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
1975 changed files with 70874 additions and 205762 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>

59
.github/pr_labels.yml vendored
View File

@ -1,59 +0,0 @@
Root:
- changed-files:
- any-glob-to-any-file: '*'
PythonDeps:
- changed-files:
- any-glob-to-any-file: 'pyproject.toml'
Python:
- changed-files:
- all-globs-to-any-file:
- 'invokeai/**'
- '!invokeai/frontend/web/**'
PythonTests:
- changed-files:
- any-glob-to-any-file: 'tests/**'
CICD:
- 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/**
Documentation:
- 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/**'
FrontendDeps:
- 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

@ -1,66 +0,0 @@
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
## Merge Plan
<!--
A merge plan describes how this PR should be handled after it is approved.
Example merge plans:
- "This PR can be merged when approved"
- "This must be squash-merged when approved"
- "DO NOT MERGE - I will rebase and tidy commits before merging"
- "#dev-chat on discord needs to be advised of this change when it is merged"
A merge plan is particularly important for large PRs or PRs that touch the
database in any way.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?

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

@ -1,34 +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

@ -1,16 +0,0 @@
name: "Pull Request Labeler"
on:
- pull_request_target
jobs:
labeler:
permissions:
contents: read
pull-requests: write
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- uses: actions/labeler@v5
with:
configuration-path: .github/pr_labels.yml

View File

@ -1,45 +0,0 @@
name: Lint frontend
on:
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
push:
branches:
- 'main'
merge_group:
workflow_dispatch:
defaults:
run:
working-directory: invokeai/frontend/web
jobs:
lint-frontend:
if: github.event.pull_request.draft == false
runs-on: ubuntu-22.04
steps:
- name: Setup Node 18
uses: actions/setup-node@v4
with:
node-version: '18'
- name: Checkout
uses: actions/checkout@v4
- name: Setup pnpm
uses: pnpm/action-setup@v2
with:
version: '8.12.1'
- name: Install dependencies
run: 'pnpm install --prefer-frozen-lockfile'
- name: Typescript
run: 'pnpm run lint:tsc'
- name: Madge
run: 'pnpm run lint:dpdm'
- name: ESLint
run: 'pnpm run lint:eslint'
- name: Prettier
run: 'pnpm run lint:prettier'
- name: Knip
run: 'pnpm run lint:knip'

View File

@ -2,19 +2,12 @@ name: mkdocs-material
on:
push:
branches:
- 'refs/heads/main'
permissions:
contents: write
- 'main'
- 'development'
jobs:
mkdocs-material:
if: github.event.pull_request.draft == false
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 sources
uses: actions/checkout@v3
@ -25,15 +18,11 @@ jobs:
uses: actions/setup-python@v4
with:
python-version: '3.10'
cache: pip
cache-dependency-path: pyproject.toml
- name: install requirements
env:
PIP_USE_PEP517: 1
run: |
python -m \
pip install ".[docs]"
pip install -r requirements-mkdocs.txt
- name: confirm buildability
run: |

View File

@ -1,67 +0,0 @@
name: PyPI Release
on:
workflow_dispatch:
inputs:
publish_package:
description: 'Publish build on PyPi? [true/false]'
required: true
default: 'false'
jobs:
build-and-release:
if: github.repository == 'invoke-ai/InvokeAI'
runs-on: ubuntu-22.04
env:
TWINE_USERNAME: __token__
TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
TWINE_NON_INTERACTIVE: 1
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Setup Node 18
uses: actions/setup-node@v4
with:
node-version: '18'
- name: Setup pnpm
uses: pnpm/action-setup@v2
with:
version: '8.12.1'
- name: Install frontend dependencies
run: pnpm install --prefer-frozen-lockfile
working-directory: invokeai/frontend/web
- name: Build frontend
run: pnpm run build
working-directory: invokeai/frontend/web
- name: Install python dependencies
run: pip install --upgrade build twine
- name: Build python package
run: python3 -m build
- name: Upload build as workflow artifact
uses: actions/upload-artifact@v4
with:
name: dist
path: dist
- name: Check distribution
run: twine check dist/*
- name: Check PyPI versions
if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/heads/release/')
run: |
pip install --upgrade requests
python -c "\
import scripts.pypi_helper; \
EXISTS=scripts.pypi_helper.local_on_pypi(); \
print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
- name: Publish build on PyPi
if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != '' && github.event.inputs.publish_package == 'true'
run: twine upload dist/*

View File

@ -1,24 +0,0 @@
name: style checks
on:
pull_request:
push:
branches: main
jobs:
ruff:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Setup Python
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install dependencies with pip
run: |
pip install ruff
- run: ruff check --output-format=github .
- run: ruff format --check .

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

View File

@ -1,129 +0,0 @@
name: Test invoke.py pip
on:
push:
branches:
- 'main'
pull_request:
types:
- 'ready_for_review'
- 'opened'
- 'synchronize'
merge_group:
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
cancel-in-progress: true
jobs:
matrix:
if: github.event.pull_request.draft == false
strategy:
matrix:
python-version:
# - '3.9'
- '3.10'
pytorch:
- linux-cuda-11_7
- linux-rocm-5_2
- linux-cpu
- macos-default
- windows-cpu
include:
- pytorch: linux-cuda-11_7
os: ubuntu-22.04
github-env: $GITHUB_ENV
- pytorch: linux-rocm-5_2
os: ubuntu-22.04
extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
github-env: $GITHUB_ENV
- pytorch: linux-cpu
os: ubuntu-22.04
extra-index-url: 'https://download.pytorch.org/whl/cpu'
github-env: $GITHUB_ENV
- pytorch: macos-default
os: macOS-12
github-env: $GITHUB_ENV
- pytorch: windows-cpu
os: windows-2022
github-env: $env:GITHUB_ENV
name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
runs-on: ${{ matrix.os }}
env:
PIP_USE_PEP517: '1'
steps:
- name: Checkout sources
id: checkout-sources
uses: actions/checkout@v3
- name: Check for changed python files
id: changed-files
uses: tj-actions/changed-files@v41
with:
files_yaml: |
python:
- 'pyproject.toml'
- 'invokeai/**'
- '!invokeai/frontend/web/**'
- 'tests/**'
- name: set test prompt to main branch validation
if: steps.changed-files.outputs.python_any_changed == 'true'
run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
- name: setup python
if: steps.changed-files.outputs.python_any_changed == 'true'
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: pip
cache-dependency-path: pyproject.toml
- name: install invokeai
if: steps.changed-files.outputs.python_any_changed == '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'
id: run-pytest
run: pytest
# - name: run invokeai-configure
# env:
# HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
# run: >
# invokeai-configure
# --yes
# --default_only
# --full-precision
# # can't use fp16 weights without a GPU
# - name: run invokeai
# id: run-invokeai
# env:
# # Set offline mode to make sure configure preloaded successfully.
# HF_HUB_OFFLINE: 1
# HF_DATASETS_OFFLINE: 1
# TRANSFORMERS_OFFLINE: 1
# INVOKEAI_OUTDIR: ${{ github.workspace }}/results
# run: >
# invokeai
# --no-patchmatch
# --no-nsfw_checker
# --precision=float32
# --always_use_cpu
# --use_memory_db
# --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
# --from_file ${{ env.TEST_PROMPTS }}
# - name: Archive results
# env:
# INVOKEAI_OUTDIR: ${{ github.workspace }}/results
# uses: actions/upload-artifact@v3
# with:
# name: results
# path: ${{ env.INVOKEAI_OUTDIR }}

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

@ -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
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
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
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
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
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
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
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
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
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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
Model in an end-use application - e.g. chatbot, translator, image
generator.
"Third Parties" means individuals or legal entities that are not under
common control with Licensor or You.
"Contribution" means any work of authorship, including the original
version of the Model and any modifications or additions to that Model
or Derivatives of the Model thereof, that is intentionally submitted
to Licensor for inclusion in the Model by the copyright owner or by an
individual or Legal Entity authorized to submit on behalf of the
copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent to
the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control
systems, and issue tracking systems that are managed by, or on behalf
of, the Licensor for the purpose of discussing and improving the
Model, but excluding communication that is conspicuously marked or
otherwise designated in writing by the copyright owner as "Not a
Contribution."
"Contributor" means Licensor and any individual or Legal Entity on
behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Model.
Section II: INTELLECTUAL PROPERTY RIGHTS
Both copyright and patent grants apply to the Model, Derivatives of
the Model and Complementary Material. The Model and Derivatives of the
Model are subject to additional terms as described in
Section III.
Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare, publicly display, publicly
perform, sublicense, and distribute the Complementary Material, the
Model, and Derivatives of the Model.
Grant of Patent License. Subject to the terms and conditions of this
License and where and as applicable, each Contributor hereby grants to
You a perpetual, worldwide, non-exclusive, no-charge, royalty-free,
irrevocable (except as stated in this paragraph) patent license to
make, have made, use, offer to sell, sell, import, and otherwise
transfer the Model and the Complementary Material, where such license
applies only to those patent claims licensable by such Contributor
that are necessarily infringed by their Contribution(s) alone or by
combination of their Contribution(s) with the Model to which such
Contribution(s) was submitted. If You institute patent litigation
against any entity (including a cross-claim or counterclaim in a
lawsuit) alleging that the Model and/or Complementary Material or a
Contribution incorporated within the Model and/or Complementary
Material constitutes direct or contributory patent infringement, then
any patent licenses granted to You under this License for the Model
and/or Work shall terminate as of the date such litigation is asserted
or filed.
Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
Distribution and Redistribution. You may host for Third Party remote
access purposes (e.g. software-as-a-service), reproduce and distribute
copies of the Model or Derivatives of the Model thereof in any medium,
with or without modifications, provided that You meet the following
conditions: Use-based restrictions as referenced in paragraph 5 MUST
be included as an enforceable provision by You in any type of legal
agreement (e.g. a license) governing the use and/or distribution of
the Model or Derivatives of the Model, and You shall give notice to
subsequent users You Distribute to, that the Model or Derivatives of
the Model are subject to paragraph 5. This provision does not apply to
the use of Complementary Material. You must give any Third Party
recipients of the Model or Derivatives of the Model a copy of this
License; You must cause any modified files to carry prominent notices
stating that You changed the files; You must retain all copyright,
patent, trademark, and attribution notices excluding those notices
that do not pertain to any part of the Model, Derivatives of the
Model. You may add Your own copyright statement to Your modifications
and may provide additional or different license terms and conditions -
respecting paragraph 4.a. - for use, reproduction, or Distribution of
Your modifications, or for any such Derivatives of the Model as a
whole, provided Your use, reproduction, and Distribution of the Model
otherwise complies with the conditions stated in this License.
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
uses. You may use the Model subject to this License, including only
for lawful purposes and in accordance with the License. Use may
include creating any content with, finetuning, updating, running,
training, evaluating and/or reparametrizing the Model. You shall
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).
The Output You Generate. Except as set forth herein, Licensor claims
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
License.
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.
Trademarks and related. Nothing in this License permits You to make
use of Licensors trademarks, trade names, logos or to otherwise
suggest endorsement or misrepresent the relationship between the
parties; and any rights not expressly granted herein are reserved by
the Licensors.
Disclaimer of Warranty. Unless required by applicable law or agreed to
in writing, Licensor provides the Model and the Complementary Material
(and each Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Model, Derivatives of
the Model, and the Complementary Material and assume any risks
associated with Your exercise of permissions under this License.
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 liable to You
for damages, including any direct, indirect, special, incidental, or
consequential damages of any character arising as a result of this
License or out of the use or inability to use the Model and the
Complementary Material (including but not limited to damages for loss
of goodwill, work stoppage, computer failure or malfunction, or any
and all other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
Accepting Warranty or Additional Liability. While redistributing the
Model, Derivatives of the Model and the Complementary Material
thereof, You may choose to offer, and charge a fee for, acceptance of
support, warranty, indemnity, or other liability obligations and/or
rights consistent with this License. However, in accepting such
obligations, You may act only on Your own behalf and on Your sole
responsibility, not on behalf of any other Contributor, and only if
You agree to indemnify, defend, and hold each Contributor harmless for
any liability incurred by, or claims asserted against, such
Contributor by reason of your accepting any such warranty or
additional liability.
If any provision of this License is held to be invalid, illegal or
unenforceable, the remaining provisions shall be unaffected thereby
and remain valid as if such provision had not been set forth herein.
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
can be used to harm an individual;
* To defame, disparage or otherwise harass others;
* For fully automated decision making that adversely impacts an
individuals legal rights or otherwise creates or modifies a
binding, enforceable obligation;
* For any use intended to or which has the effect of discriminating
against or harming individuals or groups based on online or offline
social behavior or known or predicted personal or personality
characteristics;
* To exploit any of the vulnerabilities of a specific group of persons
based on their age, social, physical or mental characteristics, in
order to materially distort the behavior of a person pertaining to
that group in a manner that causes or is likely to cause that person
or another person physical or psychological harm;
* For any use intended to or which has the effect of discriminating
against individuals or groups based on legally protected
characteristics or categories;
* To provide medical advice and medical results interpretation;
* To generate or disseminate information for the purpose to be used
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

