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1
+ {
2
+ "nbformat": 4,
3
+ "nbformat_minor": 0,
4
+ "metadata": {
5
+ "colab": {
6
+ "provenance": []
7
+ },
8
+ "kernelspec": {
9
+ "name": "python3",
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+ "display_name": "Python 3"
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+ },
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+ "language_info": {
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+ "name": "python"
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+ }
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+ },
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "source": [
20
+ "# Cast civitai trained LoRa in torch.bfloat16 to Tensor Art Compatible torch.float16 dtype\n",
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+ "\n",
22
+ "Created by Adcom: https://tensor.art/u/743241123023077878"
23
+ ],
24
+ "metadata": {
25
+ "id": "YDCnQpDdqDe4"
26
+ }
27
+ },
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+ {
29
+ "cell_type": "code",
30
+ "source": [
31
+ "#initialize\n",
32
+ "import torch\n",
33
+ "from safetensors.torch import load_file\n",
34
+ "from google.colab import drive\n",
35
+ "drive.mount('/content/drive')"
36
+ ],
37
+ "metadata": {
38
+ "id": "1oxeJYHRqxQC",
39
+ "outputId": "5397ceb1-cd98-4477-f472-d766beac79fb",
40
+ "colab": {
41
+ "base_uri": "https://localhost:8080/"
42
+ }
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+ },
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+ "execution_count": 1,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
50
+ "Mounted at /content/drive\n"
51
+ ]
52
+ }
53
+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "cgi = load_file('/content/drive/MyDrive/Saved from Chrome/cgi_style.safetensors')"
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+ ],
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+ "metadata": {
61
+ "id": "JuGDCX5272Bh"
62
+ },
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+ "execution_count": 10,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "cgi = load_file('/content/drive/MyDrive/Saved from Chrome/cgi_style.safetensors')\n",
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+ "iris = load_file('/content/drive/MyDrive/Saved from Chrome/proud_iris.safetensors')\n",
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+ "nudism = load_file('/content/drive/MyDrive/Saved from Chrome/nudism.safetensors')"
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+ ],
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+ "metadata": {
74
+ "id": "FftDdBRG7su6"
75
+ },
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+ "execution_count": 107,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "for key in cgi:\n",
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+ " cgi[f'{key}'] = cgi[f'{key}'].to(dtype=torch.float16)\n",
84
+ " iris[f'{key}'] = iris[f'{key}'].to(dtype=torch.float16)\n",
85
+ " nudism[f'{key}'] = nudism[f'{key}'].to(dtype=torch.float16)"
86
+ ],
87
+ "metadata": {
88
+ "id": "RII9SEqh8KH2"
89
+ },
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+ "execution_count": 108,
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+ "outputs": []
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+ },
93
+ {
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+ "cell_type": "code",
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+ "source": [
96
+ "import torch\n",
97
+ "import torch.nn as nn\n",
98
+ "#define metric for similarity\n",
99
+ "tgt_dim = torch.Size([64, 3072])\n",
100
+ "cos0 = nn.CosineSimilarity(dim=1)\n",
101
+ "cos = nn.CosineSimilarity(dim=1)\n",
102
+ "\n",
103
+ "\n",
104
+ "def sim(tgt , ref ,key):\n",
105
