github.com/invoke-ai/InvokeAI/releases/latest
[translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
[translation status link]: https://hosted.weblate.org/engage/invokeai/
[nvidia docker docs]: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
[amd docker docs]: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html
[latest release link]: https://github.com/invoke-ai/InvokeAI/releases
</div>
This is a fork of
[CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion),
the open source text-to-image generator. It provides a streamlined
process with various new features and options to aid the image
generation process. It runs on Windows, Mac and Linux machines, with
GPU cards with as little as 4 GB of RAM. It provides both a polished
Web interface (see below), and an easy-to-use command-line interface.
**Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
<div align="center"><img src="docs/assets/invoke-web-server-1.png" width=640></div>
_Note: This fork is rapidly evolving. Please use the
[Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
requests. Be sure to use the provided templates. They will help aid diagnose issues faster._
## Table of Contents
1. [Installation](#installation)
2. [Hardware Requirements](#hardware-requirements)
3. [Features](#features)
4. [Latest Changes](#latest-changes)
5. [Troubleshooting](#troubleshooting)
6. [Contributing](#contributing)
7. [Contributors](#contributors)
8. [Support](#support)
9. [Further Reading](#further-reading)
### Installation
This fork is supported across multiple platforms. You can find individual installation instructions
below.
- #### [Linux](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_LINUX/)
- #### [Windows](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_WINDOWS/)
- #### [Macintosh](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_MAC/)
### Hardware Requirements
#### System
You wil need one of the following:
- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
- An Apple computer with an M1 chip.
#### Memory
- At least 12 GB Main Memory RAM.
#### Disk
- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
**Note**
If you have a Nvidia 10xx series card (e.g. the 1080ti), please
run the dream script in full-precision mode as shown below.
Similarly, specify full-precision mode on Apple M1 hardware.
Precision is auto configured based on the device. If however you encounter
errors like 'expected type Float but found Half' or 'not implemented for Half'
you can try starting `invoke.py` with the `--precision=float32` flag:
```bash
(invokeai) ~/InvokeAI$ python scripts/invoke.py --precision=float32
```
### Features
#### Major Features
- [Web Server](https://invoke-ai.github.io/InvokeAI/features/WEB/)
- [Interactive Command Line Interface](https://invoke-ai.github.io/InvokeAI/features/CLI/)
- [Image To Image](https://invoke-ai.github.io/InvokeAI/features/IMG2IMG/)
- [Inpainting Support](https://invoke-ai.github.io/InvokeAI/features/INPAINTING/)
- [Outpainting Support](https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/)
- [Upscaling, face-restoration and outpainting](https://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/)
- [Reading Prompts From File](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#reading-prompts-from-a-file)
- [Prompt Blending](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#prompt-blending)
- [Thresholding and Perlin Noise Initialization Options](https://invoke-ai.github.io/InvokeAI/features/OTHER/#thresholding-and-perlin-noise-initialization-options)
- [Negative/Unconditioned Prompts](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#negative-and-unconditioned-prompts)
- [Variations](https://invoke-ai.github.io/InvokeAI/features/VARIATIONS/)
- [Personalizing Text-to-Image Generation](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/)
- [Simplified API for text to image generation](https://invoke-ai.github.io/InvokeAI/features/OTHER/#simplified-api)
#### Other Features
- [Google Colab](https://invoke-ai.github.io/InvokeAI/features/OTHER/#google-colab)
- [Seamless Tiling](https://invoke-ai.github.io/InvokeAI/features/OTHER/#seamless-tiling)
- [Shortcut: Reusing Seeds](https://invoke-ai.github.io/InvokeAI/features/OTHER/#shortcuts-reusing-seeds)
- [Preload Models](https://invoke-ai.github.io/InvokeAI/features/OTHER/#preload-models)
### Latest Changes
### v2.1.0 major changes <small>(2 November 2022)</small>
- [Inpainting](https://invoke-ai.github.io/InvokeAI/features/INPAINTING/) support in the WebGUI
- Greatly improved navigation and user experience in the [WebGUI](https://invoke-ai.github.io/InvokeAI/features/WEB/)
- The prompt syntax has been enhanced with [prompt weighting, cross-attention and prompt merging](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/).
- You can now load [multiple models and switch among them quickly](https://docs.google.com/presentation/d/1WywGA1rny7bpFh7CLSdTr4nNpVKdlUeT0Bj0jCsILyU/edit?usp=sharing) without leaving the CLI.