@ -1,63 +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 "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 "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!)"
# 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
# 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
# Installer zip file
installer-zip:
cd installer && ./create_installer.sh
# Tag the release
tag-release:
cd installer && ./tag_release.sh

490
README.md
View File

@ -1,22 +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
## To learn more about Invoke, or implement our Business solutions, visit [invoke.com](https://www.invoke.com/about)
_Formerly known as lstein/stable-diffusion_
![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]
[![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]
[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
[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
@ -27,395 +28,174 @@
[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
[translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
[translation status link]: https://hosted.weblate.org/engage/invokeai/
</div>
InvokeAI is a leading creative engine built to empower professionals
and enthusiasts alike. Generate and create stunning visual media using
the latest AI-driven technologies. InvokeAI offers an industry leading
Web Interface, interactive Command Line Interface, and also serves as
the foundation for multiple commercial products.
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**: [[How to
Install](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)] [<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/issues">Bug Reports</a>]
[<a
href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion,
Ideas & Q&A</a>]
[<a
href="https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/">Contributing</a>]
**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">
<div align="center"><img src="docs/assets/invoke-web-server-1.png" width=640></div>
![Highlighted Features - Canvas and Workflows](https://github.com/invoke-ai/InvokeAI/assets/31807370/708f7a82-084f-4860-bfbe-e2588c53548d)
</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
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)
**Getting Started**
1. 🏁 [Quick Start](#quick-start)
3. 🖥️ [Hardware Requirements](#hardware-requirements)
### Installation
**More About Invoke**
1. 🌟 [Features](#features)
2. 📣 [Latest Changes](#latest-changes)
3. 🛠️ [Troubleshooting](#troubleshooting)
This fork is supported across multiple platforms. You can find individual installation instructions
below.
**Supporting the Project**
1. 🤝 [Contributing](#contributing)
2. 👥 [Contributors](#contributors)
3. 💕 [Support](#support)
- #### [Linux](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_LINUX/)
## Quick Start
- #### [Windows](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_WINDOWS/)
For full installation and upgrade instructions, please see:
[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALLATION/)
- #### [Macintosh](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_MAC/)
If upgrading from version 2.3, please read [Migrating a 2.3 root
directory to 3.0](#migrating-to-3) first.
### Hardware Requirements
### Automatic Installer (suggested for 1st time users)
#### System
1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/latest)
You wil need one of the following:
2. Download the .zip file for your OS (Windows/macOS/Linux).
- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
- An Apple computer with an M1 chip.
3. Unzip the file.
#### Memory
4. **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 return. **Linux:** run `install.sh`.
- At least 12 GB Main Memory RAM.
5. You'll be asked to confirm the location of the folder in which
to install InvokeAI and its image generation model files. Pick a
location with at least 15 GB of free memory. More if you plan on
installing lots of models.
#### Disk
6. Wait while the installer does its thing. After installing the software,
the installer will launch a script that lets you configure InvokeAI and
select a set of starting image generation models.
- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
7. Find the folder that InvokeAI was installed into (it is not the
same as the unpacked zip file directory!) The default location of this
folder (if you didn't change it in step 5) is `~/invokeai` on
Linux/Mac systems, and `C:\Users\YourName\invokeai` on Windows. This directory will contain launcher scripts named `invoke.sh` and `invoke.bat`.
**Note**
8. On Windows systems, double-click on the `invoke.bat` file. On
macOS, open a Terminal window, drag `invoke.sh` from the folder into
the Terminal, and press return. On Linux, run `invoke.sh`
If you have a Nvidia 10xx series card (e.g. the 1080ti), please
run the dream script in full-precision mode as shown below.
9. Press 2 to open the "browser-based UI", press enter/return, wait a
minute or two for Stable Diffusion to start up, then open your browser
and go to http://localhost:9090.
Similarly, specify full-precision mode on Apple M1 hardware.
10. Type `banana sushi` in the box on the top left and click `Invoke`
### Command-Line Installation (for developers and users familiar with Terminals)
You must have Python 3.10 through 3.11 installed on your machine. Earlier or
later versions are not supported.
Node.js also needs to be installed along with `pnpm` (can be installed with
the command `npm install -g pnpm` if needed)
1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
```terminal
mkdir invokeai
````
3. Create a virtual environment named `.venv` inside this directory and activate it:
```terminal
cd invokeai
python -m venv .venv --prompt InvokeAI
```
4. Activate the virtual environment (do it every time you run InvokeAI)
_For Linux/Mac users:_
```sh
source .venv/bin/activate
```
_For Windows users:_
```ps
.venv\Scripts\activate
```
5. Install the InvokeAI module and its dependencies. Choose the command suited for your platform & GPU.
_For Windows/Linux with an NVIDIA GPU:_
```terminal
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
```
_For Linux with an AMD GPU:_
```sh
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.6
```
_For non-GPU systems:_
```terminal
pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
```
_For Macintoshes, either Intel or M1/M2/M3:_
```sh
pip install InvokeAI --use-pep517
```
6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
```terminal
invokeai-configure --root .
```
Don't miss the dot at the end!
7. Launch the web server (do it every time you run InvokeAI):
```terminal
invokeai-web
```
8. Point your browser to http://localhost:9090 to bring up the web interface.
9. Type `banana sushi` in the box on the top left and click `Invoke`.
Be sure to activate the virtual environment each time before re-launching InvokeAI,
using `source .venv/bin/activate` or `.venv\Scripts\activate`.
## Detailed Installation Instructions
This fork is supported across Linux, Windows and Macintosh. Linux
users can use either an Nvidia-based card (with CUDA support) or an
AMD card (using the ROCm driver). For full installation and upgrade
instructions, please see:
[InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_SOURCE/)
<a name="migrating-to-3"></a>
### Migrating a v2.3 InvokeAI root directory
The InvokeAI root directory is where the InvokeAI startup file,
installed models, and generated images are stored. It is ordinarily
named `invokeai` and located in your home directory. The contents and
layout of this directory has changed between versions 2.3 and 3.0 and
cannot be used directly.
We currently recommend that you use the installer to create a new root
directory named differently from the 2.3 one, e.g. `invokeai-3` and
then use a migration script to copy your 2.3 models into the new
location. However, if you choose, you can upgrade this directory in
place. This section gives both recipes.
#### Creating a new root directory and migrating old models
This is the safer recipe because it leaves your old root directory in
place to fall back on.
1. Follow the instructions above to create and install InvokeAI in a
directory that has a different name from the 2.3 invokeai directory.
In this example, we will use "invokeai-3"
2. When you are prompted to select models to install, select a minimal
set of models, such as stable-diffusion-v1.5 only.
3. After installation is complete launch `invokeai.sh` (Linux/Mac) or
`invokeai.bat` and select option 8 "Open the developers console". This
will take you to the command line.
4. Issue the command `invokeai-migrate3 --from /path/to/v2.3-root --to
/path/to/invokeai-3-root`. Provide the correct `--from` and `--to`
paths for your v2.3 and v3.0 root directories respectively.
This will copy and convert your old models from 2.3 format to 3.0
format and create a new `models` directory in the 3.0 directory. The
old models directory (which contains the models selected at install
time) will be renamed `models.orig` and can be deleted once you have
confirmed that the migration was successful.
If you wish, you can pass the 2.3 root directory to both `--from` and
`--to` in order to update in place. Warning: this directory will no
longer be usable with InvokeAI 2.3.
#### Migrating in place
For the adventurous, you may do an in-place upgrade from 2.3 to 3.0
without touching the command line. ***This recipe does not work on
Windows platforms due to a bug in the Windows version of the 2.3
upgrade script.** See the next section for a Windows recipe.
##### For Mac and Linux Users:
1. Launch the InvokeAI launcher script in your current v2.3 root directory.
2. Select option [9] "Update InvokeAI" to bring up the updater dialog.
3. Select option [1] to upgrade to the latest release.
4. Once the upgrade is finished you will be returned to the launcher
menu. Select option [6] "Re-run the configure script to fix a broken
install or to complete a major upgrade".
This will run the configure script against the v2.3 directory and
update it to the 3.0 format. The following files will be replaced:
- The invokeai.init file, replaced by invokeai.yaml
- The models directory
- The configs/models.yaml model index
The original versions of these files will be saved with the suffix
".orig" appended to the end. Once you have confirmed that the upgrade
worked, you can safely remove these files. Alternatively you can
restore a working v2.3 directory by removing the new files and
restoring the ".orig" files' original names.
##### For Windows Users:
Windows Users can upgrade with the
1. Enter the 2.3 root directory you wish to upgrade
2. Launch `invoke.sh` or `invoke.bat`
3. Select the "Developer's console" option [8]
4. Type the following commands
```
pip install "invokeai @ https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v3.0.0" --use-pep517 --upgrade
invokeai-configure --root .
```
(Replace `v3.0.0` with the current release number if this document is out of date).
The first command will install and upgrade new software to run
InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
You may now launch the WebUI in the usual way, by selecting option [1]
from the launcher script
#### Migrating Images
The migration script will migrate your invokeai settings and models,
including textual inversion models, LoRAs and merges that you may have
installed previously. However it does **not** migrate the generated
images stored in your 2.3-format outputs directory. To do this, you
need to run an additional step:
1. From a working InvokeAI 3.0 root directory, start the launcher and
enter menu option [8] to open the "developer's console".
2. At the developer's console command line, type the command:
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-import-images
(invokeai) ~/InvokeAI$ python scripts/invoke.py --precision=float32
```
3. This will lead you through the process of confirming the desired
source and destination for the imported images. The images will
appear in the gallery board of your choice, and contain the
original prompt, model name, and other parameters used to generate
the image.
(Many kudos to **techjedi** for contributing this script.)
### Features
## Hardware Requirements
#### Major Features
InvokeAI is supported across Linux, Windows and macOS. Linux
users can use either an Nvidia-based card (with CUDA support) or an
AMD card (using the ROCm driver).
- [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)
### System
You will need one of the following:
- An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB
