Upload LoRa_Merge_Script.ipynb

LoRa_Merge_Script.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Cast civitai trained LoRa in torch.bfloat16 to Tensor Art Compatible torch.float16 dtype\n",
+        "\n",
+        "Created by Adcom: https://tensor.art/u/743241123023077878"
+      ],
+      "metadata": {
+        "id": "YDCnQpDdqDe4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#initialize\n",
+        "import torch\n",
+        "from safetensors.torch import load_file\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "id": "CBVTifA_ZwdC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "\n",
+        "doll = load_file('/content/drive/MyDrive/Saved from Chrome/dolls.safetensors')\n",
+        "euro = load_file('/content/drive/MyDrive/Saved from Chrome/euro.safetensors')\n",
+        "scale = load_file('/content/drive/MyDrive/Saved from Chrome/scale.safetensors')\n",
+        "cgi = load_file('/content/drive/MyDrive/Saved from Chrome/cgi.safetensors')\n",
+        "guns = load_file('/content/drive/MyDrive/Saved from Chrome/guns.safetensors')\n",
+        "iris = load_file('/content/drive/MyDrive/Saved from Chrome/iris.safetensors')\n",
+        "\n",
+        "for key in doll:\n",
+        "  doll[f'{key}'] = doll[f'{key}'].to(device = device , dtype=torch.float16)\n",
+        "  euro[f'{key}'] = euro[f'{key}'].to(device = device , dtype=torch.float16)\n",
+        "  scale[f'{key}'] = scale[f'{key}'].to(device = device , dtype=torch.float16)\n",
+        "  iris[f'{key}'] = iris[f'{key}'].to(device = device , dtype=torch.float16)\n",
+        "  cgi[f'{key}'] = cgi[f'{key}'].to(device = device , dtype=torch.float16)\n",
+        "  guns[f'{key}'] = guns[f'{key}'].to(device = device , dtype=torch.float16)"
+      ],
+      "metadata": {
+        "id": "1oxeJYHRqxQC"
+      },
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cgi = load_file('/content/drive/MyDrive/Saved from Chrome/cgi_style.safetensors')"
+      ],
+      "metadata": {
+        "id": "JuGDCX5272Bh"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#cgi = load_file('/content/drive/MyDrive/Saved from Chrome/cgi_style.safetensors')\n",
+        "doll = load_file('/content/drive/MyDrive/Saved from Chrome/dolls.safetensors')\n",
+        "euro = load_file('/content/drive/MyDrive/Saved from Chrome/euro.safetensors')\n",
+        "scale = load_file('/content/drive/MyDrive/Saved from Chrome/scale.safetensors')\n",
+        "cgi = load_file('/content/drive/MyDrive/Saved from Chrome/cgi.safetensors')\n",
+        "guns = load_file('/content/drive/MyDrive/Saved from Chrome/guns.safetensors')\n",
+        "iris = load_file('/content/drive/MyDrive/Saved from Chrome/iris.safetensors')"
+      ],
+      "metadata": {
+        "id": "FftDdBRG7su6"
+      },
+      "execution_count": 57,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for key in doll:\n",
+        "  doll[f'{key}'] = doll[f'{key}'].to(dtype=torch.float16)\n",
+        "  euro[f'{key}'] = euro[f'{key}'].to(dtype=torch.float16)\n",
+        "  scale[f'{key}'] = scale[f'{key}'].to(dtype=torch.float16)"
+      ],
+      "metadata": {
+        "id": "RII9SEqh8KH2"
+      },
+      "execution_count": 60,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "#define metric for similarity\n",
+        "tgt_dim = torch.Size([64, 3072])\n",
+        "cos0 = nn.CosineSimilarity(dim=1)\n",
+        "cos = nn.CosineSimilarity(dim=1)\n",
+        "\n",
+        "\n",
+        "def sim(tgt , ref ,key):\n",
+        "  return torch.sum(torch.abs(cos(tgt, ref[f'{key}'])))  + torch.sum(torch.abs(cos0(tgt, ref[f'{key}'])))\n",
+        "#-----#\n",
+        "\n",
+        "from torch import linalg as LA\n",
+        "\n",
+        "LA.matrix_norm\n",
+        "def rand_search(A , B , key , iters):\n",
+        "  tgt_norm = (LA.matrix_norm(A[f'{key}']) + LA.matrix_norm(B[f'{key}']))/2\n",
+        "  tgt_avg = (A[f'{key}'] + B[f'{key}'])/2\n",
+        "\n",
+        "  max_sim = (sim(tgt_avg , A , key) + sim(tgt_avg , B , key))\n",
+        "  cand = tgt_avg\n",
+        "\n",
+        "  for iter in range(iters):\n",
+        "    rand = torch.ones(tgt_dim)*(-0.5)  + torch.rand(tgt_dim)\n",
+        "    rand = rand  * (tgt_norm/LA.matrix_norm(rand))\n",
+        "    #rand = (rand + tgt_avg)/2\n",
