{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ipSgY-L3z8kG"
   },
   "source": [
    "# Fine Tune Stable Diffusion\n",
    "\n",
    "Fine tuning Stable Diffusion on Pokemon, \n",
    "for more details see the [Lambda Labs examples repo](https://github.com/LambdaLabsML/examples). \n",
    "\n",
    "We recommend using a multi-GPU machine, for example an instance from [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud). If running on Colab this notebook is likely to need a GPU with >16GB of VRAM and a runtime with high RAM, which will almost certainly need Colab Pro or Pro+. (If you get errors suchs as `Killed` or `CUDA out of memory` then one of these is not sufficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "bjNGOU6Pz8kH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'stable-diffusion'...\n",
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      "remote: Total 1546 (delta 458), reused 425 (delta 425), pack-reused 1052\u001b[K\n",
      "Receiving objects: 100% (1546/1546), 67.92 MiB | 69.07 MiB/s, done.\n",
      "Resolving deltas: 100% (980/980), done.\n",
      "/home/ubuntu/stable-diffusion\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
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      "  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 /home/ubuntu/stable-diffusion/src/taming-transformers\n",
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      "\u001b[?25hObtaining clip from git+https://github.com/openai/CLIP.git@main#egg=clip (from -r requirements.txt (line 21))\n",
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      "  Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /home/ubuntu/stable-diffusion/src/clip\n",
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      "\u001b[?25hBuilding wheels for collected packages: albumentations, pudb, fire, antlr4-python3-runtime, test-tube, imgaug, urwid, validators, ffmpy, python-multipart\n",
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      "\u001b[?25h  Created wheel for albumentations: filename=albumentations-0.4.3-py3-none-any.whl size=60766 sha256=05eadca09805575f3074ec44f877ba2879dcd3e6da2704396d8a47032fc14ec8\n",
      "  Stored in directory: /home/ubuntu/.cache/pip/wheels/a0/37/4e/0bd417ba6a58f73329b825623d8c949e8e4ac2cdbd252b786d\n",
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      "\u001b[?25h  Created wheel for pudb: filename=pudb-2019.2-py3-none-any.whl size=63230 sha256=9ca404433ce8115d7d129aa2d81f91488890a78900d6b61012d80c1a45040a10\n",
      "  Stored in directory: /home/ubuntu/.cache/pip/wheels/48/83/f1/d8a09d401e2512bfda01ac9fc1b334885f9ddf51617c1c49f1\n",
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      "\u001b[?25h  Created wheel for fire: filename=fire-0.4.0-py2.py3-none-any.whl size=115925 sha256=2d8fe917d7538e2606619c759f146da90a339d4ddae68133d836c34179a99e3f\n",
      "  Stored in directory: /home/ubuntu/.cache/pip/wheels/1f/10/06/2a990ee4d73a8479fe2922445e8a876d38cfbfed052284c6a1\n",
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      "\u001b[?25h  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141230 sha256=b99f1dfd4d32e815c712ea6aae7b22f9111e3bcd259682fe9af85352c3255528\n",
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      "\u001b[?25h  Created wheel for test-tube: filename=test_tube-0.7.5-py3-none-any.whl size=25358 sha256=393e571100e363b2a62ec66009dcb1598e247bbb96e1bec64864272f3f19c4eb\n",
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      "\u001b[?25h  Created wheel for imgaug: filename=imgaug-0.2.6-py3-none-any.whl size=654018 sha256=73dea8c357648a92587178629acbbe4fcd8fe896308f878a5f402c5bfde2171b\n",
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      "\u001b[?25h  Created wheel for urwid: filename=urwid-2.1.2-cp38-cp38-linux_x86_64.whl size=259151 sha256=1dc46a35bdabf9cbaeb34bda8f062e82235b0fb9c9e540f2b9913c5becccb81c\n",
