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cff-version: 1.2.0 | ||
message: "If you use this software, please cite it as below." | ||
authors: | ||
- given-names: AUTOMATIC1111 | ||
title: "Stable Diffusion Web UI" | ||
date-released: 2022-08-22 | ||
url: "https://github.com/AUTOMATIC1111/stable-diffusion-webui" |
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import torch | ||
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import networks | ||
from modules import patches | ||
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class LoraPatches: | ||
def __init__(self): | ||
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) | ||
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def undo(self): | ||
self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward') | ||
self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict') | ||
self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward') | ||
self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict') | ||
self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward') | ||
self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict') | ||
self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward') | ||
self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict') | ||
self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward') | ||
self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict') | ||
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import network | ||
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class ModuleTypeNorm(network.ModuleType): | ||
def create_module(self, net: network.Network, weights: network.NetworkWeights): | ||
if all(x in weights.w for x in ["w_norm", "b_norm"]): | ||
return NetworkModuleNorm(net, weights) | ||
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return None | ||
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class NetworkModuleNorm(network.NetworkModule): | ||
def __init__(self, net: network.Network, weights: network.NetworkWeights): | ||
super().__init__(net, weights) | ||
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self.w_norm = weights.w.get("w_norm") | ||
self.b_norm = weights.w.get("b_norm") | ||
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def calc_updown(self, orig_weight): | ||
output_shape = self.w_norm.shape | ||
updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype) | ||
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if self.b_norm is not None: | ||
ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype) | ||
else: | ||
ex_bias = None | ||
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return self.finalize_updown(updown, orig_weight, output_shape, ex_bias) |
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