+ " return torch.sum(torch.abs(cos(tgt, ref[f'{key}']))) + torch.sum(torch.abs(cos0(tgt, ref[f'{key}'])))\n",
106
+ "#-----#\n",
107
+ "\n",
108
+ "from torch import linalg as LA\n",
109
+ "def rand_search(A , B , key , iters):\n",
110
+ " tgt_norm = (LA.matrix_norm(A[f'{key}']) + LA.matrix_norm(B[f'{key}']))/2\n",
111
+ " tgt_avg = (A[f'{key}'] + B[f'{key}'])/2\n",
112
+ "\n",
113
+ " max_sim = (sim(tgt_avg , A , key) + sim(tgt_avg , B , key))\n",
114
+ " cand = tgt_avg\n",
115
+ "\n",
116
+ " for iter in range(iters):\n",
117
+ " rand = torch.ones(tgt_dim)*(-0.5) + torch.rand(tgt_dim)\n",
118
+ " rand = rand * (tgt_norm/LA.matrix_norm(rand))\n",
119
+ " #rand = (rand + tgt_avg)/2\n",
120
+ " #rand = rand * (tgt_norm/LA.matrix_norm(rand))\n",
121
+ "\n",
122
+ " tmp = sim(rand,A, key) + sim(rand , B, key)\n",
123
+ " if (tmp > max_sim):\n",
124
+ " max_sim = tmp\n",
125
+ " cand = rand\n",
126
+ " print('found!')\n",
127
+ " break\n",
128
+ " #------#\n",
129
+ " print('returning')\n",
130
+ " return cand , max_sim\n",
131
+ "#-----#"
132
+ ],
133
+ "metadata": {
134
+ "id": "hJL6QEclHdHn"
135
+ },
136
+ "execution_count": 104,
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+ "outputs": []
138
+ },
139
+ {
140
+ "cell_type": "code",
141
+ "source": [
142
+ "cand , max_sim = rand_search(cgi , iris , 'lora_unet_double_blocks_0_img_attn_proj.lora_down.weight' , 1000)\n",
143
+ "print(sim(cand , iris , key))\n",
144
+ "print(sim(cand , cgi , key))"
145
+ ],
146
+ "metadata": {
147
+ "id": "ckyBSQi5Ll4F",
148
+ "outputId": "341f7192-083d-4423-f61f-4f49d5756e79",
149
+ "colab": {
150
+ "base_uri": "https://localhost:8080/"
151
+ }
152
+ },
153
+ "execution_count": 106,
154
+ "outputs": [
155
+ {
156
+ "output_type": "stream",
157
+ "name": "stdout",
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+ "text": [
159
+ "returning\n",
160
+ "tensor(91.1875, dtype=torch.float16)\n",
161
+ "tensor(90.2500, dtype=torch.float16)\n"
162
+ ]
163
+ }
164
+ ]
165
+ },
166
+ {
167
+ "cell_type": "code",
168
+ "source": [
169
+ "from safetensors.torch import load_file , save_file\n",
170
+ "\n",
171
+ "merge = load_file('/content/drive/MyDrive/Saved from Chrome/cgi_style.safetensors')\n",
172
+ "for key in cgi:\n",
173
+ " if cgi[f'{key}'].shape == torch.Size([]): continue\n",
174
+ " merge[f'{key}'] = (cgi[f'{key}'] + iris[f'{key}'])/2\n",
175
+ "\n",
176
+ "%cd /content/\n",
177
+ "save_file(merge , 'cgi_iris_1_1_1_merge.safetensors')"
178
+ ],
179
+ "metadata": {
180
+ "id": "9L_g5Zp9Du2E",
181
+ "outputId": "38661765-461a-42c3-8480-38fe7f1abe3e",
182
+ "colab": {
183
+ "base_uri": "https://localhost:8080/"
184
+ }
185
+ },
186
+ "execution_count": 113,
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+ "outputs": [
188
+ {
189
+ "output_type": "stream",
190
+ "name": "stdout",
191
+ "text": [
192
+ "/content\n"
193
+ ]
194
+ }
195
+ ]
196
+ },
197
+ {
198
+ "cell_type": "code",
199
+ "source": [
200
+ "tgt_dim = torch.Size([64, 3072])\n",
201
+ "cosa = nn.CosineSimilarity(dim=0)\n",
202
+ "cos_dim1 = nn.CosineSimilarity(dim=1)\n",
203
+ "\n",
204
+ "for key in cgi:\n",
205
+ " if not cgi[f'{key}'].shape == torch.Size([64, 3072]): continue\n",
206
+ " print(f'{key} : ')\n",
207
+ " print(torch.sum(torch.abs(cos_dim1(cgi[f'{key}'] , iris[f'{key}']))))"
208
+ ],
209
+ "metadata": {
210
+ "id": "VFNw0Nck8V6Q",
211
+ "outputId": "e48bab98-18f7-43bb-d1cf-89f3e00f7ccf",
212
+ "colab": {
213
+ "base_uri": "https://localhost:8080/"
214
+ }
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+ },
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+ "execution_count": 39,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "lora_unet_double_blocks_0_img_attn_proj.lora_down.weight : \n",
