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()
```
I have added the arabic locale files. There need to be some
modifications to the code in order to detect the language direction and
add it to the current document body properties.
For example we can use this:
import { appWithTranslation, useTranslation } from "next-i18next";
import React, { useEffect } from "react";
const { t, i18n } = useTranslation();
const direction = i18n.dir();
useEffect(() => {
document.body.dir = direction;
}, [direction]);
This should be added to the app file. It uses next-i18next to
automatically get the current language and sets the body text direction
(ltr or rtl) depending on the selected language.
## Provide informative error messages when TI and Merge scripts have
insufficient space for console UI
- 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.
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.
Some of the core features of this PR include:
- optional push image to dockerhub (will be skipped in repos which
didn't set it up)
- stop using the root user at runtime
- trigger builds also for update/docker/* and update/ci/docker/*
- always cache image from current branch and main branch
- separate caches for container flavors
- updated comments with instructions in build.sh and run.sh
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.
Users can now pick the folder to save their diffusers converted model. It can either be the same folder as the ckpt, or the invoke root models folder or a totally custom location.
Fixed a couple of bugs:
1. The original config file for the ckpt file is derived from the entry in
`models.yaml` rather than relying on the user to select. The implication
of this is that V2 ckpt models need to be assigned `v2-inference-v.yaml`
when they are first imported. Otherwise they won't convert right. Note
that currently V2 ckpts are imported with `v1-inference.yaml`, which
isn't right either.
2. Fixed a backslash in the output diffusers path, which was causing
load failures on Linux.
Remaining issues:
1. The radio buttons for selecting the model type are
nonfunctional. It feels to me like these should be moved into the
dialogue for importing ckpt/safetensors files, because this is
where the algorithm needs help from the user.
2. The output diffusers model is written into the same directory as
the input ckpt file. The CLI does it differently and stores the
diffusers model in `ROOTDIR/models/converted-ckpts`. We should
settle on one way or the other.
Converted the picker options to a Radio Group and also updated the backend to use the appropriate config if it is a v2 model that needs to be converted.
- 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.
