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an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
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(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
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get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
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You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
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---------------------------------Facebook BNB-------------------------------
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Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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SOFTWARE.

2
packages_3rdparty/webui_lora_collection/lora.py vendored
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# TODO: Implement API

68
packages_3rdparty/webui_lora_collection/lyco_helpers.py vendored
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import torch
def make_weight_cp(t, wa, wb):
temp = torch.einsum('i j k l, j r -> i r k l', t, wb)
return torch.einsum('i j k l, i r -> r j k l', temp, wa)
def rebuild_conventional(up, down, shape, dyn_dim=None):
up = up.reshape(up.size(0), -1)
down = down.reshape(down.size(0), -1)
if dyn_dim is not None:
up = up[:, :dyn_dim]
down = down[:dyn_dim, :]
return (up @ down).reshape(shape)
def rebuild_cp_decomposition(up, down, mid):
up = up.reshape(up.size(0), -1)
down = down.reshape(down.size(0), -1)
return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
'''
return a tuple of two value of input dimension decomposed by the number closest to factor
second value is higher or equal than first value.
In LoRA with Kroneckor Product, first value is a value for weight scale.
secon value is a value for weight.
Because of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
examples)
factor
-1 2 4 8 16 ...
127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127
128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16
250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25
360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30
512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32
1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64
'''
if factor > 0 and (dimension % factor) == 0:
m = factor
n = dimension // factor
if m > n:
n, m = m, n
return m, n
if factor < 0:
factor = dimension
m, n = 1, dimension
length = m + n
while m<n:
new_m = m + 1
while dimension%new_m != 0:
new_m += 1
new_n = dimension // new_m
if new_m + new_n > length or new_m>factor:
break
else:
m, n = new_m, new_n
if m > n:
n, m = m, n
return m, n

