- Using relative root addresses was causing problems when the
current working directory was changed after start time.
- This commit makes the root address absolute at start time, such
that changing the working directory later on doesn't break anything.
* partially working simple installer
* works on linux
* fix linux requirements files
* read root environment variable in right place
* fix cat invokeai.init in test workflows
* fix classical cp error in test-invoke-pip.yml
* respect --root argument now
* untested bat installers added
* windows install.bat now working
fix logic to find frontend files
* rename simple_install to "installer"
1. simple_install => 'installer'
2. source and binary install directories are removed
* enable update scripts to update requirements
- Also pin requirements to known working commits.
- This may be a breaking change; exercise with caution
- No functional testing performed yet!
* update docs and installation requirements
NOTE: This may be a breaking commit! Due to the way the installer
works, I have to push to a public branch in order to do full end-to-end
testing.
- Updated installation docs, removing binary and source installers and
substituting the "simple" unified installer.
- Pin requirements for the "http:" downloads to known working commits.
- Removed as much as possible the invoke-ai forks of others' repos.
* fix directory path for installer
* correct requirement/environment errors
* exclude zip files in .gitignore
* possible fix for dockerbuild
* ready for torture testing
- final Windows bat file tweaks
- copy environments-and-requirements to the runtime directory so that
the `update.sh` script can run.
This is not ideal, since we lose control over the
requirements. Better for the update script to pull the proper
updated requirements script from the repository.
* allow update.sh/update.bat to install arbitrary InvokeAI versions
- Can pass the zip file path to any InvokeAI release, branch, commit or tag,
and the installer will try to install it.
- Updated documentation
- Added Linux Python install hints.
* use binary installer's :err_exit function
* user diffusers 0.10.0
* added logic for CPPFLAGS on mac
* improve windows install documentation
- added information on a couple of gotchas I experienced during
windows installation, including DLL loading errors experienced
when Visual Studio C++ Redistributable was not present.
* tagged to pull from 2.2.4-rc1
- also fix error of shell window closing immediately if suitable
python not found
Co-authored-by: mauwii <Mauwii@outlook.de>
* attention maps saving to /tmp
* tidy up diffusers branch backporting of cross attention refactoring
* base64-encoding the attention maps image for generationResult
* cleanup/refactor conditioning.py
* attention maps and tokens being sent to web UI
* attention maps: restrict count to actual token count and improve robustness
* add argument type hint to image_to_dataURL function
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Co-authored-by: damian <git@damianstewart.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
In the event where no `init_mask` is given and `invert_mask` is set to True, the script will raise the following error:
```bash
AttributeError: 'NoneType' object has no attribute 'mode'
```
The new implementation will only run inversion when both variables are valid.
prompt token sequences begin with a "beginning-of-sequence" marker <bos> and end with a repeated "end-of-sequence" marker <eos> - to make a default prompt length of <bos> + 75 prompt tokens + <eos>. the .swap() code was failing to take the column for <bos> at index 0 into account. the changes here do that, and also add extra handling for a single <eos> (which may be redundant but which is included for completeness).
based on my understanding and some assumptions about how this all works, the reason .swap() nevertheless seemed to do the right thing, to some extent, is because over multiple steps the conditioning process in Stable Diffusion operates as a feedback loop. a change to token n-1 has flow-on effects to how the [1x4x64x64] latent tensor is modified by all the tokens after it, - and as the next step is processed, all the tokens before it as well. intuitively, a token's conditioning effects "echo" throughout the whole length of the prompt. so even though the token at n-1 was being edited when what the user actually wanted was to edit the token at n, it nevertheless still had some non-negligible effect, in roughly the right direction, often enough that it seemed like it was working properly.
prompt token sequences begin with a "beginning-of-sequence" marker <bos> and end with a repeated "end-of-sequence" marker <eos> - to make a default prompt length of <bos> + 75 prompt tokens + <eos>. the .swap() code was failing to take the column for <bos> at index 0 into account. the changes here do that, and also add extra handling for a single <eos> (which may be redundant but which is included for completeness).
based on my understanding and some assumptions about how this all works, the reason .swap() nevertheless seemed to do the right thing, to some extent, is because over multiple steps the conditioning process in Stable Diffusion operates as a feedback loop. a change to token n-1 has flow-on effects to how the [1x4x64x64] latent tensor is modified by all the tokens after it, - and as the next step is processed, all the tokens before it as well. intuitively, a token's conditioning effects "echo" throughout the whole length of the prompt. so even though the token at n-1 was being edited when what the user actually wanted was to edit the token at n, it nevertheless still had some non-negligible effect, in roughly the right direction, often enough that it seemed like it was working properly.
