slat_decoder_mesh failed.

[zh9586]

Question description

I'm able to access and visualize outputs["gs"] correctly via the supersplat editor, but output["glb"] returns empty. Tracing this back to the decode_slat function:

device: NVIDIA GeForce RTX 4090 (48GB)

  def decode_slat(
        self,
        slat: sp.SparseTensor,
        formats: List[str] = ["mesh", "gaussian"],
    ) -> dict:
        """
        Decode the structured latent.

        Args:
            slat (sp.SparseTensor): The structured latent.
            formats (List[str]): The formats to decode the structured latent to.

        Returns:
            dict: The decoded structured latent.
        """
        logger.info("Decoding sparse latent...")
        ret = {}
        with torch.no_grad():
            if "mesh" in formats:
                ret["mesh"] = self.models["slat_decoder_mesh"](slat)
            if "gaussian" in formats:
                ret["gaussian"] = self.models["slat_decoder_gs"](slat)
            if "gaussian_4" in formats:
                ret["gaussian_4"] = self.models["slat_decoder_gs_4"](slat)
        # if "radiance_field" in formats:
        #     ret["radiance_field"] = self.models["slat_decoder_rf"](slat)
        return ret

the issue occurs at ret["mesh"] = self.models"slat_decoder_mesh". Going further, I found that NaN values appear during the forward pass of
'sam3d_objects.model.backbone.tdfy_dit.models.structured_latent_vae.decoder_mesh.SLatMeshDecoderTdfyWrapper'. I have checked the model weight loading, and the weights are loaded correctly." Has anyone encountered similar issues? Any suggestions on what might cause NaN only in the mesh decoder?

[san5621050115But]

• def decode_slat(

  self,
  slat: sp.SparseTensor,
  formats: List[str] = ["mesh", "gaussian"],
  

  ) -> dict:
  """
  Decode the structured latent.

  Args:
      slat (sp.SparseTensor): The structured latent.
      formats (List[str]): The formats to decode the structured latent to.
  
  Returns:
      dict: The decoded structured latent.
  """
  logger.info("Decoding sparse latent...")
  ret = {}
  with torch.no_grad():
      if "mesh" in formats:
          ret["mesh"] = self.models["slat_decoder_mesh"](slat)
      if "gaussian" in formats:
          ret["gaussian"] = self.models["slat_decoder_gs"](slat)
      if "gaussian_4" in 
  

• formats:

            ret["gaussian_4"] = self.models["slat_decoder_gs_4"](slat)
    # if "radiance_field" in formats:
    #     ret["radiance_field"] = self.models["slat_decoder_rf"](slat)
    return ret