of VRAM is highly recommended for rendering using the Stable
Diffusion XL models
- An Apple computer with an M1 chip.
- An AMD-based graphics card with 4GB or more VRAM memory (Linux
only), 6-8 GB for XL rendering.
We do not recommend the GTX 1650 or 1660 series video cards. They are
unable to run in half-precision mode and do not have sufficient VRAM
to render 512x512 images.
**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.
## Features
Feature documentation can be reviewed by navigating to [the InvokeAI Documentation page](https://invoke-ai.github.io/InvokeAI/features/)
### *Web Server & UI*
InvokeAI offers a locally hosted Web Server & React Frontend, with an industry leading user experience. The Web-based UI allows for simple and intuitive workflows, and is responsive for use on mobile devices and tablets accessing the web server.
### *Unified Canvas*
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, 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*
InvokeAI offers a fully featured workflow management solution, enabling users to combine the power of nodes 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 AI provides 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*
- *SD 2.0, 2.1, XL support*
- *Upscaling Tools*
- *Embedding Manager & Support*
- *Model Manager & Support*
- *Workflow creation & management*
- *Node-Based Architecture*
#### 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
For our latest changes, view our [Release
Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
[CHANGELOG](docs/CHANGELOG.md).
### 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 **[Troubleshooting Guide](https://invoke-ai.github.io/InvokeAI/installation/010_INSTALL_AUTOMATED/#troubleshooting)** to get solutions for common installation
problems and other issues. For more help, please join our [Discord][discord link]
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
# 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.
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).
Get started with contributing by reading our [Contribution documentation](https://invoke-ai.github.io/InvokeAI/contributing/CONTRIBUTING/), joining the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) or the GitHub discussion board.
If you are unfamiliar with how
to contribute to GitHub projects, we have a new contributor checklist you can follow to get started contributing:
[New Contributor Checklist](https://invoke-ai.github.io/InvokeAI/contributing/contribution_guides/newContributorChecklist/).
We hope you enjoy using our software as much as we enjoy creating it,
and we hope that some of those of you who are reading this will elect
to become part of our community.
Welcome to InvokeAI!
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
@ -425,7 +205,13 @@ their time, hard work and effort.
### Support
For support, please use this repository's GitHub Issues tracking service, or join the [Discord][discord link].
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) 2023 by respective contributors.
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/).

View File

@ -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

View File

Before

Width:  |  Height:  |  Size: 651 KiB

After

Width:  |  Height:  |  Size: 651 KiB

View File

Before

Width:  |  Height:  |  Size: 596 KiB

After

Width:  |  Height:  |  Size: 596 KiB

View File

Before

Width:  |  Height:  |  Size: 609 KiB

After

Width:  |  Height:  |  Size: 609 KiB

View File

Before

Width:  |  Height:  |  Size: 548 KiB

After

Width:  |  Height:  |  Size: 548 KiB

View File

Before

Width:  |  Height:  |  Size: 705 KiB

After

Width:  |  Height:  |  Size: 705 KiB

View File

Before

Width:  |  Height:  |  Size: 757 KiB

After

Width:  |  Height:  |  Size: 757 KiB

View File

Before

Width:  |  Height:  |  Size: 33 KiB

After

Width:  |  Height:  |  Size: 33 KiB

View File

Before

Width:  |  Height:  |  Size: 612 KiB

After

Width:  |  Height:  |  Size: 612 KiB

View File

Before

Width:  |  Height:  |  Size: 312 KiB

After

Width:  |  Height:  |  Size: 312 KiB

View File

Before

Width:  |  Height:  |  Size: 72 KiB

After

Width:  |  Height:  |  Size: 72 KiB

View File

Before

Width:  |  Height:  |  Size: 319 KiB

After

Width:  |  Height:  |  Size: 319 KiB

View File

Before

Width:  |  Height:  |  Size: 788 KiB

After

Width:  |  Height:  |  Size: 788 KiB

View File

Before

Width:  |  Height:  |  Size: 958 KiB

After

Width:  |  Height:  |  Size: 958 KiB

View File

Before

Width:  |  Height:  |  Size: 9.4 MiB

After

Width:  |  Height:  |  Size: 9.4 MiB

View File

Before

Width:  |  Height:  |  Size: 610 KiB

After

Width:  |  Height:  |  Size: 610 KiB

View File

Before

Width:  |  Height:  |  Size: 1.1 MiB

After

Width:  |  Height:  |  Size: 1.1 MiB

View File

Before

Width:  |  Height:  |  Size: 1.3 MiB

After

Width:  |  Height:  |  Size: 1.3 MiB

View File

Before

Width:  |  Height:  |  Size: 945 KiB

After

Width:  |  Height:  |  Size: 945 KiB

View File

Before

Width:  |  Height:  |  Size: 972 KiB

After

Width:  |  Height:  |  Size: 972 KiB

View File

Before

Width:  |  Height:  |  Size: 662 KiB

After

Width:  |  Height:  |  Size: 662 KiB

View File

Before

Width:  |  Height:  |  Size: 302 KiB

After

Width:  |  Height:  |  Size: 302 KiB

View File

Before

Width:  |  Height:  |  Size: 2.2 MiB

After

Width:  |  Height:  |  Size: 2.2 MiB

File diff suppressed because it is too large Load Diff

View File

@ -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

View File

@ -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)

View File

@ -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

View File

@ -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

View File

@ -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

View File

@ -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

View File

@ -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

View File

@ -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

View File

@ -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: ['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
coverage/.gitignore vendored
View File

@ -1,4 +0,0 @@
# Ignore everything in this directory
*
# Except this file
!.gitignore

View File

Before

Width:  |  Height:  |  Size: 14 KiB

After

Width:  |  Height:  |  Size: 14 KiB

View File

Before

Width:  |  Height:  |  Size: 466 KiB

After

Width:  |  Height:  |  Size: 466 KiB

View File

Before

Width:  |  Height:  |  Size: 7.4 KiB

After

Width:  |  Height:  |  Size: 7.4 KiB

View File

Before

Width:  |  Height:  |  Size: 539 KiB

After

Width:  |  Height:  |  Size: 539 KiB

View File

Before

Width:  |  Height:  |  Size: 7.6 KiB

After

Width:  |  Height:  |  Size: 7.6 KiB

View File

Before

Width:  |  Height:  |  Size: 450 KiB

After

Width:  |  Height:  |  Size: 450 KiB

View File

Before

Width:  |  Height:  |  Size: 12 KiB

After

Width:  |  Height:  |  Size: 12 KiB

View File

Before

Width:  |  Height:  |  Size: 553 KiB

After

Width:  |  Height:  |  Size: 553 KiB

View File

Before

Width:  |  Height:  |  Size: 12 KiB

After

Width:  |  Height:  |  Size: 12 KiB

View File

Before

Width:  |  Height:  |  Size: 418 KiB

After

Width:  |  Height:  |  Size: 418 KiB

View File

Before

Width:  |  Height:  |  Size: 6.1 KiB

After

Width:  |  Height:  |  Size: 6.1 KiB

View File

Before

Width:  |  Height:  |  Size: 542 KiB

After

Width:  |  Height:  |  Size: 542 KiB

View File

Before

Width:  |  Height:  |  Size: 9.5 KiB

After

Width:  |  Height:  |  Size: 9.5 KiB

View File

Before

Width:  |  Height:  |  Size: 395 KiB

After

Width:  |  Height:  |  Size: 395 KiB

View File

Before

Width:  |  Height:  |  Size: 465 KiB

After

Width:  |  Height:  |  Size: 465 KiB

74
docker-build/Dockerfile Normal file
View File

@ -0,0 +1,74 @@
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
docker-build/build.sh Executable file
View File

@ -0,0 +1,81 @@
#!/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
View File

@ -0,0 +1,8 @@
#!/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
View File

@ -0,0 +1,13 @@
#!/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
View File

@ -0,0 +1,15 @@
#!/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" ${1:+$@}

View File

@ -1,18 +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.
# HOST_INVOKEAI_ROOT is the path on the docker host's filesystem where InvokeAI will store data.
# Outputs will also be stored here by default.
# If relative, it will be relative to the docker directory in which the docker-compose.yml file is located
#HOST_INVOKEAI_ROOT=../../invokeai-data
# INVOKEAI_ROOT is the path to the root of the InvokeAI repository within the container.
# INVOKEAI_ROOT=~/invokeai
# Get this value from your HuggingFace account settings page.
# HUGGING_FACE_HUB_TOKEN=
## optional variables specific to the docker setup.
# GPU_DRIVER=nvidia #| rocm
# CONTAINER_UID=1000

View File

@ -1,128 +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 TORCH_VERSION=2.1.2
ARG TORCHVISION_VERSION=0.16.2
ARG GPU_DRIVER=cuda
ARG TARGETPLATFORM="linux/amd64"
# unused but available
ARG BUILDPLATFORM
WORKDIR ${INVOKEAI_SRC}
# Install pytorch before all other pip packages
# 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 &&\
pip install $extra_index_url_arg \
torch==$TORCH_VERSION \
torchvision==$TORCHVISION_VERSION
# Install the local package.
# Editable mode helps use the same image for development:
# the local working copy can be bind-mounted into the image
# at path defined by ${INVOKEAI_SRC}
COPY invokeai ./invokeai
COPY pyproject.toml ./
RUN --mount=type=cache,target=/root/.cache/pip \
# xformers + triton fails to install on arm64
if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
pip install -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:$PATH"
RUN corepack enable
WORKDIR /build
COPY invokeai/frontend/web/ ./
RUN --mount=type=cache,target=/pnpm/store \
pnpm install --frozen-lockfile
RUN npx vite build
#### Runtime stage ---------------------------------------
FROM library/ubuntu:23.04 AS runtime
ARG DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1
RUN apt update && apt install -y --no-install-recommends \
git \
curl \
vim \
tmux \
ncdu \
iotop \
bzip2 \
gosu \
magic-wormhole \
libglib2.0-0 \
libgl1-mesa-glx \
python3-venv \
python3-pip \
build-essential \
libopencv-dev \
libstdc++-10-dev &&\
apt-get clean && apt-get autoclean
ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
ENV INVOKEAI_ROOT=/invokeai
ENV PATH="$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", "--host", "0.0.0.0"]

View File

@ -1,88 +0,0 @@
# InvokeAI Containerized
All commands should be run within the `docker` directory: `cd docker`
## Quickstart :rocket:
On a known working Linux+Docker+CUDA (Nvidia) system, execute `./run.sh` in this directory. It will take a few minutes - depending on your internet speed - to install the core models. Once the application starts up, open `http://localhost:9090` in your browser to Invoke!
For more configuration options (using an AMD GPU, custom root directory location, etc): read on.
## Detailed setup
#### 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 continues to work for now.
3. Ensure docker daemon is able to access the GPU.
- You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
#### macOS
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 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:
a. the desired location of the InvokeAI runtime directory, or
b. an existing, v3.0.0 compatible runtime directory.
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`. The runtime directory will be populated with the base configs and models necessary to start 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 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.
## 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=nvidia
```
Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
## Even Moar 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
```

View File

@ -1,63 +0,0 @@
# Copyright (c) 2023 Eugene Brodsky https://github.com/ebr
version: '3.8'
x-invokeai: &invokeai
image: "local/invokeai:latest"
build:
context: ..
dockerfile: docker/Dockerfile
# variables without a default will automatically inherit from the host environment
environment:
- INVOKEAI_ROOT
- HF_HOME
# 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
ports:
- "${INVOKEAI_PORT:-9090}:9090"
volumes:
- type: bind
source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
