def cache_init(timesteps, model_kwargs=None):
    """
    Initialization for cache.
    """
    cache_dic = {}
    cache = {}
    cache_index = {}
    cache[-1] = {}
    cache_index[-1] = {}
    cache_index["layer_index"] = {}
    cache_dic["attn_map"] = {}
    cache_dic["attn_map"][-1] = {}
    cache_dic["attn_map"][-1]["double_stream"] = {}
    cache_dic["attn_map"][-1]["single_stream"] = {}

    cache_dic["k-norm"] = {}
    cache_dic["k-norm"][-1] = {}
    cache_dic["k-norm"][-1]["double_stream"] = {}
    cache_dic["k-norm"][-1]["single_stream"] = {}

    cache_dic["v-norm"] = {}
    cache_dic["v-norm"][-1] = {}
    cache_dic["v-norm"][-1]["double_stream"] = {}
    cache_dic["v-norm"][-1]["single_stream"] = {}

    cache_dic["cross_attn_map"] = {}
    cache_dic["cross_attn_map"][-1] = {}
    cache[-1]["double_stream"] = {}
    cache[-1]["single_stream"] = {}
    cache_dic["cache_counter"] = 0

    for j in range(19):
        cache[-1]["double_stream"][j] = {}
        cache_index[-1][j] = {}
        cache_dic["attn_map"][-1]["double_stream"][j] = {}
        cache_dic["attn_map"][-1]["double_stream"][j]["total"] = {}
        cache_dic["attn_map"][-1]["double_stream"][j]["txt_mlp"] = {}
        cache_dic["attn_map"][-1]["double_stream"][j]["img_mlp"] = {}

        cache_dic["k-norm"][-1]["double_stream"][j] = {}
        cache_dic["k-norm"][-1]["double_stream"][j]["txt_mlp"] = {}
        cache_dic["k-norm"][-1]["double_stream"][j]["img_mlp"] = {}

        cache_dic["v-norm"][-1]["double_stream"][j] = {}
        cache_dic["v-norm"][-1]["double_stream"][j]["txt_mlp"] = {}
        cache_dic["v-norm"][-1]["double_stream"][j]["img_mlp"] = {}

    for j in range(38):
        cache[-1]["single_stream"][j] = {}
        cache_index[-1][j] = {}
        cache_dic["attn_map"][-1]["single_stream"][j] = {}
        cache_dic["attn_map"][-1]["single_stream"][j]["total"] = {}

        cache_dic["k-norm"][-1]["single_stream"][j] = {}
        cache_dic["k-norm"][-1]["single_stream"][j]["total"] = {}

        cache_dic["v-norm"][-1]["single_stream"][j] = {}
        cache_dic["v-norm"][-1]["single_stream"][j]["total"] = {}

    mode = "ToCa"
    if mode == "original":
        cache_dic["cache_type"] = "random"  # model_kwargs['cache_type'] # no use
        cache_dic["cache_index"] = cache_index
        cache_dic["cache"] = cache
        cache_dic["fresh_ratio_schedule"] = "ToCa"  # model_kwargs['ratio_scheduler']
        cache_dic["fresh_ratio"] = 0.0  # model_kwargs['fresh_ratio']
        cache_dic["fresh_threshold"] = 1  # model_kwargs['fresh_threshold']
        cache_dic["force_fresh"] = "global"  # model_kwargs['force_fresh']
        cache_dic["soft_fresh_weight"] = 0.0  # model_kwargs['soft_fresh_weight']

    elif mode == "ToCa":
        cache_dic["cache_type"] = (
            "attention"  # Attention cache type for ToCa, use Self-Attention Weight to evaluate the importance of each token
        )
        cache_dic["cache_index"] = cache_index
        cache_dic["cache"] = cache
        cache_dic["fresh_ratio_schedule"] = "ToCa"
        cache_dic["fresh_ratio"] = 0.1
        cache_dic["fresh_threshold"] = 4
        cache_dic["force_fresh"] = "global"
        cache_dic["soft_fresh_weight"] = 0.25

    current = {}
    current["final_time"] = timesteps[-2]
    return cache_dic, current