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+ "tensor(1.6982, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_0_img_attn_qkv.lora_down.weight : \n",
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+ "tensor(1.8145, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_0_img_mlp_0.lora_down.weight : \n",
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+ "tensor(1.6309, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_0_img_mod_lin.lora_down.weight : \n",
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+ "tensor(2.6211, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_0_txt_attn_proj.lora_down.weight : \n",
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+ "tensor(2.3203, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_0_txt_attn_qkv.lora_down.weight : \n",
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+ "tensor(2.3027, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_0_txt_mlp_0.lora_down.weight : \n",
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+ "tensor(2.5898, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_0_txt_mod_lin.lora_down.weight : \n",
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+ "tensor(2.7402, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_10_img_attn_proj.lora_down.weight : \n",
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+ "tensor(2.0410, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_10_img_attn_qkv.lora_down.weight : \n",
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+ "tensor(1.3350, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_10_img_mlp_0.lora_down.weight : \n",
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+ "tensor(2.0020, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_10_img_mod_lin.lora_down.weight : \n",
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+ "tensor(2.6562, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_10_txt_attn_proj.lora_down.weight : \n",
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+ "tensor(1.1816, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_10_txt_attn_qkv.lora_down.weight : \n",
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+ "tensor(1.1348, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_10_txt_mlp_0.lora_down.weight : \n",
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+ "tensor(3.0156, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_10_txt_mod_lin.lora_down.weight : \n",
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+ "tensor(1.4746, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_11_img_attn_proj.lora_down.weight : \n",
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+ "tensor(1.8359, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_11_img_attn_qkv.lora_down.weight : \n",
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+ "tensor(1.5312, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_11_img_mlp_0.lora_down.weight : \n",
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+ "lora_unet_double_blocks_11_img_mod_lin.lora_down.weight : \n",
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+ "lora_unet_double_blocks_11_txt_attn_proj.lora_down.weight : \n",
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+ "lora_unet_double_blocks_11_txt_attn_qkv.lora_down.weight : \n",
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+ "tensor(1.8594, dtype=torch.float16)\n",
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+ "lora_unet_double_blocks_11_txt_mod_lin.lora_down.weight : \n",
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631
+ "tensor(2.7051, dtype=torch.float16)\n",
632
+ "lora_unet_single_blocks_33_modulation_lin.lora_down.weight : \n",
633
+ "tensor(5.2930, dtype=torch.float16)\n",
634
+ "lora_unet_single_blocks_34_linear1.lora_down.weight : \n",
635
+ "tensor(2.6738, dtype=torch.float16)\n",
636