target: ${INVOKEAI_ROOT:-/invokeai}
- ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
# - ${INVOKEAI_MODELS_DIR:-${INVOKEAI_ROOT:-/invokeai/models}}
# - ${INVOKEAI_MODELS_CONFIG_PATH:-${INVOKEAI_ROOT:-/invokeai/configs/models.yaml}}
tty: true
stdin_open: true
# # Example of running alternative commands/scripts in the container
# command:
# - bash
# - -c
# - |
# invokeai-model-install --yes --default-only --config_file ${INVOKEAI_ROOT}/config_custom.yaml
# invokeai-nodes-web --host 0.0.0.0
services:
invokeai-nvidia:
<<: *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

View File

@ -1,65 +0,0 @@
#!/bin/bash
set -e -o pipefail
### Container entrypoint
# Runs the CMD as defined by the Dockerfile or passed to `docker run`
# Can be used to configure the runtime dir
# Bypass by using ENTRYPOINT or `--entrypoint`
### Set INVOKEAI_ROOT pointing to a valid runtime directory
# Otherwise configure the runtime dir first.
### Configure the InvokeAI runtime directory (done by default)):
# docker run --rm -it <this image> --configure
# or skip with --no-configure
### Set the CONTAINER_UID envvar to match your user.
# Ensures files created in the container are owned by you:
# docker run --rm -it -v /some/path:/invokeai -e CONTAINER_UID=$(id -u) <this image>
# Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS.
USER_ID=${CONTAINER_UID:-1000}
USER=ubuntu
usermod -u ${USER_ID} ${USER} 1>/dev/null
configure() {
# Configure the runtime directory
if [[ -f ${INVOKEAI_ROOT}/invokeai.yaml ]]; then
echo "${INVOKEAI_ROOT}/invokeai.yaml exists. InvokeAI is already configured."
echo "To reconfigure InvokeAI, delete the above file."
echo "======================================================================"
else
mkdir -p "${INVOKEAI_ROOT}"
chown --recursive ${USER} "${INVOKEAI_ROOT}"
gosu ${USER} invokeai-configure --yes --default_only
fi
}
## Skip attempting to configure.
## Must be passed first, before any other args.
if [[ $1 != "--no-configure" ]]; then
configure
else
shift
fi
### Set the $PUBLIC_KEY env var to enable SSH access.
# We do not install openssh-server in the image by default to avoid bloat.
# but it is useful to have the full SSH server e.g. on Runpod.
# (use SCP to copy files to/from the image, etc)
if [[ -v "PUBLIC_KEY" ]] && [[ ! -d "${HOME}/.ssh" ]]; then
apt-get update
apt-get install -y openssh-server
pushd "$HOME"
mkdir -p .ssh
echo "${PUBLIC_KEY}" > .ssh/authorized_keys
chmod -R 700 .ssh
popd
service ssh start
fi
cd "${INVOKEAI_ROOT}"
# Run the CMD as the Container User (not root).
exec gosu ${USER} "$@"

View File

@ -1,32 +0,0 @@
#!/usr/bin/env bash
set -e -o pipefail
run() {
local scriptdir=$(dirname "${BASH_SOURCE[0]}")
cd "$scriptdir" || exit 1
local build_args=""
local profile=""
touch .env
build_args=$(awk '$1 ~ /=[^$]/ && $0 !~ /^#/ {print "--build-arg " $0 " "}' .env) &&
profile="$(awk -F '=' '/GPU_DRIVER/ {print $2}' .env)"
[[ -z "$profile" ]] && profile="nvidia"
local service_name="invokeai-$profile"
if [[ ! -z "$build_args" ]]; then
printf "%s\n" "docker compose build args:"
printf "%s\n" "$build_args"
fi
docker compose build $build_args $service_name
unset build_args
printf "%s\n" "starting service $service_name"
docker compose --profile "$profile" up -d "$service_name"
docker compose logs -f
}
run

View File

@ -1,60 +0,0 @@
# InvokeAI - A Stable Diffusion Toolkit
Stable Diffusion distribution by InvokeAI: https://github.com/invoke-ai
The Docker image tracks the `main` branch of the InvokeAI project, which means it includes the latest features, but may contain some bugs.
Your working directory is mounted under the `/workspace` path inside the pod. The models are in `/workspace/invokeai/models`, and outputs are in `/workspace/invokeai/outputs`.
> **Only the /workspace directory will persist between pod restarts!**
> **If you _terminate_ (not just _stop_) the pod, the /workspace will be lost.**
## Quickstart
1. Launch a pod from this template. **It will take about 5-10 minutes to run through the initial setup**. Be patient.
1. Wait for the application to load.
- TIP: you know it's ready when the CPU usage goes idle
- You can also check the logs for a line that says "_Point your browser at..._"
1. Open the Invoke AI web UI: click the `Connect` => `connect over HTTP` button.
1. Generate some art!
## Other things you can do
At any point you may edit the pod configuration and set an arbitrary Docker command. For example, you could run a command to downloads some models using `curl`, or fetch some images and place them into your outputs to continue a working session.
If you need to run *multiple commands*, define them in the Docker Command field like this:
`bash -c "cd ${INVOKEAI_ROOT}/outputs; wormhole receive 2-foo-bar; invoke.py --web --host 0.0.0.0"`
### Copying your data in and out of the pod
This image includes a couple of handy tools to help you get the data into the pod (such as your custom models or embeddings), and out of the pod (such as downloading your outputs). Here are your options for getting your data in and out of the pod:
- **SSH server**:
1. Make sure to create and set your Public Key in the RunPod settings (follow the official instructions)
1. Add an exposed port 22 (TCP) in the pod settings!
1. When your pod restarts, you will see a new entry in the `Connect` dialog. Use this SSH server to `scp` or `sftp` your files as necessary, or SSH into the pod using the fully fledged SSH server.
- [**Magic Wormhole**](https://magic-wormhole.readthedocs.io/en/latest/welcome.html):
1. On your computer, `pip install magic-wormhole` (see above instructions for details)
1. Connect to the command line **using the "light" SSH client** or the browser-based console. _Currently there's a bug where `wormhole` isn't available when connected to "full" SSH server, as described above_.
1. `wormhole send /workspace/invokeai/outputs` will send the entire `outputs` directory. You can also send individual files.
1. Once packaged, you will see a `wormhole receive <123-some-words>` command. Copy it
1. Paste this command into the terminal on your local machine to securely download the payload.
1. It works the same in reverse: you can `wormhole send` some models from your computer to the pod. Again, save your files somewhere in `/workspace` or they will be lost when the pod is stopped.
- **RunPod's Cloud Sync feature** may be used to sync the persistent volume to cloud storage. You could, for example, copy the entire `/workspace` to S3, add some custom models to it, and copy it back from S3 when launching new pod configurations. Follow the Cloud Sync instructions.
### Disable the NSFW checker
The NSFW checker is enabled by default. To disable it, edit the pod configuration and set the following command:
```
invoke --web --host 0.0.0.0 --no-nsfw_checker
```
---
Template ©2023 Eugene Brodsky [ebr](https://github.com/ebr)

View File

@ -4,671 +4,150 @@ title: Changelog
# :octicons-log-16: **Changelog**
## v2.3.5 <small>(22 May 2023)</small>
This release (along with the post1 and post2 follow-on releases) expands support for additional LoRA and LyCORIS models, upgrades diffusers versions, and fixes a few bugs.
### LoRA and LyCORIS Support Improvement
A number of LoRA/LyCORIS fine-tune files (those which alter the text encoder as well as the unet model) were not having the desired effect in InvokeAI. This bug has now been fixed. Full documentation of LoRA support is available at InvokeAI LoRA Support.
Previously, InvokeAI did not distinguish between LoRA/LyCORIS models based on Stable Diffusion v1.5 vs those based on v2.0 and 2.1, leading to a crash when an incompatible model was loaded. This has now been fixed. In addition, the web pulldown menus for LoRA and Textual Inversion selection have been enhanced to show only those files that are compatible with the currently-selected Stable Diffusion model.
Support for the newer LoKR LyCORIS files has been added.
### Library Updates and Speed/Reproducibility Advancements
The major enhancement in this version is that NVIDIA users no longer need to decide between speed and reproducibility. Previously, if you activated the Xformers library, you would see improvements in speed and memory usage, but multiple images generated with the same seed and other parameters would be slightly different from each other. This is no longer the case. Relative to 2.3.5 you will see improved performance when running without Xformers, and even better performance when Xformers is activated. In both cases, images generated with the same settings will be identical.
Here are the new library versions:
Library Version
Torch 2.0.0
Diffusers 0.16.1
Xformers 0.0.19
Compel 1.1.5
Other Improvements
### Performance Improvements
When a model is loaded for the first time, InvokeAI calculates its checksum for incorporation into the PNG metadata. This process could take up to a minute on network-mounted disks and WSL mounts. This release noticeably speeds up the process.
### Bug Fixes
The "import models from directory" and "import from URL" functionality in the console-based model installer has now been fixed.
When running the WebUI, we have reduced the number of times that InvokeAI reaches out to HuggingFace to fetch the list of embeddable Textual Inversion models. We have also caught and fixed a problem with the updater not correctly detecting when another instance of the updater is running
## v2.3.4 <small>(7 April 2023)</small>
What's New in 2.3.4
This features release adds support for LoRA (Low-Rank Adaptation) and LyCORIS (Lora beYond Conventional) models, as well as some minor bug fixes.
### LoRA and LyCORIS Support
LoRA files contain fine-tuning weights that enable particular styles, subjects or concepts to be applied to generated images. LyCORIS files are an extended variant of LoRA. InvokeAI supports the most common LoRA/LyCORIS format, which ends in the suffix .safetensors. You will find numerous LoRA and LyCORIS models for download at Civitai, and a small but growing number at Hugging Face. Full documentation of LoRA support is available at InvokeAI LoRA Support.( Pre-release note: this page will only be available after release)
To use LoRA/LyCORIS models in InvokeAI:
Download the .safetensors files of your choice and place in /path/to/invokeai/loras. This directory was not present in earlier version of InvokeAI but will be created for you the first time you run the command-line or web client. You can also create the directory manually.
Add withLora(lora-file,weight) to your prompts. The weight is optional and will default to 1.0. A few examples, assuming that a LoRA file named loras/sushi.safetensors is present:
family sitting at dinner table eating sushi withLora(sushi,0.9)
family sitting at dinner table eating sushi withLora(sushi, 0.75)
family sitting at dinner table eating sushi withLora(sushi)
Multiple withLora() prompt fragments are allowed. The weight can be arbitrarily large, but the useful range is roughly 0.5 to 1.0. Higher weights make the LoRA's influence stronger. Negative weights are also allowed, which can lead to some interesting effects.
Generate as you usually would! If you find that the image is too "crisp" try reducing the overall CFG value or reducing individual LoRA weights. As is the case with all fine-tunes, you'll get the best results when running the LoRA on top of the model similar to, or identical with, the one that was used during the LoRA's training. Don't try to load a SD 1.x-trained LoRA into a SD 2.x model, and vice versa. This will trigger a non-fatal error message and generation will not proceed.
You can change the location of the loras directory by passing the --lora_directory option to `invokeai.
### New WebUI LoRA and Textual Inversion Buttons
This version adds two new web interface buttons for inserting LoRA and Textual Inversion triggers into the prompt as shown in the screenshot below.
Clicking on one or the other of the buttons will bring up a menu of available LoRA/LyCORIS or Textual Inversion trigger terms. Select a menu item to insert the properly-formatted withLora() or <textual-inversion> prompt fragment into the positive prompt. The number in parentheses indicates the number of trigger terms currently in the prompt. You may click the button again and deselect the LoRA or trigger to remove it from the prompt, or simply edit the prompt directly.
Currently terms are inserted into the positive prompt textbox only. However, some textual inversion embeddings are designed to be used with negative prompts. To move a textual inversion trigger into the negative prompt, simply cut and paste it.