228
packages_3rdparty/webui_lora_collection/network.py vendored
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from __future__ import annotations
import os
from collections import namedtuple
import enum
import torch.nn as nn
import torch.nn.functional as F
from modules import sd_models, cache, errors, hashes, shared
import modules.models.sd3.mmdit
NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
class SdVersion(enum.Enum):
Unknown = 1
SD1 = 2
SD2 = 3
SDXL = 4
class NetworkOnDisk:
def __init__(self, name, filename):
self.name = name
self.filename = filename
self.metadata = {}
self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
def read_metadata():
metadata = sd_models.read_metadata_from_safetensors(filename)
return metadata
if self.is_safetensors:
try:
self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
except Exception as e:
errors.display(e, f"reading lora {filename}")
if self.metadata:
m = {}
for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
m[k] = v
self.metadata = m
self.alias = self.metadata.get('ss_output_name', self.name)
self.hash = None
self.shorthash = None
self.set_hash(
self.metadata.get('sshs_model_hash') or
hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
''
)
self.sd_version = self.detect_version()
def detect_version(self):
if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
return SdVersion.SDXL
elif str(self.metadata.get('ss_v2', "")) == "True":
return SdVersion.SD2
elif len(self.metadata):
return SdVersion.SD1
return SdVersion.Unknown
def set_hash(self, v):
self.hash = v
self.shorthash = self.hash[0:12]
if self.shorthash:
import networks
networks.available_network_hash_lookup[self.shorthash] = self
def read_hash(self):
if not self.hash:
self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
def get_alias(self):
import networks
if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
return self.name
else:
return self.alias
class Network: # LoraModule
def __init__(self, name, network_on_disk: NetworkOnDisk):
self.name = name
self.network_on_disk = network_on_disk
self.te_multiplier = 1.0
self.unet_multiplier = 1.0
self.dyn_dim = None
self.modules = {}
self.bundle_embeddings = {}
self.mtime = None
self.mentioned_name = None
"""the text that was used to add the network to prompt - can be either name or an alias"""
class ModuleType:
def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
return None
class NetworkModule:
def __init__(self, net: Network, weights: NetworkWeights):
self.network = net
self.network_key = weights.network_key
self.sd_key = weights.sd_key
self.sd_module = weights.sd_module
if isinstance(self.sd_module, modules.models.sd3.mmdit.QkvLinear):
s = self.sd_module.weight.shape
self.shape = (s[0] // 3, s[1])
elif hasattr(self.sd_module, 'weight'):
self.shape = self.sd_module.weight.shape
elif isinstance(self.sd_module, nn.MultiheadAttention):
# For now, only self-attn use Pytorch's MHA
# So assume all qkvo proj have same shape
self.shape = self.sd_module.out_proj.weight.shape
else:
self.shape = None
self.ops = None
self.extra_kwargs = {}
if isinstance(self.sd_module, nn.Conv2d):
self.ops = F.conv2d
self.extra_kwargs = {
'stride': self.sd_module.stride,
'padding': self.sd_module.padding
}
elif isinstance(self.sd_module, nn.Linear):
self.ops = F.linear
elif isinstance(self.sd_module, nn.LayerNorm):
self.ops = F.layer_norm
self.extra_kwargs = {
'normalized_shape': self.sd_module.normalized_shape,
'eps': self.sd_module.eps
}
elif isinstance(self.sd_module, nn.GroupNorm):
self.ops = F.group_norm
self.extra_kwargs = {
'num_groups': self.sd_module.num_groups,
'eps': self.sd_module.eps
}
self.dim = None
self.bias = weights.w.get("bias")
self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
self.scale = weights.w["scale"].item() if "scale" in weights.w else None
self.dora_scale = weights.w.get("dora_scale", None)
self.dora_norm_dims = len(self.shape) - 1
def multiplier(self):
if 'transformer' in self.sd_key[:20]:
return self.network.te_multiplier
else:
return self.network.unet_multiplier
def calc_scale(self):
if self.scale is not None:
return self.scale
if self.dim is not None and self.alpha is not None:
return self.alpha / self.dim
return 1.0
def apply_weight_decompose(self, updown, orig_weight):
# Match the device/dtype
orig_weight = orig_weight.to(updown.dtype)
dora_scale = self.dora_scale.to(device=orig_weight.device, dtype=updown.dtype)
updown = updown.to(orig_weight.device)
merged_scale1 = updown + orig_weight
merged_scale1_norm = (
merged_scale1.transpose(0, 1)
.reshape(merged_scale1.shape[1], -1)
.norm(dim=1, keepdim=True)
.reshape(merged_scale1.shape[1], *[1] * self.dora_norm_dims)
.transpose(0, 1)
)
dora_merged = (
merged_scale1 * (dora_scale / merged_scale1_norm)
)
final_updown = dora_merged - orig_weight
return final_updown
def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
if self.bias is not None:
updown = updown.reshape(self.bias.shape)
updown += self.bias.to(orig_weight.device, dtype=updown.dtype)
updown = updown.reshape(output_shape)
if len(output_shape) == 4:
updown = updown.reshape(output_shape)
if orig_weight.size().numel() == updown.size().numel():
updown = updown.reshape(orig_weight.shape)
if ex_bias is not None:
ex_bias = ex_bias * self.multiplier()
updown = updown * self.calc_scale()
if self.dora_scale is not None:
updown = self.apply_weight_decompose(updown, orig_weight)
return updown * self.multiplier(), ex_bias
def calc_updown(self, target):
raise NotImplementedError()
def forward(self, x, y):
"""A general forward implementation for all modules"""
if self.ops is None:
raise NotImplementedError()
else:
updown, ex_bias = self.calc_updown(self.sd_module.weight)
return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs)

27
packages_3rdparty/webui_lora_collection/network_full.py vendored
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@ -1,27 +0,0 @@
import network
class ModuleTypeFull(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["diff"]):
return NetworkModuleFull(net, weights)
return None
class NetworkModuleFull(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.weight = weights.w.get("diff")
self.ex_bias = weights.w.get("diff_b")
def calc_updown(self, orig_weight):
output_shape = self.weight.shape
updown = self.weight.to(orig_weight.device)
if self.ex_bias is not None:
ex_bias = self.ex_bias.to(orig_weight.device)
else:
ex_bias = None
return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)