      "  Stored in directory: /home/ubuntu/.cache/pip/wheels/28/71/e4/38b5d81438105d0e3db5016cf2eea6fa796d89d96a04451d4d\n",
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      "\u001b[?25h  Created wheel for validators: filename=validators-0.20.0-py3-none-any.whl size=19565 sha256=ce8b7264206c377ed61a5137f9478235b238b5eef59f2d9d3d57f465081c718e\n",
      "  Stored in directory: /home/ubuntu/.cache/pip/wheels/19/09/72/3eb74d236bb48bd0f3c6c3c83e4e0c5bbfcbcad7c6c3539db8\n",
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      "\u001b[?25h  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4709 sha256=9ba5c4d9c1060bdf85262d3b1f0b5f642fee4561b75d8cd791fd81b560e5820b\n",
      "  Stored in directory: /home/ubuntu/.cache/pip/wheels/ff/5b/59/913b443e7369dc04b61f607a746b6f7d83fb65e2e19fcc958d\n",
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      "\u001b[?25h  Created wheel for python-multipart: filename=python_multipart-0.0.5-py3-none-any.whl size=31669 sha256=697dd66bbef6fe75521d762322eca5730884ea9ccd9648fef2cd4f83470c2ba9\n",
      "  Stored in directory: /home/ubuntu/.cache/pip/wheels/9e/fc/1c/cf980e6413d3ee8e70cd8f39e2366b0f487e3e221aeb452eb0\n",
      "Successfully built albumentations pudb fire antlr4-python3-runtime test-tube imgaug urwid validators ffmpy python-multipart\n",
      "Installing collected packages: wcwidth, urwid, tokenizers, rfc3986, pydub, monotonic, ffmpy, einops, commonmark, braceexpand, antlr4-python3-runtime, xxhash, webdataset, watchdog, validators, urllib3, uc-micro-py, tzdata, toml, taming-transformers, smmap, semver, rich, regex, python-multipart, pympler, pyDeprecate, pycryptodome, pyarrow, pudb, protobuf, orjson, opencv-python-headless, opencv-python, omegaconf, multidict, mdurl, latent-diffusion, imageio-ffmpeg, imageio, h11, ftfy, fsspec, frozenlist, fire, dill, bcrypt, backports.zoneinfo, backoff, async-timeout, yarl, uvicorn, torchmetrics, torch-fidelity, starlette, pytz-deprecation-shim, pydeck, paramiko, multiprocess, markdown-it-py, linkify-it-py, kornia, imgaug, httpcore, gitdb, clip, aiosignal, tzlocal, test-tube, responses, mdit-py-plugins, huggingface-hub, httpx, gitpython, fastapi, analytics-python, altair, albumentations, aiohttp, transformers, streamlit, diffusers, pytorch-lightning, gradio, datasets\n",
      "  Running setup.py develop for taming-transformers\n",
      "  Running setup.py develop for latent-diffusion\n",
      "  Running setup.py develop for clip\n",
      "Successfully installed aiohttp-3.8.3 aiosignal-1.2.0 albumentations-0.4.3 altair-4.2.0 analytics-python-1.4.0 antlr4-python3-runtime-4.8 async-timeout-4.0.2 backoff-1.10.0 backports.zoneinfo-0.2.1 bcrypt-4.0.0 braceexpand-0.1.7 clip commonmark-0.9.1 datasets-2.4.0 diffusers-0.3.0 dill-0.3.5.1 einops-0.3.0 fastapi-0.85.0 ffmpy-0.3.0 fire-0.4.0 frozenlist-1.3.1 fsspec-2022.8.2 ftfy-6.1.1 gitdb-4.0.9 gitpython-3.1.28 gradio-3.1.4 h11-0.12.0 httpcore-0.15.0 httpx-0.23.0 huggingface-hub-0.10.0 imageio-2.9.0 imageio-ffmpeg-0.4.2 imgaug-0.2.6 kornia-0.6.0 latent-diffusion linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.1 mdurl-0.1.2 monotonic-1.6 multidict-6.0.2 multiprocess-0.70.13 omegaconf-2.1.1 opencv-python-4.5.5.64 opencv-python-headless-4.6.0.66 orjson-3.8.0 paramiko-2.11.0 protobuf-3.20.3 pudb-2019.2 pyDeprecate-0.3.1 pyarrow-9.0.0 pycryptodome-3.15.0 pydeck-0.8.0b3 pydub-0.25.1 pympler-1.0.1 python-multipart-0.0.5 pytorch-lightning-1.4.2 pytz-deprecation-shim-0.1.0.post0 regex-2022.9.13 responses-0.18.0 rfc3986-1.5.0 rich-12.6.0 semver-2.13.0 smmap-5.0.0 starlette-0.20.4 streamlit-1.13.0 taming-transformers test-tube-0.7.5 tokenizers-0.12.1 toml-0.10.2 torch-fidelity-0.3.0 torchmetrics-0.6.0 transformers-4.22.2 tzdata-2022.4 tzlocal-4.2 uc-micro-py-1.0.1 urllib3-1.26.12 urwid-2.1.2 uvicorn-0.18.3 validators-0.20.0 watchdog-2.1.9 wcwidth-0.2.5 webdataset-0.2.5 xxhash-3.0.0 yarl-1.8.1\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: keras in /usr/lib/python3/dist-packages (2.9.0)\n",
