Enhancements:
1. Directory-based imports will not attempt to import components of diffusers models.
2. Diffuser directory imports now supported
3. Files that end with .ckpt that are not Stable Diffusion models (such as VAEs) are
skipped during import.
Bugs identified in Psychedelicious's review:
1. The invokeai-configure form now tracks the current contents of `invokeai.init` correctly.
2. The autoencoders are no longer treated like installable models, but instead are
mandatory support models. They will no longer appear in `models.yaml`
Bugs identified in Damian's review:
1. If invokeai-model-install is started before the root directory is initialized, it will
call invokeai-configure to fix the matter.
2. Fix bug that was causing empty `models.yaml` under certain conditions.
3. Made import textbox smaller
4. Hide the "convert to diffusers" options if nothing to import.
- Fixed the test for token length; tested on several .pt and .bin files
- Also added a __main__ entrypoint for CLI.py, to make pdb debugging a bit
more convenient.
- The checkpoint conversion script was generating diffusers models
with the safety checker set to null. This resulted in models
that could not be merged with ones that have the safety checker
activated.
- This PR fixes the issue by incorporating the safety checker into
all 1.x-derived checkpoints, regardless of user's nsfw_checker setting.
- quashed multiple bugs in model conversion and importing
- found old issue in handling of resume of interrupted downloads
- will require extensive testing
1. Now works with sites that produce lots of redirects, such as CIVITAI
2. Derive name of destination model file from HTTP Content-Disposition header,
if present.
3. Swap \\ for / in file paths provided by users, to hopefully fix issues with
Windows.
This PR adds a new attributer to ldm.generate, `embedding_trigger_strings`:
```
gen = Generate(...)
strings = gen.embedding_trigger_strings
strings = gen.embedding_trigger_strings()
```
The trigger strings will change when the model is updated to show only
those strings which are compatible with the current
model. Dynamically-downloaded triggers from the HF Concepts Library
will only show up after they are used for the first time. However, the
full list of concepts available for download can be retrieved
programatically like this:
```
from ldm.invoke.concepts_lib import HuggingFAceConceptsLibrary
concepts = HuggingFaceConceptsLibrary()
trigger_strings = concepts.list_concepts()
```
1. The invokeai-configure script has now been refactored. The work of
selecting and downloading initial models at install time is now done
by a script named invokeai-initial-models (module
name is ldm.invoke.config.initial_model_select)
The calling arguments for invokeai-configure have not changed, so
nothing should break. After initializing the root directory, the
script calls invokeai-initial-models to let the user select the
starting models to install.
2. invokeai-initial-models puts up a console GUI with checkboxes to
indicate which models to install. It respects the --default_only
and --yes arguments so that CI will continue to work.
3. User can now edit the VAE assigned to diffusers models in the CLI.
4. Fixed a bug that caused a crash during model loading when the VAE
is set to None, rather than being empty.
This commit cleans up the code that did bulk imports of legacy model
files. The code has been refactored, and the user is now offered the
option of importing all the model files found in the directory, or
selecting which ones to import.
- This PR introduces a CLI prompt for the proper configuration file to
use when converting a ckpt file, in order to support both inpainting
and v2 models files.
- When user tries to directly !import a v2 model, it prints out a proper
warning that v2 ckpts are not directly supported.
- If CLI asked to convert the currently loaded model, the model would crash
on the first rendering. CLI will now refuse to convert a model loaded
in memory (probably a good idea in any case).
- CLI will offer the `v1-inpainting-inference.yaml` as the configuration
file when importing an inpainting a .ckpt or .safetensors file that
has "inpainting" in the name. Otherwise it offers `v1-inference.yaml`
as the default.
Previously conversions of .ckpt and .safetensors files to diffusers
models were failing with channel mismatch errors. This is corrected
with this PR.
- The model_manager convert_and_import() method now accepts the path
to the checkpoint file's configuration file, using the parameter
`original_config_file`. For inpainting files this should be set to
the full path to `v1-inpainting-inference.yaml`.
- If no configuration file is provided in the call, then the presence
of an inpainting file will be inferred at the
`ldm.ckpt_to_diffuser.convert_ckpt_to_diffUser()` level by looking
for the string "inpaint" in the path. AUTO1111 does something
similar to this, but it is brittle and not recommended.
- This PR also changes the model manager model_names() method to return
the model names in case folded sort order.
- Rename configure_invokeai.py to invokeai_configure.py to be
consistent with installed script name
- Remove warning message about half-precision models not being
available during the model download process.
- adjust estimated file size reported by configure
- guesstimate disk space needed for "all" models
- fix up the "latest" tag to be named 'v2.3-latest'
`torch` wasn't seeing the environment variable. I suspect this is because it was imported before the variable was set, so was running with a different environment.
Many `torch` ops are supported on MPS so this wasn't noticed immediately, but some samplers like k_dpm_2 still use unsupported operations and need this fallback.
* remove non maintained Dockerfile
* adapt Docker related files to latest changes
- also build the frontend when building the image
- skip user response if INVOKE_MODEL_RECONFIGURE is set
- split INVOKE_MODEL_RECONFIGURE to support more than one argument
* rename `docker-build` dir to `docker`
* update build-container.yml
- rename image to invokeai
- add cpu flavor
- add metadata to build summary
- enable caching
- remove build-cloud-img.yml
* fix yarn cache path, link copyjob
* refactor ckpt_to_diffuser to allow converted pipeline to remain in memory
- This idea was introduced by Damian
- Note that although I attempted to use the updated HuggingFace module
pipelines/stable_diffusion/convert_from_ckpt.py, it was unable to
convert safetensors files for reasons I didn't dig into.
- Default is to extract EMA weights.
* add --ckpt_convert option to load legacy ckpt files as diffusers models
- not quite working - I'm getting artifacts and glitches in the
converted diffuser models
- leave as draft for time being
* do not include safety checker in converted files
* add ability to control which vae is used
API now allows the caller to pass an external VAE model to the
checkpoint conversion process. In this way, if an external VAE is
specified in the checkpoint's config stanza, this VAE will be used
when constructing the diffusers model.
Tested with both regular and inpainting 1.X models.
Not tested with SD 2.X models!
---------
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
Co-authored-by: Damian Stewart <null@damianstewart.com>
- fixes a spurious "unknown model name" error when trying to edit the
short name of an existing model.
- relaxes naming requirements to include the ':' and '/' characters
in model names
1) Downgrade numpy to avoid dependency conflict with numba
2) Move all non ldm/invoke files into `invokeai`. This includes assets, backend, frontend, and configs.
3) Fix up way that the backend finds the frontend and the generator finds the NSFW caution.png icon.
This allows reliable distribution of the initial 'configs' directory
with the Python package, and enables the configuration script to be running
from anywhere, as long as the virtual environment is available on the sys.path
