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import torch
import networks
from modules import patches, shared
class LoraPatches:
def __init__(self):
self.active = False
self.Linear_forward = None
self.Linear_load_state_dict = None
self.Conv2d_forward = None
self.Conv2d_load_state_dict = None
self.GroupNorm_forward = None
self.GroupNorm_load_state_dict = None
self.LayerNorm_forward = None
self.LayerNorm_load_state_dict = None
self.MultiheadAttention_forward = None
self.MultiheadAttention_load_state_dict = None
def apply(self):
if self.active or shared.opts.lora_force_diffusers:
return
self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict)
self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict)
self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward)
self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict)
self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward)
self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict)
self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward)
self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict)
networks.timer['load'] = 0
networks.timer['apply'] = 0
networks.timer['restore'] = 0
self.active = True
def undo(self):
if not self.active or shared.opts.lora_force_diffusers:
return
self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward') # pylint: disable=E1128
self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict') # pylint: disable=E1128
self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward') # pylint: disable=E1128
self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict') # pylint: disable=E1128
self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward') # pylint: disable=E1128
self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict') # pylint: disable=E1128
self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward') # pylint: disable=E1128
self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict') # pylint: disable=E1128
self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward') # pylint: disable=E1128
self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict') # pylint: disable=E1128
patches.originals.pop(__name__, None)
self.active = False
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