diff --git a/packages_3rdparty/comfyui_lora_collection/LICENSE b/modules_forge/packages/comfy/LICENSE
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diff --git a/packages_3rdparty/comfyui_lora_collection/lora.py b/modules_forge/packages/comfy/lora.py
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diff --git a/packages_3rdparty/gguf/LICENSE b/modules_forge/packages/gguf/LICENSE
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diff --git a/packages_3rdparty/gguf/README.md b/modules_forge/packages/gguf/README.md
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diff --git a/packages_3rdparty/gguf/__init__.py b/modules_forge/packages/gguf/__init__.py
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diff --git a/packages_3rdparty/gguf/constants.py b/modules_forge/packages/gguf/constants.py
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diff --git a/packages_3rdparty/gguf/gguf_reader.py b/modules_forge/packages/gguf/gguf_reader.py
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diff --git a/packages_3rdparty/gguf/gguf_writer.py b/modules_forge/packages/gguf/gguf_writer.py
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diff --git a/packages_3rdparty/gguf/lazy.py b/modules_forge/packages/gguf/lazy.py
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diff --git a/packages_3rdparty/gguf/metadata.py b/modules_forge/packages/gguf/metadata.py
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diff --git a/packages_3rdparty/gguf/vocab.py b/modules_forge/packages/gguf/vocab.py
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diff --git a/packages_3rdparty/README.md b/packages_3rdparty/README.md
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--- a/packages_3rdparty/README.md
+++ /dev/null
@@ -1 +0,0 @@
-Please follow the standard of https://github.com/opencv/opencv/tree/315f85d4f484c1e2fa043c73ac3fdd9fc5997ee7/3rdparty when PR or modifying files.
diff --git a/packages_3rdparty/webui_lora_collection/LICENSE.txt b/packages_3rdparty/webui_lora_collection/LICENSE.txt
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index caed17a8..00000000
--- a/packages_3rdparty/webui_lora_collection/LICENSE.txt
+++ /dev/null
@@ -1,688 +0,0 @@
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diff --git a/packages_3rdparty/webui_lora_collection/lora.py b/packages_3rdparty/webui_lora_collection/lora.py
deleted file mode 100644
index e95db050..00000000
--- a/packages_3rdparty/webui_lora_collection/lora.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# TODO: Implement API
-
diff --git a/packages_3rdparty/webui_lora_collection/lyco_helpers.py b/packages_3rdparty/webui_lora_collection/lyco_helpers.py
deleted file mode 100644
index 3d4efd7e..00000000
--- a/packages_3rdparty/webui_lora_collection/lyco_helpers.py
+++ /dev/null
@@ -1,68 +0,0 @@
-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 length or new_m>factor:
- break
- else:
- m, n = new_m, new_n
- if m > n:
- n, m = m, n
- return m, n
-
diff --git a/packages_3rdparty/webui_lora_collection/network.py b/packages_3rdparty/webui_lora_collection/network.py
deleted file mode 100644
index 89987438..00000000
--- a/packages_3rdparty/webui_lora_collection/network.py
+++ /dev/null
@@ -1,228 +0,0 @@
-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)
-
diff --git a/packages_3rdparty/webui_lora_collection/network_full.py b/packages_3rdparty/webui_lora_collection/network_full.py
deleted file mode 100644
index cf5fbbb2..00000000
--- a/packages_3rdparty/webui_lora_collection/network_full.py
+++ /dev/null
@@ -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)
diff --git a/packages_3rdparty/webui_lora_collection/network_glora.py b/packages_3rdparty/webui_lora_collection/network_glora.py
deleted file mode 100644
index efe5c681..00000000
--- a/packages_3rdparty/webui_lora_collection/network_glora.py
+++ /dev/null
@@ -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)
diff --git a/packages_3rdparty/webui_lora_collection/network_hada.py b/packages_3rdparty/webui_lora_collection/network_hada.py
deleted file mode 100644
index d179b29e..00000000
--- a/packages_3rdparty/webui_lora_collection/network_hada.py
+++ /dev/null
@@ -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)
diff --git a/packages_3rdparty/webui_lora_collection/network_ia3.py b/packages_3rdparty/webui_lora_collection/network_ia3.py
deleted file mode 100644
index 549b9a75..00000000
--- a/packages_3rdparty/webui_lora_collection/network_ia3.py
+++ /dev/null
@@ -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)
diff --git a/packages_3rdparty/webui_lora_collection/network_lokr.py b/packages_3rdparty/webui_lora_collection/network_lokr.py
deleted file mode 100644
index 4f3128e5..00000000
--- a/packages_3rdparty/webui_lora_collection/network_lokr.py
+++ /dev/null
@@ -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)
diff --git a/packages_3rdparty/webui_lora_collection/network_lora.py b/packages_3rdparty/webui_lora_collection/network_lora.py
deleted file mode 100644
index 8ee26c31..00000000
--- a/packages_3rdparty/webui_lora_collection/network_lora.py
+++ /dev/null
@@ -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()
-
-
diff --git a/packages_3rdparty/webui_lora_collection/network_norm.py b/packages_3rdparty/webui_lora_collection/network_norm.py
deleted file mode 100644
index d25afcbb..00000000
--- a/packages_3rdparty/webui_lora_collection/network_norm.py
+++ /dev/null
@@ -1,28 +0,0 @@
-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)
diff --git a/packages_3rdparty/webui_lora_collection/network_oft.py b/packages_3rdparty/webui_lora_collection/network_oft.py
deleted file mode 100644
index 925be21a..00000000
--- a/packages_3rdparty/webui_lora_collection/network_oft.py
+++ /dev/null
@@ -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)