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"@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Setup Cube 3D" ], "metadata": { "id": "s6skCw3w0TaF" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "fjejfzJLxrfu", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c08b4334-54f3-4dab-938c-5df644bbf922" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'cube'...\n", "remote: Enumerating objects: 82, done.\u001b[K\n", "remote: Counting objects: 100% (30/30), done.\u001b[K\n", "remote: Compressing objects: 100% (21/21), done.\u001b[K\n", "remote: Total 82 (delta 15), reused 18 (delta 9), pack-reused 52 (from 1)\u001b[K\n", "Receiving objects: 100% (82/82), 25.14 MiB | 31.20 MiB/s, done.\n", "Resolving deltas: 100% (17/17), done.\n" ] } ], "source": [ "!git clone https://github.com/Roblox/cube" ] }, { "cell_type": "code", "source": [ "%cd /content/cube" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ENHjlVjDyAx5", "outputId": "5a868d2a-59f6-455d-9d47-ec12b11bec99" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/cube\n" ] } ] }, { "cell_type": "code", "source": [ "!huggingface-cli download Roblox/cube3d-v0.1 --local-dir ./model_weights" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "6W4RbGhByEE5", "outputId": "62734f6f-9064-43c1-e6ab-bb745dfac8b6" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\rFetching 6 files: 0% 0/6 [00:00=1.20 in /usr/local/lib/python3.11/dist-packages (from trimesh) (2.0.2)\n", "Collecting antlr4-python3-runtime==4.9.* (from omegaconf)\n", " Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m7.5 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satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2025.1.31)\n", "Downloading trimesh-4.6.5-py3-none-any.whl (708 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m708.9/708.9 kB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading omegaconf-2.3.0-py3-none-any.whl (79 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading warp_lang-1.6.2-py3-none-manylinux2014_x86_64.whl (127.4 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.4/127.4 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hBuilding wheels for collected packages: antlr4-python3-runtime\n", " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144555 sha256=49a1caff39bdb240ad5e4506dbaa73d98efd4085d8f5e9fdf3a8ef2f9786b911\n", " Stored in directory: /root/.cache/pip/wheels/1a/97/32/461f837398029ad76911109f07047fde1d7b661a147c7c56d1\n", "Successfully built antlr4-python3-runtime\n", "Installing collected packages: antlr4-python3-runtime, warp-lang, trimesh, omegaconf\n", "Successfully installed antlr4-python3-runtime-4.9.3 omegaconf-2.3.0 trimesh-4.6.5 warp-lang-1.6.2\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "pydevd_plugins" ] }, "id": "a01de8989f3f4b47bc63c981631c89c3" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "%cd /content/cube" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Sm8Eu0t4y9Ls", "outputId": "0eb36d15-9713-4432-95c6-6e9e64f88224" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/cube\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Create Inference Engine" ], "metadata": { "id": "qg65EFHp0aiz" } }, { "cell_type": "code", "source": [ "from cube3d.inference.engine import Engine\n", "import torch\n", "# load ckpt\n", "config_path = \"cube3d/configs/open_model.yaml\"\n", "gpt_ckpt_path = \"model_weights/shape_gpt.safetensors\"\n", "shape_ckpt_path = \"model_weights/shape_tokenizer.safetensors\"\n", "engine = Engine(\n", " config_path,\n", " gpt_ckpt_path,\n", " shape_ckpt_path,\n", " device=torch.device(\"cuda\"),\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 368, "referenced_widgets": [ "c94473c48d6f46d1882aa3a0a6c76842", "d825b222efc04b76ba3fc80d26978b22", "4141fad6cea3489abba17a568026f4a6", "7b1c150524a24aaebb0f2f985939edc9", "a88535250d3b49ef9d39ad7f94faa67e", "fa8b6792d432433194b0494c523748fa", "4bca5118a6894271a8d452f91d779e4d", "f421eb4e19ef47fbb728487eee19a899", "8587bb43a25e427890e51b371b37977c", "ee73abd9d00d4160819d46ac6289ae7b", "405c4ecec41f4f5b824e31d822a3d3a1", "f28440704168450e8d7f8898355d4b37", "dbd91d03c00d4b0aaa7490898ba5c4bf", "79a2de46e1b147ce899e85008e110799", "df3179415020435e8fedadc6a3357237", "ad8b0cc37f2148a498b2098b6a6de9ea", "6bf4535b6fbf449294e20cab7cef8b03", "014b5433035b49c2873362e8248d6e96", "c021e91153624b4ea42e54958e4a4f58", "2314f83ab01946eb8a29fa2069628d31", "1ac26a027f0e45eb8b03e9e953f53f71", "0b1efd80a021434abe22ae00547ebf45", "a078e1de96e24f38ac7584b7bb169a6b", "5f8850bfe4364348bbab4663cf380c20", "8dc9a5fe1a0a404585cb681ac26be786", "b1f0be34c1b14ff49b27713f8ef0bc93", "3bee5ef2705a45e187c874d6cc46c731", "5108be0c663f4c78b3f76939a8b16623", "3b2c7a76a10e4ab2a82c16215d2167e2", "ca3cee06d80d45a79bf7b564701429b8", "2b966ed681ab42f88c4b64a394be7e0f", "0aa03670526e4c079cd63a5710cababd", "ce32be5d1abf4c8d88517ca793792508", "82d1786e0a5a4e859f6440e92f4fb1a5", "c46881c0e82e4fa2a9c2372386225608", "cfcbdb92c2394b089115a8c3deab6992", "e52d164174f34b309079ef0ea6a090cd", "780bb758ac38442598e52bd0f601cbfc", "d7cc49bb8e734dbf8c2fdc9700994698", "617a7e54586f4c5cba08a165aeb1b4d6", "8aaf0d7f2f5845bfbd4e4159b3eb8cfb", "020219c2acbf474e9079f958703425fb", "76a5f43010104af3b3caf7795fa4da01", "094f62ff33544f8a91dd1a7a62d1f2b8", "e298ddd32fd74f6cade726ff8481c306", "fa679a5193754673810cb678a0fb0115", "e4a3174219434a1eba876de59c271f2e", "7fb6eb4ca3a249bbb1d05a024e9a8b41", "9b890678d14c48a580a3d6abdf3c766f", "b9556038e9c54576985a744bf3e2b2d9", "1a22cee6cff2477ca0cc112982ece186", "8c2846e3590548c793bfe44da8319cd1", "9853af2d467c4d6d8c26d4d5426dccd5", "b425798457b04d8b8490dd895a61b338", "f7a0c18e9fdf4db197c5771d1f571f8c", "0363c6dffb634f39b64cab0572acb231", "3a9f81e6288442218156ed41d12bd7af", "a81d14370f5d4206a359aa06cc8cc84d", "bd5e4284bd75459d8e91a2a3fbcd9d53", "c459a615437c4f90a67476a286c7dd1a", "97bece89236f4981bbed8aa01b480968", "3872192b8cf944b383f89cfb3280cfc3", "194b3c24e2614080bbe7844a830fc087", "7deac37a0d2c4b2f9c6da10821ebb10c", "f93f3ee703af46bbba91e9317d13e54b", "81f618cb8c8b4c93a053d8c824582676", "3a8f5635551c4ab68bad64eede70a739", "307b840b05174535830b4f2246b83250", "eaadc19ee23a44f2acf511e3229e6d3a", "14f1b859654c4ad594adbf3541e64687", "122cdc89d44d470a9d8458d125cad9d8", "61af717a1fe5480abaaa8498f952721c", "4ea588545b7e402a87ca0134252ab770", "1c12057eacf240b2a26d471284efac57", "bf3bbc938bc14c2dbbc8451cab91b333", "707cc8e5e5ba42c68974149a836bb509", "ff9777081c554fc9ad6916ea5c2e12cf" ] }, "id": "lRt4dnfRzA92", "outputId": "cb0cc557-08bf-4b10-9ea3-61f937f0b3ae" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/4.52k [00:00\n", "\n", "\n", "
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