Method decode

src/diffusers/models/vq_model.py:140–156

(
        self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True, shape=None
    )

    @apply_forward_hook
    def decode(
        self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True, shape=None
    ) -> Union[DecoderOutput, torch.FloatTensor]:
        # also go through quantization layer
        if not force_not_quantize:
            quant, _, _ = self.quantize(h)
        elif self.config.lookup_from_codebook:
            quant = self.quantize.get_codebook_entry(h, shape)
        else:
            quant = h
        quant2 = self.post_quant_conv(quant)
        dec = self.decoder(quant2, quant if self.config.norm_type == "spatial" else None)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    def forward(
        self, sample: torch.FloatTensor, return_dict: bool = True