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README.md
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@ -19,5 +19,37 @@ Makes it easy to fine tune Stable Diffusion on your own dataset. For example gen
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> Girl with a pearl earring, Cute Obama creature, Donald Trump, Boris Johnson, Totoro, Hello Kitty
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For a step by step guide see the [notebook in this repo](./tpde_finetune.ipynb) (adapted from [Lambda Labs examples repo](https://github.com/LambdaLabsML/examples).)
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For a step by step guide see the [Lambda Labs examples repo](https://github.com/LambdaLabsML/examples).
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## Image variations
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[](https://47725.gradio.app/)
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[](https://colab.research.google.com/drive/1JqNbI_kDq_Gth2MIYdsphgNgyGIJxBgB?usp=sharing)
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[]()
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For more details on the Image Variation model see the [model card](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned).
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- Get access to a Linux machine with a decent NVIDIA GPU (e.g. on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud))
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- Clone this repo
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- Make sure PyTorch is installed and then install other requirements: `pip install -r requirements.txt`
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- Get model from huggingface hub [lambdalabs/stable-diffusion-image-conditioned](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned/blob/main/sd-clip-vit-l14-img-embed_ema_only.ckpt)
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- Put model in `models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt`
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- Run `scripts/image_variations.py` or `scripts/gradio_variations.py`
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All together:
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```
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git clone https://github.com/justinpinkney/stable-diffusion.git
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cd stable-diffusion
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mkdir -p models/ldm/stable-diffusion-v1
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wget https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned/resolve/main/sd-clip-vit-l14-img-embed_ema_only.ckpt -O models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt
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pip install -r requirements.txt
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python scripts/gradio_variations.py
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```
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Then you should see this:
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[](https://twitter.com/Buntworthy/status/1565704770056294400)
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Trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda](https://lambdalabs.com/)
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@ -1,737 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ipSgY-L3z8kG"
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},
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"source": [
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"# Fine Tune Stable Diffusion\n",
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"\n",
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"Fine tuning Stable Diffusion on Pokemon, \n",
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"for more details see the [Lambda Labs examples repo](https://github.com/LambdaLabsML/examples). \n",
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"\n",
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"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)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "bjNGOU6Pz8kH"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Cloning into 'stable-diffusion'...\n",
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"remote: Enumerating objects: 1546, done.\u001b[K\n",
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"remote: Counting objects: 100% (494/494), done.\u001b[K\n",
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"remote: Compressing objects: 100% (69/69), done.\u001b[K\n",
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"remote: Total 1546 (delta 458), reused 425 (delta 425), pack-reused 1052\u001b[K\n",
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"Receiving objects: 100% (1546/1546), 67.92 MiB | 69.07 MiB/s, done.\n",
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"Resolving deltas: 100% (980/980), done.\n",
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"/home/ubuntu/stable-diffusion\n",
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"Obtaining taming-transformers from git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers (from -r requirements.txt (line 20))\n",
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" Cloning https://github.com/CompVis/taming-transformers.git (to revision master) to ./src/taming-transformers\n",
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" 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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" Resolved https://github.com/CompVis/taming-transformers.git to commit 24268930bf1dce879235a7fddd0b2355b84d7ea6\n",
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" Preparing metadata (setup.py) ... \u001b[?25ldone\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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|
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" 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",
|
||||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m113.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||||
"\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": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"background_execution": "on",
|
||||
"machine_shape": "hm",
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in a new issue