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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([1., 1., 1., 1., 1.], device='mps:0')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/johnnydevriese/miniforge3/envs/pytorch-1-12/lib/python3.10/site-packages/torch/_tensor_str.py:103: UserWarning: The operator 'aten::bitwise_and.Tensor_out' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1659484612588/work/aten/src/ATen/mps/MPSFallback.mm:11.)\n",
" nonzero_finite_vals = torch.masked_select(tensor_view, torch.isfinite(tensor_view) & tensor_view.ne(0))\n"
]
}
],
"source": [
"import torch\n",
"\n",
"import torch\n",
"\n",
"\n",
"# Check that MPS is available\n",
"if not torch.backends.mps.is_available():\n",
" if not torch.backends.mps.is_built():\n",
" print(\"MPS not available because the current PyTorch install was not \"\n",
" \"built with MPS enabled.\")\n",
" else:\n",
" print(\"MPS not available because the current MacOS version is not 12.3+ \"\n",
" \"and/or you do not have an MPS-enabled device on this machine.\")\n",
"\n",
"else:\n",
" mps_device = torch.device(\"mps\")\n",
"\n",
" # Create a Tensor directly on the mps device\n",
" x = torch.ones(5, device=mps_device)\n",
" # Or\n",
" # x = torch.ones(5, device=\"mps\")\n",
" print(x)\n",
"\n",
" # # Any operation happens on the GPU\n",
" # y = x * 2\n",
"\n",
" # # Move your model to mps just like any other device\n",
" # model = YourFavoriteNet()\n",
" # model.to(mps_device)\n",
"\n",
" # # Now every call runs on the GPU\n",
" # pred = model(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Installing collected packages: zipp, tqdm, regex, pyyaml, filelock, importlib-metadata, huggingface-hub, diffusers\n",
"Successfully installed diffusers-0.2.4 filelock-3.8.0 huggingface-hub-0.9.0 importlib-metadata-4.12.0 pyyaml-6.0 regex-2022.8.17 tqdm-4.64.0 zipp-3.8.1\n",
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"Building wheels for collected packages: tokenizers\n",
" Building wheel for tokenizers (pyproject.toml) ... \u001b[?25lerror\n",
" \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
" \n",
" \u001b[31m×\u001b[0m \u001b[32mBuilding wheel for tokenizers \u001b[0m\u001b[1;32m(\u001b[0m\u001b[32mpyproject.toml\u001b[0m\u001b[1;32m)\u001b[0m did not run successfully.\n",
" \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
" \u001b[31m╰─>\u001b[0m \u001b[31m[51 lines of output]\u001b[0m\n",
" \u001b[31m \u001b[0m running bdist_wheel\n",
" \u001b[31m \u001b[0m running build\n",
" \u001b[31m \u001b[0m running build_py\n",
" \u001b[31m \u001b[0m creating build\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers/models\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/models/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/models\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers/decoders\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/decoders/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/decoders\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers/normalizers\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/normalizers/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/normalizers\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers/pre_tokenizers\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/pre_tokenizers/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/pre_tokenizers\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers/processors\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/processors/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/processors\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers/trainers\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/trainers/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/trainers\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers/implementations\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/implementations/byte_level_bpe.