diff --git a/diffusers/SDXL_DreamBooth_LoRA_.ipynb b/diffusers/SDXL_DreamBooth_LoRA_.ipynb index 28b73d95..74da209b 100644 --- a/diffusers/SDXL_DreamBooth_LoRA_.ipynb +++ b/diffusers/SDXL_DreamBooth_LoRA_.ipynb @@ -1,12248 +1,12248 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "MFNkvjgn1lPI" - }, - "source": [ - "## Fine-tuning Stable Diffusion XL with DreamBooth and LoRA on a free-tier Colab Notebook 🧨\n", - "\n", - "In this notebook, we show how to fine-tune [Stable Diffusion XL (SDXL)](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl) with [DreamBooth](https://huggingface.co/docs/diffusers/main/en/training/dreambooth) and [LoRA](https://huggingface.co/docs/diffusers/main/en/training/lora) on a T4 GPU.\n", - "\n", - "SDXL consists of a much larger UNet and two text encoders that make the cross-attention context quite larger than the previous variants.\n", - "\n", - "So, to pull this off, we will make use of several tricks such as gradient checkpointing, mixed-precision, and 8-bit Adam. So, hang tight and let's get started πŸ§ͺ" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pdM59WIoS01O" - }, - "source": [ - "## Setup πŸͺ“" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XUxRrLfLMnBb", - "outputId": "e8792843-7ba4-4678-da74-c87d2eab331b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thu Nov 23 06:47:16 2023 \n", - "+-----------------------------------------------------------------------------+\n", - "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\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 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", - "| N/A 49C P8 9W / 70W | 0MiB / 15360MiB | 0% Default |\n", - "| | | N/A |\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": [ - "# Check the GPU\n", - "!nvidia-smi" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "55U57alsRIek", - "outputId": "c263d790-ccbd-497c-8db7-d2ceedf2d11b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.8/211.8 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ], - "source": [ - "# Install dependencies.\n", - "!pip install bitsandbytes transformers accelerate peft -q" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FJXRjGZa1vFZ" - }, - "source": [ - "Make sure to install `diffusers` from `main`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9atNWUXFZlVe", - "outputId": "4b3b1fab-6f8d-4ad9-fe6b-b5cb27526a0e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" - ] - } - ], - "source": [ - "!pip install git+https://github.com/huggingface/diffusers.git -q" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d2TRPaxGx3mE" - }, - "source": [ - "Download diffusers SDXL DreamBooth training script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zQpqmq7rx3mE", - "outputId": "a8066139-6e92-4225-befc-4bb5987cc55e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2023-11-23 06:48:12-- https://raw.githubusercontent.com/huggingface/diffusers/main/examples/dreambooth/train_dreambooth_lora_sdxl.py\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 72845 (71K) [text/plain]\n", - "Saving to: β€˜train_dreambooth_lora_sdxl.py’\n", - "\n", - "train_dreambooth_lo 100%[===================>] 71.14K --.-KB/s in 0.001s \n", - "\n", - "2023-11-23 06:48:12 (46.6 MB/s) - β€˜train_dreambooth_lora_sdxl.py’ saved [72845/72845]\n", - "\n" - ] - } - ], - "source": [ - "!wget https://raw.githubusercontent.com/huggingface/diffusers/main/examples/dreambooth/train_dreambooth_lora_sdxl.py" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CRBcvYAyS8bU" - }, - "source": [ - "## Dataset 🐢" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8UNkbXwj126Q" - }, - "source": [ - "**Let's get our training data!**\n", - "For this example, we'll download some images from the hub\n", - "\n", - "If you already have a dataset on the hub you wish to use, you can skip this part and go straight to: \"Prep for\n", - "training πŸ’»\" section, where you'll simply specify the dataset name.\n", - "\n", - "If your images are saved locally, and/or you want to add BLIP generated captions,\n", - "pick option 1 or 2 below.