+        "    #rand = rand  * (tgt_norm/LA.matrix_norm(rand))\n",
+        "\n",
+        "    tmp = sim(rand,A, key) + sim(rand , B, key)\n",
+        "    if (tmp > max_sim):\n",
+        "      max_sim = tmp\n",
+        "      cand = rand\n",
+        "      print('found!')\n",
+        "      break\n",
+        "  #------#\n",
+        "  print('returning')\n",
+        "  return cand  , max_sim\n",
+        "#-----#"
+      ],
+      "metadata": {
+        "id": "hJL6QEclHdHn"
+      },
+      "execution_count": 104,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cand , max_sim = rand_search(cgi , iris , 'lora_unet_double_blocks_0_img_attn_proj.lora_down.weight' , 1000)\n",
+        "print(sim(cand , iris , key))\n",
+        "print(sim(cand , cgi , key))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ckyBSQi5Ll4F",
+        "outputId": "341f7192-083d-4423-f61f-4f49d5756e79"
+      },
+      "execution_count": 106,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "returning\n",
+            "tensor(91.1875, dtype=torch.float16)\n",
+            "tensor(90.2500, dtype=torch.float16)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(torch.rand(1).to(dtype=torch.float16)*3).item()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XLwslN61hiIJ",
+        "outputId": "9e3cbba6-3727-4772-f453-fecf8a408790"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.2138671875"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.rand(1).to(dtype=torch.float16)*10"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AKwh0lZ1f8dJ",
+        "outputId": "59186526-bd73-4efe-925a-3e7a9c738e53"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor([6.8555], dtype=torch.float16)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "#define metric for similarity\n",
+        "tgt_dim = torch.Size([64, 3072])\n",
+        "cos0 = nn.CosineSimilarity(dim=0)\n",
+        "\n",
+        "\n",
+        "\n",
+        "cos = nn.CosineSimilarity(dim=1)\n",
+        "\n",
+        "\n",
+        "def sim(tgt , ref ,key):\n",
+        "  return torch.sum(torch.abs(cos(tgt, ref[f'{key}'])))  + torch.sum(torch.abs(cos0(tgt, ref[f'{key}'])))\n",
+        "#-----#"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SNCvvkb2h3Zb",
+        "outputId": "725fabd1-3fe2-4ac2-f24c-5f9309d45e4a"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "7.715576171875"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from safetensors.torch import load_file , save_file\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "from torch import linalg as LA\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "#define metric for similarity\n",
+        "cos0 = nn.CosineSimilarity(dim=0).to(device)\n",
+        "final_score = 0\n",
+        "highest_score = 0\n",
+        "w_cgi = 1\n",
+        "w_doll = 2\n",
+        "w_euro = 2\n",
+        "w_guns = 1\n",
+        "w_iris = 2\n",
+        "w_scale = 1\n",
+        "\n",
+        "w_noise = 0.00001 * (w_cgi + w_doll + w_euro  + w_guns + w_iris + w_scale)\n",
+        "fixed_noise = {}\n",
+        "\n",
+        "#for key in doll:\n",
+        "#  fixed_noise[f'{key}'] = torch.zeros(doll[f'{key}'].shape).to(device = device , dtype=torch.float16)\n",
+        "#------#\n",
+        "#w_offset = 0* (w1+w2+w3)\n",
+        "#_w_offset = 0\n",
+        "\n",
+        "W =  (w_cgi + w_doll + w_euro  + w_guns + w_iris + w_scale + w_noise)*torch.ones(1).to(device = device,dtype=torch.float16)\n",
+        "\n",
+        "SCALE = 0.0001\n",
+        "one = torch.ones(1).to(dtype=torch.float16).to(device)\n",
+        "\n",
+        "for attempt in range(1000):\n",
+        "  print(f'attempt no : {attempt+1} ')\n",
+        "  merge = load_file('/content/drive/MyDrive/Saved from Chrome/dolls.safetensors')\n",
+        "  for key in doll:\n",
+        "    tgt_dim = doll[f'{key}'].shape\n",
+        "    if tgt_dim == torch.Size([]): continue\n",
+        "    r_cgi = torch.rand(1).to(device = device,dtype=torch.float16).item()*w_cgi\n",
+        "    r_doll = torch.rand(1).to(device = device,dtype=torch.float16).item()*w_doll\n",
+        "    r_euro = torch.rand(1).to(device = device,dtype=torch.float16).item()*w_euro\n",
+        "    r_guns = torch.rand(1).to(device = device,dtype=torch.float16).item()*w_guns\n",
