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https://github.com/invoke-ai/InvokeAI
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102 lines
4.4 KiB
Markdown
102 lines
4.4 KiB
Markdown
---
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title: Upscale
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---
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## **GFPGAN and Real-ESRGAN Support**
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The script also provides the ability to do face restoration and upscaling with the help of GFPGAN
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and Real-ESRGAN respectively.
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As of version 1.14, environment.yaml will install the Real-ESRGAN package into the standard install
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location for python packages, and will put GFPGAN into a subdirectory of "src" in the
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stable-diffusion directory. (The reason for this is that the standard GFPGAN distribution has a
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minor bug that adversely affects image color.) Upscaling with Real-ESRGAN should "just work" without
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further intervention. Simply pass the --upscale (-U) option on the dream> command line, or indicate
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the desired scale on the popup in the Web GUI.
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For **GFPGAN** to work, there is one additional step needed. You will need to download and copy the
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GFPGAN [models file](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth)
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into **src/gfpgan/experiments/pretrained_models**. On Mac and Linux systems, here's how you'd do it
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using **wget**:
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```bash
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> wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth src/gfpgan/experiments/pretrained_models/
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```
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Make sure that you're in the stable-diffusion directory when you do this.
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Alternatively, if you have GFPGAN installed elsewhere, or if you are using an earlier version of
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this package which asked you to install GFPGAN in a sibling directory, you may use the
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`--gfpgan_dir` argument with `dream.py` to set a custom path to your GFPGAN directory. _There are
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other GFPGAN related boot arguments if you wish to customize further._
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!!! warning "Internet connection needed"
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Users whose GPU machines are isolated from the Internet (e.g.
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on a University cluster) should be aware that the first time you run dream.py with GFPGAN and
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Real-ESRGAN turned on, it will try to download model files from the Internet. To rectify this, you
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may run `python3 scripts/preload_models.py` after you have installed GFPGAN and all its
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dependencies.
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## **Usage**
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You will now have access to two new prompt arguments.
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### **Upscaling**
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`-U : <upscaling_factor> <upscaling_strength>`
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The upscaling prompt argument takes two values. The first value is a scaling factor and should be
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set to either `2` or `4` only. This will either scale the image 2x or 4x respectively using
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different models.
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You can set the scaling stength between `0` and `1.0` to control intensity of the of the scaling.
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This is handy because AI upscalers generally tend to smooth out texture details. If you wish to
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retain some of those for natural looking results, we recommend using values between `0.5 to 0.8`.
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If you do not explicitly specify an upscaling_strength, it will default to 0.75.
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### **Face Restoration**
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`-G : <gfpgan_strength>`
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This prompt argument controls the strength of the face restoration that is being applied. Similar to
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upscaling, values between `0.5 to 0.8` are recommended.
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You can use either one or both without any conflicts. In cases where you use both, the image will be
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first upscaled and then the face restoration process will be executed to ensure you get the highest
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quality facial features.
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`--save_orig`
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When you use either `-U` or `-G`, the final result you get is upscaled or face modified. If you want
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to save the original Stable Diffusion generation, you can use the `-save_orig` prompt argument to
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save the original unaffected version too.
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### **Example Usage**
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```bash
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dream> superman dancing with a panda bear -U 2 0.6 -G 0.4
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```
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This also works with img2img:
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```bash
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dream> a man wearing a pineapple hat -I path/to/your/file.png -U 2 0.5 -G 0.6
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```
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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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If you wish to stop during the image generation but want to upscale or face restore a particular
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generated image, pass it again with the same prompt and generated seed along with the `-U` and `-G`
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prompt arguments to perform those actions.
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