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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 | |