Add a check and explanation for tensor with all NaNs.

    return torch.autocast("cuda")

class NansException(Exception):

    pass

def test_for_nans(x, where):

    from modules import shared

    if not torch.all(torch.isnan(x)).item():

        return

    if where == "unet":

        message = "A tensor with all NaNs was produced in Unet."

        if not shared.cmd_opts.no_half:

            message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try using --no-half commandline argument to fix this."

    elif where == "vae":

        message = "A tensor with all NaNs was produced in VAE."

        if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae:

            message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."

    else:

        message = "A tensor with all NaNs was produced."

    raise NansException(message)

# MPS workaround for https://github.com/pytorch/pytorch/issues/79383

orig_tensor_to = torch.Tensor.to

def tensor_to_fix(self, *args, **kwargs):

            torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )

        orig_narrow = torch.narrow

        torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )

                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)

            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]

            for x in x_samples_ddim:

                devices.test_for_nans(x, "vae")

            x_samples_ddim = torch.stack(x_samples_ddim).float()

            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})

        devices.test_for_nans(x_out, "unet")

        if opts.live_preview_content == "Prompt":

            store_latent(x_out[0:uncond.shape[0]])

        elif opts.live_preview_content == "Negative prompt":