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/implementations\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/implementations/sentencepiece_unigram.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/implementations\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/implementations/sentencepiece_bpe.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/implementations\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/implementations/base_tokenizer.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/implementations\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/implementations/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/implementations\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/implementations/char_level_bpe.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/implementations\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/implementations/bert_wordpiece.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/implementations\n",
" \u001b[31m \u001b[0m creating build/lib.macosx-11.0-arm64-cpython-310/tokenizers/tools\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/tools/__init__.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/tools\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/tools/visualizer.py -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/tools\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/__init__.pyi -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/models/__init__.pyi -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/models\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/decoders/__init__.pyi -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/decoders\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/normalizers/__init__.pyi -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/normalizers\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/pre_tokenizers/__init__.pyi -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/pre_tokenizers\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/processors/__init__.pyi -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/processors\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/trainers/__init__.pyi -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/trainers\n",
" \u001b[31m \u001b[0m copying py_src/tokenizers/tools/visualizer-styles.css -> build/lib.macosx-11.0-arm64-cpython-310/tokenizers/tools\n",
" \u001b[31m \u001b[0m running build_ext\n",
" \u001b[31m \u001b[0m running build_rust\n",
" \u001b[31m \u001b[0m error: can't find Rust compiler\n",
" \u001b[31m \u001b[0m \n",
" \u001b[31m \u001b[0m If you are using an outdated pip version, it is possible a prebuilt wheel is available for this package but pip is not able to install from it. Installing from the wheel would avoid the need for a Rust compiler.\n",
" \u001b[31m \u001b[0m \n",
" \u001b[31m \u001b[0m To update pip, run:\n",
" \u001b[31m \u001b[0m \n",
" \u001b[31m \u001b[0m pip install --upgrade pip\n",
" \u001b[31m \u001b[0m \n",
" \u001b[31m \u001b[0m and then retry package installation.\n",
" \u001b[31m \u001b[0m \n",
" \u001b[31m \u001b[0m If you did intend to build this package from source, try installing a Rust compiler from your system package manager and ensure it is on the PATH during installation. Alternatively, rustup (available at https://rustup.rs) is the recommended way to download and update the Rust compiler toolchain.\n",
" \u001b[31m \u001b[0m \u001b[31m[end of output]\u001b[0m\n",
" \n",
" \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
"\u001b[?25h\u001b[31m ERROR: Failed building wheel for tokenizers\u001b[0m\u001b[31m\n",
"\u001b[0mFailed to build tokenizers\n",
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"Successfully built lxml\n",