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EvTS06eEx3mF" - }, - "source": [ - "**Option 1:** upload example images from your local files:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "TqqhZ9R9x3mG" - }, - "outputs": [], - "source": [ - "import os\n", - "from google.colab import files\n", - "\n", - "# pick a name for the image folder\n", - "local_dir = \"./dog/\" #@param\n", - "os.makedirs(local_dir)\n", - "os.chdir(local_dir)\n", - "\n", - "# choose and upload local images into the newly created directory\n", - "uploaded_images = files.upload()\n", - "os.chdir(\"/content\") # back to parent directory" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E3aCuZZGx3mG" - }, - "source": [ - "**Option 2:** download example images from the hub:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 255, - "referenced_widgets": [ - "9a9859c3ac1f44af81db5fb5b821aee6", - "2499d42c707445ff8ae3f51b16026556", - "3c5e6c23bcde435ea0f820c8d4d63c11", - "1cd29ae69fef4f00b74327d4b642282c", - "7fb77e4194444c0c9739952e453bcba6", - "fa4de672fcbb4ceea28e97abe2827f7b", - "9637e8cbccb64d8c9138dcdb5e3a52c1", - "93ea3a472f19449a92b8fb8db896abfe", - "9d20b6328a404d3bb894abe9c1fe458b", - "5bc3ce3d3de6481ba7fd0ec1eff7012e", - "76dc51a005ca4e03ab51e792ca4db288", - "18516eb5cb6f4d8b8094b30ae633577d", - "46c4d70931204b37a9b67312048263df", - "b48748776dee47fdad7226ddf933010e", - "5c894c4e5c02428f807e570baae6d640", - "07e1472de1974c42bc7f721825dd9c60", - "2cd06d29f6914ba1bd09a1a98612d6d8", - "00ddb1b212334fffa98c8717ae18c94e", - "785ebbc397c7453093b810f4b60338ef", - "1d29417430da4fefb424089a5fe65375", - "929d24e77001416da24a05323e11e7de", - "b5515a1a20124e83a30144361d5a9e3e", - "758acd0c4e6141e38df22a78be883212", - "aa3d2d03d4c8430abac1e9b8058c5fce", - "c24a081a1e0a4dda973506d7254613ac", - "391237a27e5d496799955dab5a76a0b9", - "8f6326ed0b5341c581e86d88c8e043a6", - "f7abc400b22f4ebd8f47b057c6b2a283", - "9a90b166ef5341e19616cfd8a7805dc3", - "3f3a470891d34f9ab87e1d4fe1b9932e", - "07830c077c784479afcd59dfcae87687", - "8055adde4617432c81771c058980d2ae", - "37d80818c7ea4e59ab996a57942f0615", - "7f95e47b7bbd43788e9cfed6570dfbe1", - "3da311b52b9f4159a2e4cf775fb50a38", - "4315465a99874d8681a49904ec80d812", - "c24d62b089a940c08c120b81cc31111c", - "430f2344b69a4f2dac54254582366020", - "acbdc11b2de04c25b78f04e663112557", - "e22adbce96cb4baa9e3cf0641fc488be", - "bbcec8f9ee5a4923bc0b0b8d992be783", - "e9a6d822c853426baa1664ffb50884fd", - "3019c364a59940929291a348645ae6b9", - "02289e74efb44aefb9df7b3fa13916ad", - "2562d9fe03d343be8f2016b24de1aadc", - "9fac496da01f42828436f907005cae7f", - "2166b3f4d384473c8334ba7715f5c61c", - "f73be3a5295a4e3f9128d1cb09a80c41", - "b09ed57a45724d469398e2a93645546a", - "4adb199157ca45a1adbc3e37a32e1bef", - "233d2ceee8ac4ba3ba338ff8ee71b7b4", - "34dcef51467644c6a572364df698dc62", - "224e11b9806845e0874174942dc1b5ee", - "b212a297fc64477fb05d29678ef82843", - "6cbe07ba74c54b1e87c0d1381583d381", - "0ebc3d3a317e416b9de9adf26dd1f8f7", - "739e3b518fc844d4a8fd8e35c7777c5a", - "5404fc14dd284181bd85efb189b05014", - "509b855737d04166986ff765ade20377", - "f56bbab3296f4c84841c5aac6b99b4f5", - "aa9e7e721ecb48fc9e0d402c11cc822a", - "acfe30f070534097be815dba40b64901", - "f4c326893bc14424b1f2747d0e7f42f2", - "995e455075fe4716b820fafaa90d28ca", - "104dd3bcc2d7420ba59481f937e9af98", - "2dd5daefb5c24aad8e925d33ddc5cdc6" - ] - }, - "id": "-BC36MOBdIWO", - "outputId": "a38fd182-61eb-454d-fd5d-50cb19df3e28" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9a9859c3ac1f44af81db5fb5b821aee6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Fetching 5 files: 0%| | 0/5 [00:00" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import glob\n", - "\n", - "# change path to display images from your local dir\n", - "img_paths = \"./dog/*.jpeg\"\n", - "imgs = [Image.open(path) for path in glob.glob(img_paths)]\n", - "\n", - "num_imgs_to_preview = 5\n", - "image_grid(imgs[:num_imgs_to_preview], 1, num_imgs_to_preview)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SqrxnDMUTADH" - }, - "source": [ - "### Generate custom captions with BLIP\n", - "Load BLIP to auto caption your images:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 311, - "referenced_widgets": [ - "08cb0277a7724ad0a782529e3a1b929f", - "5cf3ce3702924f99b142841812d7f739", - "5d0ee26223e44c9f9fbbeb6dac6d5b0d", - "333e2d13a4914c2189c514f9a0758b76", - "838b59066d5e4fd189ddd594f0960fd3", - "b7bd817dbc1e436c8a59d82c5148ec10", - "fd8676ffe23842e88ddb7b7632331af2", - "a9209dfd55d44ced8ec9647a86a6bcec", - "77018507646d4890beb02d898c7ce5c8", - "a973e5bf2b844c59b9d0ccf853727dff", - "5758bb6401064701bcae6ea85f89132a", - "e3bbd6c7582e4f3c83c074719a345733", - "01fd3d6b5c8645fe822041b0a93f7d29", - "1ed2293ea7ed477082f1de112ad58208", - "b613861df19e44b5a1e4b95fea6b2070", - "618d58179581431e9d032df138f80cdd", - "6ba74817a3744cf59f15c716e1d99678", - "bd892bb073634abb8ca648b73934c188", - "71fe2eca70ff441280d61a5755e9f005", - "b9272e18b7ce410da792f96c02868188", - "da9e0703f7d24dff9c92a279493109c5", - "fec546336d7b42fcbaa38d60bb21beb9", - "74ad9bfa1960432c8477e540bb829ec6", - "148621033ecd43caa464a1ff5e44b1af", - "08de35b1df7c4dd2808d69cc3cdef71c", - "8bcbd116981549baa30f9701cc105893", - "d0e515928b594201b95a2218e0ec0708", - "fb5ca753a72c482392fff9e3ba92da01", - "994e90625a3d4cab98739644157d233c", - "cf6cdb3c1b33470ab59eccd901acfc01", - "fa1545a16612492eac8892f960c33efc", - "cfb8ea92d049485092da2a0c72f39efe", - "57a76b34c0ca42748f15de3a9ce593c5", - "a70ded4f518b4bf68d04059486390fe4", - "35c8463ada1b47e7b7873ab2d0a2b774", - "a62d17c71a884b8da2deeff4b55e5a3e", - "a2c1e494e9b74625b423c2a3dc4e3407", - "c02910f8ca8646e1899aa8f81840f675", - "a0f6754080b941c8857a09e5e5c64922", - "719ed1674d6e4e1e93253336b742ed10", - "2c62fe02b70c419c89e23646641a1e28", - "11ce8ccd8e7f41a7a601859cea317fe3", - "32edebcea421415dae93350722422b68", - "e3bd6173408d466a95ca869088b2b718", - "0f3883b8127746ff9ebbaa0811b66da1", - "d97d6b22f2414a8cb1cf4ba71fb0692d", - "9f25e29b688d4931a94bafc66625f777", - "a93b8d95d20745ba9bd83d42755c0a53", - "86bdaa0da8c04416856b4c9325e80a7a", - "bffa8079ea0140e3a27ab8c6b161caac", - "fff32b96cb934b0387bc4718111ae889", - "c1377ca9c9f34da391b8b12223916782", - "cbef8c757cb446049e9af82c3ed161b5", - "56e525584a6c4bf19439532ec8e36e3c", - "8c8b64dbb813404baa1c7f25943d592d", - "be7b68cbc5b74df4984a9d18d39c1787", - "898352ac14314f369ff4ce5effb92fac", - "38e169c2b0a44d5ea92d7ecc519171ba", - "75a096ad39684877bd1d99a1dadc5372", - "f0a22afd70f047adafa6486abc39a760", - "020d86ad45d6424386b12c609f22c4d2", - "32cb02212bee4422b2a26d64024b1947", - "822060b5f33d41428e24c03d835318c0", - "b351150bce8e4028b48a851819edf44c", - "b0d9a703f36944a293c977fed9841ee4", - "b2830c87032d4789a33d650244e01acc", - "334cf989046542559b0ce929dcafd41c", - "dfdb89b60df543ac89137c8229e09120", - "c1758562dbd8434f84df9f3fa19bbccb", - "66d3c8e4bc8b49eaa5dc2e54612cd7b0", - "ea573f55de994943bbc1e9640a911b4e", - "9061e671864141d989629d87d2a26759", - "6b1f9c375bde41d9bbc0504b9ecfc87e", - "5c177bc9903c42e3b9480a6552df09f1", - "8b4a483b8e2241afb73717041778ced2", - "767590d6b93647ebabf24848f0afc0c1", - "40c82cc1708245d692577560cd773a83", - "041ff941714b4af09dcd96446f1a7678", - "0d6214691d734dbda4fdc7411d78cc8c", - "5bef70257f634d86af2a3b420044c918", - "61d945b14ed94408a7877f36213b660e", - "06f19e6c4ec044f89478a0aad66f0741", - "94e0d90ed71e49369fd76a319eb4f0ed", - "528d5d9441ce42e7a149ff2ac8c04117", - "6f82accefd43441ba74a77afd478c7dd", - "349478838b644ef781c7d8da6d5c71f5", - "09dda9ec9310476d86f3ecf4aa8ed8f3", - "9d6d7e867d0a4fbcabd13eaf8e86bb2e" - ] - }, - "id": "qZK1QuLqx3mH", - "outputId": "d69e853e-8231-49c6-d010-434fa6f52487" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "08cb0277a7724ad0a782529e3a1b929f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e3bbd6c7582e4f3c83c074719a345733", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "preprocessor_config.json: 0%| | 0.00/287 [00:00 \n", - " sys.exit(main())\n", - " File \"/usr/local/lib/python3.10/dist-packages/accelerate/commands/accelerate_cli.py\", line 47, in main\n", - " args.func(args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py\", line 994, in launch_command\n", - " simple_launcher(args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py\", line 636, in simple_launcher\n", - " raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)\n", - "subprocess.CalledProcessError: Command '['/usr/bin/python3', 'train_dreambooth_lora_sdxl.py', '--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0', '--pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix', '--dataset_name=dog', '--output_dir=corgy_dog_LoRA', '--caption_column=prompt', '--mixed_precision=fp16', '--instance_prompt=a photo of TOK dog', '--resolution=1024', '--train_batch_size=1', '--gradient_accumulation_steps=3', '--gradient_checkpointing', '--learning_rate=1e-4', '--snr_gamma=5.0', '--lr_scheduler=constant', '--lr_warmup_steps=0', '--mixed_precision=fp16', '--use_8bit_adam', '--max_train_steps=500', '--checkpointing_steps=717', '--seed=0', '--push_to_hub']' died with .