33
packages_3rdparty/webui_lora_collection/network_glora.py vendored
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@ -1,33 +0,0 @@
import network
class ModuleTypeGLora(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]):
return NetworkModuleGLora(net, weights)
return None
# adapted from https://github.com/KohakuBlueleaf/LyCORIS
class NetworkModuleGLora(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
if hasattr(self.sd_module, 'weight'):
self.shape = self.sd_module.weight.shape
self.w1a = weights.w["a1.weight"]
self.w1b = weights.w["b1.weight"]
self.w2a = weights.w["a2.weight"]
self.w2b = weights.w["b2.weight"]
def calc_updown(self, orig_weight):
w1a = self.w1a.to(orig_weight.device)
w1b = self.w1b.to(orig_weight.device)
w2a = self.w2a.to(orig_weight.device)
w2b = self.w2b.to(orig_weight.device)
output_shape = [w1a.size(0), w1b.size(1)]
updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a))
return self.finalize_updown(updown, orig_weight, output_shape)

55
packages_3rdparty/webui_lora_collection/network_hada.py vendored
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@ -1,55 +0,0 @@
import lyco_helpers
import network
class ModuleTypeHada(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]):
return NetworkModuleHada(net, weights)
return None
class NetworkModuleHada(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
if hasattr(self.sd_module, 'weight'):
self.shape = self.sd_module.weight.shape
self.w1a = weights.w["hada_w1_a"]
self.w1b = weights.w["hada_w1_b"]
self.dim = self.w1b.shape[0]
self.w2a = weights.w["hada_w2_a"]
self.w2b = weights.w["hada_w2_b"]
self.t1 = weights.w.get("hada_t1")
self.t2 = weights.w.get("hada_t2")
def calc_updown(self, orig_weight):
w1a = self.w1a.to(orig_weight.device)
w1b = self.w1b.to(orig_weight.device)
w2a = self.w2a.to(orig_weight.device)
w2b = self.w2b.to(orig_weight.device)
output_shape = [w1a.size(0), w1b.size(1)]
if self.t1 is not None:
output_shape = [w1a.size(1), w1b.size(1)]
t1 = self.t1.to(orig_weight.device)
updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
output_shape += t1.shape[2:]
else:
if len(w1b.shape) == 4:
output_shape += w1b.shape[2:]
updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
if self.t2 is not None:
t2 = self.t2.to(orig_weight.device)
updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
else:
updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
updown = updown1 * updown2
return self.finalize_updown(updown, orig_weight, output_shape)

30
packages_3rdparty/webui_lora_collection/network_ia3.py vendored
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@ -1,30 +0,0 @@
import network
class ModuleTypeIa3(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["weight"]):
return NetworkModuleIa3(net, weights)
return None
class NetworkModuleIa3(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.w = weights.w["weight"]
self.on_input = weights.w["on_input"].item()
def calc_updown(self, orig_weight):
w = self.w.to(orig_weight.device)
output_shape = [w.size(0), orig_weight.size(1)]
if self.on_input:
output_shape.reverse()
else:
w = w.reshape(-1, 1)
updown = orig_weight * w
return self.finalize_updown(updown, orig_weight, output_shape)

