video/tune_a_video.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/softmurata/colab_notebooks/blob/main/video/tune_a_video.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# download stable-diffusion-v1-4 checkpoints\n",
        "%cd /content\n",
        "!git lfs install\n",
        "!git clone https://huggingface.co/CompVis/stable-diffusion-v1-4"
      ],
      "metadata": {
        "id": "SrD5ozRfkJTv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l2VJQNiDfCcZ"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/showlab/Tune-A-Video.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tips: torch, torchvisionはcomment out\n",
        "%cd /content/Tune-A-Video/\n",
        "!pip install -r requirements.txt\n",
        "!pip install -U xformers"
      ],
      "metadata": {
        "id": "ROpckIYFiIQH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/Tune-A-Video/"
      ],
      "metadata": {
        "id": "FWTmIBKqkZPj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!accelerate launch train_tuneavideo.py --config=\"configs/man-surfing.yaml\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9H-lUfAiZAv",
        "outputId": "76705c05-083b-42fc-99c9-20451483d2d1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:accelerate.commands.launch:The following values were not passed to `accelerate launch` and had defaults used instead:\n",
            "\t`--num_processes` was set to a value of `1`\n",
            "\t`--num_machines` was set to a value of `1`\n",
            "\t`--mixed_precision` was set to a value of `'no'`\n",
            "\t`--dynamo_backend` was set to a value of `'no'`\n",
            "To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\n",
            "WARNING:xformers:A matching Triton is not available, some optimizations will not be enabled.\n",
            "Error caught was: No module named 'triton'\n",
            "INFO:__main__:Distributed environment: NO\n",
            "Num processes: 1\n",
            "Process index: 0\n",
            "Local process index: 0\n",
            "Device: cuda\n",
            "Mixed precision type: fp16\n",
            "\n",
            "{'variance_type', 'prediction_type'} was not found in config. Values will be initialized to default values.\n",
            "{'norm_num_groups'} was not found in config. Values will be initialized to default values.\n",
            "{'use_linear_projection', 'resnet_time_scale_shift', 'num_class_embeds', 'only_cross_attention', 'dual_cross_attention', 'class_embed_type', 'mid_block_type', 'upcast_attention'} was not found in config. Values will be initialized to default values.\n",
            "{'prediction_type'} was not found in config. Values will be initialized to default values.\n",
            "INFO:__main__:***** Running training *****\n",
            "INFO:__main__:  Num examples = 1\n",
            "INFO:__main__:  Num Epochs = 300\n",
            "INFO:__main__:  Instantaneous batch size per device = 1\n",
            "INFO:__main__:  Total train batch size (w. parallel, distributed & accumulation) = 1\n",
            "INFO:__main__:  Gradient Accumulation steps = 1\n",
            "INFO:__main__:  Total optimization steps = 300\n",
            "Steps:  33% 100/300 [05:33<11:10,  3.35s/it, lr=3e-5, step_loss=0.239]\n",
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            "\n",
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            "\n",
            "  0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
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            "100% 50/50 [01:18<00:00,  1.58s/it]\n",
            "INFO:__main__:Saved samples to ./outputs/man-surfing_lr3e-5_seed33/2023-02-08T01-11-37/samples/sample-100.gif\n",
            "Steps:  67% 200/300 [16:42<05:35,  3.35s/it, lr=3e-5, step_loss=0.353]\n",
            "  0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
            "  0% 0/50 [00:00<?, ?it/s]\u001b[A\n",
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            "100% 50/50 [01:18<00:00,  1.58s/it]\n",
            "INFO:__main__:Saved samples to ./outputs/man-surfing_lr3e-5_seed33/2023-02-08T01-11-37/samples/sample-200.gif\n",
            "Steps: 100% 300/300 [27:51<00:00,  3.36s/it, lr=3e-5, step_loss=0.56]\n",
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            "\n",
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            "\n",
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            "INFO:__main__:Saved samples to ./outputs/man-surfing_lr3e-5_seed33/2023-02-08T01-11-37/samples/sample-300.gif\n",
            "Steps: 100% 300/300 [33:25<00:00,  3.36s/it, lr=3e-5, step_loss=0.0428]{'prediction_type'} was not found in config. Values will be initialized to default values.\n",
            "Configuration saved in ./outputs/man-surfing_lr3e-5_seed33/2023-02-08T01-11-37/model_index.json\n",
            "Configuration saved in ./outputs/man-surfing_lr3e-5_seed33/2023-02-08T01-11-37/vae/config.json\n",
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            "Configuration saved in ./outputs/man-surfing_lr3e-5_seed33/2023-02-08T01-11-37/unet/config.json\n",
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            "Configuration saved in ./outputs/man-surfing_lr3e-5_seed33/2023-02-08T01-11-37/scheduler/scheduler_config.json\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install triton"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKPwU-bqvch5",
        "outputId": "9f642b85-86d6-4634-bf8c-274302c8bd5b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting triton\n",
            "  Downloading triton-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m76.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (from triton) (1.13.1+cu116)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from triton) (3.9.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch->triton) (4.4.0)\n",
            "Installing collected packages: triton\n",
            "Successfully installed triton-1.1.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title inference\n",
        "from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline\n",
        "from tuneavideo.models.unet import UNet3DConditionModel\n",
        "from tuneavideo.util import save_videos_grid\n",
        "import torch\n",
        "\n",
        "pretrained_model_path = \"/content/stable-diffusion-v1-4\"\n",
        "unet_model_path = \"./outputs/man-surfing_lr3e-5_seed33/2023-02-08T01-11-37\"\n",
        "unet = UNet3DConditionModel.from_pretrained(unet_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')\n",
        "pipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to(\"cuda\")\n",
        "pipe.enable_xformers_memory_efficient_attention()\n",
        "\n",
        "prompt = \"a panda is surfing\"\n",
        "video = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos\n",
        "\n",
        "save_videos_grid(video, f\"./{prompt}.gif\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "56dc28a546e44072a42397d0f4b62c44",
            "3e60fd3984dc4dc58135346ef4934e31",
            "fe210b3e35f04de88e295b76196df0e3",
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            "584e53489a2f4f43866dc049043b8ab7",
            "7566e110e877485e8e7fa583f5a0dd76",
            "8b479051c99b4706bd617904edeedcdf",
            "389aff774673429ca5cfe422c5ad5dcb",
            "ba8004edc67643c2ac0764e3bbb2a560",
            "df3d041ade324ad18689ce8fd7af49b6"
          ]
        },
        "id": "LmRkwX9EvOpl",
        "outputId": "5b063ec8-c072-4aa1-97cf-53e425fff237"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        }
      ]
    }
  ]
}