- latent1: The path of the first style vector. Come from `dst.npy` generated by Pixel2Style2Pixel or `dst.fitting.npy` generated by StyleGANv2 Fitting module
- latent2: The path of the second style vector. The source is the same as the first style vector
- weights: The two style vectors are mixed in different proportions at different levels. For a resolution of 1024, there are 18 levels. For a resolution of 512, there are 16 levels, and so on.
The more in front, the more it affects the whole of the mixed image. The more behind, the more it affects the details of the mixed image.
The more in front, the more it affects the whole of the mixed image. The more behind, the more it affects the details of the mixed image. In the figure below we show the fusion results of different weights for reference.
- need_align: whether to crop the image to an image that can be recognized by the model. For an image that has been cropped, such as the `src.png` that is pre-generated when Pixel2Style2Pixel is used to generate the style vector, the need_align parameter may not be filled in
- start_lr: learning rate at the begin of training
- final_lr: learning rate at the end of training
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@@ -72,6 +72,25 @@ The result of mixing two style vectors in a specific ratio:
