cosmos_predict1/tokenizer/modules/distributions.py

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"""The distribution modes to use for continuous image tokenizers."""

import torch


class IdentityDistribution(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, parameters):
        return parameters, (torch.tensor([0.0]), torch.tensor([0.0]))


class GaussianDistribution(torch.nn.Module):
    def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0):
        super().__init__()
        self.min_logvar = min_logvar
        self.max_logvar = max_logvar

    def sample(self, mean, logvar):
        std = torch.exp(0.5 * logvar)
        return mean + std * torch.randn_like(mean)

    def forward(self, parameters):
        mean, logvar = torch.chunk(parameters, 2, dim=1)
        logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar)
        return self.sample(mean, logvar), (mean, logvar)