      "Collecting keras\n",
      "  Downloading keras-2.10.0-py2.py3-none-any.whl (1.7 MB)\n",
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      "\u001b[?25hInstalling collected packages: keras\n",
      "Successfully installed keras-2.10.0\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/justinpinkney/stable-diffusion.git\n",
    "%cd stable-diffusion\n",
    "!pip install --upgrade pip\n",
    "!pip install -r requirements.txt\n",
    "\n",
    "!pip install --upgrade keras # on lambda stack we need to upgrade keras\n",
    "#!pip uninstall -y torchtext # on colab we need to remove torchtext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "m1AkWL270DSE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Oct  7 07:47:59 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |\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  NVIDIA A100-SXM...  On   | 00000000:06:00.0 Off |                    0 |\n",
      "| N/A   33C    P0    46W / 400W |      0MiB / 40960MiB |      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": 8,
   "metadata": {
    "id": "FTf_OdfEz8kI"
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-df3cd5287312>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParisDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../VLoD/\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3/dist-packages/torch/utils/data/dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT_co\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__add__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Dataset[T_co]'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'ConcatDataset[T_co]'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Check the dataset\n",
    "# from datasets import load_dataset\n",
    "# ds = load_dataset(\"lambdalabs/pokemon-blip-captions\", split=\"train\")\n",
    "# sample = ds[0]\n",
    "# display(sample[\"image\"].resize((256, 256)))\n",
    "# print(sample[\"text\"])\n",
    "from paris_dataloader import ParisDataset\n",
    "\n",
    "ds = ParisDataset(\"../VLoD/\", 512, 512, None, [])\n",
    "\n",
    "# sample = ds[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5V1uUVjQz8kI"
   },
   "source": [
    "To get the weights you need to you'll need to [go to the model card](https://huggingface.co/CompVis/stable-diffusion-v1-4-original), read the license and tick the checkbox if you agree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "PbMtzkytz8kI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: huggingface_hub in /home/ubuntu/.local/lib/python3.8/site-packages (0.10.0)\n",
      "Requirement already satisfied: packaging>=20.9 in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface_hub) (21.3)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface_hub) (4.3.0)\n",
      "Requirement already satisfied: requests in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface_hub) (2.28.1)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/lib/python3/dist-packages (from huggingface_hub) (5.3.1)\n",
      "Requirement already satisfied: tqdm in /home/ubuntu/.local/lib/python3.8/site-packages (from huggingface_hub) (4.64.1)\n",