+        "    r_iris = torch.rand(1).to(device = device,dtype=torch.float16).item()*w_iris\n",
+        "    r_scale = torch.rand(1).to(device = device,dtype=torch.float16).item()*w_scale\n",
+        "    #------#\n",
+        "    noise = torch.rand(tgt_dim).to(device = device,dtype=torch.float16)\n",
+        "    noise_norm = LA.matrix_norm(noise).to(device = device,dtype=torch.float16).item()\n",
+        "    noise = (w_noise/noise_norm)*noise.to(device = device,dtype=torch.float16)\n",
+        "    #-----#\n",
+        "    merge[f'{key}'] = r_cgi * cgi[f'{key}'] #overwrite\n",
+        "    merge[f'{key}'] = merge[f'{key}'] + r_doll * doll[f'{key}']\n",
+        "    merge[f'{key}'] = merge[f'{key}'] + r_euro * euro[f'{key}']\n",
+        "    merge[f'{key}'] = merge[f'{key}'] + r_guns * guns[f'{key}']\n",
+        "    merge[f'{key}'] = merge[f'{key}'] + r_iris * iris[f'{key}']\n",
+        "    merge[f'{key}'] = merge[f'{key}'] + r_scale * scale[f'{key}']\n",
+        "    merge[f'{key}'] = ((merge[f'{key}'] + noise)/W).to(device = device,dtype=torch.float16)\n",
+        "  #-------#\n",
+        "  score = torch.zeros(1).to(device = device, dtype=torch.float32)\n",
+        "  #----#\n",
+        "  NUM_ITERS = 10\n",
+        "  for iter in range(NUM_ITERS):\n",
+        "    for key in doll:\n",
+        "      tgt_dim = doll[f'{key}'].shape\n",
+        "      if tgt_dim == torch.Size([]): continue\n",
+        "      vec = torch.rand(tgt_dim[0]).to(device = device,dtype=torch.float16)\n",
+        "      cgi_out = torch.matmul(vec , cgi[f'{key}']).to(device = device,dtype=torch.float16)\n",
+        "      doll_out = torch.matmul(vec , doll[f'{key}']).to(device = device,dtype=torch.float16)\n",
+        "      euro_out = torch.matmul(vec , euro[f'{key}']).to(device = device,dtype=torch.float16)\n",
+        "      guns_out = torch.matmul(vec , guns[f'{key}']).to(device = device,dtype=torch.float16)\n",
+        "      iris_out = torch.matmul(vec , iris[f'{key}']).to(device = device,dtype=torch.float16)\n",
+        "      scale_out = torch.matmul(vec , scale[f'{key}']).to(device = device,dtype=torch.float16)\n",
+        "      merge_out = torch.matmul(vec , merge[f'{key}']).to(device = device,dtype=torch.float16)\n",
+        "      #-------#\n",
+        "      sim_value_cgi = torch.abs(cos0(cgi_out , merge_out)).to(device = device,dtype=torch.float32)*SCALE\n",
+        "      sim_value_doll = torch.abs(cos0(doll_out , merge_out)).to(device = device,dtype=torch.float32)*SCALE\n",
+        "      sim_value_euro = torch.abs(cos0(euro_out , merge_out)).to(device = device,dtype=torch.float32)*SCALE\n",
+        "      sim_value_guns = torch.abs(cos0(guns_out , merge_out)).to(device = device,dtype=torch.float32)*SCALE\n",
+        "      sim_value_iris = torch.abs(cos0(iris_out , merge_out)).to(device = device,dtype=torch.float32)*SCALE\n",
+        "      sim_value_scale = torch.abs(cos0(scale_out , merge_out)).to(device = device,dtype=torch.float32)*SCALE\n",
+        "      score = score  + SCALE*(sim_value_cgi + 2*sim_value_doll +  2*sim_value_euro + sim_value_guns + 2*sim_value_iris + sim_value_scale)/9  #<--- This score can be anything at all\n",
+        "      #----#\n",
+        "  #-----#\n",
+        "\n",
+        "  final_score = (1000/(NUM_ITERS * SCALE))*score.to(device = 'cpu' , dtype=torch.float32).item()\n",
+        "  if (final_score>highest_score) :\n",
+        "    highest_score = final_score\n",
+        "    print('new highscore!')\n",
+        "    print(f'score : {final_score} pts')\n",
+        "    #------#\n",
+        "    save_file(merge , 'all_merge_R4.safetensors')\n",
+        "  #------#\n",
+        "\n",
+        "print(f'------------')\n",
+        "print(f'Final score : {highest_score} pts')\n",
+        "\n",
+        "\n",
+        "#all R1 23.190992578747682\n",
+        "\n",
+        "#all R2 23.333244826062582\n",
+        "\n",
+        "#all R3 23.34471355425194\n",
+        "\n",
+        "#all R4 23.402637452818453"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "9L_g5Zp9Du2E",
+        "outputId": "a3aa2bde-061e-43f5-ca35-96bdc470be80"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "attempt no : 1 \n",