+ "lora_unet_single_blocks_34_modulation_lin.lora_down.weight : \n",
637
+ "tensor(4.7852, dtype=torch.float16)\n",
638
+ "lora_unet_single_blocks_35_linear1.lora_down.weight : \n",
639
+ "tensor(2.5117, dtype=torch.float16)\n",
640
+ "lora_unet_single_blocks_35_modulation_lin.lora_down.weight : \n",
641
+ "tensor(6.7734, dtype=torch.float16)\n",
642
+ "lora_unet_single_blocks_36_linear1.lora_down.weight : \n",
643
+ "tensor(1.8418, dtype=torch.float16)\n",
644
+ "lora_unet_single_blocks_36_modulation_lin.lora_down.weight : \n",
645
+ "tensor(6.5859, dtype=torch.float16)\n",
646
+ "lora_unet_single_blocks_37_linear1.lora_down.weight : \n",
647
+ "tensor(2.4473, dtype=torch.float16)\n",
648
+ "lora_unet_single_blocks_37_modulation_lin.lora_down.weight : \n",
649
+ "tensor(2.5742, dtype=torch.float16)\n",
650
+ "lora_unet_single_blocks_3_linear1.lora_down.weight : \n",
651
+ "tensor(2.5566, dtype=torch.float16)\n",
652
+ "lora_unet_single_blocks_3_modulation_lin.lora_down.weight : \n",
653
+ "tensor(4.7148, dtype=torch.float16)\n",
654
+ "lora_unet_single_blocks_4_linear1.lora_down.weight : \n",
655
+ "tensor(2.2832, dtype=torch.float16)\n",
656
+ "lora_unet_single_blocks_4_modulation_lin.lora_down.weight : \n",
657
+ "tensor(2.0566, dtype=torch.float16)\n",
658
+ "lora_unet_single_blocks_5_linear1.lora_down.weight : \n",
659
+ "tensor(2.2109, dtype=torch.float16)\n",
660
+ "lora_unet_single_blocks_5_modulation_lin.lora_down.weight : \n",
661
+ "tensor(2.7793, dtype=torch.float16)\n",
662
+ "lora_unet_single_blocks_6_linear1.lora_down.weight : \n",
663
+ "tensor(3.0176, dtype=torch.float16)\n",
664
+ "lora_unet_single_blocks_6_modulation_lin.lora_down.weight : \n",
665
+ "tensor(2.9180, dtype=torch.float16)\n",
666
+ "lora_unet_single_blocks_7_linear1.lora_down.weight : \n",
667
+ "tensor(2.2461, dtype=torch.float16)\n",
668
+ "lora_unet_single_blocks_7_modulation_lin.lora_down.weight : \n",
669
+ "tensor(2.1074, dtype=torch.float16)\n",
670
+ "lora_unet_single_blocks_8_linear1.lora_down.weight : \n",
671
+ "tensor(3.0391, dtype=torch.float16)\n",
672
+ "lora_unet_single_blocks_8_modulation_lin.lora_down.weight : \n",
673
+ "tensor(2.0039, dtype=torch.float16)\n",
674
+ "lora_unet_single_blocks_9_linear1.lora_down.weight : \n",
675
+ "tensor(3.8789, dtype=torch.float16)\n",
676
+ "lora_unet_single_blocks_9_modulation_lin.lora_down.weight : \n",
677
+ "tensor(4.0547, dtype=torch.float16)\n"
678
+ ]
679
+ }
680
+ ]
681
+ },
682
+ {
683
+ "cell_type": "markdown",
684
+ "source": [
685
+ "<---- Upload your civiai trained .safetensor file to Google Colab before running the next cell\n",
686
+ "\n"
687
+ ],
688
+ "metadata": {
689
+ "id": "oDAUwfFzqzgj"
690
+ }
691
+ },
692
+ {
693
+ "cell_type": "code",
694
+ "execution_count": null,
695
+ "metadata": {
696
+ "id": "WQZ3BZn1p-pw"
697
+ },
698
+ "outputs": [],
699
+ "source": [
700
+ "civiai_lora = '' # @param {type:'string' ,placeholder:'ex. civitai_trained_e19.safetensors'}\n",
701
+ "tensor_art_filename = '' # @param {type:'string' ,placeholder:'ex. e19.safetensors'}\n",
702
+ "%cd /content/\n",
703
+ "tgt = load_file(f'{civiai_lora}')\n",
704
+ "for key in tgt:\n",
705
+ " tgt[f'{key}'] = tgt[f'{key}'].to(dtype=torch.float16)\n",
706
+ "%cd /content/\n",
707
+ "save_file(tgt , f'{tensor_art_filename}')"
708
+ ]
709
+ },
710
+ {
711
+ "cell_type": "markdown",
712
+ "source": [
713
+ "Download the new .safetensor file to your device.\n",
714
+ "\n",
715
+ "Downloading from CoLab Notebook will seemingly do nothing for ~5min. Then the file will download , so be patient.\n",
716
+ "\n",
717
+ "For faster/more consistent downloads , download your .safetensor file from your Google Drive"
718
+ ],
719
+ "metadata": {
720
+ "id": "blnBW-U4rAS7"
721
+ }
722
+ }
723
+ ]
724
+ }