nal image size but that’s okay though because the model will transform it into the final 512x512 image dimensions later. 💡 The height and width are divided by 8 because the vae model has 3 down-sampling layers. You can check by running the following:   Copied 2 ** (len(vae.config.block_out_channels) - 1) == 8   Copied >>> latents = torch.randn(
...     (batch_size, unet.config.in_channels, height // 8, width // 8),
...     generator=generator,