- The installation process (via `scripts/preload_models.py`) now lets you select among several popular [Stable Diffusion models](https://invoke-ai.github.io/InvokeAI/installation/INSTALLING_MODELS/) and downloads and installs them on your behalf. Among other models, this script will install the current Stable Diffusion 1.5 model as well as a StabilityAI variable autoencoder (VAE) which improves face generation.
- Tired of struggling with photoeditors to get the masked region of for inpainting just right? Let the AI make the mask for you using [text masking](https://docs.google.com/presentation/d/1pWoY510hCVjz0M6X9CBbTznZgW2W5BYNKrmZm7B45q8/edit#slide=id.p). This feature allows you to specify the part of the image to paint over using just English-language phrases.
- Tired of seeing the head of your subjects cropped off? Uncrop them in the CLI with the [outcrop feature](https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/#outcrop).
- Tired of seeing your subject's bodies duplicated or mangled when generating larger-dimension images? Check out the `--hires` option in the CLI, or select the corresponding toggle in the WebGUI.
- We now support textual inversion and fine-tune .bin styles and subjects from the Hugging Face archive of [SD Concepts](https://huggingface.co/sd-concepts-library). Load the .bin file using the `--embedding_path` option. (The next version will support merging and loading of multiple simultaneous models).
<a href="https://invoke-ai.github.io/InvokeAI/CHANGELOG/>Complete Changelog</a>
- v2.0.1 (13 October 2022)
- fix noisy images at high step count when using k* samplers
- dream.py script now calls invoke.py module directly rather than
via a new python process (which could break the environment)
- v2.0.0 (9 October 2022)
- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains
for backward compatibility.
- Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
- Support for <a href="https://invoke-ai.github.io/InvokeAI/features/INPAINTING/">inpainting</a> and <a href="https://invoke-ai.github.io/InvokeAI/features/OUTPAINTING/">outpainting</a>
- img2img runs on all k* samplers
- Support for <a href="https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#negative-and-unconditioned-prompts">negative prompts</a>
- Support for CodeFormer face reconstruction
- Support for Textual Inversion on Macintoshes
- Support in both WebGUI and CLI for <a href="https://invoke-ai.github.io/InvokeAI/features/POSTPROCESS/">post-processing of previously-generated images</a>
using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E infinite canvas),
and "embiggen" upscaling. See the `!fix` command.
- New `--hires` option on `invoke>` line allows <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/#txt2img">larger images to be created without duplicating elements</a>, at the cost of some performance.
- New `--perlin` and `--threshold` options allow you to add and control variation
during image generation (see <a href="https://github.com/invoke-ai/InvokeAI/blob/main/docs/features/OTHER.md#thresholding-and-perlin-noise-initialization-options">Thresholding and Perlin Noise Initialization</a>
- Extensive metadata now written into PNG files, allowing reliable regeneration of images
and tweaking of previous settings.
- Command-line completion in `invoke.py` now works on Windows, Linux and Mac platforms.
- Improved <a href="https://invoke-ai.github.io/InvokeAI/features/CLI/">command-line completion behavior</a>.
New commands added:
- List command-line history with `!history`
- Search command-line history with `!search`
- Clear history with `!clear`
- Deprecated `--full_precision` / `-F`. Simply omit it and `invoke.py` will auto
configure. To switch away from auto use the new flag like `--precision=float32`.
For older changelogs, please visit the **[CHANGELOG](https://invoke-ai.github.io/InvokeAI/CHANGELOG#v114-11-september-2022)**.
### Troubleshooting
Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
problems and other issues.
# Contributing
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
cleanup, testing, or code reviews, is very much encouraged to do so. If you are unfamiliar with how
to contribute to GitHub projects, here is a
[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github).
A full set of contribution guidelines, along with templates, are in progress, but for now the most
important thing is to **make your pull request against the "development" branch**, and not against
"main". This will help keep public breakage to a minimum and will allow you to propose more radical
changes.
### Contributors
This fork is a combined effort of various people from across the world.
[Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for
their time, hard work and effort.
### Support
For support, please use this repository's GitHub Issues tracking service. Feel free to send me an
email if you use and like the script.
Original portions of the software are Copyright (c) 2020
[Lincoln D. Stein](https://github.com/lstein)
### Further Reading
Please see the original README for more information on this software and underlying algorithm,
located in the file [README-CompViz.md](https://invoke-ai.github.io/InvokeAI/other/README-CompViz/).

