Nisaar_DreamBooth_Custom_colab.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Crgax-BInpVb",
        "outputId": "25c6d389-af56-4dbb-9fbf-22c75d587035",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Dreambooth-Stable-Diffusion'...\n",
            "remote: Enumerating objects: 907, done.\u001b[K\n",
            "remote: Counting objects: 100% (32/32), done.\u001b[K\n",
            "remote: Compressing objects: 100% (25/25), done.\u001b[K\n",
            "remote: Total 907 (delta 11), reused 23 (delta 7), pack-reused 875\u001b[K\n",
            "Receiving objects: 100% (907/907), 16.98 MiB | 34.64 MiB/s, done.\n",
            "Resolving deltas: 100% (499/499), done.\n",
            "/content/Dreambooth-Stable-Diffusion\n"
          ]
        }
      ],
      "source": [
        "#@title Load repo (if needed)\n",
        "!git clone https://github.com/JoePenna/Dreambooth-Stable-Diffusion\n",
        "%cd Dreambooth-Stable-Diffusion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsenBwgDnvjq",
        "outputId": "47c23309-71af-4a09-d1dd-88f97d5207cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Jan  4 09:15:07 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  A100-SXM4-40GB      Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   41C    P0    54W / 400W |      0MiB / 40536MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWbJ6QKwoBwK",
        "outputId": "a2198515-daf5-4cca-8916-f2510c9b622c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "qeTrc2vOeiNh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3b0d617a-9262-4ce0-e75b-190ad5ca8c15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 KB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: antlr4-python3-runtime\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144575 sha256=ba173e062083b1931537defe7a6aae3140ff707320eb169117b498e127ad76b2\n",
            "  Stored in directory: /root/.cache/pip/wheels/b1/a3/c2/6df046c09459b73cc9bb6c4401b0be6c47048baf9a1617c485\n",
            "Successfully built antlr4-python3-runtime\n",
            "Installing collected packages: antlr4-python3-runtime, omegaconf\n",
            "Successfully installed antlr4-python3-runtime-4.9.3 omegaconf-2.3.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting einops\n",
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            "\u001b[?25hInstalling collected packages: einops\n",
            "Successfully installed einops-0.6.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pytorch-lightning==1.6.5\n",
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            "Installing collected packages: pyDeprecate, torchmetrics, pytorch-lightning\n",
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            "Building wheels for collected packages: test-tube\n",
            "  Building wheel for test-tube (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for test-tube: filename=test_tube-0.7.5-py3-none-any.whl size=25357 sha256=0f65342bdaf171f3600023d00af50e8f46c9c1d36d7bda1f635091658c42670d\n",
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            "Installing collected packages: test-tube\n",
            "Successfully installed test-tube-0.7.5\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
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            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.11.1 tokenizers-0.13.2 transformers-4.25.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting kornia\n",
            "  Downloading kornia-0.6.9-py2.py3-none-any.whl (569 kB)\n",
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            "Installing collected packages: kornia\n",
            "Successfully installed kornia-0.6.9\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Obtaining taming-transformers from git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers\n",
            "  Cloning https://github.com/CompVis/taming-transformers.git (to revision master) to ./src/taming-transformers\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/CompVis/taming-transformers.git /content/Dreambooth-Stable-Diffusion/src/taming-transformers\n",
            "  Resolved https://github.com/CompVis/taming-transformers.git to commit 3ba01b241669f5ade541ce990f7650a3b8f65318\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (from taming-transformers) (1.13.0+cu116)\n",
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            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch->taming-transformers) (4.4.0)\n",
            "Installing collected packages: taming-transformers\n",
            "  Running setup.py develop for taming-transformers\n",
            "Successfully installed taming-transformers-0.0.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Obtaining clip from git+https://github.com/openai/CLIP.git@main#egg=clip\n",
            "  Cloning https://github.com/openai/CLIP.git (to revision main) to ./src/clip\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /content/Dreambooth-Stable-Diffusion/src/clip\n",
            "  Resolved https://github.com/openai/CLIP.git to commit d50d76daa670286dd6cacf3bcd80b5e4823fc8e1\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting ftfy\n",
            "  Downloading ftfy-6.1.1-py3-none-any.whl (53 kB)\n",
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            "Installing collected packages: ftfy, clip\n",
            "  Running setup.py develop for clip\n",
            "Successfully installed clip-1.0 ftfy-6.1.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting setuptools==59.5.0\n",
            "  Downloading setuptools-59.5.0-py3-none-any.whl (952 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m952.4/952.4 KB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: setuptools\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 57.4.0\n",
            "    Uninstalling setuptools-57.4.0:\n",
            "      Successfully uninstalled setuptools-57.4.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ipython 7.9.0 requires jedi>=0.10, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed setuptools-59.5.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pillow==9.0.1\n",
            "  Downloading Pillow-9.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n",
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            "\u001b[?25hInstalling collected packages: pillow\n",
            "  Attempting uninstall: pillow\n",
            "    Found existing installation: Pillow 7.1.2\n",
            "    Uninstalling Pillow-7.1.2:\n",
            "      Successfully uninstalled Pillow-7.1.2\n",
            "Successfully installed pillow-9.0.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torchmetrics==0.6.0\n",
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            "Installing collected packages: torchmetrics\n",
            "  Attempting uninstall: torchmetrics\n",
            "    Found existing installation: torchmetrics 0.11.0\n",
            "    Uninstalling torchmetrics-0.11.0:\n",
            "      Successfully uninstalled torchmetrics-0.11.0\n",
            "Successfully installed torchmetrics-0.6.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Obtaining file:///content/Dreambooth-Stable-Diffusion\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (from latent-diffusion==0.0.1) (1.13.0+cu116)\n",
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from latent-diffusion==0.0.1) (4.64.1)\n",
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            "Installing collected packages: latent-diffusion\n",
            "  Running setup.py develop for latent-diffusion\n",
            "Successfully installed latent-diffusion-0.0.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting protobuf==3.20.1\n",
            "  Downloading protobuf-3.20.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: protobuf\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.19.6\n",
            "    Uninstalling protobuf-3.19.6:\n",
            "      Successfully uninstalled protobuf-3.19.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.9.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.1 which is incompatible.\n",
            "tensorboard 2.9.1 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.1 which is incompatible.\n",
            "googleapis-common-protos 1.57.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-translate 3.8.4 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-language 2.6.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-firestore 2.7.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-datastore 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery 3.4.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.17.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed protobuf-3.20.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.8/dist-packages (4.4.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from gdown) (1.15.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.8/dist-packages (from gdown) (2.25.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from gdown) (4.64.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from gdown) (3.8.2)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.8/dist-packages (from gdown) (4.6.3)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (2.10)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pydrive in /usr/local/lib/python3.8/dist-packages (1.3.1)\n",
            "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.8/dist-packages (from pydrive) (4.1.3)\n",
            "Requirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.8/dist-packages (from pydrive) (2.70.0)\n",
            "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.8/dist-packages (from pydrive) (6.0)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.15.0 in /usr/local/lib/python3.8/dist-packages (from google-api-python-client>=1.2->pydrive) (0.17.4)\n",
            "Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 in /usr/local/lib/python3.8/dist-packages (from google-api-python-client>=1.2->pydrive) (2.11.0)\n",