\n" - ] - } - ], - "source": [ - "#!/usr/bin/env bash\n", - "!accelerate launch train_dreambooth_lora_sdxl.py \\\n", - " --pretrained_model_name_or_path=\"stabilityai/stable-diffusion-xl-base-1.0\" \\\n", - " --pretrained_vae_model_name_or_path=\"madebyollin/sdxl-vae-fp16-fix\" \\\n", - " --dataset_name=\"dog\" \\\n", - " --output_dir=\"corgy_dog_LoRA\" \\\n", - " --caption_column=\"prompt\"\\\n", - " --mixed_precision=\"fp16\" \\\n", - " --instance_prompt=\"a photo of TOK dog\" \\\n", - " --resolution=1024 \\\n", - " --train_batch_size=1 \\\n", - " --gradient_accumulation_steps=3 \\\n", - " --gradient_checkpointing \\\n", - " --learning_rate=1e-4 \\\n", - " --snr_gamma=5.0 \\\n", - " --lr_scheduler=\"constant\" \\\n", - " --lr_warmup_steps=0 \\\n", - " --mixed_precision=\"fp16\" \\\n", - " --use_8bit_adam \\\n", - " --max_train_steps=500 \\\n", - " --checkpointing_steps=717 \\\n", - " --seed=\"0\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i11tgWz5x3mK" - }, - "source": [ - "### Save your model to the hub and check it out πŸ”₯" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7uyy9n54EtBY" - }, - "outputs": [], - "source": [ - "from huggingface_hub import whoami\n", - "from pathlib import Path\n", - "#@markdown make sure the `output_dir` you specify here is the same as the one used for training\n", - "output_dir = \"corgy_dog_LoRA\" #@param\n", - "username = whoami(token=Path(\"/root/.cache/huggingface/\"))[\"name\"]\n", - "repo_id = f\"{username}/{output_dir}\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 449, - "referenced_widgets": [ - "ffbd60ac22ea41478175f89f611e4fd7", - "19bc0912cf6541da861f5f4b804d68e5", - "a1901fae89614db7bd6ca849d7d3e359", - "b7f5df22dfd34814b15e0bb2ff29cef2", - "d8a363d77a3b48aa9ea2d52ce83aac88", - "1a669bec45fd4cb9af63e8119cbc858c", - "479ee1a3a2f643798e1de1ff5e16a305", - "18088571d7014c95afde6b7a46275c14", - "69166eb0c2fb41e7943b586c30db46df", - "4b612a0885154e6396f9ffc781639e00", - "141c87e818c84099b4434a9ad30b7cb8", - "6fddb9649fa9428f8d9d254f6172a82a", - "af213843aac34ac29891fd1f04b95797", - "8ead471506474b69b8b8612c14b24851", - "d29a0855ba4e4489a5470b433f81ba4d", - "e9022e3fb8c147abba6ff912b9075365", - "440ebf932b7e450988c6320cac8997b7", - "cfa332f30cbc415ca1d14d9a30ec1c46", - "3a77977b5d0f4947b7943a1706176a2e", - "532e4fd43b354d5698c20012b3744d5c", - "b20f602431ec4172b3214aad66dc7d2d", - "ffd12bbe1d2f4237a1648665c1240eab", - "eb884589026b447282fd037a900e7a44", - "98caebcdd0b140568650e2c35f62ab39", - "6cb3b0340cf0458e9d340a99b27894db", - "5a7db33da5fc43c0a221df01fb44dd5a", - "7bcc72e2351d4a42ae24b844239848a3", - "a4090232d3ec4df3b8fa511ec7ead4d7", - "a62fa882e5ce464eb404bb3731adbfcc", - "5ceb5c6daf1b4da19e12f863fcb8a2ec", - "20a839a811894d68ab30e7ed2b41a048", - "6368fb8add4444a7a0ddc7cc10198f28", - "7a28677e22d1486bb7fa94d90ff1a3cc", - "3d21a19853b543f5a534cdf22fa7e40c", - "e3f287e6c7a742bfbef5029c8d408db9", - "7ef50664c5df4e6bb54d02a3a3bc2e7d", - "459cdd69cdbe42ee90d937cc1019d361", - "b69d9160aca0435ca0e2032b7de0dd70", - "7c6d71338ef64bd49497daf3c8b5fca6", - "c5670c8dfbb34486ad8addc6df5cc301", - "87269073a63e441fac456c942c05fa88", - "1907b7f46bca4de1a59a15511aa75c67", - "fc5f6895a58e4da99781efaac23c3c13", - "43c58cac15e8404793605f7fc241861c", - "40ffc573dd5c4e7a88fc3e5871851790", - "7261b659aac54741a9de0db50b41c77d", - "c72dd9a21a6c4e41a7cc7062e72e97bf", - "6dc2d68fc4ca403197564521a1ba150b", - "d7876a2e2b5c4f86952d61fd89ccce06", - "c8961da57dfe4bf596b331deb368ecc9", - "9a9df59518294857a2554e017a512bc7", - "f632475f230042229e020320f2c23f6c", - "937bbd50e51c4866901f864296f6335a", - "5c322a054923452ab29675149ef43ee8", - "3a380ce08ce549e7aa4a6f4510d2a329", - "0704502ce9d24854b9f07e51f638c539", - "ceefe7264ef9452b8cc40205a9b22f42", - "5f88f59ba375480fbaf837e9b77cf9ba", - "033cbf5b719144c4abb0ca6414a927ef", - "9f757a62e10b4ba6b1825c35967a2dba", - "334d6f25220c4b13a3ea42f77b33dca7", - "565693fc50f747179d51a30d2ab0e0f6", - "c1e7df8f461e48dba6a854610554c610", - "b28121d0bf9c4beaaf3deac162bfe858", - "6b40ed217673469899a82a2570c950f7", - "47ea3ac012a7463487ffa46c5184dc69" - ] - }, - "id": "smLheRkvEQ9H", - "outputId": "453363a1-3625-4890-a170-dd0729d40f37" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:xformers:WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n", - " PyTorch 2.1.0+cu121 with CUDA 1201 (you have 2.1.0+cu118)\n", - " Python 3.10.13 (you have 3.10.12)\n", - " Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n", - " Memory-efficient attention, SwiGLU, sparse and more won't be available.