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lora-training-data

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Delete lora_merge.ipynb

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- {
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- "nbformat": 4,
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- "nbformat_minor": 0,
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- "metadata": {
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- "colab": {
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- "provenance": []
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- },
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- "kernelspec": {
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- "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": [
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- "# Cast civitai trained LoRa in torch.bfloat16 to Tensor Art Compatible torch.float16 dtype\n",
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- "\n",
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- "Created by Adcom: https://tensor.art/u/743241123023077878"
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- ],
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- "metadata": {
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- "id": "YDCnQpDdqDe4"
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- }
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "#initialize\n",
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- "import torch\n",
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- "from safetensors.torch import load_file\n",
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- "from google.colab import drive\n",
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- "drive.mount('/content/drive')"
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- ],
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- "metadata": {
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- "id": "1oxeJYHRqxQC",
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- "outputId": "5397ceb1-cd98-4477-f472-d766beac79fb",
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- }
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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": [
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- "Mounted at /content/drive\n"
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- ]
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- }
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- ]
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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": {
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- "id": "JuGDCX5272Bh"
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- },
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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": {
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- "id": "FftDdBRG7su6"
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- },
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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",
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- " iris[f'{key}'] = iris[f'{key}'].to(dtype=torch.float16)\n",
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- " nudism[f'{key}'] = nudism[f'{key}'].to(dtype=torch.float16)"
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- ],
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- "metadata": {
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- "id": "RII9SEqh8KH2"
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- },
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- "execution_count": 108,
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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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- "import torch\n",
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- "import torch.nn as nn\n",
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- "#define metric for similarity\n",
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- "tgt_dim = torch.Size([64, 3072])\n",
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- "cos0 = nn.CosineSimilarity(dim=1)\n",
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- "cos = nn.CosineSimilarity(dim=1)\n",
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- "\n",
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- "\n",
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- "def sim(tgt , ref ,key):\n",
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- " return torch.sum(torch.abs(cos(tgt, ref[f'{key}']))) + torch.sum(torch.abs(cos0(tgt, ref[f'{key}'])))\n",
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- "#-----#\n",
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- "\n",
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- "from torch import linalg as LA\n",
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- "def rand_search(A , B , key , iters):\n",
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- " tgt_norm = (LA.matrix_norm(A[f'{key}']) + LA.matrix_norm(B[f'{key}']))/2\n",
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- " tgt_avg = (A[f'{key}'] + B[f'{key}'])/2\n",
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- "\n",
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- " max_sim = (sim(tgt_avg , A , key) + sim(tgt_avg , B , key))\n",
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- " cand = tgt_avg\n",
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- "\n",
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- " for iter in range(iters):\n",
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- " rand = torch.ones(tgt_dim)*(-0.5) + torch.rand(tgt_dim)\n",
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- " rand = rand * (tgt_norm/LA.matrix_norm(rand))\n",
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- " #rand = (rand + tgt_avg)/2\n",
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- " #rand = rand * (tgt_norm/LA.matrix_norm(rand))\n",
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- "\n",
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- " tmp = sim(rand,A, key) + sim(rand , B, key)\n",
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- " if (tmp > max_sim):\n",
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- " max_sim = tmp\n",
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- " cand = rand\n",
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- " print('found!')\n",
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- " break\n",
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- " #------#\n",
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- " print('returning')\n",
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- " return cand , max_sim\n",
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- "#-----#"
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- ],
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- "metadata": {
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- "id": "hJL6QEclHdHn"
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- },
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- "execution_count": 104,
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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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- "cand , max_sim = rand_search(cgi , iris , 'lora_unet_double_blocks_0_img_attn_proj.lora_down.weight' , 1000)\n",
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- "print(sim(cand , iris , key))\n",
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- "print(sim(cand , cgi , key))"