+            "new highscore!\n",
+            "score : 23.264414267032407 pts\n",
+            "attempt no : 2 \n",
+            "attempt no : 3 \n",
+            "attempt no : 4 \n",
+            "new highscore!\n",
+            "score : 23.29399467271287 pts\n",
+            "attempt no : 5 \n",
+            "attempt no : 6 \n",
+            "attempt no : 7 \n",
+            "attempt no : 8 \n",
+            "attempt no : 9 \n",
+            "attempt no : 10 \n",
+            "attempt no : 11 \n",
+            "new highscore!\n",
+            "score : 23.362628780887462 pts\n",
+            "attempt no : 12 \n",
+            "attempt no : 13 \n",
+            "attempt no : 14 \n",
+            "attempt no : 15 \n",
+            "attempt no : 16 \n",
+            "attempt no : 17 \n",
+            "attempt no : 18 \n",
+            "attempt no : 19 \n",
+            "attempt no : 20 \n",
+            "attempt no : 21 \n",
+            "attempt no : 22 \n",
+            "attempt no : 23 \n",
+            "new highscore!\n",
+            "score : 23.37011210329365 pts\n",
+            "attempt no : 24 \n",
+            "attempt no : 25 \n",
+            "attempt no : 26 \n",
+            "attempt no : 27 \n",
+            "attempt no : 28 \n",
+            "attempt no : 29 \n",
+            "attempt no : 30 \n",
+            "attempt no : 31 \n",
+            "attempt no : 32 \n",
+            "attempt no : 33 \n",
+            "attempt no : 34 \n",
+            "new highscore!\n",
+            "score : 23.402637452818453 pts\n",
+            "attempt no : 35 \n",
+            "attempt no : 36 \n",
+            "attempt no : 37 \n",
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+            "attempt no : 84 \n",
+            "attempt no : 85 \n",
+            "attempt no : 86 \n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-33-037249458db5>\u001b[0m in \u001b[0;36m<cell line: 31>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     61\u001b[0m       \u001b[0mtgt_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdoll\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'{key}'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mtgt_dim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m       \u001b[0mvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtgt_dim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m       \u001b[0mcgi_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvec\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mcgi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'{key}'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m       \u001b[0mdoll_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvec\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mdoll\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'{key}'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "    for key in doll:\n",
+        "      if final_score<38.5: break\n",
+        "      _w_offset = w_offset\n",
+        "      W =  (w1+w2+w3 + w_noise + _w_offset)*torch.ones(1).to(device = device,dtype=torch.float16)\n",
+        "      tgt_dim = doll[f'{key}'].shape\n",
+        "      if tgt_dim == torch.Size([]): continue\n",
+        "      fixed_noise[f'{key}'] = fixed_noise[f'{key}'] + merge[f'{key}']\n",
+        "      fixed_noise[f'{key}'] = (fixed_noise[f'{key}'] * (w_offset*torch.ones(1).to(device = device,dtype=torch.float16)/LA.matrix_norm(fixed_noise[f'{key}']))).to(device = device,dtype=torch.float16)"
+      ],
+      "metadata": {
+        "id": "jWFHMJN6TqDq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "  vec = torch.rand(tgt_dim[0]).to(dtype=torch.float16)\n",
+        "  same = torch.abs(cos0(vec ,vec))"
+      ],
+      "metadata": {
+        "id": "k7Pq-kDbuNnQ"
+      },
+      "execution_count": 64,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "same"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ANBPfP7tuOoa",
+        "outputId": "24300487-f874-4f1b-beb7-0f441ec7df4a"
+      },
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor(1., dtype=torch.float16)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.ones(1).to(dtype=torch.float16)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zN92j8JJuQ6G",
