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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/daspartho/bored-ape-diffusion/blob/main/main.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setup"
      ],
      "metadata": {
        "id": "usnKpp5PeMOj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installing required libraries"
      ],
      "metadata": {
        "id": "7hcAwhJEkz0R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install -qq -U diffusers datasets transformers accelerate ftfy pyarrow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JL2H8qoCblOc",
        "outputId": "78576b94-13da-46ac-eff4-52c91d4d7249"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 453 kB 19.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 451 kB 56.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 5.8 MB 48.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 191 kB 66.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 53 kB 1.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 36.0 MB 57 kB/s \n",
            "\u001b[K     |████████████████████████████████| 182 kB 52.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 132 kB 71.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 67.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 56.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 48.3 MB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pandas-gbq 0.17.9 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 10.0.1 which is incompatible.\n",
            "db-dtypes 1.0.4 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 10.0.1 which is incompatible.\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installing Git-LFS to upload your model checkpoints"
      ],
      "metadata": {
        "id": "9X-sVeyBk4y8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!sudo apt -qq install git-lfs\n",
        "!git config --global credential.helper store"
      ],
      "metadata": {
        "id": "2Mqo13bNgbs9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define convenience functions"
      ],
      "metadata": {
        "id": "7a_FeA2klCX1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "from PIL import Image\n",
        "\n",
        "def show_images(x):\n",
        "    \"\"\"Given a batch of images x, make a grid and convert to PIL\"\"\"\n",
        "    x = x * 0.5 + 0.5  # Map from (-1, 1) back to (0, 1)\n",
        "    grid = torchvision.utils.make_grid(x)\n",
        "    grid_im = grid.detach().cpu().permute(1, 2, 0).clip(0, 1) * 255\n",
        "    grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))\n",
        "    return grid_im\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "7l-MxwPtcQQg"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download training dataset"
      ],
      "metadata": {
        "id": "XfNkPbPAEPng"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll be downloading [this collection of 10000 Bored Apes](https://huggingface.co/datasets/daspartho/bored-ape) i've uploaded on HF Hub :)"
      ],
      "metadata": {
        "id": "ungfNKwrleeK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from torchvision import transforms\n",
        "\n",
        "dataset = load_dataset(\"daspartho/bored-ape\", split=\"train\")\n",
        "\n",
        "image_size = 64\n",
        "\n",
        "batch_size = 64\n",
        "\n",
        "# Define data transformations\n",
        "preprocess = transforms.Compose(\n",
        "    [\n",
        "        transforms.Resize((image_size, image_size)),  # Resize\n",
        "        transforms.ToTensor(),  # Convert to tensor (0, 1)\n",
        "        transforms.Normalize([0.5], [0.5]),  # Map to (-1, 1)\n",
        "    ]\n",
        ")\n",
        "\n",
        "def transform(examples):\n",
        "    images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n",
        "    return {\"images\": images}\n",
        "\n",
        "dataset.set_transform(transform)\n",
        "\n",
        "# Create a dataloader from the dataset to serve up the transformed images in batches\n",
        "train_dataloader = torch.utils.data.DataLoader(\n",
        "    dataset, batch_size=batch_size, shuffle=True\n",
        ")"
      ],
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        "id": "Ti2zelanEVio",