By default the Textual Inversion menu only shows locally installed models found at startup time in /path/to/invokeai/embeddings. However, InvokeAI has the ability to dynamically download and install additional Textual Inversion embeddings from the HuggingFace Concepts Library. You may choose to display the most popular of these (with five or more likes) in the Textual Inversion menu by going to Settings and turning on "Show Textual Inversions from HF Concepts Library." When this option is activated, the locally-installed TI embeddings will be shown first, followed by uninstalled terms from Hugging Face. See The Hugging Face Concepts Library and Importing Textual Inversion files for more information.
### Minor features and fixes
This release changes model switching behavior so that the command-line and Web UIs save the last model used and restore it the next time they are launched. It also improves the behavior of the installer so that the pip utility is kept up to date.
### Known Bugs in 2.3.4
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
## v2.3.3 <small>(28 March 2023)</small>
This is a bugfix and minor feature release.
### Bugfixes
Since version 2.3.2 the following bugs have been fixed:
Bugs
When using legacy checkpoints with an external VAE, the VAE file is now scanned for malware prior to loading. Previously only the main model weights file was scanned.
Textual inversion will select an appropriate batchsize based on whether xformers is active, and will default to xformers enabled if the library is detected.
The batch script log file names have been fixed to be compatible with Windows.
Occasional corruption of the .next_prefix file (which stores the next output file name in sequence) on Windows systems is now detected and corrected.
Support loading of legacy config files that have no personalization (textual inversion) section.
An infinite loop when opening the developer's console from within the invoke.sh script has been corrected.
Documentation fixes, including a recipe for detecting and fixing problems with the AMD GPU ROCm driver.
Enhancements
It is now possible to load and run several community-contributed SD-2.0 based models, including the often-requested "Illuminati" model.
The "NegativePrompts" embedding file, and others like it, can now be loaded by placing it in the InvokeAI embeddings directory.
If no --model is specified at launch time, InvokeAI will remember the last model used and restore it the next time it is launched.
On Linux systems, the invoke.sh launcher now uses a prettier console-based interface. To take advantage of it, install the dialog package using your package manager (e.g. sudo apt install dialog).
When loading legacy models (safetensors/ckpt) you can specify a custom config file and/or a VAE by placing like-named files in the same directory as the model following this example:
my-favorite-model.ckpt
my-favorite-model.yaml
my-favorite-model.vae.pt # or my-favorite-model.vae.safetensors
### Known Bugs in 2.3.3
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
## v2.3.2 <small>(11 March 2023)</small>
This is a bugfix and minor feature release.
### Bugfixes
Since version 2.3.1 the following bugs have been fixed:
Black images appearing for potential NSFW images when generating with legacy checkpoint models and both --no-nsfw_checker and --ckpt_convert turned on.
Black images appearing when generating from models fine-tuned on Stable-Diffusion-2-1-base. When importing V2-derived models, you may be asked to select whether the model was derived from a "base" model (512 pixels) or the 768-pixel SD-2.1 model.
The "Use All" button was not restoring the Hi-Res Fix setting on the WebUI
When using the model installer console app, models failed to import correctly when importing from directories with spaces in their names. A similar issue with the output directory was also fixed.
Crashes that occurred during model merging.
Restore previous naming of Stable Diffusion base and 768 models.
Upgraded to latest versions of diffusers, transformers, safetensors and accelerate libraries upstream. We hope that this will fix the assertion NDArray > 2**32 issue that MacOS users have had when generating images larger than 768x768 pixels. Please report back.
As part of the upgrade to diffusers, the location of the diffusers-based models has changed from models/diffusers to models/hub. When you launch InvokeAI for the first time, it will prompt you to OK a one-time move. This should be quick and harmless, but if you have modified your models/diffusers directory in some way, for example using symlinks, you may wish to cancel the migration and make appropriate adjustments.
New "Invokeai-batch" script
### Invoke AI Batch
2.3.2 introduces a new command-line only script called invokeai-batch that can be used to generate hundreds of images from prompts and settings that vary systematically. This can be used to try the same prompt across multiple combinations of models, steps, CFG settings and so forth. It also allows you to template prompts and generate a combinatorial list like:
a shack in the mountains, photograph
a shack in the mountains, watercolor
a shack in the mountains, oil painting
a chalet in the mountains, photograph
a chalet in the mountains, watercolor
a chalet in the mountains, oil painting
a shack in the desert, photograph
...
If you have a system with multiple GPUs, or a single GPU with lots of VRAM, you can parallelize generation across the combinatorial set, reducing wait times and using your system's resources efficiently (make sure you have good GPU cooling).
To try invokeai-batch out. Launch the "developer's console" using the invoke launcher script, or activate the invokeai virtual environment manually. From the console, give the command invokeai-batch --help in order to learn how the script works and create your first template file for dynamic prompt generation.
### Known Bugs in 2.3.2
These are known bugs in the release.
The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
Windows Defender will sometimes raise a Trojan alert for the codeformer.pth face restoration model. As far as we have been able to determine, this is a false positive and can be safely whitelisted.
## v2.3.1 <small>(22 February 2023)</small>
This is primarily a bugfix release, but it does provide several new features that will improve the user experience.
### Enhanced support for model management
InvokeAI now makes it convenient to add, remove and modify models. You can individually import models that are stored on your local system, scan an entire folder and its subfolders for models and import them automatically, and even directly import models from the internet by providing their download URLs. You also have the option of designating a local folder to scan for new models each time InvokeAI is restarted.
There are three ways of accessing the model management features:
From the WebUI, click on the cube to the right of the model selection menu. This will bring up a form that allows you to import models individually from your local disk or scan a directory for models to import.
Using the Model Installer App
Choose option (5) download and install models from the invoke launcher script to start a new console-based application for model management. You can use this to select from a curated set of starter models, or import checkpoint, safetensors, and diffusers models from a local disk or the internet. The example below shows importing two checkpoint URLs from popular SD sites and a HuggingFace diffusers model using its Repository ID. It also shows how to designate a folder to be scanned at startup time for new models to import.
Command-line users can start this app using the command invokeai-model-install.
Using the Command Line Client (CLI)
The !install_model and !convert_model commands have been enhanced to allow entering of URLs and local directories to scan and import. The first command installs .ckpt and .safetensors files as-is. The second one converts them into the faster diffusers format before installation.
Internally InvokeAI is able to probe the contents of a .ckpt or .safetensors file to distinguish among v1.x, v2.x and inpainting models. This means that you do not need to include "inpaint" in your model names to use an inpainting model. Note that Stable Diffusion v2.x models will be autoconverted into a diffusers model the first time you use it.
Please see INSTALLING MODELS for more information on model management.
### An Improved Installer Experience
The installer now launches a console-based UI for setting and changing commonly-used startup options:
After selecting the desired options, the installer installs several support models needed by InvokeAI's face reconstruction and upscaling features and then launches the interface for selecting and installing models shown earlier. At any time, you can edit the startup options by launching invoke.sh/invoke.bat and entering option (6) change InvokeAI startup options
Command-line users can launch the new configure app using invokeai-configure.
This release also comes with a renewed updater. To do an update without going through a whole reinstallation, launch invoke.sh or invoke.bat and choose option (9) update InvokeAI . This will bring you to a screen that prompts you to update to the latest released version, to the most current development version, or any released or unreleased version you choose by selecting the tag or branch of the desired version.
Command-line users can run this interface by typing invokeai-configure
### Image Symmetry Options
There are now features to generate horizontal and vertical symmetry during generation. The way these work is to wait until a selected step in the generation process and then to turn on a mirror image effect. In addition to generating some cool images, you can also use this to make side-by-side comparisons of how an image will look with more or fewer steps. Access this option from the WebUI by selecting Symmetry from the image generation settings, or within the CLI by using the options --h_symmetry_time_pct and --v_symmetry_time_pct (these can be abbreviated to --h_sym and --v_sym like all other options).
### A New Unified Canvas Look
This release introduces a beta version of the WebUI Unified Canvas. To try it out, open up the settings dialogue in the WebUI (gear icon) and select Use Canvas Beta Layout:
Refresh the screen and go to to Unified Canvas (left side of screen, third icon from the top). The new layout is designed to provide more space to work in and to keep the image controls close to the image itself:
Model conversion and merging within the WebUI
The WebUI now has an intuitive interface for model merging, as well as for permanent conversion of models from legacy .ckpt/.safetensors formats into diffusers format. These options are also available directly from the invoke.sh/invoke.bat scripts.
An easier way to contribute translations to the WebUI
We have migrated our translation efforts to Weblate, a FOSS translation product. Maintaining the growing project's translations is now far simpler for the maintainers and community. Please review our brief translation guide for more information on how to contribute.
Numerous internal bugfixes and performance issues
### Bug Fixes
This releases quashes multiple bugs that were reported in 2.3.0. Major internal changes include upgrading to diffusers 0.13.0, and using the compel library for prompt parsing. See Detailed Change Log for a detailed list of bugs caught and squished.
Summary of InvokeAI command line scripts (all accessible via the launcher menu)
Command Description
invokeai Command line interface
invokeai --web Web interface
invokeai-model-install Model installer with console forms-based front end
invokeai-ti --gui Textual inversion, with a console forms-based front end
invokeai-merge --gui Model merging, with a console forms-based front end
invokeai-configure Startup configuration; can also be used to reinstall support models
invokeai-update InvokeAI software updater
### Known Bugs in 2.3.1
These are known bugs in the release.
MacOS users generating 768x768 pixel images or greater using diffusers models may experience a hard crash with assertion NDArray > 2**32 This appears to be an issu...
## v2.3.0 <small>(15 January 2023)</small>
**Transition to diffusers
Version 2.3 provides support for both the traditional `.ckpt` weight
checkpoint files as well as the HuggingFace `diffusers` format. This
