def get_special_dtypes_update(self, model, torch_dtype: "torch.dtype") -> Dict[str, "torch.dtype"]:
        """
        returns dtypes for modules that are not quantized - used for the computation of the device_map in case one
        passes a str as a device_map. The method will use the `modules_to_not_convert` that is modified in
        `_process_model_before_weight_loading`. `diffusers` models don't have any `modules_to_not_convert` attributes
        yet but this can change soon in the future.

        Args:
            model (`~diffusers.models.modeling_utils.ModelMixin`):
                The model to quantize
            torch_dtype (`torch.dtype`):
                The dtype passed in `from_pretrained` method.
        """

        return {
            name: torch_dtype
            for name, _ in model.named_parameters()
            if any(m in name for m in self.modules_to_not_convert)
        }

        returns dtypes for modules that are not quantized - used for the computation of the device_map in case one
        passes a str as a device_map. The method will use the `modules_to_not_convert` that is modified in
        `_process_model_before_weight_loading`. `diffusers` models don't have any `modules_to_not_convert` attributes
        yet but this can change soon in the future.

        Args:
            model (`~diffusers.models.modeling_utils.ModelMixin`):
                The model to quantize
            torch_dtype (`torch.dtype`):
                The dtype passed in `from_pretrained` method.
        
src/diffusers/quantizers/base.py