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123 lines
4.8 KiB
Markdown
123 lines
4.8 KiB
Markdown
---
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title: Postprocessing
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---
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# :material-image-edit: Postprocessing
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## Intro
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This extension provides the ability to restore faces and upscale images.
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## Face Fixing
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The default face restoration module is GFPGAN. The default upscale is
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Real-ESRGAN. For an alternative face restoration module, see
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[CodeFormer Support](#codeformer-support) below.
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As of version 1.14, environment.yaml will install the Real-ESRGAN package into
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the standard install location for python packages, and will put GFPGAN into a
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subdirectory of "src" in the InvokeAI directory. Upscaling with Real-ESRGAN
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should "just work" without further intervention. Simply indicate the desired scale on
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the popup in the Web GUI.
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**GFPGAN** requires a series of downloadable model files to work. These are
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loaded when you run `invokeai-configure`. If GFPAN is failing with an
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error, please run the following from the InvokeAI directory:
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```bash
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invokeai-configure
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```
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If you do not run this script in advance, the GFPGAN module will attempt to
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download the models files the first time you try to perform facial
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reconstruction.
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### Upscaling
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Open the upscaling dialog by clicking on the "expand" icon located
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above the image display area in the Web UI:
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<figure markdown>
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</figure>
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There are three different upscaling parameters that you can
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adjust. The first is the scale itself, either 2x or 4x.
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The second is the "Denoising Strength." Higher values will smooth out
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the image and remove digital chatter, but may lose fine detail at
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higher values.
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Third, "Upscale Strength" allows you to adjust how the You can set the
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scaling stength between `0` and `1.0` to control the intensity of the
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scaling. AI upscalers generally tend to smooth out texture details. If
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you wish to retain some of those for natural looking results, we
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recommend using values between `0.5 to 0.8`.
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[This figure](../assets/features/upscaling-montage.png) illustrates
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the effects of denoising and strength. The original image was 512x512,
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4x scaled to 2048x2048. The "original" version on the upper left was
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scaled using simple pixel averaging. The remainder use the ESRGAN
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upscaling algorithm at different levels of denoising and strength.
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<figure markdown>
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{ width=720 }
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</figure>
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Both denoising and strength default to 0.75.
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### Face Restoration
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InvokeAI offers alternative two face restoration algorithms,
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[GFPGAN](https://github.com/TencentARC/GFPGAN) and
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[CodeFormer](https://huggingface.co/spaces/sczhou/CodeFormer). These
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algorithms improve the appearance of faces, particularly eyes and
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mouths. Issues with faces are less common with the latest set of
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Stable Diffusion models than with the original 1.4 release, but the
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restoration algorithms can still make a noticeable improvement in
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certain cases. You can also apply restoration to old photographs you
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upload.
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To access face restoration, click the "smiley face" icon in the
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toolbar above the InvokeAI image panel. You will be presented with a
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dialog that offers a choice between the two algorithm and sliders that
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allow you to adjust their parameters. Alternatively, you may open the
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left-hand accordion panel labeled "Face Restoration" and have the
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restoration algorithm of your choice applied to generated images
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automatically.
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Like upscaling, there are a number of parameters that adjust the face
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restoration output. GFPGAN has a single parameter, `strength`, which
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controls how much the algorithm is allowed to adjust the
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image. CodeFormer has two parameters, `strength`, and `fidelity`,
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which together control the quality of the output image as described in
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the [CodeFormer project
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page](https://shangchenzhou.com/projects/CodeFormer/). Default values
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are 0.75 for both parameters, which achieves a reasonable balance
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between changing the image too much and not enough.
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[This figure](../assets/features/restoration-montage.png) illustrates
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the effects of adjusting GFPGAN and CodeFormer parameters.
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<figure markdown>
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{ width=720 }
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</figure>
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!!! note
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GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid crashes and memory overloads
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during the Stable Diffusion process, these effects are applied after Stable Diffusion has completed
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its work.
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In single image generations, you will see the output right away but when you are using multiple
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iterations, the images will first be generated and then upscaled and face restored after that
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process is complete. While the image generation is taking place, you will still be able to preview
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the base images.
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## How to disable
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If, for some reason, you do not wish to load the GFPGAN and/or ESRGAN libraries,
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you can disable them on the invoke.py command line with the `--no_restore` and
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`--no_esrgan` options, respectively.
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