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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"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": "1oxeJYHRqxQC",
"outputId": "5397ceb1-cd98-4477-f472-d766beac79fb",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"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",
"iris = load_file('/content/drive/MyDrive/Saved from Chrome/proud_iris.safetensors')\n",
"nudism = load_file('/content/drive/MyDrive/Saved from Chrome/nudism.safetensors')"
],
"metadata": {
"id": "FftDdBRG7su6"
},
"execution_count": 107,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for key in cgi:\n",
" cgi[f'{key}'] = cgi[f'{key}'].to(dtype=torch.float16)\n",
" iris[f'{key}'] = iris[f'{key}'].to(dtype=torch.float16)\n",
" nudism[f'{key}'] = nudism[f'{key}'].to(dtype=torch.float16)"
],
"metadata": {
"id": "RII9SEqh8KH2"
},
"execution_count": 108,
"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",
"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": {
"id": "ckyBSQi5Ll4F",
"outputId": "341f7192-083d-4423-f61f-4f49d5756e79",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"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": [
"from safetensors.torch import load_file , save_file\n",
"\n",
"merge = load_file('/content/drive/MyDrive/Saved from Chrome/cgi_style.safetensors')\n",
"for key in cgi:\n",
" if cgi[f'{key}'].shape == torch.Size([]): continue\n",
" merge[f'{key}'] = (cgi[f'{key}'] + iris[f'{key}'])/2\n",
"\n",
"%cd /content/\n",
"save_file(merge , 'cgi_iris_1_1_1_merge.safetensors')"
],
"metadata": {
"id": "9L_g5Zp9Du2E",
"outputId": "38661765-461a-42c3-8480-38fe7f1abe3e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 113,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content\n"
]
}
]
},
{
"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": {
"id": "VFNw0Nck8V6Q",
"outputId": "e48bab98-18f7-43bb-d1cf-89f3e00f7ccf",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"lora_unet_double_blocks_0_img_attn_qkv.lora_down.weight : \n",
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]
}
]
},
{
"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"
}
}
]
} |