            "Requirement already satisfied: google-auth<3.0.0dev,>=1.19.0 in /usr/local/lib/python3.8/dist-packages (from google-api-python-client>=1.2->pydrive) (2.15.0)\n",
            "Requirement already satisfied: uritemplate<5,>=3.0.1 in /usr/local/lib/python3.8/dist-packages (from google-api-python-client>=1.2->pydrive) (4.1.1)\n",
            "Requirement already satisfied: google-auth-httplib2>=0.1.0 in /usr/local/lib/python3.8/dist-packages (from google-api-python-client>=1.2->pydrive) (0.1.0)\n",
            "Requirement already satisfied: six>=1.6.1 in /usr/local/lib/python3.8/dist-packages (from oauth2client>=4.0.0->pydrive) (1.15.0)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.8/dist-packages (from oauth2client>=4.0.0->pydrive) (4.9)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.8/dist-packages (from oauth2client>=4.0.0->pydrive) (0.2.8)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.8/dist-packages (from oauth2client>=4.0.0->pydrive) (0.4.8)\n",
            "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.8/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.2->pydrive) (2.25.1)\n",
            "Collecting protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5\n",
            "  Downloading protobuf-4.21.12-cp37-abi3-manylinux2014_x86_64.whl (409 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m409.8/409.8 KB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: googleapis-common-protos<2.0dev,>=1.56.2 in /usr/local/lib/python3.8/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.2->pydrive) (1.57.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3.0.0dev,>=1.19.0->google-api-python-client>=1.2->pydrive) (5.2.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.2->pydrive) (1.24.3)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.2->pydrive) (4.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.2->pydrive) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5->google-api-python-client>=1.2->pydrive) (2022.12.7)\n",
            "Installing collected packages: protobuf\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.1\n",
            "    Uninstalling protobuf-3.20.1:\n",
            "      Successfully uninstalled protobuf-3.20.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.9.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 4.21.12 which is incompatible.\n",
            "tensorflow-metadata 1.12.0 requires protobuf<4,>=3.13, but you have protobuf 4.21.12 which is incompatible.\n",
            "tensorboard 2.9.1 requires protobuf<3.20,>=3.9.2, but you have protobuf 4.21.12 which is incompatible.\n",
            "pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 4.21.12 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed protobuf-4.21.12\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.9/153.9 KB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m452.9/452.9 KB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m191.5/191.5 KB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m48.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.8/dist-packages (0.11.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (2.25.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (3.8.2)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (21.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.64.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (1.26.13)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2.10)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: ipywidgets==7.7.1 in /usr/local/lib/python3.8/dist-packages (7.7.1)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.8/dist-packages (from ipywidgets==7.7.1) (3.6.1)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.8/dist-packages (from ipywidgets==7.7.1) (5.7.1)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.8/dist-packages (from ipywidgets==7.7.1) (0.2.0)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.8/dist-packages (from ipywidgets==7.7.1) (7.9.0)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from ipywidgets==7.7.1) (3.0.5)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.8/dist-packages (from ipywidgets==7.7.1) (5.3.4)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.8/dist-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1) (6.1.12)\n",
            "Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.8/dist-packages (from ipykernel>=4.5.1->ipywidgets==7.7.1) (6.0.4)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.8/dist-packages (from ipython>=4.0.0->ipywidgets==7.7.1) (4.8.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.8/dist-packages (from ipython>=4.0.0->ipywidgets==7.7.1) (59.5.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.8/dist-packages (from ipython>=4.0.0->ipywidgets==7.7.1) (0.7.5)\n",
            "Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.8/dist-packages (from ipython>=4.0.0->ipywidgets==7.7.1) (0.18.2)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from ipython>=4.0.0->ipywidgets==7.7.1) (4.4.2)\n",
            "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from ipython>=4.0.0->ipywidgets==7.7.1) (2.0.10)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.8/dist-packages (from ipython>=4.0.0->ipywidgets==7.7.1) (0.2.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from ipython>=4.0.0->ipywidgets==7.7.1) (2.6.1)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.8/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (5.7.16)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.10->ipython>=4.0.0->ipywidgets==7.7.1) (0.8.3)\n",
            "Requirement already satisfied: nbformat in /usr/local/lib/python3.8/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (5.7.1)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (0.13.3)\n",
            "Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.8/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (23.2.1)\n",
            "Requirement already satisfied: nbconvert<6.0 in /usr/local/lib/python3.8/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (5.6.1)\n",
            "Requirement already satisfied: jinja2<=3.0.0 in /usr/local/lib/python3.8/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (2.11.3)\n",
            "Requirement already satisfied: jupyter-core>=4.4.0 in /usr/local/lib/python3.8/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (5.1.1)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.8/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (1.8.0)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.8/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (0.15.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets==7.7.1) (2.8.2)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets==7.7.1) (0.2.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets==7.7.1) (1.15.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect->ipython>=4.0.0->ipywidgets==7.7.1) (0.7.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2<=3.0.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (2.0.1)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.8/dist-packages (from jupyter-core>=4.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (2.6.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (0.8.4)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.8/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (0.6.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (1.5.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.8/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (0.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.8/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (0.7.1)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.8/dist-packages (from nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (5.0.1)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.8/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (2.16.2)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.8/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (4.3.3)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (22.2.0)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (5.10.1)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (0.19.2)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.8/dist-packages (from bleach->nbconvert<6.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (0.5.1)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1) (3.11.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting captionizer==1.0.1\n",
            "  Downloading captionizer-1.0.1-py3-none-any.whl (13 kB)\n",
            "Installing collected packages: captionizer\n",
            "Successfully installed captionizer-1.0.1\n"
          ]
        }
      ],
      "source": [
        "#@title BUILD ENV\n",
        "!pip install omegaconf\n",
        "!pip install einops\n",
        "!pip install pytorch-lightning==1.6.5\n",
        "!pip install test-tube\n",
        "!pip install transformers\n",
        "!pip install kornia\n",
        "!pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers\n",
        "!pip install -e git+https://github.com/openai/CLIP.git@main#egg=clip\n",
        "!pip install setuptools==59.5.0\n",
        "!pip install pillow==9.0.1\n",
        "!pip install torchmetrics==0.6.0\n",
        "!pip install -e .\n",
        "!pip install protobuf==3.20.1\n",
        "!pip install gdown\n",
        "!pip install pydrive\n",
        "!pip install -qq diffusers[\"training\"]==0.3.0 transformers ftfy\n",
        "!pip install -qq \"ipywidgets>=7,<8\"\n",
        "!pip install huggingface_hub\n",
        "!pip install ipywidgets==7.7.1\n",
        "!pip install captionizer==1.0.1\n",
        "\n",
        "import os\n",
        "os._exit(00)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BETxnfhpnpVd",
        "outputId": "7bfe4342-6414-424a-b06d-c0e827f7137e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Errno 2] No such file or directory: 'Dreambooth-Stable-Diffusion'\n",
            "/content/Dreambooth-Stable-Diffusion\n"
          ]
        }
      ],
      "source": [
        "#@title # Required - Navigate back to the directory.\n",