+        "outputId": "b810f4e6-a8f3-426a-ae52-ffbd44fb3f00"
+      },
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor([1.], dtype=torch.float16)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        ""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "py-JMJzhsAI4",
+        "outputId": "207cd809-031c-48e3-af0a-98bc114d910e"
+      },
+      "execution_count": 85,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "score : 45.8125 pts\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%cd /content/\n",
+        "save_file(merge , 'doll_euro_scale_R_merge.safetensors')"
+      ],
+      "metadata": {
+        "id": "7qogsYsAr2QU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "9wzLwurSpwpL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test = torch.rand(tgt_dim)\n",
+        "vec = torch.rand(tgt_dim[0])"
+      ],
+      "metadata": {
+        "id": "DHdy4DptowYG"
+      },
+      "execution_count": 47,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tgt_dim[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WeNJ0bquphtx",
+        "outputId": "442bfb2e-c1ab-4549-a4ea-ca80d3cc9a7d"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "9216"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(torch.matmul(vec,test)).shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xqZp3Xo8pQuW",
+        "outputId": "68e5c25e-3391-45e7-9c73-45e0174ddbc1"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "torch.Size([64])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tgt_dim = torch.Size([64, 3072])\n",
+        "cosa = nn.CosineSimilarity(dim=0)\n",
+        "cos_dim1 = nn.CosineSimilarity(dim=1)\n",
+        "\n",
+        "for key in cgi:\n",
+        "  if not cgi[f'{key}'].shape == torch.Size([64, 3072]): continue\n",
+        "  print(f'{key} : ')\n",
+        "  print(torch.sum(torch.abs(cos_dim1(cgi[f'{key}'] , iris[f'{key}']))))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VFNw0Nck8V6Q",
+        "outputId": "e48bab98-18f7-43bb-d1cf-89f3e00f7ccf"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "lora_unet_double_blocks_0_img_attn_proj.lora_down.weight : \n",
+            "tensor(1.6982, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_0_img_attn_qkv.lora_down.weight : \n",
+            "tensor(1.8145, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_0_img_mlp_0.lora_down.weight : \n",
+            "tensor(1.6309, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_0_img_mod_lin.lora_down.weight : \n",
+            "tensor(2.6211, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_0_txt_attn_proj.lora_down.weight : \n",
+            "tensor(2.3203, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_0_txt_attn_qkv.lora_down.weight : \n",
+            "tensor(2.3027, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_0_txt_mlp_0.lora_down.weight : \n",
+            "tensor(2.5898, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_0_txt_mod_lin.lora_down.weight : \n",
+            "tensor(2.7402, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_10_img_attn_proj.lora_down.weight : \n",
+            "tensor(2.0410, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_10_img_attn_qkv.lora_down.weight : \n",
+            "tensor(1.3350, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_10_img_mlp_0.lora_down.weight : \n",
+            "tensor(2.0020, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_10_img_mod_lin.lora_down.weight : \n",
+            "tensor(2.6562, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_10_txt_attn_proj.lora_down.weight : \n",
+            "tensor(1.1816, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_10_txt_attn_qkv.lora_down.weight : \n",
+            "tensor(1.1348, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_10_txt_mlp_0.lora_down.weight : \n",
+            "tensor(3.0156, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_10_txt_mod_lin.lora_down.weight : \n",
+            "tensor(1.4746, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_11_img_attn_proj.lora_down.weight : \n",
+            "tensor(1.8359, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_11_img_attn_qkv.lora_down.weight : \n",
+            "tensor(1.5312, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_11_img_mlp_0.lora_down.weight : \n",