\n", - " Set XFORMERS_MORE_DETAILS=1 for more details\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ffbd60ac22ea41478175f89f611e4fd7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "events.out.tfevents.1700722958.c9c71b08d6c0.3723.1: 0%| | 0.00/3.23k [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import display, Markdown\n", - "\n", - "link_to_model = f\"https://huggingface.co/{repo_id}\"\n", - "display(Markdown(\"### Your model has finished training.\\nAccess it here: {}\".format(link_to_model)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Dvu9e6q_x3mK" - }, - "source": [ - "Let's generate some images with it!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bH7-YJwMcyra" - }, - "source": [ - "## Inference πŸ•" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297, - "referenced_widgets": [ - "98d34476a91e479692c0c6967dda42ab", - "2e89119c8373413db8645493aae2beb6", - "0e90af7327f44b808ab386d5edfc9c1c", - "a500c01210934daaa3758e4ba95b3508", - "7b6f50640a164eb09b5a051916575303", - "8dee983c410d4f22bdcd885a8e15df30", - "93e04c6282c945549c532a91f966373b", - "806353cbe138461b8dad777b8d79f7d3", - "d908430becdb47ea82118daed6da1808", - "03e73340b5174324b5028461154f0a11", - "35ac5511129c4fcb9eec0d8e406dbed9", - 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So, hang tight and let's get started πŸ§ͺ" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pdM59WIoS01O" + }, + "source": [ + "## Setup πŸͺ“" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XUxRrLfLMnBb", + "outputId": "e8792843-7ba4-4678-da74-c87d2eab331b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Thu Nov 23 06:47:16 2023 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\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 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 49C P8 9W / 70W | 0MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\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": [ + "# Check the GPU\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "55U57alsRIek", + "outputId": "c263d790-ccbd-497c-8db7-d2ceedf2d11b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.8/211.8 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "# Install dependencies.\n", + "!pip install bitsandbytes transformers accelerate peft -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FJXRjGZa1vFZ" + }, + "source": [ + "Make sure to install `diffusers` from `main`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9atNWUXFZlVe", + "outputId": "4b3b1fab-6f8d-4ad9-fe6b-b5cb27526a0e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ], + "source": [ + "!pip install git+https://github.com/huggingface/diffusers.git -q" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d2TRPaxGx3mE" + }, + "source": [ + "Download diffusers SDXL DreamBooth training script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zQpqmq7rx3mE", + "outputId": "a8066139-6e92-4225-befc-4bb5987cc55e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-11-23 06:48:12-- https://raw.githubusercontent.com/huggingface/diffusers/main/examples/dreambooth/train_dreambooth_lora_sdxl.py\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 72845 (71K) [text/plain]\n", + "Saving to: β€˜train_dreambooth_lora_sdxl.py’\n", + "\n", + "train_dreambooth_lo 100%[===================>] 71.14K --.-KB/s in 0.001s \n", + "\n", + "2023-11-23 06:48:12 (46.6 MB/s) - β€˜train_dreambooth_lora_sdxl.py’ saved [72845/72845]\n", + "\n" + ] + } + ], + "source": [ + "!wget https://raw.githubusercontent.com/huggingface/diffusers/main/examples/dreambooth/train_dreambooth_lora_sdxl.py" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CRBcvYAyS8bU" + }, + "source": [ + "## Dataset 🐢" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8UNkbXwj126Q" + }, + "source": [ + "**Let's get our training data!**\n", + "For this example, we'll download some images from the hub\n", + "\n", + "If you already have a dataset on the hub you wish to use, you can skip this part and go straight to: \"Prep for\n", + "training πŸ’»\" section, where you'll simply specify the dataset name.