        "outputId": "fc56c625-f1ad-4698-c713-784cd088e0d3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/819 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration daspartho--bored-ape-e5adc902b844de1c\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset imagefolder/default (download: 711.43 MiB, generated: 739.16 MiB, post-processed: Unknown size, total: 1.42 GiB) to /root/.cache/huggingface/datasets/daspartho___parquet/daspartho--bored-ape-e5adc902b844de1c/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
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            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "model_id": "9493b356aac2487baa4459638e7bd90c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/373M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab1d295538c14088bba855a85157bcd9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/373M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9ea40ff0fab4e57b8624856b7a84d75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e46c68b112a433faddc24e7db875e35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a40b3f42d41e49228d002c959dd8ac81"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/daspartho___parquet/daspartho--bored-ape-e5adc902b844de1c/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's grab a batch of images and view some of them"
      ],
      "metadata": {
        "id": "qDK4HUDHlaJ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xb = next(iter(train_dataloader))[\"images\"].to(device)[:8]\n",
        "print(\"X shape:\", xb.shape)\n",
        "show_images(xb).resize((8 * 64, 64), resample=Image.NEAREST)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "ypfzqG73c7RL",
        "outputId": "7e9f9a97-6578-42eb-e62f-e5618e799a8a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape: torch.Size([8, 3, 64, 64])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x64 at 0x7F89B0253970>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Noise Scheduler"
      ],
      "metadata": {
        "id": "tWN8LttifRZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining a noise scheduler with 'cosine' schedule, which may be better for small image sizes"
      ],
      "metadata": {
        "id": "aMUoEhqZmCN6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DDPMScheduler\n",
        "\n",
        "noise_scheduler = DDPMScheduler(\n",
        "    num_train_timesteps=1000, beta_schedule=\"squaredcos_cap_v2\"\n",
        ")"
      ],
      "metadata": {
        "id": "T8tZRD-ffkvE"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can plot $\\sqrt{\\bar{\\alpha}_t}$ (labelled as `sqrt_alpha_prod`) and $\\sqrt{(1 - \\bar{\\alpha}_t)}$ (labelled as `sqrt_one_minus_alpha_prod`) to view how the input (x) and the noise are scaled and mixed across different timesteps"
      ],
      "metadata": {
        "id": "2xxgghyimrLz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(noise_scheduler.alphas_cumprod.cpu() ** 0.5, label=r\"${\\sqrt{\\bar{\\alpha}_t}}$\")\n",
        "plt.plot((1 - noise_scheduler.alphas_cumprod.cpu()) ** 0.5, label=r\"$\\sqrt{(1 - \\bar{\\alpha}_t)}$\")\n",
        "plt.legend(fontsize=\"x-large\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "EJVXstKrfoui",
        "outputId": "99ff488a-4250-4119-cbf6-894407d5e123"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we can now use the noise scheduler to add noise in different amounts using the noise_scheduler.add_noise function like so"
      ],
      "metadata": {
        "id": "V6Siq-J1m0OQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "timesteps = torch.linspace(0, 999, 8).long().to(device)\n",
        "noise = torch.randn_like(xb)\n",
        "noisy_xb = noise_scheduler.add_noise(xb, noise, timesteps)\n",
        "print(\"Noisy X shape\", noisy_xb.shape)\n",
        "show_images(noisy_xb).resize((8 * 64, 64), resample=Image.NEAREST)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "rZ6tbhKGf22S",
        "outputId": "b90779df-5cc3-4e77-81ab-d699204c22f6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Noisy X shape torch.Size([8, 3, 64, 64])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x64 at 0x7F883016C520>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define the Model"
      ],
      "metadata": {
        "id": "HAaFkm43gUD-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create a U-net for our desired image size using the handy UNet2DModel class which Diffusers provides that creates the desired architecture in PyTorch"
      ],
      "metadata": {
        "id": "kvArhRwCnGOi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import UNet2DModel\n",
        "\n",
        "# Create a model\n",
        "model = UNet2DModel(\n",
        "    sample_size=image_size,  # the target image resolution\n",
