hotfix to 2.3.4 (invoke-ai#3188)

- Pin diffusers to 0.14
- Small fix to LoRA loading routine that was preventing placement of
LoRA files in subdirectories.
- Bump version to 2.3.4.post1

Move v2.3-latest tag to tip

release 2.3.4

Move tag to commit with correct rc version.

Release 2.3.3 (invoke-ai#3058)

(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 invoke-ai#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.invoke-ai#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.

making this a prerelease for zipfile purposes

making this a prerelease for zipfile purposes

add basic autocomplete functionality to node cli (invoke-ai#3035)

- Commands, invocations and their parameters will now autocomplete using
introspection.
- Two types of parameter *arguments* will also autocomplete:
  - --sampler_name  will autocomplete the scheduler name
  - --model will autocomplete the model name
- There don't seem to be commands for reading/writing image files yet,
so path autocompletion is not implemented

enhancements to update routines

- 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.

bump rc version number