OpenAI_Point_E_Text_to_3D_to_Blender.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "BTd1D3C-6-30",
        "outputId": "e3091b56-7dd2-4529-d1df-6eee8d8a1650"
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      "execution_count": null,
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        {
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          "name": "stdout",
          "text": [
            "Wed Dec 21 06:53:07 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\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  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   54C    P0    26W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install plotly -q"
      ],
      "metadata": {
        "id": "kt8YLZ7gB_5R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RNpaNpz84lxU"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/openai/point-e"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd point-e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iS6PYQ_p61H6",
        "outputId": "8838dab7-35fe-4b3b-b16d-be3e83be20d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/point-e\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -e ."
      ],
      "metadata": {
        "id": "kFQZvSQm7BQQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Imports"
      ],
      "metadata": {
        "id": "DWHrZY3N7PkL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config\n",
        "from point_e.diffusion.sampler import PointCloudSampler\n",
        "from point_e.models.download import load_checkpoint\n",
        "from point_e.models.configs import MODEL_CONFIGS, model_from_config\n",
        "from point_e.util.plotting import plot_point_cloud"
      ],
      "metadata": {
        "id": "yM-Lj7gu7Edm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Models"
      ],
      "metadata": {
        "id": "Qd4byG0C7Q5a"
      }
    },
    {
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      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "print('creating base model...')\n",
        "base_name = 'base40M-textvec'\n",
        "base_model = model_from_config(MODEL_CONFIGS[base_name], device)\n",
        "base_model.eval()\n",
        "base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])\n",
        "\n",
        "print('creating upsample model...')\n",
        "upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)\n",
        "upsampler_model.eval()\n",
        "upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])\n",
        "\n",
        "print('downloading base checkpoint...')\n",
        "base_model.load_state_dict(load_checkpoint(base_name, device))\n",
        "\n",
        "print('downloading upsampler checkpoint...')\n",
        "upsampler_model.load_state_dict(load_checkpoint('upsample', device))"
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          "name": "stdout",
          "text": [
            "creating base model...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████| 890M/890M [00:15<00:00, 60.0MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "creating upsample model...\n",
            "downloading base checkpoint...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/161M [00:00<?, ?iB/s]"
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          "text": [
            "downloading upsampler checkpoint...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/162M [00:00<?, ?iB/s]"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sampler = PointCloudSampler(\n",
        "    device=device,\n",
        "    models=[base_model, upsampler_model],\n",
        "    diffusions=[base_diffusion, upsampler_diffusion],\n",
        "    num_points=[1024, 4096 - 1024],\n",
        "    aux_channels=['R', 'G', 'B'],\n",
        "    guidance_scale=[3.0, 0.0],\n",
        "    model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all\n",
        ")"
      ],
      "metadata": {
        "id": "LynlAPV-7O6H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set a prompt to condition on.\n",
        "prompt = 'a traffic cone '\n",
        "\n",
        "# Produce a sample from the model.\n",
        "samples = None\n",
        "for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt]))):\n",
        "    samples = x"
      ],
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            "d12f5e12bdb24115a0887a92f0a3d7d8",
            "0c1abfb28b8a4060bbe6dad967ea4be3",
            "a7f5f96f7ffa43f38290f18163f4e3f4",
            "750b9454162f48a08fea3fd11f872548",
            "0bdf53b5b8bd4d588e3b2c6f9790cb55",
            "9276cc78ff0f4f19925fa98e143a8c50",
            "388067a8c7344a13814aaa29f4f5368a",
            "9f4426b4e3254df9b63fcb8442a73764",
            "4b313cda2e794ccd86516a9a92d4a265",
            "e380ec9fbcf7465587ebdb554df9c9e4"
          ]
        },
        "id": "Kr3wL6Ee7bfs",