        "    in_channels=3,  # the number of input channels, 3 for RGB images\n",
        "    out_channels=3,  # the number of output channels\n",
        "    layers_per_block=2,  # how many ResNet layers to use per UNet block\n",
        "    block_out_channels=(64, 128, 128, 256),  # More channels -> more parameters\n",
        "    down_block_types=(\n",
        "        \"DownBlock2D\",  # a regular ResNet downsampling block\n",
        "        \"DownBlock2D\",\n",
        "        \"AttnDownBlock2D\",  # a ResNet downsampling block with spatial self-attention\n",
        "        \"AttnDownBlock2D\",\n",
        "    ),\n",
        "    up_block_types=(\n",
        "        \"AttnUpBlock2D\",\n",
        "        \"AttnUpBlock2D\",  # a ResNet upsampling block with spatial self-attention\n",
        "        \"UpBlock2D\",\n",
        "        \"UpBlock2D\",  # a regular ResNet upsampling block\n",
        "    ),\n",
        ")\n",
        "model.to(device);"
      ],
      "metadata": {
        "id": "UU7ZMhjpgTl8"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's pass in a batch of data and some random timesteps through the model and produces an output the same shape as the input data"
      ],
      "metadata": {
        "id": "QApaCuLNnfQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "    model_prediction = model(noisy_xb, timesteps).sample\n",
        "model_prediction.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Q8098KJg4vG",
        "outputId": "e8c266b1-9244-4da2-f676-b0072f0da315"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8, 3, 64, 64])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training Loop"
      ],
      "metadata": {
        "id": "7JsH4q0HhFVk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A minimal training loop"
      ],
      "metadata": {
        "id": "rRbj7-ZooDAZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.AdamW(model.parameters(), lr=4e-4)\n",
        "\n",
        "losses = []\n",
        "\n",
        "for epoch in range(20):\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "        clean_images = batch[\"images\"].to(device)\n",
        "        # Sample noise to add to the images\n",
        "        noise = torch.randn(clean_images.shape).to(clean_images.device)\n",
        "        bs = clean_images.shape[0]\n",
        "\n",
        "        # Sample a random timestep for each image\n",
        "        timesteps = torch.randint(\n",
        "            0, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device\n",
        "        ).long()\n",
        "\n",
        "        # Add noise to the clean images according to the noise magnitude at each timestep\n",
        "        noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n",
        "\n",
        "        # Get the model prediction\n",
        "        noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n",
        "\n",
        "        # Calculate the loss\n",
        "        loss = F.mse_loss(noise_pred, noise)\n",
        "        loss.backward(loss)\n",
        "        losses.append(loss.item())\n",
        "\n",
        "        # Update the model parameters with the optimizer\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "    loss_last_epoch = sum(losses[-len(train_dataloader) :]) / len(train_dataloader)\n",
        "    print(f\"Epoch:{epoch+1}, loss: {loss_last_epoch}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3ZKiWcxhIP1",
        "outputId": "5ec20cc0-65d9-4058-c5bb-3cbdad5c3277"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:1, loss: 0.14032686373610406\n",
            "Epoch:2, loss: 0.07494249534170339\n",
            "Epoch:3, loss: 0.05690267831561672\n",
            "Epoch:4, loss: 0.04720293348477145\n",
            "Epoch:5, loss: 0.04005653165565554\n",
            "Epoch:6, loss: 0.03710380664012235\n",
            "Epoch:7, loss: 0.0326306944368941\n",
            "Epoch:8, loss: 0.02801479410475037\n",
            "Epoch:9, loss: 0.026851495099105654\n",
            "Epoch:10, loss: 0.02375918933469209\n",
            "Epoch:11, loss: 0.024341612555988275\n",
            "Epoch:12, loss: 0.0215647898341535\n",
            "Epoch:13, loss: 0.020122374224055346\n",
            "Epoch:14, loss: 0.01875028107908501\n",
            "Epoch:15, loss: 0.02002343845666404\n",
            "Epoch:16, loss: 0.01742931737143333\n",
            "Epoch:17, loss: 0.01753757232028967\n",
            "Epoch:18, loss: 0.01648577350743447\n",
            "Epoch:19, loss: 0.017952318534634675\n",