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- ],
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- "metadata": {
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- "id": "ckyBSQi5Ll4F",
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- "outputId": "341f7192-083d-4423-f61f-4f49d5756e79",
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- }
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- },
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- "execution_count": 106,
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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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- "returning\n",
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- "tensor(91.1875, dtype=torch.float16)\n",
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- "tensor(90.2500, dtype=torch.float16)\n"
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- ]
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- }
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- ]
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "from safetensors.torch import load_file , save_file\n",
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- "\n",
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- "merge = load_file('/content/drive/MyDrive/Saved from Chrome/cgi_style.safetensors')\n",
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- "for key in cgi:\n",
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- " if cgi[f'{key}'].shape == torch.Size([]): continue\n",
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- " merge[f'{key}'] = (cgi[f'{key}'] + iris[f'{key}'])/2\n",
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- "\n",
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- "%cd /content/\n",
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- "save_file(merge , 'cgi_iris_1_1_1_merge.safetensors')"
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- ],
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- "metadata": {
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- "id": "9L_g5Zp9Du2E",
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- "outputId": "38661765-461a-42c3-8480-38fe7f1abe3e",
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- }
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- },
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- "execution_count": 113,
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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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- "/content\n"
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- ]
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- }
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- ]
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "tgt_dim = torch.Size([64, 3072])\n",
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- "cosa = nn.CosineSimilarity(dim=0)\n",
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- "cos_dim1 = nn.CosineSimilarity(dim=1)\n",
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- "\n",
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- "for key in cgi:\n",
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- " if not cgi[f'{key}'].shape == torch.Size([64, 3072]): continue\n",
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- " print(f'{key} : ')\n",
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- " print(torch.sum(torch.abs(cos_dim1(cgi[f'{key}'] , iris[f'{key}']))))"
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- ],
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- "metadata": {
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- "id": "VFNw0Nck8V6Q",
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- "outputId": "e48bab98-18f7-43bb-d1cf-89f3e00f7ccf",
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- }
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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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- "tensor(2.1465, dtype=torch.float16)\n",
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- "lora_unet_double_blocks_11_img_mod_lin.lora_down.weight : \n",
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- "tensor(3.9277, dtype=torch.float16)\n",
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- "lora_unet_double_blocks_11_txt_attn_proj.lora_down.weight : \n",
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- "tensor(1.7246, dtype=torch.float16)\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_mlp_0.lora_down.weight : \n",
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- "lora_unet_double_blocks_11_txt_mod_lin.lora_down.weight : \n",
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- "lora_unet_double_blocks_12_img_attn_proj.lora_down.weight : \n",
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- "lora_unet_double_blocks_12_img_attn_qkv.lora_down.weight : \n",
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- "lora_unet_double_blocks_12_img_mlp_0.lora_down.weight : \n",
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- "lora_unet_double_blocks_12_img_mod_lin.lora_down.weight : \n",
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- "lora_unet_double_blocks_12_txt_attn_proj.lora_down.weight : \n",
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- "lora_unet_double_blocks_13_img_attn_qkv.lora_down.weight : \n",
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- "lora_unet_double_blocks_13_img_mlp_0.lora_down.weight : \n",
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- "lora_unet_double_blocks_14_img_attn_qkv.lora_down.weight : \n",
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- "lora_unet_double_blocks_14_img_mlp_0.lora_down.weight : \n",
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- "lora_unet_double_blocks_16_img_attn_qkv.lora_down.weight : \n",
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- "lora_unet_double_blocks_16_img_mlp_0.lora_down.weight : \n",
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- "lora_unet_double_blocks_16_img_mod_lin.lora_down.weight : \n",
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- "lora_unet_double_blocks_16_txt_attn_proj.lora_down.weight : \n",
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- "lora_unet_double_blocks_16_txt_attn_qkv.lora_down.weight : \n",
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- "lora_unet_double_blocks_17_img_mod_lin.lora_down.weight : \n",
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- "lora_unet_double_blocks_17_txt_attn_proj.lora_down.weight : \n",
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- "lora_unet_double_blocks_17_txt_attn_qkv.lora_down.weight : \n",
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- "lora_unet_double_blocks_17_txt_mlp_0.lora_down.weight : \n",
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- "tensor(2.3887, dtype=torch.float16)\n",
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- "lora_unet_double_blocks_18_img_attn_qkv.lora_down.weight : \n",
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- "tensor(1.6738, dtype=torch.float16)\n",
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- "lora_unet_double_blocks_18_img_mlp_0.lora_down.weight : \n",
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- "tensor(3.7500, dtype=torch.float16)\n",
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- "tensor(2.7285, dtype=torch.float16)\n",
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- "tensor(2.0410, dtype=torch.float16)\n",
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- "tensor(2.0586, dtype=torch.float16)\n",
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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
- }