Method forward

python_coreml_stable_diffusion/unet.py:259–272

(self, hidden_states, res_hidden_states_tuple, temb=None)

            self.upsamplers = nn.ModuleList([Upsample2D(out_channels)])

    def forward(self, hidden_states, res_hidden_states_tuple, temb=None):
        for resnet in self.resnets:
            res_hidden_states = res_hidden_states_tuple[-1]
            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
            hidden_states = torch.cat([hidden_states, res_hidden_states],
                                      dim=1)

            hidden_states = resnet(hidden_states, temb)

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states)

        return hidden_states


class CrossAttnDownBlock2D(nn.Module):