            "Epoch:20, loss: 0.01664763518199799\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting the loss"
      ],
      "metadata": {
        "id": "aL5eWXyGoKZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
        "axs[0].plot(losses)\n",
        "axs[1].plot(np.log(losses))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "lEMCH9vUiDv2",
        "outputId": "75f48391-0292-447f-beb5-6dc80d0a2545"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generating Images"
      ],
      "metadata": {
        "id": "AGw4OhO3ptx5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Option 1: Create pipeline"
      ],
      "metadata": {
        "id": "VIdXthAJp3nk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DDPMPipeline\n",
        "\n",
        "image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler)"
      ],
      "metadata": {
        "id": "O0sZ9efYptiz"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_output = image_pipe()\n",
        "pipeline_output.images[0]"
      ],
      "metadata": {
        "id": "Pv-727h4qA-W",
        "outputId": "5c45172d-f98d-4a92-b07a-9ab6aa9bbf1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "ef491ebdad714838a8ddadbc4d2902b2",
            "4356debe043148ab8458e73ff3bbc75b",
            "24f120de962c425c92bdbda2e49b33b9",
            "bdbcb8d6e0454e5db1977b792b009434",
            "ef696e9816464ebbb9e928bb2303cc58",
            "5b96a862c84342b7889ed4ec9f931890",
            "0e7a547f540e4f818c8c5c749c0b974b",
            "2a9b41e5370b4a15859a478b4323a7ba",
            "0c3b0ff0ea6b4e3c84f4e24a4de205c6",
            "323d82d306b541ed9610f3d040887974",
            "a4bc799253d244b19cb62a2bad75e198"
          ]
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef491ebdad714838a8ddadbc4d2902b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=64x64 at 0x7F88302FC1F0>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "save the pipeline to a local folder"
      ],
      "metadata": {
        "id": "Mpg30rDFoSX-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "image_pipe.save_pretrained(\"bored-ape-diffusion\")"
      ],
      "metadata": {
        "id": "yEE5d6z6qCtx"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inspecting the folder contents"
      ],
      "metadata": {
        "id": "0mi8JgktoWSZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls bored-ape-diffusion/"
      ],
      "metadata": {
        "id": "NTZdCgUEqKnb",
        "outputId": "96ff941e-a1b6-40b0-8f23-ff66db797bba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model_index.json  scheduler  unet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls bored-ape-diffusion/unet/"
      ],
      "metadata": {
        "id": "-5g87LdXqYfU",
        "outputId": "54659844-fe3b-4536-d66f-d9af25a95b97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "config.json  diffusion_pytorch_model.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Option 2: Writing a Sampling Loop"
      ],
      "metadata": {
        "id": "p0iIPm7HrDm6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We begin with random noise, and run through the scheduler timesteps from most to least noisy, removing a small amount of noise each step based on the model prediction"
      ],
      "metadata": {
        "id": "N91tXP6OoebG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random starting point (8 random images):\n",
        "sample = torch.randn(8, 3, 64, 64).to(device)\n",
        "\n",
        "for i, t in enumerate(noise_scheduler.timesteps):\n",
        "\n",
        "    # Get model pred\n",
        "    with torch.no_grad():\n",
        "        residual = model(sample, t).sample\n",
        "\n",
        "    # Update sample with step\n",
        "    sample = noise_scheduler.step(residual, t, sample).prev_sample\n",
        "\n",
        "show_images(sample)"
      ],
      "metadata": {
        "id": "fVn41Jjcqpz0",
        "outputId": "6f204c47-c8b6-437a-fddf-24471d8efa9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=530x68 at 0x7F882E23BF70>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Push model to HF Hub"
      ],
      "metadata": {
        "id": "YjuMlz_0hXkk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Login with an access token with write permission"
      ],
      "metadata": {
        "id": "UwEPFa6Ap2YD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "9x8WLHvlh29P",
        "outputId": "3311517b-bc35-469d-8ee2-d63ac31fd85b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "b319f2a8f6c747ca8ac8c2ee77cbb9f4",
