def ldm_transform_latent()

in consistencydecoder/__init__.py [0:0]

• 12 lines of code
• 4 McCabe index (conditional complexity)

    def ldm_transform_latent(z, extra_scale_factor=1):
        channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294]
        channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034]

        if len(z.shape) != 4:
            raise ValueError()

        z = z * 0.18215
        channels = [z[:, i] for i in range(z.shape[1])]

        channels = [
            extra_scale_factor * (c - channel_means[i]) / channel_stds[i]
            for i, c in enumerate(channels)
        ]
        return torch.stack(channels, dim=1)