introduces several changes you should know about.
1. The models.yaml format has been updated. There are now two
different type of configuration stanza. The traditional ckpt
one will look like this, with a `format` of `ckpt` and a
`weights` field that points to the absolute or ROOTDIR-relative
location of the ckpt file.
```
inpainting-1.5:
description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
repo_id: runwayml/stable-diffusion-inpainting
format: ckpt
width: 512
height: 512
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
```
A configuration stanza for a diffusers model hosted at HuggingFace will look like this,
with a `format` of `diffusers` and a `repo_id` that points to the
repository ID of the model on HuggingFace:
```
stable-diffusion-2.1:
description: Stable Diffusion version 2.1 diffusers model (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1
format: diffusers
```
A configuration stanza for a diffuers model stored locally should
look like this, with a `format` of `diffusers`, but a `path` field
that points at the directory that contains `model_index.json`:
```
waifu-diffusion:
description: Latest waifu diffusion 1.4
format: diffusers
path: models/diffusers/hakurei-haifu-diffusion-1.4
```
2. In order of precedence, InvokeAI will now use HF_HOME, then
XDG_CACHE_HOME, then finally default to `ROOTDIR/models` to
store HuggingFace diffusers models.
Consequently, the format of the models directory has changed to
mimic the HuggingFace cache directory. When HF_HOME and XDG_HOME
are not set, diffusers models are now automatically downloaded
and retrieved from the directory `ROOTDIR/models/diffusers`,
while other models are stored in the directory
`ROOTDIR/models/hub`. This organization is the same as that used
by HuggingFace for its cache management.
This allows you to share diffusers and ckpt model files easily with
other machine learning applications that use the HuggingFace
libraries. To do this, set the environment variable HF_HOME
before starting up InvokeAI to tell it what directory to
cache models in. To tell InvokeAI to use the standard HuggingFace
cache directory, you would set HF_HOME like this (Linux/Mac):
`export HF_HOME=~/.cache/huggingface`
Both HuggingFace and InvokeAI will fall back to the XDG_CACHE_HOME
environment variable if HF_HOME is not set; this path
takes precedence over `ROOTDIR/models` to allow for the same sharing
with other machine learning applications that use HuggingFace
libraries.
3. If you upgrade to InvokeAI 2.3.* from an earlier version, there
will be a one-time migration from the old models directory format
to the new one. You will see a message about this the first time
you start `invoke.py`.
4. Both the front end back ends of the model manager have been
rewritten to accommodate diffusers. You can import models using
their local file path, using their URLs, or their HuggingFace
repo_ids. On the command line, all these syntaxes work:
```
!import_model stabilityai/stable-diffusion-2-1-base
!import_model /opt/sd-models/sd-1.4.ckpt
!import_model https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/blob/main/PaperCut_v1.ckpt
```
**KNOWN BUGS (15 January 2023)
1. On CUDA systems, the 768 pixel stable-diffusion-2.0 and
stable-diffusion-2.1 models can only be run as `diffusers` models
when the `xformer` library is installed and configured. Without
`xformers`, InvokeAI returns black images.
2. Inpainting and outpainting have regressed in quality.
Both these issues are being actively worked on.
## v2.2.4 <small>(11 December 2022)</small>
**the `invokeai` directory**
Previously there were two directories to worry about, the directory that
contained the InvokeAI source code and the launcher scripts, and the `invokeai`
directory that contained the models files, embeddings, configuration and
outputs. With the 2.2.4 release, this dual system is done away with, and
everything, including the `invoke.bat` and `invoke.sh` launcher scripts, now
live in a directory named `invokeai`. By default this directory is located in
your home directory (e.g. `\Users\yourname` on Windows), but you can select
where it goes at install time.
After installation, you can delete the install directory (the one that the zip
file creates when it unpacks). Do **not** delete or move the `invokeai`
directory!
**Initialization file `invokeai/invokeai.init`**
You can place frequently-used startup options in this file, such as the default
number of steps or your preferred sampler. To keep everything in one place, this
file has now been moved into the `invokeai` directory and is named
`invokeai.init`.
**To update from Version 2.2.3**
The easiest route is to download and unpack one of the 2.2.4 installer files.
When it asks you for the location of the `invokeai` runtime directory, respond
with the path to the directory that contains your 2.2.3 `invokeai`. That is, if
`invokeai` lives at `C:\Users\fred\invokeai`, then answer with `C:\Users\fred`
and answer "Y" when asked if you want to reuse the directory.
The `update.sh` (`update.bat`) script that came with the 2.2.3 source installer
does not know about the new directory layout and won't be fully functional.
**To update to 2.2.5 (and beyond) there's now an update path**
As they become available, you can update to more recent versions of InvokeAI
using an `update.sh` (`update.bat`) script located in the `invokeai` directory.
Running it without any arguments will install the most recent version of
InvokeAI. Alternatively, you can get set releases by running the `update.sh`
script with an argument in the command shell. This syntax accepts the path to
the desired release's zip file, which you can find by clicking on the green
"Code" button on this repository's home page.
**Other 2.2.4 Improvements**
- Fix InvokeAI GUI initialization by @addianto in #1687
- fix link in documentation by @lstein in #1728
- Fix broken link by @ShawnZhong in #1736
- Remove reference to binary installer by @lstein in #1731
- documentation fixes for 2.2.3 by @lstein in #1740
- Modify installer links to point closer to the source installer by @ebr in
#1745
- add documentation warning about 1650/60 cards by @lstein in #1753
- Fix Linux source URL in installation docs by @andybearman in #1756
- Make install instructions discoverable in readme by @damian0815 in #1752
- typo fix by @ofirkris in #1755
- Non-interactive model download (support HUGGINGFACE_TOKEN) by @ebr in #1578
- fix(srcinstall): shell installer - cp scripts instead of linking by @tildebyte
in #1765
- stability and usage improvements to binary & source installers by @lstein in
#1760
- fix off-by-one bug in cross-attention-control by @damian0815 in #1774
- Eventually update APP_VERSION to 2.2.3 by @spezialspezial in #1768
- invoke script cds to its location before running by @lstein in #1805
- Make PaperCut and VoxelArt models load again by @lstein in #1730
- Fix --embedding_directory / --embedding_path not working by @blessedcoolant in
#1817
- Clean up readme by @hipsterusername in #1820
- Optimized Docker build with support for external working directory by @ebr in
#1544
- disable pushing the cloud container by @mauwii in #1831
- Fix docker push github action and expand with additional metadata by @ebr in
#1837
- Fix Broken Link To Notebook by @VedantMadane in #1821
- Account for flat models by @spezialspezial in #1766
- Update invoke.bat.in isolate environment variables by @lynnewu in #1833
- Arch Linux Specific PatchMatch Instructions & fixing conda install on linux by
@SammCheese in #1848
- Make force free GPU memory work in img2img by @addianto in #1844
- New installer by @lstein
## v2.2.3 <small>(2 December 2022)</small>
!!! Note
This point release removes references to the binary installer from the
installation guide. The binary installer is not stable at the current
time. First time users are encouraged to use the "source" installer as
described in [Installing InvokeAI with the Source Installer](installation/deprecated_documentation/INSTALL_SOURCE.md)
With InvokeAI 2.2, this project now provides enthusiasts and professionals a
robust workflow solution for creating AI-generated and human facilitated
compositions. Additional enhancements have been made as well, improving safety,
ease of use, and installation.
Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
512x768 image (and less for smaller images), and is compatible with
Windows/Linux/Mac (M1 & M2).
You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
introduces the main WebUI enhancement for version 2.2 -
[The Unified Canvas](features/UNIFIED_CANVAS.md). This new workflow is the
biggest enhancement added to the WebUI to date, and unlocks a stunning amount of
potential for users to create and iterate on their creations. The following
sections describe what's new for InvokeAI.
## v2.2.2 <small>(30 November 2022)</small>
!!! note
The binary installer is not ready for prime time. First time users are recommended to install via the "source" installer accessible through the links at the bottom of this page.****
With InvokeAI 2.2, this project now provides enthusiasts and professionals a
robust workflow solution for creating AI-generated and human facilitated
compositions. Additional enhancements have been made as well, improving safety,
ease of use, and installation.
Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
512x768 image (and less for smaller images), and is compatible with
Windows/Linux/Mac (M1 & M2).
You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
introduces the main WebUI enhancement for version 2.2 -
[The Unified Canvas](https://invoke-ai.github.io/InvokeAI/features/UNIFIED_CANVAS/).
This new workflow is the biggest enhancement added to the WebUI to date, and
unlocks a stunning amount of potential for users to create and iterate on their
creations. The following sections describe what's new for InvokeAI.
## v2.2.0 <small>(2 December 2022)</small>
With InvokeAI 2.2, this project now provides enthusiasts and professionals a
robust workflow solution for creating AI-generated and human facilitated
compositions. Additional enhancements have been made as well, improving safety,
ease of use, and installation.
Optimized for efficiency, InvokeAI needs only ~3.5GB of VRAM to generate a
512x768 image (and less for smaller images), and is compatible with
Windows/Linux/Mac (M1 & M2).
You can see the [release video](https://youtu.be/hIYBfDtKaus) here, which
introduces the main WebUI enhancement for version 2.2 -
[The Unified Canvas](features/UNIFIED_CANVAS.md). This new workflow is the
biggest enhancement added to the WebUI to date, and unlocks a stunning amount of
potential for users to create and iterate on their creations. The following
sections describe what's new for InvokeAI.
## v2.1.3 <small>(13 November 2022)</small>
- A choice of installer scripts that automate installation and configuration.
See
[Installation](installation/INSTALLATION.md).
- A streamlined manual installation process that works for both Conda and
PIP-only installs. See
[Manual Installation](installation/020_INSTALL_MANUAL.md).
- The ability to save frequently-used startup options (model to load, steps,
sampler, etc) in a `.invokeai` file. See
[Client](deprecated/CLI.md)
- Support for AMD GPU cards (non-CUDA) on Linux machines.
- Multiple bugs and edge cases squashed.
## v2.1.0 <small>(2 November 2022)</small>
- update mac instructions to use invokeai for env name by @willwillems in #1030
- Update .gitignore by @blessedcoolant in #1040
- reintroduce fix for m1 from #579 missing after merge by @skurovec in #1056
- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in #1060
- Print out the device type which is used by @manzke in #1073
- Hires Addition by @hipsterusername in #1063
- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by
@skurovec in #1081
- Forward dream.py to invoke.py using the same interpreter, add deprecation
warning by @db3000 in #1077
- fix noisy images at high step counts by @lstein in #1086
- Generalize facetool strength argument by @db3000 in #1078
- Enable fast switching among models at the invoke> command line by @lstein in
#1066
- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in #1095
- Update generate.py by @unreleased in #1109
- Update 'ldm' env to 'invokeai' in troubleshooting steps by @19wolf in #1125
- Fixed documentation typos and resolved merge conflicts by @rupeshs in #1123
- Fix broken doc links, fix malaprop in the project subtitle by @majick in #1131
- Only output facetool parameters if enhancing faces by @db3000 in #1119
