Anastasiia_Ponkratova_CV_L9_modele_dyfuzyjne.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/s20488/COV/blob/main/Anastasiia_Ponkratova_CV_L9_modele_dyfuzyjne.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Wprowadzenie do modeli dyfuzyjnych i biblioteki diffusers"
      ],
      "metadata": {
        "id": "k8HKgfAPKflY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Instalujemy biblioteki."
      ],
      "metadata": {
        "id": "UIQzXFvBKkMj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install -qq -U diffusers datasets transformers accelerate ftfy pyarrow==14.0.1"
      ],
      "metadata": {
        "id": "RhXAFvZ6KfEK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f421b53-0039-461d-dfe6-652bc849af33"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.1/9.1 MB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.4/54.4 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.0/38.0 MB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m37.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pomocnicze funkcje, które przydadzą nam się później."
      ],
      "metadata": {
        "id": "mN6bOieeK3Lk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "nccfU37YKZyE"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn.functional as F\n",
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image\n",
        "\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",
        "\n",
        "def make_grid(images, size=64):\n",
        "    \"\"\"Given a list of PIL images, stack them together into a line for easy viewing\"\"\"\n",
        "    output_im = Image.new(\"RGB\", (size * len(images), size))\n",
        "    for i, im in enumerate(images):\n",
        "        output_im.paste(im.resize((size, size)), (i * size, 0))\n",
        "    return output_im\n",
        "\n",
        "\n",
        "# Mac users may need device = 'mps' (untested)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import YouTubeVideo\n",
        "\n",
        "YouTubeVideo(\"W4Mcuh38wyM\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "QU3pyzNjNhb4",
        "outputId": "e7619fd4-0a39-48df-c89c-4044a4b76dff"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x791c6a71e8c0>"
            ],
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"400\"\n",
              "            height=\"300\"\n",
              "            src=\"https://www.youtube.com/embed/W4Mcuh38wyM\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "            \n",
              "        ></iframe>\n",
              "        "
            ],
            "image/jpeg": 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\n"
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import StableDiffusionPipeline\n",
        "\n",
        "# Check out https://huggingface.co/sd-dreambooth-library for loads of models from the community\n",
        "model_id = \"sd-dreambooth-library/mr-potato-head\"\n",
        "\n",
        "# Load the pipeline\n",
        "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(\n",
        "    device\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772,
          "referenced_widgets": [
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            "076decb60f3942eaab0980d803b95064",
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            "fdd60cff3b2f4f4480c20183473c0816",
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            "0a804481f56a47c2a23ca1ed80b894ba",
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        "id": "woDHfsJDOF66",
        "outputId": "b05e2cec-5eb6-4c63-ebf5-668b58abf97f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
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        {
          "output_type": "stream",
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          "text": [
            "vae/diffusion_pytorch_model.safetensors not found\n"
          ]
        },
        {
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        {
          "output_type": "display_data",
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        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Pipelines loaded with `dtype=torch.float16` cannot run with `cpu` device. It is not recommended to move them to `cpu` as running them will fail. Please make sure to use an accelerator to run the pipeline in inference, due to the lack of support for`float16` operations on this device in PyTorch. Please, remove the `torch_dtype=torch.float16` argument, or use another device for inference.\n",
            "Pipelines loaded with `dtype=torch.float16` cannot run with `cpu` device. It is not recommended to move them to `cpu` as running them will fail. Please make sure to use an accelerator to run the pipeline in inference, due to the lack of support for`float16` operations on this device in PyTorch. Please, remove the `torch_dtype=torch.float16` argument, or use another device for inference.\n",
            "Pipelines loaded with `dtype=torch.float16` cannot run with `cpu` device. It is not recommended to move them to `cpu` as running them will fail. Please make sure to use an accelerator to run the pipeline in inference, due to the lack of support for`float16` operations on this device in PyTorch. Please, remove the `torch_dtype=torch.float16` argument, or use another device for inference.\n",
            "Pipelines loaded with `dtype=torch.float16` cannot run with `cpu` device. It is not recommended to move them to `cpu` as running them will fail. Please make sure to use an accelerator to run the pipeline in inference, due to the lack of support for`float16` operations on this device in PyTorch. Please, remove the `torch_dtype=torch.float16` argument, or use another device for inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"an abstract oil painting of sks mr potato head by picasso\"\n",
        "image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n",
        "image"
      ],
      "metadata": {
        "id": "GtuMbEGaOKqI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tworzenie pipeline'u dla modelu dyfuzyjnego\n",
        "API diffusers składa się z 3 kluczowych elementów\n",
        "\n",
        "1. **Pipelines** - wysokopoziomowe klasy przygotowane do szybkiego generowania przykładów przy pomocy popularnych modeli dyfuzyjnych\n",
        "2. **Models** - popularne architektury do trenowania modelidyfuzyjnych.\n",
        "3. **Schedulers** - różne techniki generowania obrazu z szumu w trakcie inferencji oraz dodawania szumu podczas treningu.\n",
        "\n",
        "Na dzisiejszych laboratoriach storzymy sovue pipeline tworzenia obrazków motyli."
