Copy_of_SHAP_E_Text_to_3D.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/palislee/Hello-World-/blob/master/Copy_of_SHAP_E_Text_to_3D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A07fkSE80Fr_",
        "outputId": "f9d9c9ae-4a93-485a-f093-222363b20762"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'shap-e'...\n",
            "remote: Enumerating objects: 304, done.\u001b[K\n",
            "remote: Counting objects: 100% (48/48), done.\u001b[K\n",
            "remote: Compressing objects: 100% (37/37), done.\u001b[K\n",
            "remote: Total 304 (delta 19), reused 23 (delta 11), pack-reused 256\u001b[K\n",
            "Receiving objects: 100% (304/304), 11.71 MiB | 22.49 MiB/s, done.\n",
            "Resolving deltas: 100% (19/19), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/openai/shap-e"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd shap-e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSUrOeSF0RC1",
        "outputId": "893d91d6-3d8b-4055-fdcc-400bd575b8d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/shap-e\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -e ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-fLWame0qJw",
        "outputId": "35e1f732-4463-4062-f290-d3fd72efcc91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Obtaining file:///content/shap-e\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting clip@ git+https://github.com/openai/CLIP.git\n",
            "  Cloning https://github.com/openai/CLIP.git to /tmp/pip-install-_otx4sh3/clip_ecf5c957d3ea41669a4651860e427321\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /tmp/pip-install-_otx4sh3/clip_ecf5c957d3ea41669a4651860e427321\n",
            "  Resolved https://github.com/openai/CLIP.git to commit a9b1bf5920416aaeaec965c25dd9e8f98c864f16\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (3.12.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (8.4.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (2.0.0+cu118)\n",
            "Collecting fire\n",
            "  Downloading fire-0.5.0.tar.gz (88 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.3/88.3 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: humanize in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (4.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (2.27.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (4.65.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (3.7.1)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (0.19.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from shap-e==0.0.0) (1.22.4)\n",
            "Collecting blobfile\n",
            "  Downloading blobfile-2.0.2-py3-none-any.whl (74 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m74.5/74.5 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pycryptodomex~=3.8\n",
            "  Downloading pycryptodomex-3.17-cp35-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: lxml~=4.9 in /usr/local/lib/python3.10/dist-packages (from blobfile->shap-e==0.0.0) (4.9.2)\n",
            "Requirement already satisfied: urllib3<3,>=1.25.3 in /usr/local/lib/python3.10/dist-packages (from blobfile->shap-e==0.0.0) (1.26.15)\n",
            "Collecting ftfy\n",
            "  Downloading ftfy-6.1.1-py3-none-any.whl (53 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from clip@ git+https://github.com/openai/CLIP.git->shap-e==0.0.0) (2022.10.31)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from clip@ git+https://github.com/openai/CLIP.git->shap-e==0.0.0) (0.15.1+cu118)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fire->shap-e==0.0.0) (1.16.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from fire->shap-e==0.0.0) (2.3.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->shap-e==0.0.0) (2.8.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->shap-e==0.0.0) (23.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->shap-e==0.0.0) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->shap-e==0.0.0) (1.4.4)\n",
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            "Building wheels for collected packages: clip, fire\n",
            "  Building wheel for clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369398 sha256=f3e22e6c05776411029b81feaa8f66442b6439defe6abb3db450aaee26ff1ca6\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-d861pv8v/wheels/da/2b/4c/d6691fa9597aac8bb85d2ac13b112deb897d5b50f5ad9a37e4\n",
            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fire: filename=fire-0.5.0-py2.py3-none-any.whl size=116952 sha256=53aed103179e74f6f6e17ec68992bd34cb4b834de70b232b7935602e95b29ac6\n",
            "  Stored in directory: /root/.cache/pip/wheels/90/d4/f7/9404e5db0116bd4d43e5666eaa3e70ab53723e1e3ea40c9a95\n",
            "Successfully built clip fire\n",
            "Installing collected packages: pycryptodomex, ftfy, fire, blobfile, clip, shap-e\n",
            "  Running setup.py develop for shap-e\n",
            "Successfully installed blobfile-2.0.2 clip-1.0 fire-0.5.0 ftfy-6.1.1 pycryptodomex-3.17 shap-e-0.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "from shap_e.diffusion.sample import sample_latents\n",
