Allow creation of zero vectors for TI

    with devices.autocast():

        cond_model([""])  # will send cond model to GPU if lowvram/medvram is active

    embedded = cond_model.encode_embedding_init_text(init_text, num_vectors_per_token)

    #cond_model expects at least some text, so we provide '*' as backup.

    embedded = cond_model.encode_embedding_init_text(init_text or '*', num_vectors_per_token)

    vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)

    #Only copy if we provided an init_text, otherwise keep vectors as zeros

    if init_text:

        for i in range(num_vectors_per_token):

            vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]