Question about function idxBl_to_var_input

[TianheWu]

# ===================== idxBl_to_var_input: only used in VAR training, for getting teacher-forcing input =====================
    def idxBl_to_var_input(self, gt_ms_idx_Bl: List[torch.Tensor]) -> torch.Tensor:
        next_scales = []
        B = gt_ms_idx_Bl[0].shape[0]
        C = self.Cvae
        H = W = self.v_patch_nums[-1]
        SN = len(self.v_patch_nums)
        
        f_hat = gt_ms_idx_Bl[0].new_zeros(B, C, H, W, dtype=torch.float32)
        pn_next: int = self.v_patch_nums[0]
        for si in range(SN-1):
            if self.prog_si == 0 or (0 <= self.prog_si-1 < si): break   # progressive training: not supported yet, prog_si always -1
            h_BChw = F.interpolate(self.embedding(gt_ms_idx_Bl[si]).transpose_(1, 2).view(B, C, pn_next, pn_next), size=(H, W), mode='bicubic')
            f_hat.add_(self.quant_resi[si/(SN-1)](h_BChw))
            pn_next = self.v_patch_nums[si+1]
            
            next_scales.append(F.interpolate(f_hat, size=(pn_next, pn_next), mode='area').view(B, C, -1).transpose(1, 2))
            
        return torch.cat(next_scales, dim=1) if len(next_scales) else None    # cat BlCs to BLC, this should be float32

Thanks for the brilliant work! Here is a question. Why next_scales is not direct self.embedding(gt_ms_idx_Bl[si], but F.interpolate(f_hat, size=(pn_next, pn_next), mode='area') ? The term f_hat is added by zero.