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},
"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": [
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"Downloading 'assets/images/logo.png' to 'model_weights/.cache/huggingface/download/assets/images/gvouIpVGo_vjLxWDOGAmKk_SZvY=.c55efdcfde0200722d12ebe861c9e3783a20f174.incomplete'\n",
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"Download complete. Moving file to model_weights/shape_gpt.safetensors\n",
"Fetching 6 files: 100% 6/6 [02:01<00:00, 20.19s/it]\n",
"/content/cube/model_weights\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install trimesh omegaconf warp-lang transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 849
},
"id": "pxyDswp5yn-a",
"outputId": "711e421e-2208-4bad-f0b7-23c110db1d15"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting trimesh\n",
" Downloading trimesh-4.6.5-py3-none-any.whl.metadata (18 kB)\n",
"Collecting omegaconf\n",
" Downloading omegaconf-2.3.0-py3-none-any.whl.metadata (3.9 kB)\n",
"Collecting warp-lang\n",
" Downloading warp_lang-1.6.2-py3-none-manylinux2014_x86_64.whl.metadata (28 kB)\n",
"Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.49.0)\n",
"Requirement already satisfied: numpy>=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 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.26.0->transformers) (2025.3.0)\n",
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"Requirement already 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": [
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},
"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<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c94473c48d6f46d1882aa3a0a6c76842"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f28440704168450e8d7f8898355d4b37"
}
},
"metadata": {}
},
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"output_type": "display_data",
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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}
},
"metadata": {}
},
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"output_type": "display_data",
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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}
},
"metadata": {}
},
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"output_type": "display_data",
"data": {
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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}
},
"metadata": {}
},
{
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3a8f5635551c4ab68bad64eede70a739"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Generate a Mesh from text"
],
"metadata": {
"id": "J7HUuQvh0fiE"
}
},
{
"cell_type": "code",
"source": [
"input_prompt = \"vintage couch\"\n",
"# Use a lower resolution_base to accomodate limited GPU VRAM on Colab notebooks\n",
"mesh_v_f = engine.t2s([input_prompt], use_kv_cache=True, resolution_base=5.0)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GjsB5yJIzflA",
"outputId": "e52d1345-610c-4c00-fa39-8ed1b270a08e"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"generating: 100%|██████████| 512/512 [00:38<00:00, 13.26it/s]\n",
"extracting geometry: 100%|██████████| 1/1 [00:00<00:00, 2.43chunk/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Warp 1.6.2 initialized:\n",
" CUDA Toolkit 12.8, Driver 12.4\n",
" Devices:\n",
" \"cpu\" : \"x86_64\"\n",
" \"cuda:0\" : \"Tesla T4\" (15 GiB, sm_75, mempool enabled)\n",
" Kernel cache:\n",
" /root/.cache/warp/1.6.2\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Visualize the output"
],
"metadata": {
"id": "1leBVYYx0ln7"
}
},
{
"cell_type": "code",
"source": [
"# Visualize the model\n",
"import plotly.graph_objects as go\n",
"vertices = mesh_v_f[0][0]\n",
"faces = mesh_v_f[0][1]\n",
"fig = go.Figure(\n",
" data=[\n",
" go.Mesh3d(\n",
" x=vertices[:,0],\n",
" y=vertices[:,1],\n",
" z=vertices[:,2],\n",
" i=faces[:,0],\n",
" j=faces[:,1],\n",
" k=faces[:,2],\n",
" opacity=1.0)\n",
" ],\n",
" layout=dict(\n",
" scene=dict(\n",
" xaxis=dict(visible=False),\n",
" yaxis=dict(visible=False),\n",
" zaxis=dict(visible=False)\n",
" )\n",
" )\n",
")\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "181uVYGWzxp1",
"outputId": "e81a899f-6d3e-4a89-a741-a0513ea87b9f"
},
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
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