"Installing collected packages: webencodings, Send2Trash, mistune, ipython-genutils, fastjsonschema, tinycss2, terminado, soupsieve, pyrsistent, prometheus-client, pandocfilters, markupsafe, lxml, jupyterlab-widgets, jupyterlab-pygments, defusedxml, bleach, attrs, jsonschema, jinja2, beautifulsoup4, argon2-cffi-bindings, nbformat, argon2-cffi, nbclient, nbconvert, notebook, widgetsnbextension, ipywidgets\n",
"Successfully installed Send2Trash-1.8.0 argon2-cffi-21.3.0 argon2-cffi-bindings-21.2.0 attrs-22.1.0 beautifulsoup4-4.11.1 bleach-5.0.1 defusedxml-0.7.1 fastjsonschema-2.16.1 ipython-genutils-0.2.0 ipywidgets-7.7.2 jinja2-3.1.2 jsonschema-4.14.0 jupyterlab-pygments-0.2.2 jupyterlab-widgets-1.1.1 lxml-4.9.1 markupsafe-2.1.1 mistune-2.0.4 nbclient-0.6.7 nbconvert-7.0.0 nbformat-5.4.0 notebook-6.4.12 pandocfilters-1.5.0 prometheus-client-0.14.1 pyrsistent-0.18.1 soupsieve-2.3.2.post1 terminado-0.15.0 tinycss2-1.1.1 webencodings-0.5.1 widgetsnbextension-3.6.1\n"
]
}
],
"source": [
"!pip install diffusers==0.2.4\n",
"!pip install transformers scipy ftfy\n",
"!pip install \"ipywidgets>=7,<8\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8f312208bf4744df84d15d15e956afb2",
"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": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GREP_COLOR=1;33\n",
"MANPATH=/opt/homebrew/share/man::\n",
"SHELL=/bin/zsh\n",
"HOMEBREW_REPOSITORY=/opt/homebrew\n",
"TMPDIR=/var/folders/4k/y4ljh2217c57vl68z1zkl0440000gn/T/\n",
"CONDA_SHLVL=2\n",
"PYTHONUNBUFFERED=1\n",
"CONDA_PROMPT_MODIFIER=(pytorch-1-12) \n",
"ORIGINAL_XDG_CURRENT_DESKTOP=undefined\n",
"MallocNanoZone=0\n",
"ZSH=/Users/johnnydevriese/.oh-my-zsh\n",
"PYTHONIOENCODING=utf-8\n",
"USER=johnnydevriese\n",
"LS_COLORS=di=34;40:ln=35;40:so=32;40:pi=33;40:ex=31;40:bd=34;46:cd=34;43:su=0;41:sg=0;46:tw=0;42:ow=0;43:\n",
"COMMAND_MODE=unix2003\n",
"CONDA_EXE=/Users/johnnydevriese/miniforge3/bin/conda\n",
"SSH_AUTH_SOCK=/private/tmp/com.apple.launchd.QcecMP1ruy/Listeners\n",
"__CF_USER_TEXT_ENCODING=0x1F5:0x0:0x0\n",
"PAGER=cat\n",
"VSCODE_AMD_ENTRYPOINT=vs/workbench/api/node/extensionHostProcess\n",
"ELECTRON_RUN_AS_NODE=1\n",
"_CE_CONDA=\n",
"LSCOLORS=exfxcxdxbxegedabagacad\n",
"CONDA_PREFIX_1=/Users/johnnydevriese/miniforge3\n",
"PATH=/Users/johnnydevriese/miniforge3/envs/pytorch-1-12/bin:/Users/johnnydevriese/miniforge3/envs/pytorch-1-12/bin:/Users/johnnydevriese/miniforge3/condabin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Applications/VMware Fusion Tech Preview.app/Contents/Public\n",
"LaunchInstanceID=82C5D7F6-FDB0-43D6-88D3-5FBAA6CD505A\n",
"CONDA_PREFIX=/Users/johnnydevriese/miniforge3/envs/pytorch-1-12\n",
"__CFBundleIdentifier=com.microsoft.VSCode\n",
"PWD=/Users/johnnydevriese/projects/machine_learning/code/diffusers\n",
"VSCODE_HANDLES_UNCAUGHT_ERRORS=true\n",
"XPC_FLAGS=0x0\n",
"_CE_M=\n",
"XPC_SERVICE_NAME=0\n",
"SHLVL=2\n",
"HOME=/Users/johnnydevriese\n",
"VSCODE_NLS_CONFIG={\"locale\":\"en-us\",\"availableLanguages\":{},\"_languagePackSupport\":true}\n",
"HOMEBREW_PREFIX=/opt/homebrew\n",
"CONDA_PYTHON_EXE=/Users/johnnydevriese/miniforge3/bin/python\n",
"LESS=-R\n",
"LOGNAME=johnnydevriese\n",
"VSCODE_IPC_HOOK=/Users/johnnydevriese/Library/Application Support/Code/1.70.2-main.sock\n",
"VSCODE_CODE_CACHE_PATH=/Users/johnnydevriese/Library/Application Support/Code/CachedData/e4503b30fc78200f846c62cf8091b76ff5547662\n",
"CONDA_DEFAULT_ENV=pytorch-1-12\n",
"VSCODE_PID=583\n",
"INFOPATH=/opt/homebrew/share/info:\n",
"HOMEBREW_CELLAR=/opt/homebrew/Cellar\n",
"VSCODE_CWD=/\n",
"SECURITYSESSIONID=186b3\n",
"LC_CTYPE=UTF-8\n",
"PYTHONPATH=/Users/johnnydevriese/.vscode/extensions/ms-toolsai.jupyter-2022.7.1102252217/pythonFiles:/Users/johnnydevriese/.vscode/extensions/ms-toolsai.jupyter-2022.7.1102252217/pythonFiles/lib/python\n",
"JUPYTER_PATH=/Users/johnnydevriese/.vscode/extensions/ms-toolsai.jupyter-2022.7.1102252217/temp/jupyter\n",
"PYDEVD_USE_FRAME_EVAL=NO\n",
"OLDPWD=/Users/johnnydevriese/projects/machine_learning/code/diffusers\n",
"CONDA_ROOT=/Users/johnnydevriese/miniforge3\n",
"JPY_PARENT_PID=7629\n",
"TERM=xterm-color\n",
"CLICOLOR=1\n",
