Method forward

k_diffusion/layers.py:309–312

(self, input, cond)

        nn.init.zeros_(self.mapper.bias)

    def forward(self, input, cond):
        weight, bias = self.mapper(cond[self.cond_key]).chunk(2, dim=-1)
        input = F.group_norm(input, self.num_groups, eps=self.eps)
        return torch.addcmul(utils.append_dims(bias, input.ndim), input, utils.append_dims(weight, input.ndim) + 1)


# Attention