+            "tensor(2.1465, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_11_img_mod_lin.lora_down.weight : \n",
+            "tensor(3.9277, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_11_txt_attn_proj.lora_down.weight : \n",
+            "tensor(1.7246, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_11_txt_attn_qkv.lora_down.weight : \n",
+            "tensor(1.8594, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_11_txt_mlp_0.lora_down.weight : \n",
+            "tensor(3.6465, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_11_txt_mod_lin.lora_down.weight : \n",
+            "tensor(2.6152, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_12_img_attn_proj.lora_down.weight : \n",
+            "tensor(1.7295, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_12_img_attn_qkv.lora_down.weight : \n",
+            "tensor(1.4795, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_12_img_mlp_0.lora_down.weight : \n",
+            "tensor(3.4043, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_12_img_mod_lin.lora_down.weight : \n",
+            "tensor(2.0137, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_12_txt_attn_proj.lora_down.weight : \n",
+            "tensor(1.4375, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_12_txt_attn_qkv.lora_down.weight : \n",
+            "tensor(1.8994, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_12_txt_mlp_0.lora_down.weight : \n",
+            "tensor(2.1152, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_12_txt_mod_lin.lora_down.weight : \n",
+            "tensor(1.2744, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_13_img_attn_proj.lora_down.weight : \n",
+            "tensor(3.0742, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_13_img_attn_qkv.lora_down.weight : \n",
+            "tensor(1.4980, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_13_img_mlp_0.lora_down.weight : \n",
+            "tensor(1.9609, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_13_img_mod_lin.lora_down.weight : \n",
+            "tensor(2.6133, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_13_txt_attn_proj.lora_down.weight : \n",
+            "tensor(1.6904, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_13_txt_attn_qkv.lora_down.weight : \n",
+            "tensor(2.1680, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_13_txt_mlp_0.lora_down.weight : \n",
+            "tensor(2.8574, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_13_txt_mod_lin.lora_down.weight : \n",
+            "tensor(1.9053, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_14_img_attn_proj.lora_down.weight : \n",
+            "tensor(1.8135, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_14_img_attn_qkv.lora_down.weight : \n",
+            "tensor(1.4033, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_14_img_mlp_0.lora_down.weight : \n",
+            "tensor(1.5547, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_14_img_mod_lin.lora_down.weight : \n",
+            "tensor(2.8906, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_14_txt_attn_proj.lora_down.weight : \n",
+            "tensor(1.1328, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_14_txt_attn_qkv.lora_down.weight : \n",
+            "tensor(1.3701, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_14_txt_mlp_0.lora_down.weight : \n",
+            "tensor(3.3145, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_14_txt_mod_lin.lora_down.weight : \n",
+            "tensor(1.2031, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_15_img_attn_proj.lora_down.weight : \n",
+            "tensor(1.5137, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_15_img_attn_qkv.lora_down.weight : \n",
+            "tensor(1.3809, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_15_img_mlp_0.lora_down.weight : \n",
+            "tensor(1.4834, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_15_img_mod_lin.lora_down.weight : \n",
+            "tensor(1.6465, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_15_txt_attn_proj.lora_down.weight : \n",
+            "tensor(1.7256, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_15_txt_attn_qkv.lora_down.weight : \n",