      ],
      "metadata": {
        "id": "oQHriQEwOaDe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Zadanie 1: Pobieramy zbiór danych"
      ],
      "metadata": {
        "id": "UU3Cupz8PtH0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# 1. Pobierz zbiór danych huggan/smithsonian_butterflies_subset,split=train\n",
        "dataset = dataset = load_dataset(\"huggan/smithsonian_butterflies_subset\", split=\"train\") # TODO\n",
        "image_size = 32\n",
        "batch_size = 64 # można zmniejszyc jakby notebook miał problem z pamięcią\n",
        "\n",
        "# definiujemy augmentacje\n",
        "# 1 -resize do image_size\n",
        "# 2 - losowy poziomy obrót\n",
        "# 3 - konwertujemy na tensor\n",
        "# 4 - normalizacja do przedziału (-1,1)\n",
        "preprocess = transforms.Compose([\n",
        "    transforms.Resize((image_size, image_size)),\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n",
        "]) # TODO\n",
        "\n",
        "def transform(examples):\n",
        "    images = [preprocess(image.convert(\"RGB\")) for image in examples[\"image\"]]\n",
        "    return {\"images\": images}\n",
        "\n",
        "\n",
        "dataset.set_transform(transform)\n",
        "\n",
        "# Tworzymy dataloader dla datasetu z określonym batch_sizem\n",
        "train_dataloader = train_dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True) # TODO"
      ],
      "metadata": {
        "id": "d4MQBd4cPsdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
            "fc6f5ca256604fa983d4790a4835f3dc",
            "d0169531d4d04797b4958ce9816baa5a",
            "9cb431e49c14482dace497a1471402f6",
            "f75a96e5fa4e4982a8f0c91dff81ca55",
            "c7b7f28ea788413b84d6b8439f2e9adb",
            "2c5d68eb385c4e19b3652580c5e4a488",
            "650f3300c3d3419fb58278fd1399d54a",
            "1d4bb57ac6ce4f7db809594392e47d70",
            "01d477a2fefc46f58cf5e2b5abb36e2e",
            "7848d813678741d195af16c10a12df4a",
            "946a5f7cec714b9a8fc72251dd9cc3ed",
            "bef84ea1cf124e0287be8d38e39b00f2",
            "e8d915dfca3f421c83c058c0e130bd5a",
            "a7c56e572e934bac8f3a1ebfac06add2",
            "d20971138da2495f8a691c5737774583",
            "f6cf58e1b4a94c70ba46e0d340b5f8c7",
            "e1f77e2bd9ea4062baa1e44f3a8cca1c",
            "5a8773f21508478b8418d830b38e8de9",
            "7fd5f48760f6403d868c7cca4f744f83",
            "2213c70a93984dbcab8b7a56ca0d93df",
            "8c2217d5491346de9570a607e68201bd",
            "bb4381a9c0b14d3a9b4fd4c3204624e8",
            "b246b6d029a448e383aef9455774f51c",
            "1a4daae2f15548a19c14ebd7f21a916f",
            "511f738b7a49480caf8ab2c0373a8076",
            "83d8635e5db74406b9bae578610b6b58",
            "96ea0f16bc734673a89038944aba0469",
            "aec7d7fb66bb4978a2309001d5513b25",
            "307950adbc7e4be09c382ee9c3b5af59",
            "db2f4ec24f124f888c4ff71d6763adc9",
            "e8434c2ce5fb47e1896822baacbc707d",
            "1b8b820118974496924a3df3e709cf01",
            "40bdf45094054c959959dd35a1be28dc",
            "f8c62e199cd04ccf8bbe8fffa05baa2c",
            "eee01215855c4492a595fe05ec630b16",
            "e277bbb0afac44bcb0bbc1dc5ed498b5",
            "72e0655ff1ef459889932bc13982fe53",
            "8a313f3a268749e98ea2a699443fdee9",
            "304fc95284f44485a1d313ac611d2017",
            "c13d4d12697d4d959fb15e2636dfb759",
            "d6787249fae94ad08e5e3950c9e546aa",
            "37e4a23a2d4c4dbb93d2961d03d18c3c",
            "70bbbeec16014eb5b7606341705ad49f",
            "baf7104112e0497b8e9822911ef1037c"
          ]
        },
        "outputId": "62157a14-3639-4703-ebbc-53e800bb87c4"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": [
              "Downloading readme:   0%|          | 0.00/609 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc6f5ca256604fa983d4790a4835f3dc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Repo card metadata block was not found. Setting CardData to empty.\n",
            "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bef84ea1cf124e0287be8d38e39b00f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/237M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b246b6d029a448e383aef9455774f51c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8c62e199cd04ccf8bbe8fffa05baa2c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sprawdzamy, czy obrazy wyświetlają się poprawnie."