\n", + "\n", + "If your images are saved locally, and/or you want to add BLIP generated captions,\n", + "pick option 1 or 2 below.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EvTS06eEx3mF" + }, + "source": [ + "**Option 1:** upload example images from your local files:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TqqhZ9R9x3mG" + }, + "outputs": [], + "source": [ + "import os\n", + "from google.colab import files\n", + "\n", + "# pick a name for the image folder\n", + "local_dir = \"./dog/\" #@param\n", + "os.makedirs(local_dir)\n", + "os.chdir(local_dir)\n", + "\n", + "# choose and upload local images into the newly created directory\n", + "uploaded_images = files.upload()\n", + "os.chdir(\"/content\") # back to parent directory" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E3aCuZZGx3mG" + }, + "source": [ + "**Option 2:** download example images from the hub:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255, + "referenced_widgets": [ + "9a9859c3ac1f44af81db5fb5b821aee6", + "2499d42c707445ff8ae3f51b16026556", + "3c5e6c23bcde435ea0f820c8d4d63c11", + "1cd29ae69fef4f00b74327d4b642282c", + "7fb77e4194444c0c9739952e453bcba6", + "fa4de672fcbb4ceea28e97abe2827f7b", + "9637e8cbccb64d8c9138dcdb5e3a52c1", + "93ea3a472f19449a92b8fb8db896abfe", + "9d20b6328a404d3bb894abe9c1fe458b", + "5bc3ce3d3de6481ba7fd0ec1eff7012e", + "76dc51a005ca4e03ab51e792ca4db288", + "18516eb5cb6f4d8b8094b30ae633577d", + "46c4d70931204b37a9b67312048263df", + "b48748776dee47fdad7226ddf933010e", + "5c894c4e5c02428f807e570baae6d640", + "07e1472de1974c42bc7f721825dd9c60", + "2cd06d29f6914ba1bd09a1a98612d6d8", + "00ddb1b212334fffa98c8717ae18c94e", + "785ebbc397c7453093b810f4b60338ef", + "1d29417430da4fefb424089a5fe65375", + "929d24e77001416da24a05323e11e7de", + "b5515a1a20124e83a30144361d5a9e3e", + "758acd0c4e6141e38df22a78be883212", + "aa3d2d03d4c8430abac1e9b8058c5fce", + "c24a081a1e0a4dda973506d7254613ac", + "391237a27e5d496799955dab5a76a0b9", + "8f6326ed0b5341c581e86d88c8e043a6", + "f7abc400b22f4ebd8f47b057c6b2a283", + "9a90b166ef5341e19616cfd8a7805dc3", + "3f3a470891d34f9ab87e1d4fe1b9932e", + "07830c077c784479afcd59dfcae87687", + "8055adde4617432c81771c058980d2ae", + "37d80818c7ea4e59ab996a57942f0615", + "7f95e47b7bbd43788e9cfed6570dfbe1", + "3da311b52b9f4159a2e4cf775fb50a38", + "4315465a99874d8681a49904ec80d812", + "c24d62b089a940c08c120b81cc31111c", + "430f2344b69a4f2dac54254582366020", + "acbdc11b2de04c25b78f04e663112557", + "e22adbce96cb4baa9e3cf0641fc488be", + "bbcec8f9ee5a4923bc0b0b8d992be783", + "e9a6d822c853426baa1664ffb50884fd", + "3019c364a59940929291a348645ae6b9", + "02289e74efb44aefb9df7b3fa13916ad", + "2562d9fe03d343be8f2016b24de1aadc", + "9fac496da01f42828436f907005cae7f", + "2166b3f4d384473c8334ba7715f5c61c", + "f73be3a5295a4e3f9128d1cb09a80c41", + "b09ed57a45724d469398e2a93645546a", + "4adb199157ca45a1adbc3e37a32e1bef", + "233d2ceee8ac4ba3ba338ff8ee71b7b4", + "34dcef51467644c6a572364df698dc62", + "224e11b9806845e0874174942dc1b5ee", + "b212a297fc64477fb05d29678ef82843", + "6cbe07ba74c54b1e87c0d1381583d381", + "0ebc3d3a317e416b9de9adf26dd1f8f7", + "739e3b518fc844d4a8fd8e35c7777c5a", + "5404fc14dd284181bd85efb189b05014", + "509b855737d04166986ff765ade20377", + "f56bbab3296f4c84841c5aac6b99b4f5", + "aa9e7e721ecb48fc9e0d402c11cc822a", + "acfe30f070534097be815dba40b64901", + "f4c326893bc14424b1f2747d0e7f42f2", + "995e455075fe4716b820fafaa90d28ca", + "104dd3bcc2d7420ba59481f937e9af98", + "2dd5daefb5c24aad8e925d33ddc5cdc6" + ] + }, + "id": "-BC36MOBdIWO", + "outputId": "a38fd182-61eb-454d-fd5d-50cb19df3e28" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9a9859c3ac1f44af81db5fb5b821aee6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 5 files: 0%| | 0/5 [00:00" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import glob\n", + "\n", + "# change path to display images from your local dir\n", + "img_paths = \"./dog/*.jpeg\"\n", + "imgs = [Image.open(path) for path in glob.glob(img_paths)]\n", + "\n", + "num_imgs_to_preview = 5\n", + "image_grid(imgs[:num_imgs_to_preview], 1, num_imgs_to_preview)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SqrxnDMUTADH" + }, + "source": [ + "### Generate custom captions with BLIP\n", + "Load BLIP to auto caption your images:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 311, + "referenced_widgets": [ + "08cb0277a7724ad0a782529e3a1b929f", + "5cf3ce3702924f99b142841812d7f739", + "5d0ee26223e44c9f9fbbeb6dac6d5b0d", + "333e2d13a4914c2189c514f9a0758b76", + "838b59066d5e4fd189ddd594f0960fd3", + "b7bd817dbc1e436c8a59d82c5148ec10", + "fd8676ffe23842e88ddb7b7632331af2", + "a9209dfd55d44ced8ec9647a86a6bcec", + "77018507646d4890beb02d898c7ce5c8", + "a973e5bf2b844c59b9d0ccf853727dff", + "5758bb6401064701bcae6ea85f89132a", + "e3bbd6c7582e4f3c83c074719a345733", + "01fd3d6b5c8645fe822041b0a93f7d29", + "1ed2293ea7ed477082f1de112ad58208", + "b613861df19e44b5a1e4b95fea6b2070", + "618d58179581431e9d032df138f80cdd", + "6ba74817a3744cf59f15c716e1d99678", + "bd892bb073634abb8ca648b73934c188", + "71fe2eca70ff441280d61a5755e9f005", + "b9272e18b7ce410da792f96c02868188", + "da9e0703f7d24dff9c92a279493109c5", + "fec546336d7b42fcbaa38d60bb21beb9", + "74ad9bfa1960432c8477e540bb829ec6", + "148621033ecd43caa464a1ff5e44b1af", + "08de35b1df7c4dd2808d69cc3cdef71c", + "8bcbd116981549baa30f9701cc105893", + "d0e515928b594201b95a2218e0ec0708", + "fb5ca753a72c482392fff9e3ba92da01", + "994e90625a3d4cab98739644157d233c", + "cf6cdb3c1b33470ab59eccd901acfc01", + "fa1545a16612492eac8892f960c33efc", + "cfb8ea92d049485092da2a0c72f39efe", + "57a76b34c0ca42748f15de3a9ce593c5", + "a70ded4f518b4bf68d04059486390fe4", + "35c8463ada1b47e7b7873ab2d0a2b774", + "a62d17c71a884b8da2deeff4b55e5a3e", + "a2c1e494e9b74625b423c2a3dc4e3407", + "c02910f8ca8646e1899aa8f81840f675", + "a0f6754080b941c8857a09e5e5c64922", + "719ed1674d6e4e1e93253336b742ed10", + "2c62fe02b70c419c89e23646641a1e28", + "11ce8ccd8e7f41a7a601859cea317fe3", + "32edebcea421415dae93350722422b68", + "e3bd6173408d466a95ca869088b2b718", + "0f3883b8127746ff9ebbaa0811b66da1", + "d97d6b22f2414a8cb1cf4ba71fb0692d", + "9f25e29b688d4931a94bafc66625f777", + "a93b8d95d20745ba9bd83d42755c0a53", + "86bdaa0da8c04416856b4c9325e80a7a", + "bffa8079ea0140e3a27ab8c6b161caac", + "fff32b96cb934b0387bc4718111ae889", + "c1377ca9c9f34da391b8b12223916782", + "cbef8c757cb446049e9af82c3ed161b5", + "56e525584a6c4bf19439532ec8e36e3c", + "8c8b64dbb813404baa1c7f25943d592d", + "be7b68cbc5b74df4984a9d18d39c1787", + "898352ac14314f369ff4ce5effb92fac", + "38e169c2b0a44d5ea92d7ecc519171ba", + "75a096ad39684877bd1d99a1dadc5372", + "f0a22afd70f047adafa6486abc39a760", + "020d86ad45d6424386b12c609f22c4d2", + "32cb02212bee4422b2a26d64024b1947", + "822060b5f33d41428e24c03d835318c0", + "b351150bce8e4028b48a851819edf44c", + "b0d9a703f36944a293c977fed9841ee4", + "b2830c87032d4789a33d650244e01acc", + "334cf989046542559b0ce929dcafd41c", + "dfdb89b60df543ac89137c8229e09120", + "c1758562dbd8434f84df9f3fa19bbccb", + "66d3c8e4bc8b49eaa5dc2e54612cd7b0", + "ea573f55de994943bbc1e9640a911b4e", + "9061e671864141d989629d87d2a26759", + "6b1f9c375bde41d9bbc0504b9ecfc87e", + "5c177bc9903c42e3b9480a6552df09f1", + "8b4a483b8e2241afb73717041778ced2", + "767590d6b93647ebabf24848f0afc0c1", + "40c82cc1708245d692577560cd773a83", + "041ff941714b4af09dcd96446f1a7678", + "0d6214691d734dbda4fdc7411d78cc8c", + "5bef70257f634d86af2a3b420044c918", + "61d945b14ed94408a7877f36213b660e", + "06f19e6c4ec044f89478a0aad66f0741", + "94e0d90ed71e49369fd76a319eb4f0ed", + "528d5d9441ce42e7a149ff2ac8c04117", + "6f82accefd43441ba74a77afd478c7dd", + "349478838b644ef781c7d8da6d5c71f5", + "09dda9ec9310476d86f3ecf4aa8ed8f3", + "9d6d7e867d0a4fbcabd13eaf8e86bb2e" + ] + }, + "id": "qZK1QuLqx3mH", + "outputId": "d69e853e-8231-49c6-d010-434fa6f52487" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "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" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08cb0277a7724ad0a782529e3a1b929f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e3bbd6c7582e4f3c83c074719a345733", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "preprocessor_config.json: 0%| | 0.00/287 [00:00 \n", + " sys.exit(main())\n", + " File \"/usr/local/lib/python3.10/dist-packages/accelerate/commands/accelerate_cli.py\", line 47, in main\n", + " args.func(args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py\", line 994, in launch_command\n", + " simple_launcher(args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py\", line 636, in simple_launcher\n", + " raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)\n", + "subprocess.CalledProcessError: Command '['/usr/bin/python3', 'train_dreambooth_lora_sdxl.py', '--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0', '--pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix', '--dataset_name=dog', '--output_dir=corgy_dog_LoRA', '--caption_column=prompt', '--mixed_precision=fp16', '--instance_prompt=a photo of TOK dog', '--resolution=1024', '--train_batch_size=1', '--gradient_accumulation_steps=3', '--gradient_checkpointing', '--learning_rate=1e-4', '--snr_gamma=5.0', '--lr_scheduler=constant', '--lr_warmup_steps=0', '--mixed_precision=fp16', '--use_8bit_adam', '--max_train_steps=500', '--checkpointing_steps=717', '--seed=0', '--push_to_hub']' died with .