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@ -21,7 +21,7 @@ This model card focuses on the model associated with the Stable Diffusion model,
# Uses
## Direct Use
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
@ -68,11 +68,11 @@ Using the model to generate content that is cruel to individuals is a misuse of
considerations.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
@ -84,7 +84,7 @@ The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
@ -108,12 +108,12 @@ filtered to images with an original size `>= 512x512`, estimated aesthetics scor
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:
![pareto](assets/v1-variants-scores.jpg)
![pareto](assets/v1-variants-scores.jpg)
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact

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@ -0,0 +1,55 @@
import argparse
import os
from ldm.invoke.args import PRECISION_CHOICES
def create_cmd_parser():
parser = argparse.ArgumentParser(description="InvokeAI web UI")
parser.add_argument(
"--host",
type=str,
help="The host to serve on",
default="localhost",
)
parser.add_argument("--port", type=int, help="The port to serve on", default=9090)
parser.add_argument(
"--cors",
nargs="*",
type=str,
help="Additional allowed origins, comma-separated",
)
parser.add_argument(
"--embedding_path",
type=str,
help="Path to a pre-trained embedding manager checkpoint - can only be set on command line",
)
# TODO: Can't get flask to serve images from any dir (saving to the dir does work when specified)
# parser.add_argument(
# "--output_dir",
# default="outputs/",
# type=str,
# help="Directory for output images",
# )
parser.add_argument(
"-v",
"--verbose",
action="store_true",
help="Enables verbose logging",
)
parser.add_argument(
"--precision",
dest="precision",
type=str,
choices=PRECISION_CHOICES,
metavar="PRECISION",
help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
default="auto",
)
parser.add_argument(
'--free_gpu_mem',
dest='free_gpu_mem',
action='store_true',
help='Force free gpu memory before final decoding',
)
return parser

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@ -0,0 +1,69 @@
from backend.modules.parse_seed_weights import parse_seed_weights
import argparse
SAMPLER_CHOICES = [
"ddim",
"k_dpm_2_a",
"k_dpm_2",
"k_euler_a",
"k_euler",
"k_heun",
"k_lms",
"plms",
]
def parameters_to_command(params):
"""
Converts dict of parameters into a `invoke.py` REPL command.
"""
switches = list()
if "prompt" in params:
switches.append(f'"{params["prompt"]}"')
if "steps" in params:
switches.append(f'-s {params["steps"]}')
if "seed" in params:
switches.append(f'-S {params["seed"]}')
if "width" in params:
switches.append(f'-W {params["width"]}')
if "height" in params:
switches.append(f'-H {params["height"]}')
if "cfg_scale" in params:
switches.append(f'-C {params["cfg_scale"]}')
if "sampler_name" in params:
switches.append(f'-A {params["sampler_name"]}')
if "seamless" in params and params["seamless"] == True:
switches.append(f"--seamless")
if "hires_fix" in params and params["hires_fix"] == True:
switches.append(f"--hires")
if "init_img" in params and len(params["init_img"]) > 0:
switches.append(f'-I {params["init_img"]}')
if "init_mask" in params and len(params["init_mask"]) > 0:
switches.append(f'-M {params["init_mask"]}')
if "init_color" in params and len(params["init_color"]) > 0:
switches.append(f'--init_color {params["init_color"]}')
if "strength" in params and "init_img" in params:
switches.append(f'-f {params["strength"]}')
if "fit" in params and params["fit"] == True:
switches.append(f"--fit")
if "facetool" in params:
switches.append(f'-ft {params["facetool"]}')
if "facetool_strength" in params and params["facetool_strength"]:
switches.append(f'-G {params["facetool_strength"]}')
elif "gfpgan_strength" in params and params["gfpgan_strength"]:
switches.append(f'-G {params["gfpgan_strength"]}')
if "codeformer_fidelity" in params:
switches.append(f'-cf {params["codeformer_fidelity"]}')
if "upscale" in params and params["upscale"]:
switches.append(f'-U {params["upscale"][0]} {params["upscale"][1]}')
if "variation_amount" in params and params["variation_amount"] > 0:
switches.append(f'-v {params["variation_amount"]}')
if "with_variations" in params:
seed_weight_pairs = ",".join(
f"{seed}:{weight}" for seed, weight in params["with_variations"]
)
switches.append(f"-V {seed_weight_pairs}")
return " ".join(switches)

