InvokeAI/docs/features/UPSCALE.md

GFPGAN and Real-ESRGAN Support

The script also provides the ability to do face restoration and upscaling with the help of GFPGAN and Real-ESRGAN respectively.

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 stable-diffusion directory. (The reason for this is that the standard GFPGAN distribution has a minor bug that adversely affects image color.) Upscaling with Real-ESRGAN should "just work" without further intervention. Simply pass the --upscale (-U) option on the dream> command line, or indicate the desired scale on the popup in the Web GUI.

For GFPGAN to work, there is one additional step needed. You will need to download and copy the GFPGAN models file into src/gfpgan/experiments/pretrained_models. On Mac and Linux systems, here's how you'd do it using wget:

> wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P src/gfpgan/experiments/pretrained_models/

Make sure that you're in the stable-diffusion directory when you do this.

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 dream.py to set a custom path to your GFPGAN directory. There are other GFPGAN related boot arguments if you wish to customize further.

Note: Internet connection needed: Users whose GPU machines are isolated from the Internet (e.g. on a University cluster) should be aware that the first time you run dream.py with GFPGAN and Real-ESRGAN turned on, it will try to download model files from the Internet. To rectify this, you may run python3 scripts/preload_models.py after you have installed GFPGAN and all its dependencies.

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 : <gfpgan_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

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

This also works with img2img:

dream> 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.