        "from shap_e.diffusion.gaussian_diffusion import diffusion_from_config\n",
        "from shap_e.models.download import load_model, load_config\n",
        "from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget"
      ],
      "metadata": {
        "id": "0m-XcU8i0WmR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n"
      ],
      "metadata": {
        "id": "DoZyEOAv0i5A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xm = load_model('transmitter', device=device)\n",
        "model = load_model('text300M', device=device)\n",
        "diffusion = diffusion_from_config(load_config('diffusion'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "7646aba7fd804aabac9612ed2893c6c2",
            "98912e23159f468d82bff1b2e607a9d0",
            "4f91a4a13786423282d7bb06c26af4ea",
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            "9c3fe3d1985447e59edd8f20ab350371",
            "1da0ac7c3e3846d4b0d63b8feb9bca10",
            "4abc8565376543cba188b184e55bb3bf",
            "99b035ae77a246f08b43ee990685ebd3",
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            "d5f169291e234d958002e8d80064030d",
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            "2b8826cf614b47f69dbdb53ef2aef455"
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        },
        "id": "RybJQ3160yFe",
        "outputId": "e4539c81-3cb2-4bf3-c299-f43822d4f599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/1.78G [00:00<?, ?iB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7646aba7fd804aabac9612ed2893c6c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|████████████████████████████████████████| 890M/890M [00:06<00:00, 144MiB/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/1.26G [00:00<?, ?iB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "1da0ac7c3e3846d4b0d63b8feb9bca10"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 1\n",
        "guidance_scale = 15.0\n",
        "prompt = \"a sword\"\n",
        "\n",
        "latents = sample_latents(\n",
        "    batch_size=batch_size,\n",
        "    model=model,\n",
        "    diffusion=diffusion,\n",
        "    guidance_scale=guidance_scale,\n",
        "    model_kwargs=dict(texts=[prompt] * batch_size),\n",
        "    progress=True,\n",
        "    clip_denoised=True,\n",
        "    use_fp16=True,\n",
        "    use_karras=True,\n",
        "    karras_steps=64,\n",
        "    sigma_min=1e-3,\n",
        "    sigma_max=160,\n",
        "    s_churn=0,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "25da386a9365408b893b6c913cea248e",
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        },
        "id": "Ye90kA6KeYjB",
        "outputId": "d3cc7917-4c6a-41aa-d10f-dd3b8af5efc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/64 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25da386a9365408b893b6c913cea248e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "render_mode = 'nerf' # you can change this to 'stf'\n",
        "size = 64 # this is the size of the renders; higher values take longer to render.\n",
        "\n",
        "cameras = create_pan_cameras(size, device)\n",
        "for i, latent in enumerate(latents):\n",
        "    images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
        "    display(gif_widget(images))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 95,
          "referenced_widgets": [
            "9089c0351ac3404ba6c1f3fe3f07fa27",
            "c81174b08ed34b66ac21f189fd13bc14",
            "e5d609c3704843ba80ae3cf95275326c"
          ]
        },
        "id": "iSN7AXg2eeW3",
        "outputId": "510c5deb-839a-4dc8-8d08-76a7051172d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HTML(value='<img src=\"data:image/gif;base64,R0lGODlhQABAAIMAAAAAADMzM2YzM2ZmM2YzZjNmZmZmZmaZZpmZZmaZmZmZmZnMmZ…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9089c0351ac3404ba6c1f3fe3f07fa27"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example of saving the latents as meshes.\n",
        "from shap_e.util.notebooks import decode_latent_mesh\n",
        "\n",
        "for i, latent in enumerate(latents):\n",
        "    with open(f'example_mesh_{i}.ply', 'wb') as f:\n",
        "        decode_latent_mesh(xm, latent).tri_mesh().write_ply(f)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCWLAsNuehAU",
        "outputId": "646e54a5-a026-40b2-c378-39cc300ebe81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/shap-e/shap_e/models/stf/renderer.py:286: UserWarning: exception rendering with PyTorch3D: No module named 'pytorch3d'\n",
            "  warnings.warn(f\"exception rendering with PyTorch3D: {exc}\")\n",
            "/content/shap-e/shap_e/models/stf/renderer.py:287: UserWarning: falling back on native PyTorch renderer, which does not support full gradients\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pWb6eNF2f1aJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}