- quashed multiple bugs in model conversion and importing
- found old issue in handling of resume of interrupted downloads
- will require extensive testing
Tensors with diffusers no longer have to be multiples of 8. This broke Perlin noise generation. We now generate noise for the next largest multiple of 8 and return a cropped result. Fixes#2674.
`generator` now asks `InvokeAIDiffuserComponent` to do postprocessing work on latents after every step. Thresholding - now implemented as replacing latents outside of the threshold with random noise - is called at this point. This postprocessing step is also where we can hook up symmetry and other image latent manipulations in the future.
Note: code at this layer doesn't need to worry about MPS as relevant torch functions are wrapped and made MPS-safe by `generator.py`.
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.
- The invokeai-ti and invokeai-merge scripts will crash if there is not enough space
in the console to fit the user interface (even after responsive formatting).
- This PR intercepts the errors and prints a useful error message advising user to
make window larger.
- fix unused variables and f-strings found by pyflakes
- use global_converted_ckpts_dir() to find location of diffusers
- fixed bug in model_manager that was causing the description of converted
models to read "Optimized version of {model_name}'
Strategize slicing based on free [V]RAM when not using xformers. Free [V]RAM is evaluated at every generation. When there's enough memory, the entire generation occurs without slicing. If there is not enough free memory, we use diffusers' sliced attention.
- Adds an update action to launcher script
- This action calls new python script `invokeai-update`, which prompts
user to update to latest release version, main development version,
or an arbitrary git tag or branch name.
- It then uses `pip` to update to whatever tag was specified.
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.
rather than bypassing any path with diffusers in it, im specifically bypassing model.safetensors and diffusion_pytorch_model.safetensors both of which should be diffusers files in most cases.
- 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.
- The following were supposed to be equivalent, but the latter crashes:
```
invoke> banana sushi
invoke> --prompt="banana sushi"
```
This PR fixes the problem.
- Fixes#2548
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.
- Issue is that if insufficient diffusers models are defined in
models.yaml the frontend would ungraciously crash.
- Now it emits appropriate error messages telling user what the problem
is.
- This fixes an edge case crash when the textual inversion frontend
tried to display the list of models and no default model defined
in models.yaml
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
This allows the --log_tokenization option to be used as a command line argument (or from invokeai.init), making it possible to view tokenization information in the terminal when using the web interface.
- 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
Crashes would occur in the invokeai-configure script if no HF token
was found in cache and the user declines to provide one when prompted.
The reason appears to be that on Linux systems getpass_asterisk()
raises an EOFError when no input is provided
On windows10, getpass_asterisk() does not raise the EOFError, but
returns an empty string instead. This patch detects this and raises
the exception so that the control logic is preserved.
- Added modest adaptive behavior; if the screen is wide enough the three
checklists of models will be arranged in a horizontal row.
- Added color support
* 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>
1. only load triton on linux machines
2. require pip >= 23.0 so that editable installs can run without setup.py
3. model files default to SD-1.5, not 2.1
4. use diffusers model of inpainting rather than ckpt
5. selected a new set of initial models based on # of likes at huggingface
- implement the following pattern for finding data files under both
regular and editable install conditions:
import invokeai.foo.bar as bar
path = bar.__path__[0]
- this *seems* to work reliably with Python 3.9. Testing on 3.10 needs
to be performed.
- 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.
* Update --hires_fix
Change `--hires_fix` to calculate initial width and height based on the model's resolution (if available) and with a minimum size.
- install.sh is now a thin wrapper around the pythonized install script
- install.bat not done yet - to follow
- user messaging is tailored to the current platform (paste shortcuts, file paths, etc)
- emit invoke.sh/invoke.bat scripts to the runtime dir
- improve launch scripts (add help option, etc)
- only emit the platform-specific scripts
if the config directory is missing, initialize it using the standard
process of copying it over, instead of failing to create the config file
this can happen if the user is re-running the config script in a directory which
already has the init file, but no configs dir
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