        "outputId": "a9ef43f3-3afb-408f-ddbb-b9e9a5011df7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ef6fbceeaf14d57b26abb6a3e3fd59e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pc = sampler.output_to_point_clouds(samples)[0]"
      ],
      "metadata": {
        "id": "_tNYina579Wj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75),(0.75, 0.75, 0.75)))"
      ],
      "metadata": {
        "id": "nbCQcZgI7dz2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "224dba14-3d0e-47de-99cc-59577a2a8345"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n"
      ],
      "metadata": {
        "id": "--by1iuM8Era"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig_plotly = go.Figure(\n",
        "        data=[\n",
        "            go.Scatter3d(\n",
        "                x=pc.coords[:,0], y=pc.coords[:,1], z=pc.coords[:,2], \n",
        "                mode='markers',\n",
        "                marker=dict(\n",
        "                  size=2,\n",
        "                  color=['rgb({},{},{})'.format(r,g,b) for r,g,b in zip(pc.channels[\"R\"], pc.channels[\"G\"], pc.channels[\"B\"])],\n",
        "              )\n",
        "            )\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",
        "    )"
      ],
      "metadata": {
        "id": "2qEw9PPt8Ijq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig_plotly.show(renderer=\"colab\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "A_89OKDQ-hlE",
        "outputId": "b93820c2-282f-46af-9e39-45e15b58175d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "        var display = window.getComputedStyle(gd).display;\n",
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              "            Plotly.purge(gd);\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from point_e.util.pc_to_mesh import marching_cubes_mesh"
      ],
      "metadata": {
        "id": "cg0iLpcieC08"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "print('creating SDF model...')\n",
        "name = 'sdf'\n",
        "model = model_from_config(MODEL_CONFIGS[name], device)\n",
        "model.eval()\n",
        "\n",
        "print('loading SDF model...')\n",
        "model.load_state_dict(load_checkpoint(name, device))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "38cc885f98bc4c00a2c061940eb4525b",
            "60f9381c0ca240bb8221c2a4e7748b62",
            "63c0e9808d5949158b87f82aaa044e9e",
            "6de2ca09c5df41ce9f2e7029a8576e26",
            "1c98414a42a4498fbb51bec22b8b4ecd",
            "40f44d97999a41a4b65d1a71e33a3336",
            "6ef5109475604d089dc9a69873ef6f12",
            "474a00ef1ec04a7fada998fb6a3c68ad",
            "30eb9feebc9c49518cad6e53e2799304",
            "c062ed6673ec4dc2adb08452321af36c",
            "a5b4ff3b089240369b8f80b3d46ce1a1"
          ]
        },
        "id": "G8HBh3uae9Kn",
        "outputId": "c88430ea-d461-4d76-a2a1-6cd45691d781"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "creating SDF model...\n",
            "loading SDF model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/38.0M [00:00<?, ?iB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38cc885f98bc4c00a2c061940eb4525b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import skimage.measure as measure\n"
      ],
      "metadata": {
        "id": "oijij4dIfLT5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Produce a mesh (with vertex colors)\n",
        "mesh = marching_cubes_mesh(\n",
        "    pc=pc,\n",
        "    model=model,\n",
        "    batch_size=4096,\n",
        "    grid_size=32, # increase to 128 for resolution used in evals\n",
        "    progress=True,\n",
        ")"
      ],
      "metadata": {
        "id": "DTG-Gw6v9i5V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "938fd4f6218646ffb2ec9550a5e7ea5b",
            "5a315d99c35b4bbbb37a9798f12ddb17",
            "98315394000b42a4985cb169e9c01b40",
            "2ed20702177b4a0eb637a54d84b65442",
            "e35245eb1cce48959f4eb8919e9c50ae",
            "6383deb1ea23483fb8b37563afe7af27",
            "e56e34e0fd9546ca8aec719e51b8a10f",
            "6d0916b3d0534c1a95b3cb40e1ca6213",
            "fac6165be9ab422b99e7924c746a5df3",
            "23f58ca407584c9584c240e843793195",
            "5840ca7c4f1e4e258dd13ae389e95987"
          ]
        },
        "outputId": "3836d7ad-ee08-4105-8cd9-923ce0f71049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "938fd4f6218646ffb2ec9550a5e7ea5b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Write the mesh to a PLY file to import into some other program.\n",
        "with open('traffic_cone.ply', 'wb') as f:\n",
        "    mesh.write_ply(f)"
      ],
      "metadata": {
        "id": "1HZl6kEDd6tp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1O7l_a1ufQX2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}