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Merge remote-tracking branch 'upstream/development' into mkdocs-updates

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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.
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/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`