      "Requirement already satisfied: filelock in /usr/lib/python3/dist-packages (from huggingface_hub) (3.0.12)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/lib/python3/dist-packages (from packaging>=20.9->huggingface_hub) (2.4.6)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ubuntu/.local/lib/python3.8/site-packages (from requests->huggingface_hub) (1.26.12)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests->huggingface_hub) (2019.11.28)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests->huggingface_hub) (2.8)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /home/ubuntu/.local/lib/python3.8/site-packages (from requests->huggingface_hub) (2.1.1)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9f0de6955b64459b64cd3a1dc9e6c0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install huggingface_hub\n",
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "vnnbNNycz8kI"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a817e5f3ef0d40eeb9c919870e2d26c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/7.70G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "ckpt_path = hf_hub_download(repo_id=\"CompVis/stable-diffusion-v-1-4-original\", filename=\"sd-v1-4-full-ema.ckpt\", use_auth_token=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ubuntu/.cache/huggingface/hub/models--CompVis--stable-diffusion-v-1-4-original/snapshots/0834a76f88354683d3f7ef271cadd28f4757a8cc/sd-v1-4-full-ema.ckpt'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ckpt_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y8VDyuQxz8kJ"
   },
   "source": [
    "Set your parameters below depending on your GPU setup, the settings below were used for training on a 2xA6000 machine, (the A6000 has 48GB of VRAM). On this set up good results are achieved in around 6 hours.\n",
    "\n",
    "You can make up for using smaller batches or fewer gpus by accumulating batches:\n",
    "\n",
    "`total batch size = batach size * n gpus * accumulate batches`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "WVssEQJfz8kJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPUs: 0,\n"
     ]
    }
   ],
   "source": [
    "# 2xA6000:\n",
    "BATCH_SIZE = 2\n",
    "N_GPUS = 1\n",
    "ACCUMULATE_BATCHES = 1\n",
    "\n",
    "gpu_list = \",\".join((str(x) for x in range(N_GPUS))) + \",\"\n",
    "print(f\"Using GPUs: {gpu_list}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "w7sLO53fz8kJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "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",
      "Moving 0 files to the new cache system\n",
      "0it [00:00, ?it/s]\n",
      "usage: main.py [-h] [--finetune_from [FINETUNE_FROM]] [-n [NAME]]\n",
      "               [-r [RESUME]] [-b [base_config.yaml [base_config.yaml ...]]]\n",
      "               [-t [TRAIN]] [--no-test [NO_TEST]] [-p PROJECT] [-d [DEBUG]]\n",
      "               [-s SEED] [-f POSTFIX] [-l LOGDIR] [--scale_lr [SCALE_LR]]\n",
      "               [--logger [LOGGER]]\n",
      "               [--checkpoint_callback [CHECKPOINT_CALLBACK]]\n",
      "               [--default_root_dir DEFAULT_ROOT_DIR]\n",
      "               [--gradient_clip_val GRADIENT_CLIP_VAL]\n",
      "               [--gradient_clip_algorithm GRADIENT_CLIP_ALGORITHM]\n",
      "               [--process_position PROCESS_POSITION] [--num_nodes NUM_NODES]\n",
      "               [--num_processes NUM_PROCESSES] [--devices DEVICES]\n",
      "               [--gpus GPUS] [--auto_select_gpus [AUTO_SELECT_GPUS]]\n",
      "               [--tpu_cores TPU_CORES] [--ipus IPUS]\n",
      "               [--log_gpu_memory LOG_GPU_MEMORY]\n",
      "               [--progress_bar_refresh_rate PROGRESS_BAR_REFRESH_RATE]\n",
      "               [--overfit_batches OVERFIT_BATCHES]\n",
      "               [--track_grad_norm TRACK_GRAD_NORM]\n",
      "               [--check_val_every_n_epoch CHECK_VAL_EVERY_N_EPOCH]\n",
      "               [--fast_dev_run [FAST_DEV_RUN]]\n",
      "               [--accumulate_grad_batches ACCUMULATE_GRAD_BATCHES]\n",
      "               [--max_epochs MAX_EPOCHS] [--min_epochs MIN_EPOCHS]\n",