"GIT_PAGER=cat\n",
"MPLBACKEND=module://matplotlib_inline.backend_inline\n",
"_=/usr/bin/env\n",
"PYTORCH_ENABLE_MPS_FALLBACK=1\n"
]
}
],
"source": [
"! env PYTORCH_ENABLE_MPS_FALLBACK=1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.\n"
]
},
{
"ename": "NotImplementedError",
"evalue": "The operator 'aten::index.Tensor' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on https://github.com/pytorch/pytorch/issues/77764. As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/Users/johnnydevriese/projects/machine_learning/code/diffusers/hf_diffusers.ipynb Cell 5\u001b[0m in \u001b[0;36m<cell line: 21>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/johnnydevriese/projects/machine_learning/code/diffusers/hf_diffusers.ipynb#W1sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m prompt \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39ma photo of an astronaut riding a horse on mars\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/johnnydevriese/projects/machine_learning/code/diffusers/hf_diffusers.ipynb#W1sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m \u001b[39m# with autocast(\"mps\"):\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/johnnydevriese/projects/machine_learning/code/diffusers/hf_diffusers.ipynb#W1sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m image \u001b[39m=\u001b[39m pipe(prompt)[\u001b[39m\"\u001b[39m\u001b[39msample\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m0\u001b[39m] \n\u001b[1;32m <a href='vscode-notebook-cell:/Users/johnnydevriese/projects/machine_learning/code/diffusers/hf_diffusers.ipynb#W1sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m image\u001b[39m.\u001b[39msave(\u001b[39m\"\u001b[39m\u001b[39mastronaut_rides_horse.png\u001b[39m\u001b[39m\"\u001b[39m)\n",
"File \u001b[0;32m~/miniforge3/envs/pytorch-1-12/lib/python3.10/site-packages/torch/autograd/grad_mode.py:27\u001b[0m, in \u001b[0;36m_DecoratorContextManager.__call__.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m 25\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 26\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclone():\n\u001b[0;32m---> 27\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"File \u001b[0;32m~/miniforge3/envs/pytorch-1-12/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:82\u001b[0m, in \u001b[0;36mStableDiffusionPipeline.__call__\u001b[0;34m(self, prompt, height, width, num_inference_steps, guidance_scale, eta, generator, output_type, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[39m# get prompt text embeddings\u001b[39;00m\n\u001b[1;32m 75\u001b[0m text_input \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtokenizer(\n\u001b[1;32m 76\u001b[0m prompt,\n\u001b[1;32m 77\u001b[0m padding\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mmax_length\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 80\u001b[0m return_tensors\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mpt\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 81\u001b[0m )\n\u001b[0;32m---> 82\u001b[0m text_embeddings \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtext_encoder(text_input\u001b[39m.\u001b[39;49minput_ids\u001b[39m.\u001b[39;49mto(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdevice))[\u001b[39m0\u001b[39m]\n\u001b[1;32m 84\u001b[0m \u001b[39m# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[39m# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[39m# corresponds to doing no classifier free guidance.\u001b[39;00m\n\u001b[1;32m 87\u001b[0m do_classifier_free_guidance \u001b[39m=\u001b[39m guidance_scale \u001b[39m>\u001b[39m \u001b[39m1.0\u001b[39m\n",
"File \u001b[0;32m~/miniforge3/envs/pytorch-1-12/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1129\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