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@ -0,0 +1,47 @@
def parse_seed_weights(seed_weights):
"""
Accepts seed weights as string in "12345:0.1,23456:0.2,3456:0.3" format
Validates them
If valid: returns as [[12345, 0.1], [23456, 0.2], [3456, 0.3]]
If invalid: returns False
"""
# Must be a string
if not isinstance(seed_weights, str):
return False
# String must not be empty
if len(seed_weights) == 0:
return False
pairs = []
for pair in seed_weights.split(","):
split_values = pair.split(":")
# Seed and weight are required
if len(split_values) != 2:
return False
if len(split_values[0]) == 0 or len(split_values[1]) == 1:
return False
# Try casting the seed to int and weight to float
try:
seed = int(split_values[0])
weight = float(split_values[1])
except ValueError:
return False
# Seed must be 0 or above
if not seed >= 0:
return False
# Weight must be between 0 and 1
if not (weight >= 0 and weight <= 1):
return False
# This pair is valid
pairs.append([seed, weight])
# All pairs are valid
return pairs

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@ -0,0 +1,27 @@
# This file describes the alternative machine learning models
# available to InvokeAI script.
#
# To add a new model, follow the examples below. Each
# model requires a model config file, a weights file,
# and the width and height of the images it
# was trained on.
stable-diffusion-1.5:
description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
weights: ./models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
config: ./configs/stable-diffusion/v1-inference.yaml
width: 512
height: 512
vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
default: true
stable-diffusion-1.4:
description: Stable Diffusion inference model version 1.4
config: configs/stable-diffusion/v1-inference.yaml
weights: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
width: 512
height: 512
inpainting-1.5:
weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
config: configs/stable-diffusion/v1-inpainting-inference.yaml
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
description: RunwayML SD 1.5 model optimized for inpainting

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@ -0,0 +1,110 @@
model:
base_learning_rate: 5.0e-03
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: caption
image_size: 64
channels: 4
cond_stage_trainable: true # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
embedding_reg_weight: 0.0
personalization_config:
target: ldm.modules.embedding_manager.EmbeddingManager
params:
placeholder_strings: ["*"]
initializer_words: ["sculpture"]
per_image_tokens: false
num_vectors_per_token: 1
progressive_words: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
data:
target: main.DataModuleFromConfig
params:
batch_size: 1
num_workers: 2
wrap: false
train:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: train
per_image_tokens: false
repeats: 100
validation:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: val
per_image_tokens: false
repeats: 10
lightning:
modelcheckpoint:
params:
every_n_train_steps: 500
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 500
max_images: 8
increase_log_steps: False
trainer:
benchmark: True
max_steps: 4000000
# max_steps: 4000

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@ -0,0 +1,103 @@
model:
base_learning_rate: 5.0e-03
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: caption
image_size: 64
channels: 4
cond_stage_trainable: true # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
embedding_reg_weight: 0.0
personalization_config:
target: ldm.modules.embedding_manager.EmbeddingManager
params:
placeholder_strings: ["*"]
initializer_words: ["painting"]
per_image_tokens: false
num_vectors_per_token: 1
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
data:
target: main.DataModuleFromConfig
params:
batch_size: 2
num_workers: 16
wrap: false
train:
target: ldm.data.personalized_style.PersonalizedBase
params:
size: 512
set: train
per_image_tokens: false
repeats: 100
validation:
target: ldm.data.personalized_style.PersonalizedBase
params:
size: 512
set: val
per_image_tokens: false
repeats: 10
lightning:
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 500
max_images: 8
increase_log_steps: False
trainer:
benchmark: True

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@ -0,0 +1,79 @@
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
personalization_config:
target: ldm.modules.embedding_manager.EmbeddingManager
params:
placeholder_strings: ["*"]
initializer_words: ['face', 'man', 'photo', 'africanmale']
per_image_tokens: false
num_vectors_per_token: 1
progressive_words: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.WeightedFrozenCLIPEmbedder