- Update gitignore to ignore codeformer weights at new location by
@spezialspezial in #1136
- fix links to point to invoke-ai.github.io #1117 by @mauwii in #1143
- Rework-mkdocs by @mauwii in #1144
- add option to CLI and pngwriter that allows user to set PNG compression level
by @lstein in #1127
- Fix img2img DDIM index out of bound by @wfng92 in #1137
- Fix gh actions by @mauwii in #1128
- update mac instructions to use invokeai for env name by @willwillems in #1030
- Update .gitignore by @blessedcoolant in #1040
- reintroduce fix for m1 from #579 missing after merge by @skurovec in #1056
- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in #1060
- Print out the device type which is used by @manzke in #1073
- Hires Addition by @hipsterusername in #1063
- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by
@skurovec in #1081
- Forward dream.py to invoke.py using the same interpreter, add deprecation
warning by @db3000 in #1077
- fix noisy images at high step counts by @lstein in #1086
- Generalize facetool strength argument by @db3000 in #1078
- Enable fast switching among models at the invoke> command line by @lstein in
#1066
- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in #1095
- Fixed documentation typos and resolved merge conflicts by @rupeshs in #1123
- Only output facetool parameters if enhancing faces by @db3000 in #1119
- add option to CLI and pngwriter that allows user to set PNG compression level
by @lstein in #1127
- Fix img2img DDIM index out of bound by @wfng92 in #1137
- Add text prompt to inpaint mask support by @lstein in #1133
- Respect http[s] protocol when making socket.io middleware by @damian0815 in
#976
- WebUI: Adds Codeformer support by @psychedelicious in #1151
- Skips normalizing prompts for web UI metadata by @psychedelicious in #1165
- Add Asymmetric Tiling by @carson-katri in #1132
- Web UI: Increases max CFG Scale to 200 by @psychedelicious in #1172
- Corrects color channels in face restoration; Fixes #1167 by @psychedelicious
in #1175
- Flips channels using array slicing instead of using OpenCV by @psychedelicious
in #1178
- Fix typo in docs: s/Formally/Formerly by @noodlebox in #1176
- fix clipseg loading problems by @lstein in #1177
- Correct color channels in upscale using array slicing by @wfng92 in #1181
- Web UI: Filters existing images when adding new images; Fixes #1085 by
@psychedelicious in #1171
- fix a number of bugs in textual inversion by @lstein in #1190
- Improve !fetch, add !replay command by @ArDiouscuros in #882
- Fix generation of image with s>1000 by @holstvoogd in #951
- Web UI: Gallery improvements by @psychedelicious in #1198
- Update CLI.md by @krummrey in #1211
- outcropping improvements by @lstein in #1207
- add support for loading VAE autoencoders by @lstein in #1216
- remove duplicate fix_func for MPS by @wfng92 in #1210
- Metadata storage and retrieval fixes by @lstein in #1204
- nix: add shell.nix file by @Cloudef in #1170
- Web UI: Changes vite dist asset paths to relative by @psychedelicious in #1185
- Web UI: Removes isDisabled from PromptInput by @psychedelicious in #1187
- Allow user to generate images with initial noise as on M1 / mps system by
@ArDiouscuros in #981
- feat: adding filename format template by @plucked in #968
- Web UI: Fixes broken bundle by @psychedelicious in #1242
- Support runwayML custom inpainting model by @lstein in #1243
- Update IMG2IMG.md by @talitore in #1262
- New dockerfile - including a build- and a run- script as well as a GH-Action
by @mauwii in #1233
- cut over from karras to model noise schedule for higher steps by @lstein in
#1222
- Prompt tweaks by @lstein in #1268
- Outpainting implementation by @Kyle0654 in #1251
- fixing aspect ratio on hires by @tjennings in #1249
- Fix-build-container-action by @mauwii in #1274
- handle all unicode characters by @damian0815 in #1276
- adds models.user.yml to .gitignore by @JakeHL in #1281
- remove debug branch, set fail-fast to false by @mauwii in #1284
- Protect-secrets-on-pr by @mauwii in #1285
- Web UI: Adds initial inpainting implementation by @psychedelicious in #1225
- fix environment-mac.yml - tested on x64 and arm64 by @mauwii in #1289
- Use proper authentication to download model by @mauwii in #1287
- Prevent indexing error for mode RGB by @spezialspezial in #1294
- Integrate sd-v1-5 model into test matrix (easily expandable), remove
unecesarry caches by @mauwii in #1293
- add --no-interactive to configure_invokeai step by @mauwii in #1302
- 1-click installer and updater. Uses micromamba to install git and conda into a
contained environment (if necessary) before running the normal installation
script by @cmdr2 in #1253
- configure_invokeai.py script downloads the weight files by @lstein in #1290
## v2.0.1 <small>(13 October 2022)</small>
- 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)
## v2.1.0 (2 November 2022)
- update mac instructions to use invokeai for env name by @willwillems in https://github.com/invoke-ai/InvokeAI/pull/1030
- Update .gitignore by @blessedcoolant in https://github.com/invoke-ai/InvokeAI/pull/1040
- reintroduce fix for m1 from https://github.com/invoke-ai/InvokeAI/pull/579 missing after merge by @skurovec in https://github.com/invoke-ai/InvokeAI/pull/1056
- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in https://github.com/invoke-ai/InvokeAI/pull/1060
- Print out the device type which is used by @manzke in https://github.com/invoke-ai/InvokeAI/pull/1073
- Hires Addition by @hipsterusername in https://github.com/invoke-ai/InvokeAI/pull/1063
- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by @skurovec in https://github.com/invoke-ai/InvokeAI/pull/1081
- Forward dream.py to invoke.py using the same interpreter, add deprecation warning by @db3000 in https://github.com/invoke-ai/InvokeAI/pull/1077
- fix noisy images at high step counts by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1086
- Generalize facetool strength argument by @db3000 in https://github.com/invoke-ai/InvokeAI/pull/1078
- Enable fast switching among models at the invoke> command line by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1066
- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in https://github.com/invoke-ai/InvokeAI/pull/1095
- Update generate.py by @unreleased in https://github.com/invoke-ai/InvokeAI/pull/1109
- Update 'ldm' env to 'invokeai' in troubleshooting steps by @19wolf in https://github.com/invoke-ai/InvokeAI/pull/1125
- Fixed documentation typos and resolved merge conflicts by @rupeshs in https://github.com/invoke-ai/InvokeAI/pull/1123
- Fix broken doc links, fix malaprop in the project subtitle by @majick in https://github.com/invoke-ai/InvokeAI/pull/1131
- Only output facetool parameters if enhancing faces by @db3000 in https://github.com/invoke-ai/InvokeAI/pull/1119
- Update gitignore to ignore codeformer weights at new location by @spezialspezial in https://github.com/invoke-ai/InvokeAI/pull/1136
- fix links to point to invoke-ai.github.io #1117 by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1143
- Rework-mkdocs by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1144
- add option to CLI and pngwriter that allows user to set PNG compression level by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1127
- Fix img2img DDIM index out of bound by @wfng92 in https://github.com/invoke-ai/InvokeAI/pull/1137
- Fix gh actions by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1128
- update mac instructions to use invokeai for env name by @willwillems in https://github.com/invoke-ai/InvokeAI/pull/1030
- Update .gitignore by @blessedcoolant in https://github.com/invoke-ai/InvokeAI/pull/1040
- reintroduce fix for m1 from https://github.com/invoke-ai/InvokeAI/pull/579 missing after merge by @skurovec in https://github.com/invoke-ai/InvokeAI/pull/1056
- Update Stable_Diffusion_AI_Notebook.ipynb (Take 2) by @ChloeL19 in https://github.com/invoke-ai/InvokeAI/pull/1060
- Print out the device type which is used by @manzke in https://github.com/invoke-ai/InvokeAI/pull/1073
- Hires Addition by @hipsterusername in https://github.com/invoke-ai/InvokeAI/pull/1063
- fix for "1 leaked semaphore objects to clean up at shutdown" on M1 by @skurovec in https://github.com/invoke-ai/InvokeAI/pull/1081
- Forward dream.py to invoke.py using the same interpreter, add deprecation warning by @db3000 in https://github.com/invoke-ai/InvokeAI/pull/1077
- fix noisy images at high step counts by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1086
- Generalize facetool strength argument by @db3000 in https://github.com/invoke-ai/InvokeAI/pull/1078
- Enable fast switching among models at the invoke> command line by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1066
- Fix Typo, committed changing ldm environment to invokeai by @jdries3 in https://github.com/invoke-ai/InvokeAI/pull/1095
- Fixed documentation typos and resolved merge conflicts by @rupeshs in https://github.com/invoke-ai/InvokeAI/pull/1123
- Only output facetool parameters if enhancing faces by @db3000 in https://github.com/invoke-ai/InvokeAI/pull/1119
- add option to CLI and pngwriter that allows user to set PNG compression level by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1127
- Fix img2img DDIM index out of bound by @wfng92 in https://github.com/invoke-ai/InvokeAI/pull/1137
- Add text prompt to inpaint mask support by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1133
- Respect http[s] protocol when making socket.io middleware by @damian0815 in https://github.com/invoke-ai/InvokeAI/pull/976
- WebUI: Adds Codeformer support by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1151
- Skips normalizing prompts for web UI metadata by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1165
- Add Asymmetric Tiling by @carson-katri in https://github.com/invoke-ai/InvokeAI/pull/1132
- Web UI: Increases max CFG Scale to 200 by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1172
- Corrects color channels in face restoration; Fixes #1167 by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1175
- Flips channels using array slicing instead of using OpenCV by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1178
- Fix typo in docs: s/Formally/Formerly by @noodlebox in https://github.com/invoke-ai/InvokeAI/pull/1176
- fix clipseg loading problems by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1177
- Correct color channels in upscale using array slicing by @wfng92 in https://github.com/invoke-ai/InvokeAI/pull/1181
- Web UI: Filters existing images when adding new images; Fixes #1085 by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1171
- fix a number of bugs in textual inversion by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1190
- Improve !fetch, add !replay command by @ArDiouscuros in https://github.com/invoke-ai/InvokeAI/pull/882
- Fix generation of image with s>1000 by @holstvoogd in https://github.com/invoke-ai/InvokeAI/pull/951
- Web UI: Gallery improvements by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1198
- Update CLI.md by @krummrey in https://github.com/invoke-ai/InvokeAI/pull/1211
- outcropping improvements by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1207
- add support for loading VAE autoencoders by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1216
- remove duplicate fix_func for MPS by @wfng92 in https://github.com/invoke-ai/InvokeAI/pull/1210
- Metadata storage and retrieval fixes by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1204
- nix: add shell.nix file by @Cloudef in https://github.com/invoke-ai/InvokeAI/pull/1170
- Web UI: Changes vite dist asset paths to relative by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1185
- Web UI: Removes isDisabled from PromptInput by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1187
- Allow user to generate images with initial noise as on M1 / mps system by @ArDiouscuros in https://github.com/invoke-ai/InvokeAI/pull/981
- feat: adding filename format template by @plucked in https://github.com/invoke-ai/InvokeAI/pull/968
- Web UI: Fixes broken bundle by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1242
- Support runwayML custom inpainting model by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1243
- Update IMG2IMG.md by @talitore in https://github.com/invoke-ai/InvokeAI/pull/1262
- New dockerfile - including a build- and a run- script as well as a GH-Action by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1233
- cut over from karras to model noise schedule for higher steps by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1222