        "%cd Dreambooth-Stable-Diffusion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416,
          "referenced_widgets": [
            "c9ff9604319944faac78a5d836f8e7f9",
            "523ce9007a5e4c819f38d7bc25d34823",
            "1692917eb7884b4682cd3d036b86c32b",
            "2e7afc7c6f3647af918e56b9f76c8376",
            "d8dd9c9bff48438492c2036a4d5a787e",
            "e827154a35da4d45925e87a05d93f1a8",
            "1644db5bca444f3d9e40729e8933773b",
            "475f2036dfa3478a98df05d7806e4eec",
            "ca1a99cdb3fd400e9f4976738366fde4",
            "4a647263f2c74ed182904417d336e0f2",
            "fc205dddb45140f08b5c32804366d43c",
            "6e34abc2c28149bba9040867a0e6589d",
            "e16e3b77f6154738a5fad1d1dc8c9edc",
            "241fe07a8bf149b0b579703742f4b067",
            "9f4f693eeef34050954b30a385f5a222",
            "d527ebb91f2d43b48bd2aeb3809b728c",
            "e7849047cb13404dadc1124f17eba76e"
          ]
        },
        "id": "6tjx0HcjesFo",
        "outputId": "984aba44-a397-4eec-b02e-b6d16d4d3fb8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "#@markdown Hugging Face Login\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()\n",
        "#hugging_face_token = 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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6e09b410ab1d4188b993957da997c9f8",
            "e81ccbab5c2a40edb8719b951380f59a",
            "525cae9eec18461687f3a3dcd64ccede",
            "0a7749b23feb4b81be34950f89807a22",
            "1e92f00328124126baad33dd17f0c403",
            "ae815df11b6b4cccb01b355e8815ceaa",
            "375ca5835b744e06bcd9dc87396ef9a1",
            "a8e1a6141d644bd29b3d5eb0c837cf72",
            "a7611d4bee5647b2a8ba948dfef1dada",
            "c4fc779f0f2a4508a591ca846c324965",
            "2a100fad4f014020a4c483c44345518b"
          ]
        },
        "id": "O15vMMhCevib",
        "outputId": "ff647263-e8de-4dd7-8b14-061ed430f4f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ model.ckpt successfully downloaded\n"
          ]
        }
      ],
      "source": [
        "#@markdown Download the 1.4 sd model\n",
        "from IPython.display import clear_output\n",
        "\n",
        "from huggingface_hub import hf_hub_download\n",
        "downloaded_model_path = hf_hub_download(\n",
        " repo_id=\"CompVis/stable-diffusion-v-1-4-original\",\n",
        " filename=\"sd-v1-4.ckpt\",\n",
        " use_auth_token=True\n",
        ")\n",
        "\n",
        "# Move the sd-v1-4.ckpt to the root of this directory as \"model.ckpt\"\n",
        "actual_locations_of_model_blob = !readlink -f {downloaded_model_path}\n",
        "!mv {actual_locations_of_model_blob[-1]} model.ckpt\n",
        "clear_output()\n",
        "print(\"✅ model.ckpt successfully downloaded\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N96aedTtfBjO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "outputId": "25ac8c6f-859a-4f53-becb-b2e256a23e19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Stable-Diffusion-Regularization-Images-man_euler'...\n",
            "remote: Enumerating objects: 1253, done.\u001b[K\n",
            "remote: Total 1253 (delta 0), reused 0 (delta 0), pack-reused 1253\u001b[K\n",
            "Receiving objects: 100% (1253/1253), 545.27 MiB | 63.45 MiB/s, done.\n",
            "Checking out files: 100% (1250/1250), done.\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00000.png' -> 'regularization_images/man_euler/man_euler_00000.png'\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00001.png' -> 'regularization_images/man_euler/man_euler_00001.png'\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00002.png' -> 'regularization_images/man_euler/man_euler_00002.png'\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00003.png' -> 'regularization_images/man_euler/man_euler_00003.png'\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00004.png' -> 'regularization_images/man_euler/man_euler_00004.png'\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00005.png' -> 'regularization_images/man_euler/man_euler_00005.png'\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00006.png' -> 'regularization_images/man_euler/man_euler_00006.png'\n",
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            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00014.png' -> 'regularization_images/man_euler/man_euler_00014.png'\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00015.png' -> 'regularization_images/man_euler/man_euler_00015.png'\n",
            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00016.png' -> 'regularization_images/man_euler/man_euler_00016.png'\n",
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            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_00041.png' -> 'regularization_images/man_euler/man_euler_00041.png'\n",
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            "renamed 'Stable-Diffusion-Regularization-Images-man_euler/man_euler/man_euler_01249.png' -> 'regularization_images/man_euler/man_euler_01249.png'\n"
          ]
        }
      ],
      "source": [
        "#@title # Download Regularization Images\n",
        "#@markdown We’ve created the following image sets\n",
        "#@markdown - `man_euler` - provided by Niko Pueringer (Corridor Digital) - euler @ 40 steps, CFG 7.5\n",
        "#@markdown - `man_unsplash` - pictures from various photographers\n",
        "#@markdown - `person_ddim`\n",
        "#@markdown - `woman_ddim` - provided by David Bielejeski - ddim @ 50 steps, CFG 10.0 <br />\n",
        "#@markdown - `blonde_woman` - provided by David Bielejeski - ddim @ 50 steps, CFG 10.0 <br />\n",
        "\n",
        "dataset=\"man_euler\" #@param [\"man_euler\", \"man_unsplash\", \"person_ddim\", \"woman_ddim\", \"blonde_woman\"]\n",
        "!git clone https://github.com/djbielejeski/Stable-Diffusion-Regularization-Images-{dataset}.git\n",
        "\n",
        "!mkdir -p regularization_images/{dataset}\n",
        "!mv -v Stable-Diffusion-Regularization-Images-{dataset}/{dataset}/*.* regularization_images/{dataset}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_A5FrXZV0N1g"
      },
      "outputs": [],
      "source": [
        "# Add this code above \"t = t.to('cpu')\" at /content/Dreambooth-Stable-Diffusion/ldm/models/diffusion/ddpm.py Line 1096"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A1jDpkDJ843i"
      },
      "outputs": [],
      "source": [
        "# Run this Command on Terminal\n",
        "# mkdir /content/Dreambooth-Stable-Diffusion/training_images\n",
        "# cp /content/drive/MyDrive/JOE_TRAINING_DATASET/pratikbehera/* /content/Dreambooth-Stable-Diffusion/training_images\n",
        "# cp /content/drive/MyDrive/Ruhaan/train_data/* /content/Dreambooth-Stable-Diffusion/training_images\n",
        "# cp /content/drive/MyDrive/Asgarali/* /content/Dreambooth-Stable-Diffusion/training_images\n",
        "\n",
        "#  cp /content/drive/MyDrive/JOE_TRAINING_DATASET/Soumyakantsahoo/images/* /content/Dreambooth-Stable-Diffusion/training_images\n",
        "\n",
        "# COMMAND TO TRNSFR IMAGES TO training_Images\n",
        "# mkdir /content/Dreambooth-Stable-Diffusion/training_images\n",
        "# cp /content/drive/MyDrive/Nisaar_data/Cleandata/* /content/Dreambooth-Stable-Diffusion/training_images\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "A7hmdOdOfGzs",
        "outputId": "e7b0c0d5-f0c6-43cd-a5f9-6c8d378049f8",
        "cellView": "form"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✅ 25 training images found\n"
          ]
        }
      ],
      "source": [
        "#@title # Training Images\n",
        "#@markdown ## Upload your training images\n",
        "#@markdown WARNING: Be sure to upload an even amount of images, otherwise the training inexplicably stops at 1500 steps. <br />\n",
        "#@markdown - 2-3 full body\n",
        "#@markdown - 3-5 upper body\n",
        "#@markdown - 5-12 close-up on face  <br /> <br />\n",
        "#@markdown The images should be as close as possible to the kind of images you’re trying to make (most of the time, that means no selfies).\n",
        "from google.colab import files\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Create the directory\n",
        "!rm -rf training_images\n",
        "!mkdir -p training_images\n",
        "\n",
        "# Upload the files\n",
        "uploaded = files.upload()\n",
        "for filename in uploaded.keys():\n",
        " updated_file_name = filename.replace(\" \", \"_\")\n",
        " !mv \"{filename}\" \"training_images/{updated_file_name}\"\n",
        " clear_output()\n",
        "\n",
        "# Tell the user what is going on\n",
        "training_images_file_paths = !find training_images/*\n",
        "if len(training_images_file_paths) == 0:\n",
        " print(\"❌ no training images found. Please upload images to training_images\")\n",
        "else:\n",
        " print(\"✅ \" + str(len(training_images_file_paths)) + \" training images found\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2o_fFFvfxHi",
        "outputId": "bf886a5d-7f58-4873-ccac-62ef11bdaab0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-01-04 09:17:41.518447: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n",
            "Moving 0 files to the new cache system\n",
            "0it [00:00, ?it/s]\n",
            "Global seed set to 23\n",
            "Running on GPUs 0,\n",
            "Loading model from model.ckpt\n",
            "LatentDiffusion: Running in eps-prediction mode\n",
            "DiffusionWrapper has 859.52 M params.\n",
            "making attention of type 'vanilla' with 512 in_channels\n",
            "Working with z of shape (1, 4, 64, 64) = 16384 dimensions.\n",
            "making attention of type 'vanilla' with 512 in_channels\n",
            "Downloading: 100% 961k/961k [00:00<00:00, 5.69MB/s]\n",
            "Downloading: 100% 525k/525k [00:00<00:00, 3.70MB/s]\n",
            "Downloading: 100% 389/389 [00:00<00:00, 342kB/s]\n",