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@ -0,0 +1,79 @@
model:
base_learning_rate: 7.5e-05
target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: hybrid # important
monitor: val/loss_simple_ema
scale_factor: 0.18215
finetune_keys: null
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
personalization_config:
target: ldm.modules.embedding_manager.EmbeddingManager
params:
placeholder_strings: ["*"]
initializer_words: ['face', 'man', 'photo', 'africanmale']
per_image_tokens: false
num_vectors_per_token: 1
progressive_words: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 9 # 4 data + 4 downscaled image + 1 mask
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.WeightedFrozenCLIPEmbedder

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model:
base_learning_rate: 5.0e-03
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: caption
image_size: 64
channels: 4
cond_stage_trainable: true # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
embedding_reg_weight: 0.0
personalization_config:
target: ldm.modules.embedding_manager.EmbeddingManager
params:
placeholder_strings: ["*"]
initializer_words: ['face', 'man', 'photo', 'africanmale']
per_image_tokens: false
num_vectors_per_token: 6
progressive_words: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
data:
target: main.DataModuleFromConfig
params:
batch_size: 1
num_workers: 2
wrap: false
train:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: train
per_image_tokens: false
repeats: 100
validation:
target: ldm.data.personalized.PersonalizedBase
params:
size: 512
set: val
per_image_tokens: false
repeats: 10
lightning:
modelcheckpoint:
params:
every_n_train_steps: 500
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 500
max_images: 5
increase_log_steps: False
trainer:
benchmark: False
max_steps: 6200
# max_steps: 4000

4
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# Ignore everything in this directory
*
# Except this file
!.gitignore

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74
docker-build/Dockerfile Normal file
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FROM ubuntu AS get_miniconda
SHELL ["/bin/bash", "-c"]
# install wget
RUN apt-get update \
&& apt-get install -y \
wget \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
# download and install miniconda
ARG conda_version=py39_4.12.0-Linux-x86_64
ARG conda_prefix=/opt/conda
RUN wget --progress=dot:giga -O /miniconda.sh \
https://repo.anaconda.com/miniconda/Miniconda3-${conda_version}.sh \
&& bash /miniconda.sh -b -p ${conda_prefix} \
&& rm -f /miniconda.sh
FROM ubuntu AS invokeai
# use bash
SHELL [ "/bin/bash", "-c" ]
# clean bashrc
RUN echo "" > ~/.bashrc
# Install necesarry packages
RUN apt-get update \
&& apt-get install -y \
--no-install-recommends \
gcc \
git \
libgl1-mesa-glx \
libglib2.0-0 \
pip \
python3 \
python3-dev \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
# clone repository and create symlinks
ARG invokeai_git=https://github.com/invoke-ai/InvokeAI.git
ARG project_name=invokeai
RUN git clone ${invokeai_git} /${project_name} \
&& mkdir /${project_name}/models/ldm/stable-diffusion-v1 \
&& ln -s /data/models/sd-v1-4.ckpt /${project_name}/models/ldm/stable-diffusion-v1/model.ckpt \
&& ln -s /data/outputs/ /${project_name}/outputs
# set workdir
WORKDIR /${project_name}
# install conda env and preload models
ARG conda_prefix=/opt/conda
ARG conda_env_file=environment.yml
COPY --from=get_miniconda ${conda_prefix} ${conda_prefix}
RUN source ${conda_prefix}/etc/profile.d/conda.sh \
&& conda init bash \
&& source ~/.bashrc \
&& conda env create \
--name ${project_name} \
--file ${conda_env_file} \
&& rm -Rf ~/.cache \
&& conda clean -afy \
&& echo "conda activate ${project_name}" >> ~/.bashrc \
&& ln -s /data/models/GFPGANv1.4.pth ./src/gfpgan/experiments/pretrained_models/GFPGANv1.4.pth \
&& conda activate ${project_name} \
&& python scripts/preload_models.py
# Copy entrypoint and set env
ENV CONDA_PREFIX=${conda_prefix}
ENV PROJECT_NAME=${project_name}
COPY docker-build/entrypoint.sh /
ENTRYPOINT [ "/entrypoint.sh" ]