- Prompt tweaks by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1268
- Outpainting implementation by @Kyle0654 in https://github.com/invoke-ai/InvokeAI/pull/1251
- fixing aspect ratio on hires by @tjennings in https://github.com/invoke-ai/InvokeAI/pull/1249
- Fix-build-container-action by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1274
- handle all unicode characters by @damian0815 in https://github.com/invoke-ai/InvokeAI/pull/1276
- adds models.user.yml to .gitignore by @JakeHL in https://github.com/invoke-ai/InvokeAI/pull/1281
- remove debug branch, set fail-fast to false by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1284
- Protect-secrets-on-pr by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1285
- Web UI: Adds initial inpainting implementation by @psychedelicious in https://github.com/invoke-ai/InvokeAI/pull/1225
- fix environment-mac.yml - tested on x64 and arm64 by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1289
- Use proper authentication to download model by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1287
- Prevent indexing error for mode RGB by @spezialspezial in https://github.com/invoke-ai/InvokeAI/pull/1294
- Integrate sd-v1-5 model into test matrix (easily expandable), remove unecesarry caches by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1293
- add --no-interactive to preload_models step by @mauwii in https://github.com/invoke-ai/InvokeAI/pull/1302
- 1-click installer and updater. Uses micromamba to install git and conda into a contained environment (if necessary) before running the normal installation script by @cmdr2 in https://github.com/invoke-ai/InvokeAI/pull/1253
- preload_models.py script downloads the weight files by @lstein in https://github.com/invoke-ai/InvokeAI/pull/1290
## 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 <small>(9 October 2022)</small>
- `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
backward compatibility.
- `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`
- img2img runs on all k\* samplers
- Support for
[negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
- Support for [inpainting](features/INPAINTING.md) and [outpainting](features/OUTPAINTING.md)
- img2img runs on all k* samplers
- Support for [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
- Support for CodeFormer face reconstruction
- Support for Textual Inversion on Macintoshes
- Support in both WebGUI and CLI for
[post-processing of previously-generated images](features/POSTPROCESS.md)
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
[larger images to be created without duplicating elements](deprecated/CLI.md#this-is-an-example-of-txt2img),
at the cost of some performance.
- New `--perlin` and `--threshold` options allow you to add and control
variation during image generation (see
[Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
- 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 [command-line completion behavior](deprecated/CLI.md) New commands
added:
- Support in both WebGUI and CLI for [post-processing of previously-generated images](features/POSTPROCESS.md)
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 [larger images to be created without duplicating elements](features/CLI.md#this-is-an-example-of-txt2img), at the cost of some performance.
- New `--perlin` and `--threshold` options allow you to add and control variation
during image generation (see [Thresholding and Perlin Noise Initialization](features/OTHER.md#thresholding-and-perlin-noise-initialization-options))
- 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 [command-line completion behavior](features/CLI.md)
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`.
configure. To switch away from auto use the new flag like `--precision=float32`.
## v1.14 <small>(11 September 2022)</small>
- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
- Full support for Apple hardware with M1 or M2 chips.
- Add "seamless mode" for circular tiling of image. Generates beautiful effects.
([prixt](https://github.com/prixt)).
([prixt](https://github.com/prixt)).
- Inpainting support.
- Improved web server GUI.
- Lots of code and documentation cleanups.
## v1.13 <small>(3 September 2022)</small>
- Support image variations (see [VARIATIONS](deprecated/VARIATIONS.md)
([Kevin Gibbons](https://github.com/bakkot) and many contributors and
reviewers)
- Supports a Google Colab notebook for a standalone server running on Google
hardware [Arturo Mendivil](https://github.com/artmen1516)
- Support image variations (see [VARIATIONS](features/VARIATIONS.md)
([Kevin Gibbons](https://github.com/bakkot) and many contributors and reviewers)
- Supports a Google Colab notebook for a standalone server running on Google hardware
[Arturo Mendivil](https://github.com/artmen1516)
- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling
[Kevin Gibbons](https://github.com/bakkot)
[Kevin Gibbons](https://github.com/bakkot)
- WebUI supports incremental display of in-progress images during generation
[Kevin Gibbons](https://github.com/bakkot)
[Kevin Gibbons](https://github.com/bakkot)
- A new configuration file scheme that allows new models (including upcoming
stable-diffusion-v1.5) to be added without altering the code.
([David Wager](https://github.com/maddavid12))
stable-diffusion-v1.5) to be added without altering the code.
([David Wager](https://github.com/maddavid12))
- Can specify --grid on invoke.py command line as the default.
- Miscellaneous internal bug and stability fixes.
- Works on M1 Apple hardware.
@ -680,59 +159,49 @@ sections describe what's new for InvokeAI.
- Improved file handling, including ability to read prompts from standard input.
(kudos to [Yunsaki](https://github.com/yunsaki)
- The web server is now integrated with the invoke.py script. Invoke by adding
--web to the invoke.py command arguments.
- The web server is now integrated with the invoke.py script. Invoke by adding --web to
the invoke.py command arguments.
- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
VRAM requirements are modestly reduced. Thanks to both
[Blessedcoolant](https://github.com/blessedcoolant) and
VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
[Oceanswave](https://github.com/oceanswave) for their work on this.
- You can now swap samplers on the invoke> command line.
[Blessedcoolant](https://github.com/blessedcoolant)
- You can now swap samplers on the invoke> command line. [Blessedcoolant](https://github.com/blessedcoolant)
---
## v1.11 <small>(26 August 2022)</small>
- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module.
(kudos to [Oceanswave](https://github.com/Oceanswave)
- You now can specify a seed of -1 to use the previous image's seed, -2 to use
the seed for the image generated before that, etc. Seed memory only extends
back to the previous command, but will work on all images generated with the
-n# switch.
- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
- You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
- Variant generation support temporarily disabled pending more general solution.
- Created a feature branch named **yunsaki-morphing-invoke** which adds
experimental support for iteratively modifying the prompt and its parameters.
Please
see[Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86) for
a synopsis of how this works. Note that when this feature is eventually added
to the main branch, it will may be modified significantly.
- Created a feature branch named **yunsaki-morphing-invoke** which adds experimental support for
iteratively modifying the prompt and its parameters. Please see[Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
significantly.
---
## v1.10 <small>(25 August 2022)</small>
- A barebones but fully functional interactive web server for online generation
of txt2img and img2img.
- A barebones but fully functional interactive web server for online generation of txt2img and img2img.
---
## v1.09 <small>(24 August 2022)</small>
- A new -v option allows you to generate multiple variants of an initial image
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave).
[ See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
- Added ability to personalize text to image generation (kudos to
[Oceanswave](https://github.com/Oceanswave) and
[nicolai256](https://github.com/nicolai256))
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
- Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
- Enabled all of the samplers from k_diffusion
---
## v1.08 <small>(24 August 2022)</small>
- Escape single quotes on the invoke> command before trying to parse. This
avoids parse errors.
- Escape single quotes on the invoke> command before trying to parse. This avoids
parse errors.
- Removed instruction to get Python3.8 as first step in Windows install.
Anaconda3 does it for you.
- Added bounds checks for numeric arguments that could cause crashes.
@ -742,36 +211,34 @@ sections describe what's new for InvokeAI.
## v1.07 <small>(23 August 2022)</small>
- Image filenames will now never fill gaps in the sequence, but will be assigned
the next higher name in the chosen directory. This ensures that the alphabetic
and chronological sort orders are the same.
- Image filenames will now never fill gaps in the sequence, but will be assigned the
next higher name in the chosen directory. This ensures that the alphabetic and chronological
sort orders are the same.
---
## v1.06 <small>(23 August 2022)</small>
- Added weighted prompt support contributed by
[xraxra](https://github.com/xraxra)
- Example of using weighted prompts to tweak a demonic figure contributed by
[bmaltais](https://github.com/bmaltais)
- Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
- Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
---
## v1.05 <small>(22 August 2022 - after the drop)</small>
- Filenames now use the following formats: 000010.95183149.png -- Two files
produced by the same command (e.g. -n2), 000010.26742632.png -- distinguished
by a different seed.
- Filenames now use the following formats:
000010.95183149.png -- Two files produced by the same command (e.g. -n2),
000010.26742632.png -- distinguished by a different seed.
000011.455191342.01.png -- Two files produced by the same command using
000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid
can be regenerated with the indicated key
000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid can
be regenerated with the indicated key
- It should no longer be possible for one image to overwrite another
- You can use the "cd" and "pwd" commands at the invoke> prompt to set and
retrieve the path of the output directory.
- You can use the "cd" and "pwd" commands at the invoke> prompt to set and retrieve
the path of the output directory.
---
@ -785,28 +252,26 @@ sections describe what's new for InvokeAI.
## v1.03 <small>(22 August 2022)</small>
- The original txt2img and img2img scripts from the CompViz repository have been
moved into a subfolder named "orig_scripts", to reduce confusion.
- The original txt2img and img2img scripts from the CompViz repository have been moved into
a subfolder named "orig_scripts", to reduce confusion.
---
## v1.02 <small>(21 August 2022)</small>
- A copy of the prompt and all of its switches and options is now stored in the
corresponding image in a tEXt metadata field named "Dream". You can read the
prompt using scripts/images2prompt.py, or an image editor that allows you to
explore the full metadata. **Please run "conda env update" to load the k_lms
dependencies!!**
- A copy of the prompt and all of its switches and options is now stored in the corresponding
image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
or an image editor that allows you to explore the full metadata.
**Please run "conda env update" to load the k_lms dependencies!!**
---
## v1.01 <small>(21 August 2022)</small>
- added k_lms sampling. **Please run "conda env update" to load the k_lms
dependencies!!**
- use half precision arithmetic by default, resulting in faster execution and
lower memory requirements Pass argument --full_precision to invoke.py to get
slower but more accurate image generation
- added k_lms sampling.
**Please run "conda env update" to load the k_lms dependencies!!**
- use half precision arithmetic by default, resulting in faster execution and lower memory requirements
Pass argument --full_precision to invoke.py to get slower but more accurate image generation
---

Some files were not shown because too many files have changed in this diff Show More