File size: 1,560 Bytes
3dabe4a |
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 44 45 46 47 48 49 |
from modules import shared
import numpy as np
from loractl.lib.lora_ctl_network import network, lora_weights
from loractl.lib.utils import calculate_weight, is_hires
# Patch network.Network so it reapplies properly for dynamic weights
# By default, network application is cached, with (name, te, unet, dim) as a key
# By replacing the bare properties with getters, we can ensure that we cause SD
# to reapply the network each time we change its weights, while still taking advantage
# of caching when weights are not updated.
def get_weight(m):
return calculate_weight(m, shared.state.sampling_step, shared.state.sampling_steps, step_offset=2)
def get_dynamic_te(self):
if self.name in lora_weights:
key = "te" if not is_hires() else "hrte"
w = lora_weights[self.name]
return get_weight(w.get(key, self._te_multiplier))
return get_weight(self._te_multiplier)
def get_dynamic_unet(self):
if self.name in lora_weights:
key = "unet" if not is_hires() else "hrunet"
w = lora_weights[self.name]
return get_weight(w.get(key, self._unet_multiplier))
return get_weight(self._unet_multiplier)
def set_dynamic_te(self, value):
self._te_multiplier = value
def set_dynamic_unet(self, value):
self._unet_multiplier = value
def apply():
if getattr(network.Network, "te_multiplier", None) == None:
network.Network.te_multiplier = property(get_dynamic_te, set_dynamic_te)
network.Network.unet_multiplier = property(
get_dynamic_unet, set_dynamic_unet)
|