81
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#!/usr/bin/env bash
set -e
# IMPORTANT: You need to have a token on huggingface.co to be able to download the checkpoint!!!
# configure values by using env when executing build.sh
# f.e. env ARCH=aarch64 GITHUB_INVOKE_AI=https://github.com/yourname/yourfork.git ./build.sh
source ./docker-build/env.sh || echo "please run from repository root" || exit 1
invokeai_conda_version=${INVOKEAI_CONDA_VERSION:-py39_4.12.0-${platform/\//-}}
invokeai_conda_prefix=${INVOKEAI_CONDA_PREFIX:-\/opt\/conda}
invokeai_conda_env_file=${INVOKEAI_CONDA_ENV_FILE:-environment.yml}
invokeai_git=${INVOKEAI_GIT:-https://github.com/invoke-ai/InvokeAI.git}
huggingface_token=${HUGGINGFACE_TOKEN?}
# print the settings
echo "You are using these values:"
echo -e "project_name:\t\t ${project_name}"
echo -e "volumename:\t\t ${volumename}"
echo -e "arch:\t\t\t ${arch}"
echo -e "platform:\t\t ${platform}"
echo -e "invokeai_conda_version:\t ${invokeai_conda_version}"
echo -e "invokeai_conda_prefix:\t ${invokeai_conda_prefix}"
echo -e "invokeai_conda_env_file: ${invokeai_conda_env_file}"
echo -e "invokeai_git:\t\t ${invokeai_git}"
echo -e "invokeai_tag:\t\t ${invokeai_tag}\n"
_runAlpine() {
docker run \
--rm \
--interactive \
--tty \
--mount source="$volumename",target=/data \
--workdir /data \
alpine "$@"
}
_copyCheckpoints() {
echo "creating subfolders for models and outputs"
_runAlpine mkdir models
_runAlpine mkdir outputs
echo -n "downloading sd-v1-4.ckpt"
_runAlpine wget --header="Authorization: Bearer ${huggingface_token}" -O models/sd-v1-4.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
echo "done"
echo "downloading GFPGANv1.4.pth"
_runAlpine wget -O models/GFPGANv1.4.pth https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
}
_checkVolumeContent() {
_runAlpine ls -lhA /data/models
}
_getModelMd5s() {
_runAlpine \
alpine sh -c "md5sum /data/models/*"
}
if [[ -n "$(docker volume ls -f name="${volumename}" -q)" ]]; then
echo "Volume already exists"
if [[ -z "$(_checkVolumeContent)" ]]; then
echo "looks empty, copying checkpoint"
_copyCheckpoints
fi
echo "Models in ${volumename}:"
_checkVolumeContent
else
echo -n "createing docker volume "
docker volume create "${volumename}"
_copyCheckpoints
fi
# Build Container
docker build \
--platform="${platform}" \
--tag "${invokeai_tag}" \
--build-arg project_name="${project_name}" \
--build-arg conda_version="${invokeai_conda_version}" \
--build-arg conda_prefix="${invokeai_conda_prefix}" \
--build-arg conda_env_file="${invokeai_conda_env_file}" \
--build-arg invokeai_git="${invokeai_git}" \
--file ./docker-build/Dockerfile \
.

8
docker-build/entrypoint.sh Executable file
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#!/bin/bash
set -e
source "${CONDA_PREFIX}/etc/profile.d/conda.sh"
conda activate "${PROJECT_NAME}"
python scripts/invoke.py \
${@:---web --host=0.0.0.0}

13
docker-build/env.sh Normal file
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#!/usr/bin/env bash
project_name=${PROJECT_NAME:-invokeai}
volumename=${VOLUMENAME:-${project_name}_data}
arch=${ARCH:-x86_64}
platform=${PLATFORM:-Linux/${arch}}
invokeai_tag=${INVOKEAI_TAG:-${project_name}-${arch}}
export project_name
export volumename
export arch
export platform
export invokeai_tag

15
docker-build/run.sh Executable file
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#!/usr/bin/env bash
set -e
source ./docker-build/env.sh || echo "please run from repository root" || exit 1
docker run \
--interactive \
--tty \
--rm \
--platform "$platform" \
--name "$project_name" \
--hostname "$project_name" \
--mount source="$volumename",target=/data \
--publish 9090:9090 \
"$invokeai_tag" ${1:+$@}