64
packages_3rdparty/webui_lora_collection/network_lokr.py vendored
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@ -1,64 +0,0 @@
import torch
import lyco_helpers
import network
class ModuleTypeLokr(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w)
has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w)
if has_1 and has_2:
return NetworkModuleLokr(net, weights)
return None
def make_kron(orig_shape, w1, w2):
if len(w2.shape) == 4:
w1 = w1.unsqueeze(2).unsqueeze(2)
w2 = w2.contiguous()
return torch.kron(w1, w2).reshape(orig_shape)
class NetworkModuleLokr(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.w1 = weights.w.get("lokr_w1")
self.w1a = weights.w.get("lokr_w1_a")
self.w1b = weights.w.get("lokr_w1_b")
self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim
self.w2 = weights.w.get("lokr_w2")
self.w2a = weights.w.get("lokr_w2_a")
self.w2b = weights.w.get("lokr_w2_b")
self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim
self.t2 = weights.w.get("lokr_t2")
def calc_updown(self, orig_weight):
if self.w1 is not None:
w1 = self.w1.to(orig_weight.device)
else:
w1a = self.w1a.to(orig_weight.device)
w1b = self.w1b.to(orig_weight.device)
w1 = w1a @ w1b
if self.w2 is not None:
w2 = self.w2.to(orig_weight.device)
elif self.t2 is None:
w2a = self.w2a.to(orig_weight.device)
w2b = self.w2b.to(orig_weight.device)
w2 = w2a @ w2b
else:
t2 = self.t2.to(orig_weight.device)
w2a = self.w2a.to(orig_weight.device)
w2b = self.w2b.to(orig_weight.device)
w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
if len(orig_weight.shape) == 4:
output_shape = orig_weight.shape
updown = make_kron(output_shape, w1, w2)
return self.finalize_updown(updown, orig_weight, output_shape)

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packages_3rdparty/webui_lora_collection/network_lora.py vendored
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@ -1,94 +0,0 @@
import torch
import lyco_helpers
import modules.models.sd3.mmdit
import network
from modules import devices
class ModuleTypeLora(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
return NetworkModuleLora(net, weights)
if all(x in weights.w for x in ["lora_A.weight", "lora_B.weight"]):
w = weights.w.copy()
weights.w.clear()
weights.w.update({"lora_up.weight": w["lora_B.weight"], "lora_down.weight": w["lora_A.weight"]})
return NetworkModuleLora(net, weights)
return None
class NetworkModuleLora(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.up_model = self.create_module(weights.w, "lora_up.weight")
self.down_model = self.create_module(weights.w, "lora_down.weight")
self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True)
self.dim = weights.w["lora_down.weight"].shape[0]
def create_module(self, weights, key, none_ok=False):
weight = weights.get(key)
if weight is None and none_ok:
return None
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear]
is_conv = type(self.sd_module) in [torch.nn.Conv2d]
if is_linear:
weight = weight.reshape(weight.shape[0], -1)
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
elif is_conv and key == "lora_down.weight" or key == "dyn_up":
if len(weight.shape) == 2:
weight = weight.reshape(weight.shape[0], -1, 1, 1)
if weight.shape[2] != 1 or weight.shape[3] != 1:
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
else:
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
elif is_conv and key == "lora_mid.weight":
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
elif is_conv and key == "lora_up.weight" or key == "dyn_down":
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
else:
raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
with torch.no_grad():
if weight.shape != module.weight.shape:
weight = weight.reshape(module.weight.shape)
module.weight.copy_(weight)
module.to(device=devices.cpu, dtype=devices.dtype)
module.weight.requires_grad_(False)
return module
def calc_updown(self, orig_weight):
up = self.up_model.weight.to(orig_weight.device)
down = self.down_model.weight.to(orig_weight.device)
output_shape = [up.size(0), down.size(1)]
if self.mid_model is not None:
# cp-decomposition
mid = self.mid_model.weight.to(orig_weight.device)
updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
output_shape += mid.shape[2:]
else:
if len(down.shape) == 4:
output_shape += down.shape[2:]
updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim)
return self.finalize_updown(updown, orig_weight, output_shape)
def forward(self, x, y):
self.up_model.to(device=devices.device)
self.down_model.to(device=devices.device)
return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()

28
packages_3rdparty/webui_lora_collection/network_norm.py vendored
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import network
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)
return None
class NetworkModuleNorm(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.w_norm = weights.w.get("w_norm")
self.b_norm = weights.w.get("b_norm")
def calc_updown(self, orig_weight):
output_shape = self.w_norm.shape
updown = self.w_norm.to(orig_weight.device)
if self.b_norm is not None:
ex_bias = self.b_norm.to(orig_weight.device)
else:
ex_bias = None
return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)