            "Downloading: 100% 905/905 [00:00<00:00, 794kB/s]\n",
            "Downloading: 100% 4.52k/4.52k [00:00<00:00, 3.70MB/s]\n",
            "Downloading: 100% 1.71G/1.71G [00:18<00:00, 90.8MB/s]\n",
            "Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.18.self_attn.q_proj.weight', 'vision_model.encoder.layers.20.layer_norm2.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.3.self_attn.out_proj.bias', 'vision_model.encoder.layers.22.layer_norm1.bias', 'vision_model.encoder.layers.9.self_attn.out_proj.weight', 'vision_model.encoder.layers.7.mlp.fc2.bias', 'vision_model.encoder.layers.18.layer_norm1.weight', 'vision_model.encoder.layers.16.self_attn.v_proj.weight', 'vision_model.encoder.layers.15.self_attn.k_proj.bias', 'vision_model.encoder.layers.9.self_attn.v_proj.weight', 'vision_model.encoder.layers.15.layer_norm1.weight', 'vision_model.encoder.layers.20.self_attn.k_proj.bias', 'vision_model.encoder.layers.20.mlp.fc1.weight', 'vision_model.encoder.layers.1.self_attn.q_proj.weight', 'vision_model.encoder.layers.3.layer_norm2.bias', 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'vision_model.encoder.layers.20.layer_norm1.weight', 'vision_model.encoder.layers.5.mlp.fc1.bias', 'vision_model.encoder.layers.6.self_attn.out_proj.bias', 'vision_model.encoder.layers.18.self_attn.v_proj.bias', 'vision_model.encoder.layers.0.mlp.fc2.weight', 'vision_model.encoder.layers.19.mlp.fc1.bias', 'vision_model.encoder.layers.0.layer_norm2.bias', 'vision_model.encoder.layers.21.self_attn.out_proj.bias', 'vision_model.encoder.layers.10.mlp.fc2.bias', 'vision_model.encoder.layers.13.layer_norm2.weight', 'vision_model.encoder.layers.4.layer_norm2.bias', 'vision_model.encoder.layers.8.self_attn.out_proj.bias', 'logit_scale']\n",
            "- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Restored from model.ckpt with 0 missing and 2 unexpected keys\n",
            "Unexpected Keys: ['model_ema.decay', 'model_ema.num_updates']\n",
            "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/loggers/test_tube.py:105: LightningDeprecationWarning: The TestTubeLogger is deprecated since v1.5 and will be removed in v1.7. We recommend switching to the `pytorch_lightning.loggers.TensorBoardLogger` as an alternative.\n",
            "  rank_zero_deprecation(\n",
            "Monitoring val/loss_simple_ema as checkpoint metric.\n",
            "Merged modelckpt-cfg: \n",
            "{'target': 'pytorch_lightning.callbacks.ModelCheckpoint', 'params': {'dirpath': 'logs/training_images2023-01-04T09-17-44_NisaarAgharia_Custom_model/checkpoints', 'filename': '{epoch:06}', 'verbose': True, 'save_last': True, 'monitor': 'val/loss_simple_ema', 'save_top_k': 1, 'every_n_train_steps': 500}}\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n",
            "HPU available: False, using: 0 HPUs\n",
            "#### Data #####\n",
            "train, PersonalizedBase, 1500\n",
            "reg, PersonalizedBase, 12500\n",
            "validation, PersonalizedBase, 15\n",
            "accumulate_grad_batches = 1\n",
            "++++ NOT USING LR SCALING ++++\n",
            "Setting learning rate to 1.00e-06\n",
            "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/configuration_validator.py:326: LightningDeprecationWarning: Base `LightningModule.on_train_batch_start` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.\n",
            "  rank_zero_deprecation(\n",
            "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/configuration_validator.py:335: LightningDeprecationWarning: The `on_keyboard_interrupt` callback hook was deprecated in v1.5 and will be removed in v1.7. Please use the `on_exception` callback hook instead.\n",
            "  rank_zero_deprecation(\n",
            "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/configuration_validator.py:391: LightningDeprecationWarning: The `Callback.on_pretrain_routine_start` hook has been deprecated in v1.6 and will be removed in v1.8. Please use `Callback.on_fit_start` instead.\n",
            "  rank_zero_deprecation(\n",
            "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/configuration_validator.py:342: LightningDeprecationWarning: Base `Callback.on_train_batch_end` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.\n",
            "  rank_zero_deprecation(\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "LatentDiffusion: Also optimizing conditioner params!\n",
            "\n",
            "  | Name              | Type               | Params\n",
            "---------------------------------------------------------\n",
            "0 | model             | DiffusionWrapper   | 859 M \n",
            "1 | first_stage_model | AutoencoderKL      | 83.7 M\n",
            "2 | cond_stage_model  | FrozenCLIPEmbedder | 123 M \n",
            "---------------------------------------------------------\n",
            "982 M     Trainable params\n",
            "83.7 M    Non-trainable params\n",
            "1.1 B     Total params\n",
            "4,264.941 Total estimated model params size (MB)\n",
            "Project config\n",
            "model:\n",
            "  base_learning_rate: 1.0e-06\n",
            "  target: ldm.models.diffusion.ddpm.LatentDiffusion\n",
            "  params:\n",
            "    reg_weight: 1.0\n",
            "    linear_start: 0.00085\n",
            "    linear_end: 0.012\n",
            "    num_timesteps_cond: 1\n",
            "    log_every_t: 200\n",
            "    timesteps: 1000\n",
            "    first_stage_key: image\n",
            "    cond_stage_key: caption\n",
            "    image_size: 64\n",
            "    channels: 4\n",
            "    cond_stage_trainable: true\n",
            "    conditioning_key: crossattn\n",
            "    monitor: val/loss_simple_ema\n",
            "    scale_factor: 0.18215\n",
            "    use_ema: false\n",
            "    embedding_reg_weight: 0.0\n",
            "    unfreeze_model: true\n",
            "    model_lr: 1.0e-06\n",
            "    personalization_config:\n",
            "      target: ldm.modules.embedding_manager.EmbeddingManager\n",
            "      params:\n",
            "        placeholder_strings:\n",
            "        - '*'\n",
            "        initializer_words:\n",
            "        - sculpture\n",
            "        per_image_tokens: false\n",
            "        num_vectors_per_token: 1\n",
            "        progressive_words: false\n",
            "    unet_config:\n",
            "      target: ldm.modules.diffusionmodules.openaimodel.UNetModel\n",
            "      params:\n",
            "        image_size: 32\n",
            "        in_channels: 4\n",
            "        out_channels: 4\n",
            "        model_channels: 320\n",
            "        attention_resolutions:\n",
            "        - 4\n",
            "        - 2\n",
            "        - 1\n",
            "        num_res_blocks: 2\n",
            "        channel_mult:\n",
            "        - 1\n",
            "        - 2\n",
            "        - 4\n",
            "        - 4\n",
            "        num_heads: 8\n",
            "        use_spatial_transformer: true\n",
            "        transformer_depth: 1\n",
            "        context_dim: 768\n",
            "        use_checkpoint: true\n",
            "        legacy: false\n",
            "    first_stage_config:\n",
            "      target: ldm.models.autoencoder.AutoencoderKL\n",
            "      params:\n",
            "        embed_dim: 4\n",
            "        monitor: val/rec_loss\n",
            "        ddconfig:\n",
            "          double_z: true\n",
            "          z_channels: 4\n",
            "          resolution: 512\n",
            "          in_channels: 3\n",
            "          out_ch: 3\n",
            "          ch: 128\n",
            "          ch_mult:\n",
            "          - 1\n",
            "          - 2\n",
            "          - 4\n",
            "          - 4\n",
            "          num_res_blocks: 2\n",
            "          attn_resolutions: []\n",
            "          dropout: 0.0\n",
            "        lossconfig:\n",
            "          target: torch.nn.Identity\n",
            "    cond_stage_config:\n",
            "      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder\n",
            "    ckpt_path: model.ckpt\n",
            "data:\n",
            "  target: main.DataModuleFromConfig\n",
            "  params:\n",
            "    batch_size: 1\n",
            "    num_workers: 1\n",
            "    wrap: false\n",
            "    train:\n",
            "      target: ldm.data.personalized.PersonalizedBase\n",
            "      params:\n",
            "        size: 512\n",
            "        set: train\n",
            "        per_image_tokens: false\n",
            "        repeats: 100\n",
            "        coarse_class_text: person\n",
            "        data_root: /content/Dreambooth-Stable-Diffusion/training_images\n",
            "        placeholder_token: nisaaragharia\n",
            "        token_only: false\n",
            "    reg:\n",
            "      target: ldm.data.personalized.PersonalizedBase\n",
            "      params:\n",
            "        size: 512\n",
            "        set: train\n",
            "        reg: true\n",
            "        per_image_tokens: false\n",
            "        repeats: 10\n",
            "        data_root: /content/Dreambooth-Stable-Diffusion/regularization_images/man_euler\n",
            "        coarse_class_text: person\n",
            "        placeholder_token: nisaaragharia\n",
            "    validation:\n",
            "      target: ldm.data.personalized.PersonalizedBase\n",
            "      params:\n",
            "        size: 512\n",
            "        set: val\n",
            "        per_image_tokens: false\n",
            "        repeats: 10\n",
            "        coarse_class_text: person\n",
            "        placeholder_token: nisaaragharia\n",