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@ -1,27 +0,0 @@
## Make a copy of this file named `.env` and fill in the values below.
## Any environment variables supported by InvokeAI can be specified here,
## in addition to the examples below.
## INVOKEAI_ROOT is the path *on the host system* where Invoke will store its data.
## It is mounted into the container and allows both containerized and non-containerized usage of Invoke.
# Usually this is the only variable you need to set. It can be relative or absolute.
# INVOKEAI_ROOT=~/invokeai
## HOST_INVOKEAI_ROOT and CONTAINER_INVOKEAI_ROOT can be used to control the on-host
## and in-container paths separately, if needed.
## HOST_INVOKEAI_ROOT is the path on the docker host's filesystem where Invoke will store data.
## If relative, it will be relative to the docker directory in which the docker-compose.yml file is located
## CONTAINER_INVOKEAI_ROOT is the path within the container where Invoke will expect to find the runtime directory.
## It MUST be absolute. There is usually no need to change this.
# HOST_INVOKEAI_ROOT=../../invokeai-data
# CONTAINER_INVOKEAI_ROOT=/invokeai
## INVOKEAI_PORT is the port on which the InvokeAI web interface will be available
# INVOKEAI_PORT=9090
## GPU_DRIVER can be set to either `cuda` or `rocm` to enable GPU support in the container accordingly.
# GPU_DRIVER=cuda #| rocm
## CONTAINER_UID can be set to the UID of the user on the host system that should own the files in the container.
## It is usually not necessary to change this. Use `id -u` on the host system to find the UID.
# CONTAINER_UID=1000

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@ -1,124 +0,0 @@
# syntax=docker/dockerfile:1.4
## Builder stage
FROM library/ubuntu:23.04 AS builder
ARG DEBIAN_FRONTEND=noninteractive
RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \
apt update && apt-get install -y \
git \
python3-venv \
python3-pip \
build-essential
ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
ARG GPU_DRIVER=cuda
ARG TARGETPLATFORM="linux/amd64"
# unused but available
ARG BUILDPLATFORM
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}
# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
# x86_64/CUDA is default
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m venv ${VIRTUAL_ENV} &&\
if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
elif [ "$GPU_DRIVER" = "rocm" ]; then \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
else \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
fi &&\
# xformers + triton fails to install on arm64
if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
pip install $extra_index_url_arg -e ".[xformers]"; \
else \
pip install $extra_index_url_arg -e "."; \
fi
# #### Build the Web UI ------------------------------------
FROM node:20-slim AS web-builder
ENV PNPM_HOME="/pnpm"
ENV PATH="$PNPM_HOME:$PATH"
RUN corepack use pnpm@8.x
RUN corepack enable
WORKDIR /build
COPY invokeai/frontend/web/ ./
RUN --mount=type=cache,target=/pnpm/store \
pnpm install --frozen-lockfile
RUN npx vite build
#### Runtime stage ---------------------------------------
FROM library/ubuntu:23.04 AS runtime
ARG DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1
RUN apt update && apt install -y --no-install-recommends \
git \
curl \
vim \
tmux \
ncdu \
iotop \
bzip2 \
gosu \
magic-wormhole \
libglib2.0-0 \
libgl1-mesa-glx \
python3-venv \
python3-pip \
build-essential \
libopencv-dev \
libstdc++-10-dev &&\
apt-get clean && apt-get autoclean
ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
ENV INVOKEAI_ROOT=/invokeai
ENV INVOKEAI_HOST=0.0.0.0
ENV INVOKEAI_PORT=9090
ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
ENV CONTAINER_UID=${CONTAINER_UID:-1000}
ENV CONTAINER_GID=${CONTAINER_GID:-1000}
# --link requires buldkit w/ dockerfile syntax 1.4
COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
# Link amdgpu.ids for ROCm builds
# contributed by https://github.com/Rubonnek
RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
WORKDIR ${INVOKEAI_SRC}
# build patchmatch
RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
RUN python3 -c "from patchmatch import patch_match"
RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
COPY docker/docker-entrypoint.sh ./
ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
CMD ["invokeai-web"]