119
packages_3rdparty/webui_lora_collection/network_oft.py vendored
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@ -1,119 +0,0 @@
import torch
import network
from einops import rearrange
class ModuleTypeOFT(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
return NetworkModuleOFT(net, weights)
return None
# TODO: Convert to forge patcher
# Supports both kohya-ss' implementation of COFT https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
# and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
class NetworkModuleOFT(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.lin_module = None
self.org_module: list[torch.Module] = [self.sd_module]
self.scale = 1.0
self.is_R = False
self.is_boft = False
# kohya-ss/New LyCORIS OFT/BOFT
if "oft_blocks" in weights.w.keys():
self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
self.alpha = weights.w.get("alpha", None) # alpha is constraint
self.dim = self.oft_blocks.shape[0] # lora dim
# Old LyCORIS OFT
elif "oft_diag" in weights.w.keys():
self.is_R = True
self.oft_blocks = weights.w["oft_diag"]
# self.alpha is unused
self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
is_conv = type(self.sd_module) in [torch.nn.Conv2d]
is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
if is_linear:
self.out_dim = self.sd_module.out_features
elif is_conv:
self.out_dim = self.sd_module.out_channels
elif is_other_linear:
self.out_dim = self.sd_module.embed_dim
# LyCORIS BOFT
if self.oft_blocks.dim() == 4:
self.is_boft = True
self.rescale = weights.w.get('rescale', None)
if self.rescale is not None and not is_other_linear:
self.rescale = self.rescale.reshape(-1, *[1]*(self.org_module[0].weight.dim() - 1))
self.num_blocks = self.dim
self.block_size = self.out_dim // self.dim
self.constraint = (0 if self.alpha is None else self.alpha) * self.out_dim
if self.is_R:
self.constraint = None
self.block_size = self.dim
self.num_blocks = self.out_dim // self.dim
elif self.is_boft:
self.boft_m = self.oft_blocks.shape[0]
self.num_blocks = self.oft_blocks.shape[1]
self.block_size = self.oft_blocks.shape[2]
self.boft_b = self.block_size
def calc_updown(self, orig_weight):
oft_blocks = self.oft_blocks.to(orig_weight.device)
eye = torch.eye(self.block_size, device=oft_blocks.device)
if not self.is_R:
block_Q = oft_blocks - oft_blocks.transpose(-1, -2) # ensure skew-symmetric orthogonal matrix
if self.constraint != 0:
norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device))
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
R = oft_blocks.to(orig_weight.device)
if not self.is_boft:
# This errors out for MultiheadAttention, might need to be handled up-stream
merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
merged_weight = torch.einsum(
'k n m, k n ... -> k m ...',
R,
merged_weight
)
merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
else:
# TODO: determine correct value for scale
scale = 1.0
m = self.boft_m
b = self.boft_b
r_b = b // 2
inp = orig_weight
for i in range(m):
bi = R[i] # b_num, b_size, b_size
if i == 0:
# Apply multiplier/scale and rescale into first weight
bi = bi * scale + (1 - scale) * eye
inp = rearrange(inp, "(c g k) ... -> (c k g) ...", g=2, k=2**i * r_b)
inp = rearrange(inp, "(d b) ... -> d b ...", b=b)
inp = torch.einsum("b i j, b j ... -> b i ...", bi, inp)
inp = rearrange(inp, "d b ... -> (d b) ...")
inp = rearrange(inp, "(c k g) ... -> (c g k) ...", g=2, k=2**i * r_b)
merged_weight = inp
# Rescale mechanism
if self.rescale is not None:
merged_weight = self.rescale.to(merged_weight) * merged_weight
updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype)
output_shape = orig_weight.shape
return self.finalize_updown(updown, orig_weight, output_shape)