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42f1cf6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | # Copyright (C) 2026 Boogu Team.
# This repository is a fork by Boogu Team; modifications have been made.
#
# Original work: TaylorSeer (Shenyi-Z), taylorseer_flux/cache_functions/cache_init.py
# Source: https://github.com/Shenyi-Z/TaylorSeer/blob/main/TaylorSeers-xDiT/taylorseer_flux/cache_functions/cache_init.py
# Type hinting would cause circular import, self should be `BooguImagePipeline`
def cache_init(self, num_steps: int):
"""
Initialization for cache.
"""
cache_dic = {}
cache = {}
cache_index = {}
cache[-1] = {}
cache_index[-1] = {}
cache_index["layer_index"] = {}
cache[-1]["layers_stream"] = {}
cache_dic["cache_counter"] = 0
for j in range(len(self.transformer.layers)):
cache[-1]["layers_stream"][j] = {}
cache_index[-1][j] = {}
cache_dic["Delta-DiT"] = False
cache_dic["cache_type"] = "random"
cache_dic["cache_index"] = cache_index
cache_dic["cache"] = cache
cache_dic["fresh_ratio_schedule"] = "ToCa"
cache_dic["fresh_ratio"] = 0.0
cache_dic["fresh_threshold"] = 3
cache_dic["soft_fresh_weight"] = 0.0
cache_dic["taylor_cache"] = True
cache_dic["max_order"] = 4
cache_dic["first_enhance"] = 5
current = {}
current["activated_steps"] = [0]
current["step"] = 0
current["num_steps"] = num_steps
return cache_dic, current
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