"File \u001b[0;32m~/miniforge3/envs/pytorch-1-12/lib/python3.10/site-packages/transformers/models/clip/modeling_clip.py:721\u001b[0m, in \u001b[0;36mCLIPTextModel.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 693\u001b[0m \u001b[39m@add_start_docstrings_to_model_forward\u001b[39m(CLIP_TEXT_INPUTS_DOCSTRING)\n\u001b[1;32m 694\u001b[0m \u001b[39m@replace_return_docstrings\u001b[39m(output_type\u001b[39m=\u001b[39mBaseModelOutputWithPooling, config_class\u001b[39m=\u001b[39mCLIPTextConfig)\n\u001b[1;32m 695\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 702\u001b[0m return_dict: Optional[\u001b[39mbool\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 703\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Union[Tuple, BaseModelOutputWithPooling]:\n\u001b[1;32m 704\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 705\u001b[0m \u001b[39m Returns:\u001b[39;00m\n\u001b[1;32m 706\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[39m >>> pooled_output = outputs.pooler_output # pooled (EOS token) states\u001b[39;00m\n\u001b[1;32m 720\u001b[0m \u001b[39m ```\"\"\"\u001b[39;00m\n\u001b[0;32m--> 721\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtext_model(\n\u001b[1;32m 722\u001b[0m input_ids\u001b[39m=\u001b[39;49minput_ids,\n\u001b[1;32m 723\u001b[0m attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m 724\u001b[0m position_ids\u001b[39m=\u001b[39;49mposition_ids,\n\u001b[1;32m 725\u001b[0m output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m 726\u001b[0m output_hidden_states\u001b[39m=\u001b[39;49moutput_hidden_states,\n\u001b[1;32m 727\u001b[0m return_dict\u001b[39m=\u001b[39;49mreturn_dict,\n\u001b[1;32m 728\u001b[0m )\n",
"File \u001b[0;32m~/miniforge3/envs/pytorch-1-12/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1129\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49m\u001b[39minput\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
"File \u001b[0;32m~/miniforge3/envs/pytorch-1-12/lib/python3.10/site-packages/transformers/models/clip/modeling_clip.py:656\u001b[0m, in \u001b[0;36mCLIPTextTransformer.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 652\u001b[0m last_hidden_state \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinal_layer_norm(last_hidden_state)\n\u001b[1;32m 654\u001b[0m \u001b[39m# text_embeds.shape = [batch_size, sequence_length, transformer.width]\u001b[39;00m\n\u001b[1;32m 655\u001b[0m \u001b[39m# take features from the eot embedding (eot_token is the highest number in each sequence)\u001b[39;00m\n\u001b[0;32m--> 656\u001b[0m pooled_output \u001b[39m=\u001b[39m last_hidden_state[torch\u001b[39m.\u001b[39;49marange(last_hidden_state\u001b[39m.\u001b[39;49mshape[\u001b[39m0\u001b[39;49m]), input_ids\u001b[39m.\u001b[39;49margmax(dim\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m)]\n\u001b[1;32m 658\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m return_dict:\n\u001b[1;32m 659\u001b[0m \u001b[39mreturn\u001b[39;00m (last_hidden_state, pooled_output) \u001b[39m+\u001b[39m encoder_outputs[\u001b[39m1\u001b[39m:]\n",
"\u001b[0;31mNotImplementedError\u001b[0m: The operator 'aten::index.Tensor' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on https://github.com/pytorch/pytorch/issues/77764. As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS."
]
}
],
"source": [
"# make sure you're logged in with `huggingface-cli login`\n",
"from torch import autocast\n",
"from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler\n",
"\n",
"lms = LMSDiscreteScheduler(\n",
" beta_start=0.00085, \n",
" beta_end=0.012, \n",
" beta_schedule=\"scaled_linear\"\n",
")\n",
"\n",
"pipe = StableDiffusionPipeline.from_pretrained(\n",
" \"CompVis/stable-diffusion-v1-3\", \n",
" scheduler=lms,\n",
" torch_dtype=torch.float16, \n",
" revision=\"fp16\",\n",
" use_auth_token=True\n",
").to(\"mps\")\n",
"\n",
"prompt = \"a photo of an astronaut riding a horse on mars\"\n",
"# with autocast(\"mps\"):\n",
"image = pipe(prompt)[\"sample\"][0] \n",
" \n",
"image.save(\"astronaut_rides_horse.png\")"
]
}
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