            "        data_root: /content/Dreambooth-Stable-Diffusion/training_images\n",
            "\n",
            "Lightning config\n",
            "modelcheckpoint:\n",
            "  params:\n",
            "    every_n_train_steps: 500\n",
            "callbacks:\n",
            "  image_logger:\n",
            "    target: main.ImageLogger\n",
            "    params:\n",
            "      batch_frequency: 500\n",
            "      max_images: 8\n",
            "      increase_log_steps: false\n",
            "trainer:\n",
            "  benchmark: true\n",
            "  max_steps: 2000\n",
            "  gpus: 0,\n",
            "\n",
            "Sanity Checking: 0it [00:00, ?it/s]/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
            "  rank_zero_warn(\n",
            "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
            "  rank_zero_warn(\n",
            "Training: 0it [00:00, ?it/s]/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py:2102: LightningDeprecationWarning: `Trainer.root_gpu` is deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.strategy.root_device.index` instead.\n",
            "  rank_zero_deprecation(\n",
            "Epoch 0:   0% 0/1515 [00:00<?, ?it/s] /usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/data.py:72: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n",
            "  warning_cache.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:229: UserWarning: You called `self.log('global_step', ...)` in your `training_step` but the value needs to be floating point. Converting it to torch.float32.\n",
            "  warning_cache.warn(\n",
            "Epoch 0:  33% 499/1515 [14:09<28:50,  1.70s/it, loss=0.312, v_num=0, train/loss_simple_step=0.086, train/loss_vlb_step=0.000305, train/loss_step=0.086, global_step=498.0]Data shape for DDIM sampling is (1, 4, 64, 64), eta 1.0\n",
            "Running DDIM Sampling with 50 timesteps\n",
            "\n",
            "DDIM Sampler:   0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
            "DDIM Sampler:   2% 1/50 [00:00<00:08,  5.61it/s]\u001b[A\n",
            "DDIM Sampler:   4% 2/50 [00:00<00:08,  5.83it/s]\u001b[A\n",
            "DDIM Sampler:   6% 3/50 [00:00<00:07,  5.95it/s]\u001b[A\n",
            "DDIM Sampler:   8% 4/50 [00:00<00:07,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  10% 5/50 [00:00<00:07,  6.04it/s]\u001b[A\n",
            "DDIM Sampler:  12% 6/50 [00:00<00:07,  6.08it/s]\u001b[A\n",
            "DDIM Sampler:  14% 7/50 [00:01<00:07,  6.11it/s]\u001b[A\n",
            "DDIM Sampler:  16% 8/50 [00:01<00:06,  6.14it/s]\u001b[A\n",
            "DDIM Sampler:  18% 9/50 [00:01<00:06,  6.12it/s]\u001b[A\n",
            "DDIM Sampler:  20% 10/50 [00:01<00:06,  6.11it/s]\u001b[A\n",
            "DDIM Sampler:  22% 11/50 [00:01<00:06,  6.11it/s]\u001b[A\n",
            "DDIM Sampler:  24% 12/50 [00:01<00:06,  6.13it/s]\u001b[A\n",
            "DDIM Sampler:  26% 13/50 [00:02<00:06,  6.11it/s]\u001b[A\n",
            "DDIM Sampler:  28% 14/50 [00:02<00:05,  6.11it/s]\u001b[A\n",
            "DDIM Sampler:  30% 15/50 [00:02<00:05,  6.08it/s]\u001b[A\n",
            "DDIM Sampler:  32% 16/50 [00:02<00:05,  6.11it/s]\u001b[A\n",
            "DDIM Sampler:  34% 17/50 [00:02<00:05,  6.10it/s]\u001b[A\n",
            "DDIM Sampler:  36% 18/50 [00:02<00:05,  6.08it/s]\u001b[A\n",
            "DDIM Sampler:  38% 19/50 [00:03<00:05,  6.07it/s]\u001b[A\n",
            "DDIM Sampler:  40% 20/50 [00:03<00:04,  6.08it/s]\u001b[A\n",
            "DDIM Sampler:  42% 21/50 [00:03<00:04,  6.08it/s]\u001b[A\n",
            "DDIM Sampler:  44% 22/50 [00:03<00:04,  6.09it/s]\u001b[A\n",
            "DDIM Sampler:  46% 23/50 [00:03<00:04,  6.08it/s]\u001b[A\n",
            "DDIM Sampler:  48% 24/50 [00:03<00:04,  6.13it/s]\u001b[A\n",
            "DDIM Sampler:  50% 25/50 [00:04<00:04,  6.16it/s]\u001b[A\n",
            "DDIM Sampler:  52% 26/50 [00:04<00:03,  6.12it/s]\u001b[A\n",
            "DDIM Sampler:  54% 27/50 [00:04<00:03,  6.11it/s]\u001b[A\n",
            "DDIM Sampler:  56% 28/50 [00:04<00:03,  6.04it/s]\u001b[A\n",
            "DDIM Sampler:  58% 29/50 [00:04<00:03,  6.05it/s]\u001b[A\n",
            "DDIM Sampler:  60% 30/50 [00:04<00:03,  6.06it/s]\u001b[A\n",
            "DDIM Sampler:  62% 31/50 [00:05<00:03,  6.07it/s]\u001b[A\n",
            "DDIM Sampler:  64% 32/50 [00:05<00:02,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  66% 33/50 [00:05<00:02,  6.04it/s]\u001b[A\n",
            "DDIM Sampler:  68% 34/50 [00:05<00:02,  5.85it/s]\u001b[A\n",
            "DDIM Sampler:  70% 35/50 [00:05<00:02,  5.89it/s]\u001b[A\n",
            "DDIM Sampler:  72% 36/50 [00:05<00:02,  5.94it/s]\u001b[A\n",
            "DDIM Sampler:  74% 37/50 [00:06<00:02,  5.97it/s]\u001b[A\n",
            "DDIM Sampler:  76% 38/50 [00:06<00:02,  6.00it/s]\u001b[A\n",
            "DDIM Sampler:  78% 39/50 [00:06<00:01,  6.03it/s]\u001b[A\n",
            "DDIM Sampler:  80% 40/50 [00:06<00:01,  6.08it/s]\u001b[A\n",
            "DDIM Sampler:  82% 41/50 [00:06<00:01,  6.09it/s]\u001b[A\n",
            "DDIM Sampler:  84% 42/50 [00:06<00:01,  6.10it/s]\u001b[A\n",
            "DDIM Sampler:  86% 43/50 [00:07<00:01,  6.08it/s]\u001b[A\n",
            "DDIM Sampler:  88% 44/50 [00:07<00:00,  6.09it/s]\u001b[A\n",
            "DDIM Sampler:  90% 45/50 [00:07<00:00,  6.10it/s]\u001b[A\n",
            "DDIM Sampler:  92% 46/50 [00:07<00:00,  6.09it/s]\u001b[A\n",
            "DDIM Sampler:  94% 47/50 [00:07<00:00,  6.06it/s]\u001b[A\n",
            "DDIM Sampler:  96% 48/50 [00:07<00:00,  6.07it/s]\u001b[A\n",
            "DDIM Sampler:  98% 49/50 [00:08<00:00,  6.07it/s]\u001b[A\n",
            "DDIM Sampler: 100% 50/50 [00:08<00:00,  6.06it/s]\n",
            "Data shape for DDIM sampling is (1, 4, 64, 64), eta 1.0\n",
            "Running DDIM Sampling with 50 timesteps\n",
            "\n",
            "DDIM Sampler:   0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
            "DDIM Sampler:   2% 1/50 [00:01<01:13,  1.50s/it]\u001b[A\n",
            "DDIM Sampler:   4% 2/50 [00:01<00:35,  1.37it/s]\u001b[A\n",
            "DDIM Sampler:   6% 3/50 [00:01<00:22,  2.07it/s]\u001b[A\n",
            "DDIM Sampler:   8% 4/50 [00:02<00:16,  2.73it/s]\u001b[A\n",
            "DDIM Sampler:  10% 5/50 [00:02<00:13,  3.31it/s]\u001b[A\n",
            "DDIM Sampler:  12% 6/50 [00:02<00:11,  3.79it/s]\u001b[A\n",
            "DDIM Sampler:  14% 7/50 [00:02<00:10,  4.18it/s]\u001b[A\n",
            "DDIM Sampler:  16% 8/50 [00:02<00:09,  4.49it/s]\u001b[A\n",
            "DDIM Sampler:  18% 9/50 [00:03<00:08,  4.71it/s]\u001b[A\n",
            "DDIM Sampler:  20% 10/50 [00:03<00:08,  4.88it/s]\u001b[A\n",
            "DDIM Sampler:  22% 11/50 [00:03<00:07,  5.01it/s]\u001b[A\n",
            "DDIM Sampler:  24% 12/50 [00:03<00:07,  5.09it/s]\u001b[A\n",
            "DDIM Sampler:  26% 13/50 [00:03<00:07,  5.16it/s]\u001b[A\n",
            "DDIM Sampler:  28% 14/50 [00:03<00:06,  5.21it/s]\u001b[A\n",
            "DDIM Sampler:  30% 15/50 [00:04<00:06,  5.24it/s]\u001b[A\n",
            "DDIM Sampler:  32% 16/50 [00:04<00:06,  5.26it/s]\u001b[A\n",
            "DDIM Sampler:  34% 17/50 [00:04<00:06,  5.27it/s]\u001b[A\n",
            "DDIM Sampler:  36% 18/50 [00:04<00:06,  5.28it/s]\u001b[A\n",
            "DDIM Sampler:  38% 19/50 [00:04<00:05,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  40% 20/50 [00:05<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  42% 21/50 [00:05<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  44% 22/50 [00:05<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  46% 23/50 [00:05<00:05,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  48% 24/50 [00:05<00:04,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  50% 25/50 [00:06<00:04,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  52% 26/50 [00:06<00:04,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  54% 27/50 [00:06<00:04,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  56% 28/50 [00:06<00:04,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  58% 29/50 [00:06<00:03,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  60% 30/50 [00:06<00:03,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  62% 31/50 [00:07<00:03,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  64% 32/50 [00:07<00:03,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  66% 33/50 [00:07<00:03,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  68% 34/50 [00:07<00:03,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  70% 35/50 [00:07<00:02,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  72% 36/50 [00:08<00:02,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  74% 37/50 [00:08<00:02,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  76% 38/50 [00:08<00:02,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  78% 39/50 [00:08<00:02,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  80% 40/50 [00:08<00:01,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  82% 41/50 [00:09<00:01,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  84% 42/50 [00:09<00:01,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  86% 43/50 [00:09<00:01,  5.32it/s]\u001b[A\n",
            "DDIM Sampler:  88% 44/50 [00:09<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  90% 45/50 [00:09<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  92% 46/50 [00:09<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  94% 47/50 [00:10<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  96% 48/50 [00:10<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  98% 49/50 [00:10<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler: 100% 50/50 [00:10<00:00,  4.66it/s]\n",
            "Epoch 0:  33% 500/1515 [14:31<29:30,  1.74s/it, loss=0.311, v_num=0, train/loss_simple_step=0.0068, train/loss_vlb_step=3.66e-5, train/loss_step=0.0068, global_step=499.0]/usr/local/lib/python3.8/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:378: UserWarning: `ModelCheckpoint(monitor='val/loss_simple_ema')` could not find the monitored key in the returned metrics: ['train/loss_simple', 'train/loss_simple_step', 'train/loss_vlb', 'train/loss_vlb_step', 'train/loss', 'train/loss_step', 'global_step', 'epoch', 'step']. HINT: Did you call `log('val/loss_simple_ema', value)` in the `LightningModule`?\n",