\n" + ] + } + ], + "source": [ + "#!/usr/bin/env bash\n", + "!accelerate launch train_dreambooth_lora_sdxl.py \\\n", + " --pretrained_model_name_or_path=\"stabilityai/stable-diffusion-xl-base-1.0\" \\\n", + " --pretrained_vae_model_name_or_path=\"madebyollin/sdxl-vae-fp16-fix\" \\\n", + " --dataset_name=\"dog\" \\\n", + " --output_dir=\"corgy_dog_LoRA\" \\\n", + " --caption_column=\"prompt\"\\\n", + " --mixed_precision=\"fp16\" \\\n", + " --instance_prompt=\"a photo of TOK dog\" \\\n", + " --resolution=1024 \\\n", + " --train_batch_size=1 \\\n", + " --gradient_accumulation_steps=3 \\\n", + " --gradient_checkpointing \\\n", + " --learning_rate=1e-4 \\\n", + " --snr_gamma=5.0 \\\n", + " --lr_scheduler=\"constant\" \\\n", + " --lr_warmup_steps=0 \\\n", + " --mixed_precision=\"fp16\" \\\n", + " --use_8bit_adam \\\n", + " --max_train_steps=500 \\\n", + " --checkpointing_steps=717 \\\n", + " --seed=\"0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i11tgWz5x3mK" + }, + "source": [ + "### Save your model to the hub and check it out πŸ”₯" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7uyy9n54EtBY" + }, + "outputs": [], + "source": [ + "from huggingface_hub import whoami\n", + "from pathlib import Path\n", + "#@markdown make sure the `output_dir` you specify here is the same as the one used for training\n", + "output_dir = \"corgy_dog_LoRA\" #@param\n", + "username = whoami(token=Path(\"/root/.cache/huggingface/\"))[\"name\"]\n", + "repo_id = f\"{username}/{output_dir}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 449, + "referenced_widgets": [ + "ffbd60ac22ea41478175f89f611e4fd7", + "19bc0912cf6541da861f5f4b804d68e5", + "a1901fae89614db7bd6ca849d7d3e359", + "b7f5df22dfd34814b15e0bb2ff29cef2", + "d8a363d77a3b48aa9ea2d52ce83aac88", + "1a669bec45fd4cb9af63e8119cbc858c", + "479ee1a3a2f643798e1de1ff5e16a305", + "18088571d7014c95afde6b7a46275c14", + "69166eb0c2fb41e7943b586c30db46df", + "4b612a0885154e6396f9ffc781639e00", + "141c87e818c84099b4434a9ad30b7cb8", + "6fddb9649fa9428f8d9d254f6172a82a", + "af213843aac34ac29891fd1f04b95797", + "8ead471506474b69b8b8612c14b24851", + "d29a0855ba4e4489a5470b433f81ba4d", + "e9022e3fb8c147abba6ff912b9075365", + "440ebf932b7e450988c6320cac8997b7", + "cfa332f30cbc415ca1d14d9a30ec1c46", + "3a77977b5d0f4947b7943a1706176a2e", + "532e4fd43b354d5698c20012b3744d5c", + "b20f602431ec4172b3214aad66dc7d2d", + "ffd12bbe1d2f4237a1648665c1240eab", + "eb884589026b447282fd037a900e7a44", + "98caebcdd0b140568650e2c35f62ab39", + "6cb3b0340cf0458e9d340a99b27894db", + "5a7db33da5fc43c0a221df01fb44dd5a", + "7bcc72e2351d4a42ae24b844239848a3", + "a4090232d3ec4df3b8fa511ec7ead4d7", + "a62fa882e5ce464eb404bb3731adbfcc", + "5ceb5c6daf1b4da19e12f863fcb8a2ec", + "20a839a811894d68ab30e7ed2b41a048", + "6368fb8add4444a7a0ddc7cc10198f28", + "7a28677e22d1486bb7fa94d90ff1a3cc", + "3d21a19853b543f5a534cdf22fa7e40c", + "e3f287e6c7a742bfbef5029c8d408db9", + 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"events.out.tfevents.1700722958.c9c71b08d6c0.3723.1: 0%| | 0.00/3.23k [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Markdown\n", + "\n", + "link_to_model = f\"https://huggingface.co/{repo_id}\"\n", + "display(Markdown(\"### Your model has finished training.\\nAccess it here: {}\".format(link_to_model)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dvu9e6q_x3mK" + }, + "source": [ + "Let's generate some images with it!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bH7-YJwMcyra" + }, + "source": [ + "## Inference πŸ•" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297, + "referenced_widgets": [ + "98d34476a91e479692c0c6967dda42ab", + "2e89119c8373413db8645493aae2beb6", + "0e90af7327f44b808ab386d5edfc9c1c", + "a500c01210934daaa3758e4ba95b3508", + 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