      "               [--max_steps MAX_STEPS] [--min_steps MIN_STEPS]\n",
      "               [--max_time MAX_TIME]\n",
      "               [--limit_train_batches LIMIT_TRAIN_BATCHES]\n",
      "               [--limit_val_batches LIMIT_VAL_BATCHES]\n",
      "               [--limit_test_batches LIMIT_TEST_BATCHES]\n",
      "               [--limit_predict_batches LIMIT_PREDICT_BATCHES]\n",
      "               [--val_check_interval VAL_CHECK_INTERVAL]\n",
      "               [--flush_logs_every_n_steps FLUSH_LOGS_EVERY_N_STEPS]\n",
      "               [--log_every_n_steps LOG_EVERY_N_STEPS]\n",
      "               [--accelerator ACCELERATOR] [--sync_batchnorm [SYNC_BATCHNORM]]\n",
      "               [--precision PRECISION] [--weights_summary WEIGHTS_SUMMARY]\n",
      "               [--weights_save_path WEIGHTS_SAVE_PATH]\n",
      "               [--num_sanity_val_steps NUM_SANITY_VAL_STEPS]\n",
      "               [--truncated_bptt_steps TRUNCATED_BPTT_STEPS]\n",
      "               [--resume_from_checkpoint RESUME_FROM_CHECKPOINT]\n",
      "               [--profiler PROFILER] [--benchmark [BENCHMARK]]\n",
      "               [--deterministic [DETERMINISTIC]]\n",
      "               [--reload_dataloaders_every_n_epochs RELOAD_DATALOADERS_EVERY_N_EPOCHS]\n",
      "               [--reload_dataloaders_every_epoch [RELOAD_DATALOADERS_EVERY_EPOCH]]\n",
      "               [--auto_lr_find [AUTO_LR_FIND]]\n",
      "               [--replace_sampler_ddp [REPLACE_SAMPLER_DDP]]\n",
      "               [--terminate_on_nan [TERMINATE_ON_NAN]]\n",
      "               [--auto_scale_batch_size [AUTO_SCALE_BATCH_SIZE]]\n",
      "               [--prepare_data_per_node [PREPARE_DATA_PER_NODE]]\n",
      "               [--plugins PLUGINS] [--amp_backend AMP_BACKEND]\n",
      "               [--amp_level AMP_LEVEL]\n",
      "               [--distributed_backend DISTRIBUTED_BACKEND]\n",
      "               [--move_metrics_to_cpu [MOVE_METRICS_TO_CPU]]\n",
      "               [--multiple_trainloader_mode MULTIPLE_TRAINLOADER_MODE]\n",
      "               [--stochastic_weight_avg [STOCHASTIC_WEIGHT_AVG]]\n",
      "main.py: error: argument --gpus: invalid _gpus_allowed_type value: ''\n"
     ]
    }
   ],
   "source": [
    "# Run training\n",
    "!(python main.py \\\n",
    "    -t \\\n",
    "    --base configs/stable-diffusion/paris.yaml \\\n",
    "    --gpus \"$gpu_list\" \\\n",
    "    --scale_lr False \\\n",
    "    --num_nodes 1 \\\n",
    "    --check_val_every_n_epoch 10 \\\n",
    "    --finetune_from \"$ckpt_path\" \\\n",
    "    data.params.batch_size=\"$BATCH_SIZE\" \\\n",
    "    lightning.trainer.accumulate_grad_batches=\"$ACCUMULATE_BATCHES\" \\\n",
    "    data.params.validation.params.n_gpus=\"$NUM_GPUS\" \\\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sk9wHFKgz8kJ"
   },
   "outputs": [],
   "source": [
    "# Run the model\n",
    "!(python scripts/txt2img.py \\\n",
    "    --prompt 'robotic cat with wings' \\\n",
    "    --outdir 'outputs/generated_pokemon' \\\n",
    "    --H 512 --W 512 \\\n",
    "    --n_samples 4 \\\n",
    "    --config 'configs/stable-diffusion/pokemon.yaml' \\\n",
    "    --ckpt 'path/to/your/checkpoint')"
   ]
  }
 ],
 "metadata": {
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  "colab": {
   "background_execution": "on",
   "machine_shape": "hm",
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  "kernelspec": {
   "display_name": "Python 3",
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   "name": "python3"
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  "language_info": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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  "vscode": {
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