            "  warning_cache.warn(m)\n",
            "Epoch 0, global step 500: 'val/loss_simple_ema' was not in top 1\n",
            "Pruning Checkpoint\n",
            "Checkpoint Keys: dict_keys(['epoch', 'global_step', 'pytorch-lightning_version', 'state_dict', 'loops', 'callbacks', 'optimizer_states', 'lr_schedulers'])\n",
            "Removing optimizer states from checkpoint\n",
            "This is global step 500.\n",
            "tcmalloc: large alloc 1286381568 bytes == 0x7f82060a4000 @  0x7f841b85d615 0x5d6f4c 0x51edd1 0x51ef5b 0x5aac95 0x5d8506 0x7f83f7aa05fe 0x7f83d10cfc85 0x7f83d10ca1e7 0x7f83d10d1309 0x7f83f7ab30bb 0x7f83f76b36af 0x5d80be 0x5d8d8c 0x4fedd4 0x4997c7 0x55d078 0x5d8941 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x5d8868 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x4fd8b5 0x49abe4 0x4fd8b5 0x49abe4\n",
            "tcmalloc: large alloc 1607983104 bytes == 0x7f774e5a4000 @  0x7f841b85d615 0x5d6f4c 0x51edd1 0x51ef5b 0x5aac95 0x5d8506 0x7f83f7aa05fe 0x7f83d10cfc85 0x7f83d10ca1e7 0x7f83d10d1309 0x7f83f7ab30bb 0x7f83f76b36af 0x5d80be 0x5d8d8c 0x4fedd4 0x4997c7 0x55d078 0x5d8941 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x5d8868 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x4fd8b5 0x49abe4 0x4fd8b5 0x49abe4\n",
            "tcmalloc: large alloc 2009980928 bytes == 0x7f82060a4000 @  0x7f841b85d615 0x5d6f4c 0x51edd1 0x51ef5b 0x5aac95 0x5d8506 0x7f83f7aa05fe 0x7f83d10cfc85 0x7f83d10ca1e7 0x7f83d10d1309 0x7f83f7ab30bb 0x7f83f76b36af 0x5d80be 0x5d8d8c 0x4fedd4 0x4997c7 0x55d078 0x5d8941 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x5d8868 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x4fd8b5 0x49abe4 0x4fd8b5 0x49abe4\n",
            "tcmalloc: large alloc 2512478208 bytes == 0x7f76b898e000 @  0x7f841b85d615 0x5d6f4c 0x51edd1 0x51ef5b 0x5aac95 0x5d8506 0x7f83f7aa05fe 0x7f83d10cfc85 0x7f83d10ca1e7 0x7f83d10d1309 0x7f83f7ab30bb 0x7f83f76b36af 0x5d80be 0x5d8d8c 0x4fedd4 0x4997c7 0x55d078 0x5d8941 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x5d8868 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x4fd8b5 0x49abe4 0x4fd8b5 0x49abe4\n",
            "Epoch 0:  66% 999/1515 [28:51<14:54,  1.73s/it, loss=0.221, v_num=0, train/loss_simple_step=0.0225, train/loss_vlb_step=8.77e-5, train/loss_step=0.0225, global_step=998.0]pop from empty list\n",
            "Data shape for DDIM sampling is (1, 4, 64, 64), eta 1.0\n",
            "Running DDIM Sampling with 50 timesteps\n",
            "\n",
            "DDIM Sampler:   0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
            "DDIM Sampler:   2% 1/50 [00:00<00:08,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:   4% 2/50 [00:00<00:07,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:   6% 3/50 [00:00<00:07,  5.99it/s]\u001b[A\n",
            "DDIM Sampler:   8% 4/50 [00:00<00:07,  5.97it/s]\u001b[A\n",
            "DDIM Sampler:  10% 5/50 [00:00<00:07,  5.88it/s]\u001b[A\n",
            "DDIM Sampler:  12% 6/50 [00:01<00:07,  5.84it/s]\u001b[A\n",
            "DDIM Sampler:  14% 7/50 [00:01<00:07,  5.81it/s]\u001b[A\n",
            "DDIM Sampler:  16% 8/50 [00:01<00:07,  5.79it/s]\u001b[A\n",
            "DDIM Sampler:  18% 9/50 [00:01<00:07,  5.79it/s]\u001b[A\n",
            "DDIM Sampler:  20% 10/50 [00:01<00:06,  5.77it/s]\u001b[A\n",
            "DDIM Sampler:  22% 11/50 [00:01<00:06,  5.78it/s]\u001b[A\n",
            "DDIM Sampler:  24% 12/50 [00:02<00:06,  5.79it/s]\u001b[A\n",
            "DDIM Sampler:  26% 13/50 [00:02<00:06,  5.81it/s]\u001b[A\n",
            "DDIM Sampler:  28% 14/50 [00:02<00:06,  5.83it/s]\u001b[A\n",
            "DDIM Sampler:  30% 15/50 [00:02<00:06,  5.81it/s]\u001b[A\n",
            "DDIM Sampler:  32% 16/50 [00:02<00:05,  5.79it/s]\u001b[A\n",
            "DDIM Sampler:  34% 17/50 [00:02<00:05,  5.74it/s]\u001b[A\n",
            "DDIM Sampler:  36% 18/50 [00:03<00:05,  5.71it/s]\u001b[A\n",
            "DDIM Sampler:  38% 19/50 [00:03<00:05,  5.72it/s]\u001b[A\n",
            "DDIM Sampler:  40% 20/50 [00:03<00:05,  5.68it/s]\u001b[A\n",
            "DDIM Sampler:  42% 21/50 [00:03<00:05,  5.70it/s]\u001b[A\n",
            "DDIM Sampler:  44% 22/50 [00:03<00:04,  5.70it/s]\u001b[A\n",
            "DDIM Sampler:  46% 23/50 [00:03<00:04,  5.68it/s]\u001b[A\n",
            "DDIM Sampler:  48% 24/50 [00:04<00:04,  5.71it/s]\u001b[A\n",
            "DDIM Sampler:  50% 25/50 [00:04<00:04,  5.75it/s]\u001b[A\n",
            "DDIM Sampler:  52% 26/50 [00:04<00:04,  5.80it/s]\u001b[A\n",
            "DDIM Sampler:  54% 27/50 [00:04<00:03,  5.87it/s]\u001b[A\n",
            "DDIM Sampler:  56% 28/50 [00:04<00:03,  5.93it/s]\u001b[A\n",
            "DDIM Sampler:  58% 29/50 [00:04<00:03,  5.94it/s]\u001b[A\n",
            "DDIM Sampler:  60% 30/50 [00:05<00:03,  5.97it/s]\u001b[A\n",
            "DDIM Sampler:  62% 31/50 [00:05<00:03,  5.98it/s]\u001b[A\n",
            "DDIM Sampler:  64% 32/50 [00:05<00:03,  5.99it/s]\u001b[A\n",
            "DDIM Sampler:  66% 33/50 [00:05<00:02,  6.02it/s]\u001b[A\n",
            "DDIM Sampler:  68% 34/50 [00:05<00:02,  6.02it/s]\u001b[A\n",
            "DDIM Sampler:  70% 35/50 [00:05<00:02,  6.00it/s]\u001b[A\n",
            "DDIM Sampler:  72% 36/50 [00:06<00:02,  6.00it/s]\u001b[A\n",
            "DDIM Sampler:  74% 37/50 [00:06<00:02,  6.00it/s]\u001b[A\n",
            "DDIM Sampler:  76% 38/50 [00:06<00:02,  6.00it/s]\u001b[A\n",
            "DDIM Sampler:  78% 39/50 [00:06<00:01,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  80% 40/50 [00:06<00:01,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  82% 41/50 [00:06<00:01,  5.87it/s]\u001b[A\n",
            "DDIM Sampler:  84% 42/50 [00:07<00:01,  5.90it/s]\u001b[A\n",
            "DDIM Sampler:  86% 43/50 [00:07<00:01,  5.93it/s]\u001b[A\n",
            "DDIM Sampler:  88% 44/50 [00:07<00:01,  5.94it/s]\u001b[A\n",
            "DDIM Sampler:  90% 45/50 [00:07<00:00,  5.94it/s]\u001b[A\n",
            "DDIM Sampler:  92% 46/50 [00:07<00:00,  5.96it/s]\u001b[A\n",
            "DDIM Sampler:  94% 47/50 [00:08<00:00,  5.95it/s]\u001b[A\n",
            "DDIM Sampler:  96% 48/50 [00:08<00:00,  5.92it/s]\u001b[A\n",
            "DDIM Sampler:  98% 49/50 [00:08<00:00,  5.90it/s]\u001b[A\n",
            "DDIM Sampler: 100% 50/50 [00:08<00:00,  5.87it/s]\n",
            "Data shape for DDIM sampling is (1, 4, 64, 64), eta 1.0\n",
            "Running DDIM Sampling with 50 timesteps\n",
            "\n",
            "DDIM Sampler:   0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
            "DDIM Sampler:   2% 1/50 [00:00<00:09,  5.28it/s]\u001b[A\n",
            "DDIM Sampler:   4% 2/50 [00:00<00:09,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:   6% 3/50 [00:00<00:08,  5.24it/s]\u001b[A\n",
            "DDIM Sampler:   8% 4/50 [00:00<00:08,  5.27it/s]\u001b[A\n",
            "DDIM Sampler:  10% 5/50 [00:00<00:08,  5.28it/s]\u001b[A\n",
            "DDIM Sampler:  12% 6/50 [00:01<00:08,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  14% 7/50 [00:01<00:08,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  16% 8/50 [00:01<00:07,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  18% 9/50 [00:01<00:07,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  20% 10/50 [00:01<00:07,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  22% 11/50 [00:02<00:07,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  24% 12/50 [00:02<00:07,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  26% 13/50 [00:02<00:07,  5.26it/s]\u001b[A\n",
            "DDIM Sampler:  28% 14/50 [00:02<00:06,  5.28it/s]\u001b[A\n",
            "DDIM Sampler:  30% 15/50 [00:02<00:06,  5.28it/s]\u001b[A\n",
            "DDIM Sampler:  32% 16/50 [00:03<00:06,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  34% 17/50 [00:03<00:06,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  36% 18/50 [00:03<00:06,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  38% 19/50 [00:03<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  40% 20/50 [00:03<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  42% 21/50 [00:03<00:05,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  44% 22/50 [00:04<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  46% 23/50 [00:04<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  48% 24/50 [00:04<00:05,  5.19it/s]\u001b[A\n",
            "DDIM Sampler:  50% 25/50 [00:04<00:04,  5.23it/s]\u001b[A\n",
            "DDIM Sampler:  52% 26/50 [00:04<00:04,  5.25it/s]\u001b[A\n",
            "DDIM Sampler:  54% 27/50 [00:05<00:04,  5.27it/s]\u001b[A\n",
            "DDIM Sampler:  56% 28/50 [00:05<00:04,  5.28it/s]\u001b[A\n",
            "DDIM Sampler:  58% 29/50 [00:05<00:03,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  60% 30/50 [00:05<00:03,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  62% 31/50 [00:05<00:03,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  64% 32/50 [00:06<00:03,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  66% 33/50 [00:06<00:03,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  68% 34/50 [00:06<00:03,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  70% 35/50 [00:06<00:02,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  72% 36/50 [00:06<00:02,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  74% 37/50 [00:06<00:02,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  76% 38/50 [00:07<00:02,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  78% 39/50 [00:07<00:02,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  80% 40/50 [00:07<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  82% 41/50 [00:07<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  84% 42/50 [00:07<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  86% 43/50 [00:08<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  88% 44/50 [00:08<00:01,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  90% 45/50 [00:08<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  92% 46/50 [00:08<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  94% 47/50 [00:08<00:00,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  96% 48/50 [00:09<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  98% 49/50 [00:09<00:00,  5.30it/s]\u001b[A\n",