            "869823b53d2f4a35ad5533388cc8728e",
            "6d89477fc0864fdc80263e6fe55efb02",
            "6278c1b1d85c44febbea680da5353531",
            "d8711571f71f4deb8241860648ef3358",
            "01dc00c64ceb4850a40e67ec33c3bafa",
            "a01ebdfab4fc4548b7b6143b9a683d10",
            "6fe9bc8ace8c43c78cdbf728fce10757",
            "768f9954b7c2405a9b1958444b4d01bd",
            "ee6f0cf6e6e74f8aa063290de0fa16bd",
            "78ead26810ce46b495c163e386ae0d49",
            "5e21ca81009a40cbb99d43e1c28e5b49",
            "bea7b433d18b4aedaac17d5986e46659",
            "4decfa87f9d348ac8289ea60249afe4f",
            "9249111c473c4e6ca93c1866d17ebf25",
            "6293bf7af68947ebb6fab12913c948ae",
            "00ab55ddf96447b2992e073c728f6e0c"
          ]
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting the repository name"
      ],
      "metadata": {
        "id": "eku1z31Gp-l4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import get_full_repo_name\n",
        "\n",
        "model_name = \"bored-ape-diffusion\"\n",
        "hub_model_id = get_full_repo_name(model_name)\n",
        "hub_model_id"
      ],
      "metadata": {
        "id": "ileYWDBvhati",
        "outputId": "d81c8c2b-677b-4796-e2a2-b4526f2f92ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'daspartho/bored-ape-diffusion'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a model repository on the 🤗 Hub and push our model"
      ],
      "metadata": {
        "id": "1p4uAwt2qPce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import HfApi, create_repo\n",
        "\n",
        "create_repo(hub_model_id, exist_ok=True)\n",
        "api = HfApi()\n",
        "api.upload_folder(\n",
        "    folder_path=\"bored-ape-diffusion/scheduler\", path_in_repo=\"\", repo_id=hub_model_id\n",
        ")\n",
        "api.upload_folder(folder_path=\"bored-ape-diffusion/unet\", path_in_repo=\"\", repo_id=hub_model_id)\n",
        "api.upload_file(\n",
        "    path_or_fileobj=\"bored-ape-diffusion/model_index.json\",\n",
        "    path_in_repo=\"model_index.json\",\n",
        "    repo_id=hub_model_id,\n",
        ")"
      ],
      "metadata": {
        "id": "lm17G9-ghmVn",
        "outputId": "6b899b3a-1fbd-4852-91cf-8f402e372a2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/daspartho/bored-ape-diffusion/blob/main/model_index.json'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's download the model from Hub and try generating some Bored Apes"
      ],
      "metadata": {
        "id": "ThCSo70Er1Iu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DDPMPipeline\n",
        "\n",
        "image_pipe = DDPMPipeline.from_pretrained(hub_model_id).to(device)\n",
        "pipeline_output = image_pipe()\n",
        "pipeline_output.images[0]"
      ],
      "metadata": {
        "id": "RNxEVWcah8Rm",
        "outputId": "f20f1e1d-eda8-4268-9c0f-cdcf7dd6f84f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
            "6c49fc29c9c04d69ae583f3b83869779",
            "fd714bac662b4dcc8d60b73a2e8aab24",
            "7e88316d4b654f26b7725c9aeeb7c542",
            "757b484384b54b3483e8bb4e4f8d96a1",
            "5b8b9282ee6a47d39fcf83d1da536534",
            "7d01f8ddc92e4ede9c4671472d938db0",
            "c5f4645273cf4d098f655f06d49e9ae6",
            "127e5cd831b848f18ca04f3cfd93f9fb",
            "bf431e25aeae42099caaa6b0d04d2a19",
            "b45c5412e0114867b8a03243f86082dc",
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            "36c0d154468243fb8d992d32fb8c9d67"
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        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/180 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c49fc29c9c04d69ae583f3b83869779"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c320bab53f847b9b18b39bec71e8c30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/702 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c86de619809d4f5d9d574ba94333d660"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/74.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d70986fb51b4ff6a134a73a9c222584"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/299 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f9ee39add2184cc7997d27a186171570"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4842ba571cdb4cf1bdb9f048d7ef4350"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
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\n"
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    }
  ]
}