---
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](#fixing-previously-generated-images) command.
[Outpainting and outcropping](OUTPAINTING.md) 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](#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:

```bash
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.

## 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

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

This also works with img2img:

```bash
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](https://github.com/sczhou/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](https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth)
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:

```bash
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.
