Stable-Diffusion-Vertex/Workbench/diffusers_nbexecutor.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "644ad25f",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Copyright 2022 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "7abfd671",
      "metadata": {},
      "source": [
        "## Write training code here and Click \"Execute\" for a workbench execute job\n",
        "- Use custom container built in Cloud Build and stored in Artifact Registry\n",
        "- Cloud Build command: gcloud builds submit --config cloud-build.yaml .\n",
        "- input and output directory can be /gcs/bucket_name/folder for Cloud Storage path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2243ab9f-d7db-4db5-836a-154d9616a628",
      "metadata": {
        "id": "2243ab9f-d7db-4db5-836a-154d9616a628"
      },
      "outputs": [],
      "source": [
        "MODEL_NAME=\"runwayml/stable-diffusion-v1-5\"\n",
        "INSTANCE_DIR=\"/gcs/bucket_name/input_dog\"\n",
        "OUTPUT_DIR=\"/gcs/bucket_name/dog_lora_output\"\n",
        "\n",
        "! accelerate launch ./diffusers/examples/dreambooth/train_dreambooth_lora.py \\\n",
        "    --pretrained_model_name_or_path=$MODEL_NAME  \\\n",
        "    --instance_data_dir=$INSTANCE_DIR \\\n",
        "    --output_dir=$OUTPUT_DIR \\\n",
        "    --instance_prompt=\"a photo of sks dog\" \\\n",
        "    --resolution=512 \\\n",
        "    --train_batch_size=1 \\\n",
        "    --use_8bit_adam \\\n",
        "    --mixed_precision=\"fp16\" \\\n",
        "    --gradient_accumulation_steps=1 \\\n",
        "    --learning_rate=1e-4 \\\n",
        "    --lr_scheduler=\"constant\" \\\n",
        "    --lr_warmup_steps=0 \\\n",
        "    --max_train_steps=500"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "O25rkc78ggqL",
      "metadata": {
        "id": "O25rkc78ggqL"
      },
      "source": [
        "Convert the lora .bin file to safetensor file, for used in WebUI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d52e7698-122a-4864-ad8c-55d4562c2a94",
      "metadata": {
        "id": "d52e7698-122a-4864-ad8c-55d4562c2a94"
      },
      "outputs": [],
      "source": [
        "import os;\n",
        "import re;\n",
        "import torch;\n",
        "from safetensors.torch import save_file;\n",
        "\n",
        "newDict = dict();\n",
        "checkpoint = torch.load(OUTPUT_DIR + '/pytorch_lora_weights.bin');\n",
        "for idx, key in enumerate(checkpoint):\n",
        "    newKey = re.sub('\\.processor\\.', '_', key);\n",
        "    newKey = re.sub('mid_block\\.', 'mid_block_', newKey);\n",
        "    newKey = re.sub('_lora.up.', '.lora_up.', newKey);\n",
        "    newKey = re.sub('_lora.down.', '.lora_down.', newKey);\n",
        "    newKey = re.sub('\\.(\\d+)\\.', '_\\\\1_', newKey);\n",
        "    newKey = re.sub('to_out', 'to_out_0', newKey);\n",
        "    newKey = 'lora_unet_'+newKey;\n",
        "\n",
        "    newDict[newKey] = checkpoint[key];\n",
        "\n",
        "newLoraName = 'pytorch_lora_weights.safetensors';\n",
        "print(\"Saving \" + newLoraName);\n",
        "save_file(newDict, OUTPUT_DIR + '/' + newLoraName);"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "bd0880a8",
      "metadata": {},
      "source": [
        "***It's supported to configure NFS using Executor*"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Pytorch (Local)",
      "language": "python",
      "name": "local-pytorch"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}