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Merge branch 'main' into diffusers_cross_attention_control_reimplementation

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9
docs/features/MODEL_MERGING.md
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@ -18,10 +18,9 @@ prompts you to select the models to merge, how to merge them, and the
merged model name.
Alternatively you may activate InvokeAI's virtual environment from the
command line, and call the script via `merge_models_fe.py` (the "fe"
stands for "front end"). There is also a version that accepts
command-line arguments, which you can run with the command
`merge_models.py`.
command line, and call the script via `merge_models --gui` to open up
a version that has a nice graphical front end. To get the commandline-
only version, omit `--gui`.
The user interface for the text-based interactive script is
straightforward. It shows you a series of setting fields. Use control-N (^N)
@ -47,7 +46,7 @@ under the selected name and register it with InvokeAI.
display all the diffusers-style models that InvokeAI knows about.
If you do not see the model you are looking for, then it is probably
a legacy checkpoint model and needs to be converted using the
`invoke.py` command-line client and its `!optimize` command. You
`invoke` command-line client and its `!optimize` command. You
must select at least two models to merge. The third can be left at
"None" if you desire.

14
docs/features/TEXTUAL_INVERSION.md
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@ -54,8 +54,8 @@ Please enter 1, 2, 3, or 4: [1] 3
```
From the command line, with the InvokeAI virtual environment active,
you can launch the front end with the command
`textual_inversion_fe`.
you can launch the front end with the command `textual_inversion
--gui`.
This will launch a text-based front end that will look like this:
@ -219,11 +219,9 @@ term. For example `a plate of banana sushi in <psychedelic> style`.
## **Training with the Command-Line Script**
InvokeAI also comes with a traditional command-line script for
launching textual inversion training. It is named
`textual_inversion`, and can be launched from within the
"developer's console", or from the command line after activating
InvokeAI's virtual environment.
Training can also be done using a traditional command-line script. It
can be launched from within the "developer's console", or from the
command line after activating InvokeAI's virtual environment.
It accepts a large number of arguments, which can be summarized by
passing the `--help` argument:
@ -234,7 +232,7 @@ textual_inversion --help
Typical usage is shown here:
```sh
python textual_inversion.py \
textual_inversion \
--model=stable-diffusion-1.5 \
--resolution=512 \
--learnable_property=style \