            "DDIM Sampler: 100% 50/50 [00:09<00:00,  5.29it/s]\n",
            "Epoch 0:  66% 1000/1515 [29:12<15:02,  1.75s/it, loss=0.198, v_num=0, train/loss_simple_step=0.0304, train/loss_vlb_step=0.000112, train/loss_step=0.0304, global_step=999.0]Epoch 0, global step 1000: 'val/loss_simple_ema' was not in top 1\n",
            "Pruning Checkpoint\n",
            "Checkpoint Keys: dict_keys(['epoch', 'global_step', 'pytorch-lightning_version', 'state_dict', 'loops', 'callbacks', 'optimizer_states', 'lr_schedulers'])\n",
            "Removing optimizer states from checkpoint\n",
            "This is global step 1000.\n",
            "tcmalloc: large alloc 2009980928 bytes == 0x7f82060a4000 @  0x7f841b85d615 0x5d6f4c 0x51edd1 0x51ef5b 0x5aac95 0x5d8506 0x7f83f7aa05fe 0x7f83d10cfc85 0x7f83d10ca1e7 0x7f83d10d1309 0x7f83f7ab30bb 0x7f83f76b36af 0x5d80be 0x5d8d8c 0x4fedd4 0x4997c7 0x55d078 0x5d8941 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x5d8868 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x4fd8b5 0x49abe4 0x4fd8b5 0x49abe4\n",
            "tcmalloc: large alloc 2512478208 bytes == 0x7f76b898e000 @  0x7f841b85d615 0x5d6f4c 0x51edd1 0x51ef5b 0x5aac95 0x5d8506 0x7f83f7aa05fe 0x7f83d10cfc85 0x7f83d10ca1e7 0x7f83d10d1309 0x7f83f7ab30bb 0x7f83f76b36af 0x5d80be 0x5d8d8c 0x4fedd4 0x4997c7 0x55d078 0x5d8941 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x5d8868 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x4fd8b5 0x49abe4 0x4fd8b5 0x49abe4\n",
            "Epoch 0:  99% 1499/1515 [43:49<00:28,  1.75s/it, loss=0.287, v_num=0, train/loss_simple_step=0.258, train/loss_vlb_step=0.00123, train/loss_step=0.258, global_step=1498.0]   pop from empty list\n",
            "Data shape for DDIM sampling is (1, 4, 64, 64), eta 1.0\n",
            "Running DDIM Sampling with 50 timesteps\n",
            "\n",
            "DDIM Sampler:   0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
            "DDIM Sampler:   2% 1/50 [00:00<00:08,  5.79it/s]\u001b[A\n",
            "DDIM Sampler:   4% 2/50 [00:00<00:08,  5.76it/s]\u001b[A\n",
            "DDIM Sampler:   6% 3/50 [00:00<00:08,  5.79it/s]\u001b[A\n",
            "DDIM Sampler:   8% 4/50 [00:00<00:07,  5.77it/s]\u001b[A\n",
            "DDIM Sampler:  10% 5/50 [00:00<00:07,  5.79it/s]\u001b[A\n",
            "DDIM Sampler:  12% 6/50 [00:01<00:07,  5.80it/s]\u001b[A\n",
            "DDIM Sampler:  14% 7/50 [00:01<00:07,  5.79it/s]\u001b[A\n",
            "DDIM Sampler:  16% 8/50 [00:01<00:07,  5.80it/s]\u001b[A\n",
            "DDIM Sampler:  18% 9/50 [00:01<00:07,  5.78it/s]\u001b[A\n",
            "DDIM Sampler:  20% 10/50 [00:01<00:06,  5.83it/s]\u001b[A\n",
            "DDIM Sampler:  22% 11/50 [00:01<00:06,  5.82it/s]\u001b[A\n",
            "DDIM Sampler:  24% 12/50 [00:02<00:06,  5.86it/s]\u001b[A\n",
            "DDIM Sampler:  26% 13/50 [00:02<00:06,  5.90it/s]\u001b[A\n",
            "DDIM Sampler:  28% 14/50 [00:02<00:06,  5.93it/s]\u001b[A\n",
            "DDIM Sampler:  30% 15/50 [00:02<00:05,  5.97it/s]\u001b[A\n",
            "DDIM Sampler:  32% 16/50 [00:02<00:05,  5.98it/s]\u001b[A\n",
            "DDIM Sampler:  34% 17/50 [00:02<00:05,  6.00it/s]\u001b[A\n",
            "DDIM Sampler:  36% 18/50 [00:03<00:05,  5.99it/s]\u001b[A\n",
            "DDIM Sampler:  38% 19/50 [00:03<00:05,  5.95it/s]\u001b[A\n",
            "DDIM Sampler:  40% 20/50 [00:03<00:05,  5.91it/s]\u001b[A\n",
            "DDIM Sampler:  42% 21/50 [00:03<00:04,  5.89it/s]\u001b[A\n",
            "DDIM Sampler:  44% 22/50 [00:03<00:04,  5.88it/s]\u001b[A\n",
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            "DDIM Sampler:  48% 24/50 [00:04<00:04,  5.91it/s]\u001b[A\n",
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            "DDIM Sampler:  54% 27/50 [00:04<00:03,  5.95it/s]\u001b[A\n",
            "DDIM Sampler:  56% 28/50 [00:04<00:03,  5.95it/s]\u001b[A\n",
            "DDIM Sampler:  58% 29/50 [00:04<00:03,  5.94it/s]\u001b[A\n",
            "DDIM Sampler:  60% 30/50 [00:05<00:03,  5.92it/s]\u001b[A\n",
            "DDIM Sampler:  62% 31/50 [00:05<00:03,  5.95it/s]\u001b[A\n",
            "DDIM Sampler:  64% 32/50 [00:05<00:03,  5.95it/s]\u001b[A\n",
            "DDIM Sampler:  66% 33/50 [00:05<00:02,  5.94it/s]\u001b[A\n",
            "DDIM Sampler:  68% 34/50 [00:05<00:02,  5.97it/s]\u001b[A\n",
            "DDIM Sampler:  70% 35/50 [00:05<00:02,  5.98it/s]\u001b[A\n",
            "DDIM Sampler:  72% 36/50 [00:06<00:02,  6.00it/s]\u001b[A\n",
            "DDIM Sampler:  74% 37/50 [00:06<00:02,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  76% 38/50 [00:06<00:02,  5.98it/s]\u001b[A\n",
            "DDIM Sampler:  78% 39/50 [00:06<00:01,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  80% 40/50 [00:06<00:01,  6.00it/s]\u001b[A\n",
            "DDIM Sampler:  82% 41/50 [00:06<00:01,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  84% 42/50 [00:07<00:01,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  86% 43/50 [00:07<00:01,  6.02it/s]\u001b[A\n",
            "DDIM Sampler:  88% 44/50 [00:07<00:00,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  90% 45/50 [00:07<00:00,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  92% 46/50 [00:07<00:00,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  94% 47/50 [00:07<00:00,  6.01it/s]\u001b[A\n",
            "DDIM Sampler:  96% 48/50 [00:08<00:00,  5.99it/s]\u001b[A\n",
            "DDIM Sampler:  98% 49/50 [00:08<00:00,  5.98it/s]\u001b[A\n",
            "DDIM Sampler: 100% 50/50 [00:08<00:00,  5.93it/s]\n",
            "Data shape for DDIM sampling is (1, 4, 64, 64), eta 1.0\n",
            "Running DDIM Sampling with 50 timesteps\n",
            "\n",
            "DDIM Sampler:   0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
            "DDIM Sampler:   2% 1/50 [00:00<00:09,  5.32it/s]\u001b[A\n",
            "DDIM Sampler:   4% 2/50 [00:00<00:09,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:   6% 3/50 [00:00<00:08,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:   8% 4/50 [00:00<00:08,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  10% 5/50 [00:00<00:08,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  12% 6/50 [00:01<00:08,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  14% 7/50 [00:01<00:08,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  16% 8/50 [00:01<00:07,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  18% 9/50 [00:01<00:07,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  20% 10/50 [00:01<00:07,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  22% 11/50 [00:02<00:07,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  24% 12/50 [00:02<00:07,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  26% 13/50 [00:02<00:06,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  28% 14/50 [00:02<00:06,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  30% 15/50 [00:02<00:06,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  32% 16/50 [00:03<00:06,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  34% 17/50 [00:03<00:06,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  36% 18/50 [00:03<00:06,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  38% 19/50 [00:03<00:05,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  40% 20/50 [00:03<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  42% 21/50 [00:03<00:05,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  44% 22/50 [00:04<00:05,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  46% 23/50 [00:04<00:05,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  48% 24/50 [00:04<00:04,  5.31it/s]\u001b[A\n",
            "DDIM Sampler:  50% 25/50 [00:04<00:04,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  52% 26/50 [00:04<00:04,  5.27it/s]\u001b[A\n",
            "DDIM Sampler:  54% 27/50 [00:05<00:04,  5.28it/s]\u001b[A\n",
            "DDIM Sampler:  56% 28/50 [00:05<00:04,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  58% 29/50 [00:05<00:03,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  60% 30/50 [00:05<00:03,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  62% 31/50 [00:05<00:03,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  64% 32/50 [00:06<00:03,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  66% 33/50 [00:06<00:03,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  68% 34/50 [00:06<00:03,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  70% 35/50 [00:06<00:02,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  72% 36/50 [00:06<00:02,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  74% 37/50 [00:06<00:02,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  76% 38/50 [00:07<00:02,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  78% 39/50 [00:07<00:02,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  80% 40/50 [00:07<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  82% 41/50 [00:07<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  84% 42/50 [00:07<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  86% 43/50 [00:08<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  88% 44/50 [00:08<00:01,  5.30it/s]\u001b[A\n",
            "DDIM Sampler:  90% 45/50 [00:08<00:00,  5.26it/s]\u001b[A\n",
            "DDIM Sampler:  92% 46/50 [00:08<00:00,  5.27it/s]\u001b[A\n",
            "DDIM Sampler:  94% 47/50 [00:08<00:00,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  96% 48/50 [00:09<00:00,  5.29it/s]\u001b[A\n",
            "DDIM Sampler:  98% 49/50 [00:09<00:00,  5.29it/s]\u001b[A\n",
            "DDIM Sampler: 100% 50/50 [00:09<00:00,  5.30it/s]\n",
