def get_noise_noisy_latents_and_timesteps(
    args, noise_scheduler, latents: torch.FloatTensor
):
    """
    Генерация фиксированного шума на основе содержимого latents.
    """

    if args.fixed_noise:
        # Переводим в float32 перед numpy()
        latents_bytes = latents.detach().to(torch.float32).cpu().numpy().tobytes()
        hash_int = int(hashlib.sha256(latents_bytes).hexdigest(), 16) % (2**32)

        g = torch.Generator(device=latents.device).manual_seed(hash_int)
        noise = torch.randn(latents.shape, generator=g, device=latents.device)
    else:
        noise = torch.randn_like(latents)

    if args.noise_offset:
        if args.noise_offset_random_strength:
            noise_offset = torch.rand(1, device=latents.device) * args.noise_offset
        else:
            noise_offset = args.noise_offset
        noise = custom_train_functions.apply_noise_offset(latents, noise, noise_offset, args.adaptive_noise_scale)
    if args.multires_noise_iterations:
        noise = custom_train_functions.pyramid_noise_like(
            noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount
        )

    b_size = latents.shape[0]
    min_timestep = 0 if args.min_timestep is None else args.min_timestep
    max_timestep = noise_scheduler.config.num_train_timesteps if args.max_timestep is None else args.max_timestep

    timesteps = get_timesteps(min_timestep, max_timestep, b_size, latents.device)

    if args.ip_noise_gamma:
        if args.ip_noise_gamma_random_strength:
            strength = torch.rand(1, device=latents.device) * args.ip_noise_gamma
        else:
            strength = args.ip_noise_gamma
        noisy_latents = noise_scheduler.add_noise(latents, noise + strength * torch.randn_like(latents), timesteps)
    else:
        noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

    return noise, noisy_latents, timesteps