make it so that blank ENSD does not break image generation

def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):

    eta_noise_seed_delta = opts.eta_noise_seed_delta or 0

    xs = []

    # if we have multiple seeds, this means we are working with batch size>1; this then

    # enables the generation of additional tensors with noise that the sampler will use during its processing.

    # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to

    # produce the same images as with two batches [100], [101].

    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):

    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):

        sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]

    else:

        sampler_noises = None

        if sampler_noises is not None:

            cnt = p.sampler.number_of_needed_noises(p)

            if opts.eta_noise_seed_delta > 0:

                torch.manual_seed(seed + opts.eta_noise_seed_delta)

            if eta_noise_seed_delta > 0:

                torch.manual_seed(seed + eta_noise_seed_delta)

            for j in range(cnt):

                sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))