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The issue of GANs not adjusting to varying image sizes can be addressed in Matlab by using techniques such as:

1. Resizing images: One way to address the issue of varying image sizes is to resize all images to a standardized size before training the GAN. This can be done using the imresize function in Matlab.

2. Data augmentation: Another approach to address the issue of varying image sizes is to use data augmentation techniques such as cropping, flipping and rotating. This can help to increase the variability of the training data and make the GAN more robust to images of different sizes.

3. Size-conditional GANs: A more advanced approach is to use size-conditional GANs that can generate images of different sizes. This requires the GAN to be trained on multiple image sizes and to generate new images based on the desired size.

4. Multi-scale GANs: Another approach is to use multi-scale GANs that can generate images at multiple scales simultaneously. This involves training several GANs at different scales and combining the outputs to generate images of varying sizes.

Overall, these techniques can help to address the issue of GANs not adjusting to varying image sizes in Matlab and improve the quality of the generated images.