Root directory finding algorithm is:
2) use --root argument
2) use INVOKEAI_ROOT environment variable
3) use VIRTUAL_ENV environment variable
4) use ~/invokeai
Since developer's are liable to put virtual environments in their
favorite places, not necessarily in the invokeai root directory, this
PR adds a sanity check that looks for the existence of
VIRTUAL_ENV/invokeai.init, and moves to (4) if not found.
- Fix a bug in the CLI which prevented diffusers imported by their repo_ids
from being correctly registered in the current session (though they install
correctly)
- Capitalize the "i" in Imported in the autogenerated descriptions.
1. resize installer window to give more room for configure and download forms
2. replace '\' with '/' in directory names to allow user to drag-and-drop
folders into the dialogue boxes that accept directories.
3. similar change in CLI for the !import_model and !convert_model commands
4. better error reporting when a model download fails due to network errors
5. put the launcher scripts into a loop so that menu reappears after
invokeai, merge script, etc exits. User can quit with "Q".
6. do not try to download fp16 of sd-ft-mse-vae, since it doesn't exist.
7. cleaned up status reporting when installing models
- Detect when install failed for some reason and print helpful error
message rather than stack trace.
- Detect window size and resize to minimum acceptable values to provide
better display of configure and install forms.
-At some point pathlib was added to the list of imported modules and this
broken the os.path code that assembled the sample data set.
-Now fixed by replacing os.path calls with Path methods
- Disable responsive resizing below starting dimensions (you can make
form larger, but not smaller than what it was at startup)
- Fix bug that caused multiple --ckpt_convert entries (and similar) to
be written to init file.
1. Fixed display crash when the number of installed models is less than
the number of desired columns to display them.
2. Added --ckpt_convert option to init file.
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.
Added symmetry to Invoke based on discussions with @damian0815. This can currently only be activated via the CLI with the `--h_symmetry_time_pct` and `--v_symmetry_time_pct` options. Those take values from 0.0-1.0, exclusive, indicating the percentage through generation at which symmetry is applied as a one-time operation. To have symmetry in either axis applied after the first step, use a very low value like 0.001.
- not sure why, but at some pont --ckpt_convert (which converts legacy checkpoints)
into diffusers in memory, stopped working due to float16/float32 issues.
- this commit repairs the problem
- also removed some debugging messages I found in passing
- 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.
- You can now achieve several effects:
`invokeai-configure`
This will use console-based UI to initialize invokeai.init,
download support models, and choose and download SD models
`invokeai-configure --yes`
Without activating the GUI, populate invokeai.init with default values,
download support models and download the "recommended" SD models
`invokeai-configure --default_only`
As above, but only download the default SD model (currently SD-1.5)
`invokeai-model-install`
Select and install models. This can be used to download arbitrary
models from the Internet, install HuggingFace models using their repo_id,
or watch a directory for models to load at startup time
`invokeai-model-install --yes`
Import the recommended SD models without a GUI
`invokeai-model-install --default_only`
As above, but only import the default model
- 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.
- fix alpha slider to show values from 0.01 to 0.99
- fix interpolation list to show 'difference' method for 3 models,
- and weighted_sum, sigmoid and inverse_sigmoid methods for 2
- Corrected error that caused --full-precision argument to be ignored
when models downloaded using the --yes argument.
- Improved autodetection of v1 inpainting files; no longer relies on the
file having 'inpaint' in the name.
* new OffloadingDevice loads one model at a time, on demand
* fixup! new OffloadingDevice loads one model at a time, on demand
* fix(prompt_to_embeddings): call the text encoder directly instead of its forward method
allowing any associated hooks to run with it.
* more attempts to get things on the right device from the offloader
* more attempts to get things on the right device from the offloader
* make offloading methods an explicit part of the pipeline interface
* inlining some calls where device is only used once
* ensure model group is ready after pipeline.to is called
* fixup! Strategize slicing based on free [V]RAM (#2572)
* doc(offloading): docstrings for offloading.ModelGroup
* doc(offloading): docstrings for offloading-related pipeline methods
* refactor(offloading): s/SimpleModelGroup/FullyLoadedModelGroup
* refactor(offloading): s/HotSeatModelGroup/LazilyLoadedModelGroup
to frame it is the same terms as "FullyLoadedModelGroup"
---------
Co-authored-by: Damian Stewart <null@damianstewart.com>
- 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()
```