+            "tensor(2.8672, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_15_txt_mlp_0.lora_down.weight : \n",
+            "tensor(2.1953, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_15_txt_mod_lin.lora_down.weight : \n",
+            "tensor(0.9858, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_16_img_attn_proj.lora_down.weight : \n",
+            "tensor(1.5703, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_16_img_attn_qkv.lora_down.weight : \n",
+            "tensor(1.4648, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_16_img_mlp_0.lora_down.weight : \n",
+            "tensor(1.5537, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_16_img_mod_lin.lora_down.weight : \n",
+            "tensor(2.6133, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_16_txt_attn_proj.lora_down.weight : \n",
+            "tensor(2.2559, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_16_txt_attn_qkv.lora_down.weight : \n",
+            "tensor(1.9365, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_16_txt_mlp_0.lora_down.weight : \n",
+            "tensor(2.7891, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_16_txt_mod_lin.lora_down.weight : \n",
+            "tensor(1.3174, dtype=torch.float16)\n",
+            "lora_unet_double_blocks_17_img_attn_proj.lora_down.weight : \n",
+            "tensor(2.4609, dtype=torch.float16)\n",
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+            "tensor(2.5566, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_3_modulation_lin.lora_down.weight : \n",
+            "tensor(4.7148, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_4_linear1.lora_down.weight : \n",
+            "tensor(2.2832, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_4_modulation_lin.lora_down.weight : \n",
+            "tensor(2.0566, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_5_linear1.lora_down.weight : \n",
+            "tensor(2.2109, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_5_modulation_lin.lora_down.weight : \n",
+            "tensor(2.7793, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_6_linear1.lora_down.weight : \n",
+            "tensor(3.0176, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_6_modulation_lin.lora_down.weight : \n",
+            "tensor(2.9180, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_7_linear1.lora_down.weight : \n",
+            "tensor(2.2461, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_7_modulation_lin.lora_down.weight : \n",
+            "tensor(2.1074, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_8_linear1.lora_down.weight : \n",
+            "tensor(3.0391, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_8_modulation_lin.lora_down.weight : \n",
+            "tensor(2.0039, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_9_linear1.lora_down.weight : \n",
+            "tensor(3.8789, dtype=torch.float16)\n",
+            "lora_unet_single_blocks_9_modulation_lin.lora_down.weight : \n",
+            "tensor(4.0547, dtype=torch.float16)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<---- Upload your civiai trained .safetensor file to Google Colab before running the next cell\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "oDAUwfFzqzgj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WQZ3BZn1p-pw"
+      },
+      "outputs": [],
+      "source": [
+        "civiai_lora = '' # @param {type:'string' ,placeholder:'ex. civitai_trained_e19.safetensors'}\n",
+        "tensor_art_filename = '' # @param {type:'string' ,placeholder:'ex. e19.safetensors'}\n",
+        "%cd /content/\n",
+        "tgt = load_file(f'{civiai_lora}')\n",
+        "for key in tgt:\n",
+        "  tgt[f'{key}'] = tgt[f'{key}'].to(dtype=torch.float16)\n",
+        "%cd /content/\n",
+        "save_file(tgt , f'{tensor_art_filename}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Download the new .safetensor file to your device.\n",
+        "\n",
+        "Downloading from CoLab Notebook will seemingly do nothing for ~5min. Then the file will download , so be patient.\n",
+        "\n",
+        "For faster/more consistent downloads , download your .safetensor file from your Google Drive"
+      ],
+      "metadata": {
+        "id": "blnBW-U4rAS7"
+      }
+    }
+  ]
+}