(note that this is actually release candidate 7, but I made the mistake
of including an old rc number in the branch and can't easily change it)
## Updating Root directory
- Introduced new mechanism for updating the root directory when
necessary. Currently only used to update the invoke.sh script using new
dialog colors.
- Fixed ROCm torch module version number
## Loading legacy 2.0/2.1 models
- Due to not converting the torch.dtype precision correctly, the
`load_pipeline_from_original_stable_diffusion_ckpt()` was returning
models of dtype float32 regardless of the precision setting. This caused
a precision mismatch crash.
- Problem now fixed (also see #3057 for the same fix to `main`)
## Support for a fourth textual inversion embedding file format
- This variant, exemplified by "easynegative.safetensors" has a single
'embparam' key containing a Tensor.
- Also refactored code to make it easier to read.
- Handle both pickle and safetensor formats.
## Persistent model selection
- To be consistent with WebUI parameter behavior, the currently selected
model is saved on exit and restored on restart for both WebUI and CLI
## Bug fixes
- Name of VAE cache directory was "hug", not "hub". This is fixed.
## VAE fixes
- Allow custom VAEs to be assigned to a legacy model by placing a
like-named vae file adjacent to the checkpoint file.
- The custom VAE will be picked up and incorporated into the diffusers
model if the user chooses to convert/optimize.
## Custom config file loading
- Some of the civitai models instruct users to place a custom .yaml file
adjacent to the checkpoint file. This generally wasn't working because
some of the .yaml files use FrozenCLIPEmbedder rather than
WeightedFrozenCLIPEmbedder, and our FrozenCLIPEmbedder class doesn't
handle the `personalization_config` section used by the the textual
inversion manager. Other .yaml files don't have the
`personalization_config` section at all. Both these issues are
fixed.#1685
## Consistent pytorch version
- There was an inconsistency between the pytorch version requirement in
`pyproject.toml` and the requirement in the installer (which does a
little jiggery-pokery to load torch with the right CUDA/ROCm version
prior to the main pip install. This was causing torch to be installed,
then uninstalled, and reinstalled with a different version number. This
is now fixed.
- Allow invokeai-update to update using a release, tag or branch.
- Allow CLI's root directory update routine to update directory
contents regardless of whether current version is released.
- In model importation routine, clarify wording of instructions when user is
asked to choose the type of model being imported.
- This variant, exemplified by "easynegative.safetensors" has a single
'embparam' key containing a Tensor.
- Also refactored code to make it easier to read.
- Handle both pickle and safetensor formats.
This commit fixes bugs related to the on-the-fly conversion and loading of
legacy checkpoint models built on SD-2.0 base.
- When legacy checkpoints built on SD-2.0 models were converted
on-the-fly using --ckpt_convert, generation would crash with a
precision incompatibility error.
- In addition, broken logic was causing some 2.0-derived ckpt files to
be converted into diffusers and then processed through the legacy
generation routines - not good.
- installer now installs the pretty dialog-based console launcher
- added dialogrc for custom colors
- add updater to download new launcher when users do an update
- This variant, exemplified by "easynegative.safetensors" has a single
'embparam' key containing a Tensor.
- Also refactored code to make it easier to read.
- Handle both pickle and safetensor formats.
- Imported V2 legacy models will now autoconvert into diffusers at load
time regardless of setting of --ckpt_convert.
- model manager `heuristic_import()` function now looks for side-by-side
yaml and vae files for custom configuration and VAE respectively.
Example of this:
illuminati-v1.1.safetensors illuminati-v1.1.vae.safetensors
illuminati-v1.1.yaml
When the user tries to import `illuminati-v1.1.safetensors`, the yaml
file will be used for its configuration, and the VAE will be used for
its VAE. Conversion to diffusers will happen if needed, and the yaml
file will be used to determine which V2 format (if any) to apply.
NOTE that the changes to `ckpt_to_diffusers.py` were previously reviewed
by @JPPhoto on the `main` branch and approved.
- Imported V2 legacy models will now autoconvert into diffusers
at load time regardless of setting of --ckpt_convert.
- model manager `heuristic_import()` function now looks for
side-by-side yaml and vae files for custom configuration and VAE
respectively.
Example of this:
illuminati-v1.1.safetensors
illuminati-v1.1.vae.safetensors
illuminati-v1.1.yaml
When the user tries to import `illuminati-v1.1.safetensors`, the yaml
file will be used for its configuration, and the VAE will be used for
its VAE. Conversion to diffusers will happen if needed, and the yaml
file will be used to determine which V2 format (if any) to apply.
Several related security fixes:
1. Port #2946 from main to 2.3.2 branch - this closes a hole that allows
a pickle checkpoint file to masquerade as a safetensors file.
2. Add pickle scanning to the checkpoint to diffusers conversion script.
3. Pickle scan VAE non-safetensors files
4. Avoid running scanner twice on same file during the probing and
conversion process.
5. Clean up diagnostic messages.
- The command `invokeai-batch --invoke` was created a time-stamped
logfile with colons in its name, which is a Windows no-no. This corrects
the problem by writing the timestamp out as "13-06-2023_8-35-10"
- Closes#3005
- Since 2.3.2 invokeai stores the next PNG file's numeric prefix in a
file named `.next_prefix` in the outputs directory. This avoids the
overhead of doing a directory listing to find out what file number comes
next.
- The code uses advisory locking to prevent corruption of this file in
the event that multiple invokeai's try to access it simultaneously, but
some users have experienced corruption of the file nevertheless.
- This PR addresses the problem by detecting a potentially corrupted
`.next_prefix` file and falling back to the directory listing method. A
fixed version of the file is then written out.
- Closes#3001
This PR addresses issues raised by #3008.
1. Update documentation to indicate the correct maximum batch size for
TI training when xformers is and isn't used.
2. Update textual inversion code so that the default for batch size is
aware of xformer availability.
3. Add documentation for how to launch TI with distributed learning.