            "Epoch 0:  99% 1500/1515 [44:09<00:26,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]Epoch 0, global step 1500: 'val/loss_simple_ema' was not in top 1\n",
            "Pruning Checkpoint\n",
            "Checkpoint Keys: dict_keys(['epoch', 'global_step', 'pytorch-lightning_version', 'state_dict', 'loops', 'callbacks', 'optimizer_states', 'lr_schedulers'])\n",
            "Removing optimizer states from checkpoint\n",
            "This is global step 1500.\n",
            "tcmalloc: large alloc 2512478208 bytes == 0x7f76b898e000 @  0x7f841b85d615 0x5d6f4c 0x51edd1 0x51ef5b 0x5aac95 0x5d8506 0x7f83f7aa05fe 0x7f83d10cfc85 0x7f83d10ca1e7 0x7f83d10d1309 0x7f83f7ab30bb 0x7f83f76b36af 0x5d80be 0x5d8d8c 0x4fedd4 0x4997c7 0x55d078 0x5d8941 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x5d8868 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x4fd8b5 0x49abe4 0x4fd8b5 0x49abe4\n",
            "\n",
            "Validation: 0it [00:00, ?it/s]\u001b[A\n",
            "Validation:   0% 0/15 [00:00<?, ?it/s]\u001b[A\n",
            "Validation DataLoader 0:   0% 0/15 [00:00<?, ?it/s]\u001b[A\n",
            "Validation DataLoader 0:   7% 1/15 [00:00<00:06,  2.05it/s]\u001b[A\n",
            "Epoch 0:  99% 1501/1515 [44:22<00:24,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  13% 2/15 [00:00<00:06,  2.10it/s]\u001b[A\n",
            "Epoch 0:  99% 1502/1515 [44:22<00:23,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  20% 3/15 [00:01<00:05,  2.16it/s]\u001b[A\n",
            "Epoch 0:  99% 1503/1515 [44:23<00:21,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  27% 4/15 [00:01<00:04,  2.20it/s]\u001b[A\n",
            "Epoch 0:  99% 1504/1515 [44:23<00:19,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  33% 5/15 [00:02<00:04,  2.21it/s]\u001b[A\n",
            "Epoch 0:  99% 1505/1515 [44:24<00:17,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  40% 6/15 [00:02<00:04,  2.21it/s]\u001b[A\n",
            "Epoch 0:  99% 1506/1515 [44:24<00:15,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  47% 7/15 [00:03<00:03,  2.23it/s]\u001b[A\n",
            "Epoch 0:  99% 1507/1515 [44:25<00:14,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  53% 8/15 [00:03<00:03,  2.23it/s]\u001b[A\n",
            "Epoch 0: 100% 1508/1515 [44:25<00:12,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  60% 9/15 [00:04<00:02,  2.24it/s]\u001b[A\n",
            "Epoch 0: 100% 1509/1515 [44:26<00:10,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  67% 10/15 [00:04<00:02,  2.24it/s]\u001b[A\n",
            "Epoch 0: 100% 1510/1515 [44:26<00:08,  1.77s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  73% 11/15 [00:04<00:01,  2.25it/s]\u001b[A\n",
            "Epoch 0: 100% 1511/1515 [44:26<00:07,  1.76s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  80% 12/15 [00:05<00:01,  2.25it/s]\u001b[A\n",
            "Epoch 0: 100% 1512/1515 [44:27<00:05,  1.76s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  87% 13/15 [00:05<00:00,  2.24it/s]\u001b[A\n",
            "Epoch 0: 100% 1513/1515 [44:27<00:03,  1.76s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0:  93% 14/15 [00:06<00:00,  2.24it/s]\u001b[A\n",
            "Epoch 0: 100% 1514/1515 [44:28<00:01,  1.76s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "Validation DataLoader 0: 100% 15/15 [00:06<00:00,  2.25it/s]\u001b[A\n",
            "Epoch 0: 100% 1515/1515 [44:28<00:00,  1.76s/it, loss=0.284, v_num=0, train/loss_simple_step=0.00179, train/loss_vlb_step=1.09e-5, train/loss_step=0.00179, global_step=1499.0]\n",
            "                                                            \u001b[A/usr/local/lib/python3.8/dist-packages/pytorch_lightning/trainer/trainer.py:2028: LightningDeprecationWarning: `Trainer.training_type_plugin` is deprecated in v1.6 and will be removed in v1.8. Use `Trainer.strategy` instead.\n",
            "  rank_zero_deprecation(\n",
            "Average Epoch time: 2668.71 seconds\n",
            "Average Peak memory 28701.80MiB\n",
            "Epoch 1:  33% 499/1515 [14:29<29:29,  1.74s/it, loss=0.35, v_num=0, train/loss_simple_step=0.0168, train/loss_vlb_step=7.03e-5, train/loss_step=0.0168, global_step=2e+3, train/loss_simple_epoch=0.130, train/loss_vlb_epoch=0.00324, train/loss_epoch=0.130] pop from empty list\n",
            "Data shape for DDIM sampling is (1, 4, 64, 64), eta 1.0\n",
            "Running DDIM Sampling with 50 timesteps\n",
            "\n",
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            "DDIM Sampler: 100% 50/50 [00:08<00:00,  5.92it/s]\n",
            "Data shape for DDIM sampling is (1, 4, 64, 64), eta 1.0\n",
            "Running DDIM Sampling with 50 timesteps\n",
            "\n",
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            "DDIM Sampler: 100% 50/50 [00:09<00:00,  5.30it/s]\n",
            "Epoch 1:  33% 500/1515 [14:49<30:05,  1.78s/it, loss=0.328, v_num=0, train/loss_simple_step=0.252, train/loss_vlb_step=0.00356, train/loss_step=0.252, global_step=2e+3, train/loss_simple_epoch=0.130, train/loss_vlb_epoch=0.00324, train/loss_epoch=0.130] Epoch 1, global step 2000: 'val/loss_simple_ema' reached 0.10404 (best 0.10404), saving model to '/content/Dreambooth-Stable-Diffusion/logs/training_images2023-01-04T09-17-44_NisaarAgharia_Custom_model/checkpoints/epoch=000001.ckpt' as top 1\n",
            "Pruning Checkpoint\n",
            "Checkpoint Keys: dict_keys(['epoch', 'global_step', 'pytorch-lightning_version', 'state_dict', 'loops', 'callbacks', 'optimizer_states', 'lr_schedulers'])\n",
            "Removing optimizer states from checkpoint\n",
            "This is global step 2000.\n",
            "tcmalloc: large alloc 2512478208 bytes == 0x7f76b898e000 @  0x7f841b85d615 0x5d6f4c 0x51edd1 0x51ef5b 0x5aac95 0x5d8506 0x7f83f7aa05fe 0x7f83d10cfc85 0x7f83d10ca1e7 0x7f83d10d1309 0x7f83f7ab30bb 0x7f83f76b36af 0x5d80be 0x5d8d8c 0x4fedd4 0x4997c7 0x55d078 0x5d8941 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x5d8868 0x4990ca 0x55cd91 0x5d8941 0x4997c7 0x4fd8b5 0x49abe4 0x4fd8b5 0x49abe4\n",
            "Pruning Checkpoint\n",
            "Checkpoint Keys: dict_keys(['epoch', 'global_step', 'pytorch-lightning_version', 'state_dict', 'loops', 'callbacks', 'optimizer_states', 'lr_schedulers'])\n",
            "Removing optimizer states from checkpoint\n",
            "This is global step 2000.\n",
            "Average Epoch time: 912.72 seconds\n",
            "Average Peak memory 20227.64MiB\n",
            "Epoch 1:  33% 500/1515 [15:12<30:52,  1.83s/it, loss=0.328, v_num=0, train/loss_simple_step=0.252, train/loss_vlb_step=0.00356, train/loss_step=0.252, global_step=2e+3, train/loss_simple_epoch=0.111, train/loss_vlb_epoch=0.00107, train/loss_epoch=0.111]\n",
            "Training complete. max_training_steps reached or we blew up.\n"
          ]
        }
      ],
      "source": [
        "#@title # Training\n",
        "\n",
        "#@markdown This isn't used for training, just to help you remember what your trained into the model.\n",
        "project_name = \"NisaarAgharia_Custom_model\" #@param {type:\"string\"}\n",
        "\n",
        "# MAX STEPS\n",
        "#@markdown How many steps do you want to train for?\n",
        "max_training_steps = 2000 #@param {type:\"integer\"}\n",
        "\n",
        "#@markdown Match class_word to the category of the regularization images you chose above.\n",
        "class_word = \"person\" #@param [\"man\", \"person\", \"woman\"] {allow-input: true}\n",
        "\n",
        "#@markdown This is the unique token you are incorporating into the stable diffusion model.\n",
        "token = \"nisaaragharia\" #@param {type:\"string\"}\n",
        "reg_data_root = \"/content/Dreambooth-Stable-Diffusion/regularization_images/\" + dataset\n",
        "\n",
        "!rm -rf training_images/.ipynb_checkpoints\n",
        "!python \"main.py\" \\\n",
        " --base configs/stable-diffusion/v1-finetune_unfrozen.yaml \\\n",
        " -t \\\n",
        " --actual_resume \"model.ckpt\" \\\n",
        " --reg_data_root \"{reg_data_root}\" \\\n",
        " -n \"{project_name}\" \\\n",
        " --gpus 0, \\\n",
        " --data_root \"/content/Dreambooth-Stable-Diffusion/training_images\" \\\n",
        " --max_training_steps {max_training_steps} \\\n",
        " --class_word \"{class_word}\" \\\n",
        " --token \"{token}\" \\\n",
        " --no-test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ll_ZIFNUulKJ",
        "outputId": "1d952588-8118-4d27-caa2-54bb7cd6792a",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Download your trained model file from trained_models/2023-01-04T10-18-42_NisaarAgharia_Custom_model_15_training_images_2000_max_training_steps_nisaaragharia_token_person_class_word.ckpt and use in your favorite Stable Diffusion repo!\n"
          ]
        }
      ],
      "source": [
        "#@title # Copy and name the checkpoint file\n",
        "\n",
        "directory_paths = !ls -d logs/*\n",
        "last_checkpoint_file = directory_paths[-1] + \"/checkpoints/last.ckpt\"\n",
        "training_images = !find training_images/*\n",
        "date_string = !date +\"%Y-%m-%dT%H-%M-%S\"\n",
        "file_name = date_string[-1] + \"_\" + project_name + \"_\" + str(len(training_images)) + \"_training_images_\" +  str(max_training_steps) + \"_max_training_steps_\" + token + \"_token_\" + class_word + \"_class_word.ckpt\"\n",
        "\n",
        "file_name = file_name.replace(\" \", \"_\")\n",
        "\n",
        "!mkdir -p trained_models\n",
        "!mv \"{last_checkpoint_file}\" \"trained_models/{file_name}\"\n",
        "\n",
        "print(\"Download your trained model file from trained_models/\" + file_name + \" and use in your favorite Stable Diffusion repo!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mkidEm4evn1J"
      },
      "outputs": [],
      "source": [
        "#@title Save model in google drive\n",
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')\n",
        "\n",
        "! cp trained_models/{file_name} /content/drive/MyDrive/JOE_TRAINING_DATASET/{file_name}"
      ]
    }
  ],
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