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@ -1,122 +0,0 @@
# Invoke in Docker
- Ensure that Docker can use the GPU on your system
- This documentation assumes Linux, but should work similarly under Windows with WSL2
- We don't recommend running Invoke in Docker on macOS at this time. It works, but very slowly.
## Quickstart :lightning:
No `docker compose`, no persistence, just a simple one-liner using the official images:
**CUDA:**
```bash
docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
```
**ROCm:**
```bash
docker run --device /dev/kfd --device /dev/dri --publish 9090:9090 ghcr.io/invoke-ai/invokeai:main-rocm
```
Open `http://localhost:9090` in your browser once the container finishes booting, install some models, and generate away!
> [!TIP]
> To persist your data (including downloaded models) outside of the container, add a `--volume/-v` flag to the above command, e.g.: `docker run --volume /some/local/path:/invokeai <...the rest of the command>`
## Customize the container
We ship the `run.sh` script, which is a convenient wrapper around `docker compose` for cases where custom image build args are needed. Alternatively, the familiar `docker compose` commands work just as well.
```bash
cd docker
cp .env.sample .env
# edit .env to your liking if you need to; it is well commented.
./run.sh
```
It will take a few minutes to build the image the first time. Once the application starts up, open `http://localhost:9090` in your browser to invoke!
## Docker setup in detail
#### Linux
1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
- The deprecated `docker-compose` (hyphenated) CLI probably won't work. Update to a recent version.
3. Ensure docker daemon is able to access the GPU.
- [NVIDIA docs](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
- [AMD docs](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html)
#### macOS
> [!TIP]
> You'll be better off installing Invoke directly on your system, because Docker can not use the GPU on macOS.
If you are still reading:
1. Ensure Docker has at least 16GB RAM
2. Enable VirtioFS for file sharing
3. Enable `docker compose` V2 support
This is done via Docker Desktop preferences.
### Configure the Invoke Environment
1. Make a copy of `.env.sample` and name it `.env` (`cp .env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to the desired location of the InvokeAI runtime directory. It may be an existing directory from a previous installation (post 4.0.0).
1. Execute `run.sh`
The image will be built automatically if needed.
The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. Navigate to the Model Manager tab and install some models before generating.
### Use a GPU
- Linux is *recommended* for GPU support in Docker.
- WSL2 is *required* for Windows.
- only `x86_64` architecture is supported.
The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker/NVIDIA/AMD documentation for the most up-to-date instructions for using your GPU with Docker.
To use an AMD GPU, set `GPU_DRIVER=rocm` in your `.env` file before running `./run.sh`.
## Customize
Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `run.sh`, your custom values will be used.
You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
Values are optional, but setting `INVOKEAI_ROOT` is highly recommended. The default is `~/invokeai`. Example:
```bash
INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
HUGGINGFACE_TOKEN=the_actual_token
CONTAINER_UID=1000
GPU_DRIVER=cuda
```
Any environment variables supported by InvokeAI can be set here. See the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
## Even More Customizing!
See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
### Reconfigure the runtime directory
Can be used to download additional models from the supported model list
In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
```yaml
command:
- invokeai-configure
- --yes
```
Or install models:
```yaml
command:
- invokeai-model-install
```

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@ -1,54 +0,0 @@
# Copyright (c) 2023 Eugene Brodsky https://github.com/ebr
x-invokeai: &invokeai
image: "local/invokeai:latest"
build:
context: ..
dockerfile: docker/Dockerfile
# Create a .env file in the same directory as this docker-compose.yml file
# and populate it with environment variables. See .env.sample
env_file:
- .env
# variables without a default will automatically inherit from the host environment
environment:
# if set, CONTAINER_INVOKEAI_ROOT will override the Invoke runtime directory location *inside* the container
- INVOKEAI_ROOT=${CONTAINER_INVOKEAI_ROOT:-/invokeai}
- HF_HOME
ports:
- "${INVOKEAI_PORT:-9090}:${INVOKEAI_PORT:-9090}"
volumes:
- type: bind
source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
target: ${CONTAINER_INVOKEAI_ROOT:-/invokeai}
bind:
create_host_path: true
- ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
tty: true
stdin_open: true
services:
invokeai-cuda:
<<: *invokeai
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
invokeai-cpu:
<<: *invokeai
profiles:
- cpu
invokeai-rocm:
<<: *invokeai
devices:
- /dev/kfd:/dev/kfd
- /dev/dri:/dev/dri
profiles:
- rocm

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