InvokeAI/docs/features/POSTPROCESS.md

title
Postprocessing

:material-image-edit: Postprocessing

Intro

This extension provides the ability to restore faces and upscale images.

Face restoration and upscaling can be applied at the time you generate the images, or at any later time against a previously-generated PNG file, using the !fix command. Outpainting and outcropping can only be applied after the fact.

Face Fixing

The default face restoration module is GFPGAN. The default upscale is Real-ESRGAN. For an alternative face restoration module, see [CodeFormer Support] below.

As of version 1.14, environment.yaml will install the Real-ESRGAN package into the standard install location for python packages, and will put GFPGAN into a subdirectory of "src" in the InvokeAI directory. Upscaling with Real-ESRGAN should "just work" without further intervention. Simply pass the --upscale (-U) option on the invoke> command line, or indicate the desired scale on the popup in the Web GUI.

GFPGAN requires a series of downloadable model files to work. These are loaded when you run scripts/preload_models.py. If GFPAN is failing with an error, please run the following from the InvokeAI directory:

python scripts/preload_models.py

If you do not run this script in advance, the GFPGAN module will attempt to download the models files the first time you try to perform facial reconstruction.

Alternatively, if you have GFPGAN installed elsewhere, or if you are using an earlier version of this package which asked you to install GFPGAN in a sibling directory, you may use the --gfpgan_dir argument with invoke.py to set a custom path to your GFPGAN directory. There are other GFPGAN related boot arguments if you wish to customize further.

Usage

You will now have access to two new prompt arguments.

Upscaling

-U : <upscaling_factor> <upscaling_strength>

The upscaling prompt argument takes two values. The first value is a scaling factor and should be set to either 2 or 4 only. This will either scale the image 2x or 4x respectively using different models.

You can set the scaling stength between 0 and 1.0 to control intensity of the of the scaling. This is handy because AI upscalers generally tend to smooth out texture details. If you wish to retain some of those for natural looking results, we recommend using values between 0.5 to 0.8.

If you do not explicitly specify an upscaling_strength, it will default to 0.75.

Face Restoration

-G : <facetool_strength>

This prompt argument controls the strength of the face restoration that is being applied. Similar to upscaling, values between 0.5 to 0.8 are recommended.

You can use either one or both without any conflicts. In cases where you use both, the image will be first upscaled and then the face restoration process will be executed to ensure you get the highest quality facial features.

--save_orig

When you use either -U or -G, the final result you get is upscaled or face modified. If you want to save the original Stable Diffusion generation, you can use the -save_orig prompt argument to save the original unaffected version too.

Example Usage

invoke> "superman dancing with a panda bear" -U 2 0.6 -G 0.4

This also works with img2img:

invoke> "a man wearing a pineapple hat" -I path/to/your/file.png -U 2 0.5 -G 0.6

!!! note

GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid crashes and memory overloads
during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed
its work.

In single image generations, you will see the output right away but when you are using multiple
iterations, the images will first be generated and then upscaled and face restored after that
process is complete. While the image generation is taking place, you will still be able to preview
the base images.

If you wish to stop during the image generation but want to upscale or face restore a particular generated image, pass it again with the same prompt and generated seed along with the -U and -G prompt arguments to perform those actions.

CodeFormer Support

This repo also allows you to perform face restoration using CodeFormer.

In order to setup CodeFormer to work, you need to download the models like with GFPGAN. You can do this either by running preload_models.py or by manually downloading the model file and saving it to ldm/invoke/restoration/codeformer/weights folder.

You can use -ft prompt argument to swap between CodeFormer and the default GFPGAN. The above mentioned -G prompt argument will allow you to control the strength of the restoration effect.

Usage

The following command will perform face restoration with CodeFormer instead of the default gfpgan.

<prompt> -G 0.8 -ft codeformer

Other Options

• -cf - cf or CodeFormer Fidelity takes values between 0 and 1. 0 produces high quality results but low accuracy and 1 produces lower quality results but higher accuacy to your original face.

The following command will perform face restoration with CodeFormer. CodeFormer will output a result that is closely matching to the input face.

<prompt> -G 1.0 -ft codeformer -cf 0.9

The following command will perform face restoration with CodeFormer. CodeFormer will output a result that is the best restoration possible. This may deviate slightly from the original face. This is an excellent option to use in situations when there is very little facial data to work with.

<prompt> -G 1.0 -ft codeformer -cf 0.1

Fixing Previously-Generated Images

It is easy to apply face restoration and/or upscaling to any previously-generated file. Just use the syntax !fix path/to/file.png <options>. For example, to apply GFPGAN at strength 0.8 and upscale 2X for a file named ./outputs/img-samples/000044.2945021133.png, just run:

invoke> !fix ./outputs/img-samples/000044.2945021133.png -G 0.8 -U 2

A new file named 000044.2945021133.fixed.png will be created in the output directory. Note that the !fix command does not replace the original file, unlike the behavior at generate time.

Disabling

If, for some reason, you do not wish to load the GFPGAN and/or ESRGAN libraries, you can disable them on the invoke.py command line with the --no_restore and --no_upscale options, respectively.