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store patches for Lora in a specialized module

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AUTOMATIC1111
2023-08-15 19:23:27 +03:00
parent 7327be97aa
commit f01682ee01
4 changed files with 118 additions and 61 deletions
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32
extensions-builtin/Lora/networks.py
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@@ -2,6 +2,7 @@ import logging
import os
import re
import lora_patches
import network
import network_lora
import network_hada
@@ -418,74 +419,74 @@ def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
def network_Linear_forward(self, input):
if shared.opts.lora_functional:
return network_forward(self, input, torch.nn.Linear_forward_before_network)
return network_forward(self, input, originals.Linear_forward)
network_apply_weights(self)
return torch.nn.Linear_forward_before_network(self, input)
return originals.Linear_forward(self, input)
def network_Linear_load_state_dict(self, *args, **kwargs):
network_reset_cached_weight(self)
return torch.nn.Linear_load_state_dict_before_network(self, *args, **kwargs)
return originals.Linear_load_state_dict(self, *args, **kwargs)
def network_Conv2d_forward(self, input):
if shared.opts.lora_functional:
return network_forward(self, input, torch.nn.Conv2d_forward_before_network)
return network_forward(self, input, originals.Conv2d_forward)
network_apply_weights(self)
return torch.nn.Conv2d_forward_before_network(self, input)
return originals.Conv2d_forward(self, input)
def network_Conv2d_load_state_dict(self, *args, **kwargs):
network_reset_cached_weight(self)
return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
return originals.Conv2d_load_state_dict(self, *args, **kwargs)
def network_GroupNorm_forward(self, input):
if shared.opts.lora_functional:
return network_forward(self, input, torch.nn.GroupNorm_forward_before_network)
return network_forward(self, input, originals.GroupNorm_forward)
network_apply_weights(self)
return torch.nn.GroupNorm_forward_before_network(self, input)
return originals.GroupNorm_forward(self, input)
def network_GroupNorm_load_state_dict(self, *args, **kwargs):
network_reset_cached_weight(self)
return torch.nn.GroupNorm_load_state_dict_before_network(self, *args, **kwargs)
return originals.GroupNorm_load_state_dict(self, *args, **kwargs)
def network_LayerNorm_forward(self, input):
if shared.opts.lora_functional:
return network_forward(self, input, torch.nn.LayerNorm_forward_before_network)
return network_forward(self, input, originals.LayerNorm_forward)
network_apply_weights(self)
return torch.nn.LayerNorm_forward_before_network(self, input)
return originals.LayerNorm_forward(self, input)
def network_LayerNorm_load_state_dict(self, *args, **kwargs):
network_reset_cached_weight(self)
return torch.nn.LayerNorm_load_state_dict_before_network(self, *args, **kwargs)
return originals.LayerNorm_load_state_dict(self, *args, **kwargs)
def network_MultiheadAttention_forward(self, *args, **kwargs):
network_apply_weights(self)
return torch.nn.MultiheadAttention_forward_before_network(self, *args, **kwargs)
return originals.MultiheadAttention_forward(self, *args, **kwargs)
def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
network_reset_cached_weight(self)
return torch.nn.MultiheadAttention_load_state_dict_before_network(self, *args, **kwargs)
return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
def list_available_networks():
@@ -552,6 +553,9 @@ def infotext_pasted(infotext, params):
if added:
params["Prompt"] += "\n" + "".join(added)
originals: lora_patches.LoraPatches = None
extra_network_lora = None
available_networks = {}