      ],
      "metadata": {
        "id": "7TPCduJrOSXU"
      }
    },
    {
      "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": {
        "id": "4W49VJkMQ-uW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "outputId": "887fba0b-b212-436c-94c3-f9b315e2fccd"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape: torch.Size([8, 3, 32, 32])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x64>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Zadanie 2:Tworzymy scheduler\n",
        "\n",
        "Chcemy wytrenować model poprzez iteracyjne dodawanie szumu do obrazów, a następnie podawać zaszumione obrazy do modelu.  W trakcie inferencji iteracyjnie usuwamy ten szum. W bibliotece diffusers za ten proces odpowiadają schedulers.\n",
        "\n",
        "Noise scheduler reguluje ile szumu jest dodawane w trakcie kolejnego kroku."
      ],
      "metadata": {
        "id": "tIwq_p0JRFcj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DDPMScheduler\n",
        "\n",
        "# TODO: zaimportuj Denoising Diffusion Probabilistic Model Scheduler\n",
        "noise_scheduler = DDPMScheduler(num_train_timesteps=1000) # stwórz scheduler z liczbą kroków = 1000"
      ],
      "metadata": {
        "id": "Ldwi4Ub8R13L"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Zobaczmy jak będzie się zmieniac ilość dodawanego szumu."
      ],
      "metadata": {
        "id": "MAaLlx0PSIek"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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": {
        "id": "giLGs_9HSJtM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "7cc79f2a-0326-425d-dc62-e538a45ec306"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Korzystamy z schedulera do dodania szumu w odpowiedniej ilości w zależności od kroku przy pomocy noise_scheduler.add_noise."
      ],
      "metadata": {
        "id": "jsAqivgNScEO"
      }
    },
    {
      "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": {
        "id": "8BIo-xZqSMDf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "outputId": "f160d846-282a-46f6-cbfa-fe1fa5b415ba"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Noisy X shape torch.Size([8, 3, 32, 32])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x64>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Zadanie 3: definiujemy model\n",
        "Wykorzystamy architekturę UNet. Kluczową cechą tego modelu jest to, że generuje obrazy w takim samym rozmiarze, co wejście, co jest dla nas przydatne.\n",
        "\n",
        "Skorzystamy z *UNet2Dmodel* z biblioteki diffusers."
      ],
      "metadata": {
        "id": "Qk67-tH-SxXd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import UNet2DModel\n",
        "\n",
        "# Zaimportuj i stwórz model z zadanymi wartościmi parametrów:\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",
        "# , samodzielnie określ licznę kanałów na wejściu i wyjściu\n",
        "model = UNet2DModel(\n",
        "    in_channels=3,\n",
        "    out_channels=3,\n",
        "    layers_per_block=2,\n",
        "    block_out_channels=(64, 128, 128, 256),\n",
        "    down_block_types=(\n",
        "        \"DownBlock2D\",\n",
        "        \"DownBlock2D\",\n",
        "        \"AttnDownBlock2D\",\n",
        "        \"AttnDownBlock2D\",\n",
        "    ),\n",
        "    up_block_types=(\n",
        "        \"AttnUpBlock2D\",\n",
        "        \"AttnUpBlock2D\",\n",
        "        \"UpBlock2D\",\n",
        "        \"UpBlock2D\",\n",
        "    ),\n",
        "    sample_size=(image_size, image_size)\n",
        ").to(device) # TODO"
      ],
      "metadata": {
        "id": "sJGUDpD7TOuS"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Przy większym rozmiarze obrazów stworzylibyśmy więcej up and down blocks."
      ],
      "metadata": {
        "id": "gEWpslEZT0Ru"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sprawdźmy, czy model wytworzy nam odpowiedni rozmiar obrazy dla losowych danych."
      ],
      "metadata": {
        "id": "xRjH2l8FT-wz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "    model_prediction = model(noisy_xb, timesteps).sample\n",
        "model_prediction.shape"
      ],
      "metadata": {
        "id": "bMtApbhwTkss",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b399d9be-4ef3-460b-daae-c5370314e5fe"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8, 3, 32, 32])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trening modelu:"
      ],
      "metadata": {
        "id": "Wz4J-vnaUK5v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training loop\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=4e-4)\n",
        "\n",
        "losses = []\n",
        "\n",
        "for epoch in range(30):\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",
        "    if (epoch + 1) % 5 == 0:\n",
        "        loss_last_epoch = sum(losses[-len(train_dataloader) :]) / len(train_dataloader)\n",
        "        print(f\"Epoch:{epoch+1}, loss: {loss_last_epoch}\")"
      ],
      "metadata": {
        "id": "HyT0FA4LUZGB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd0b2063-33cc-435d-b79c-601303eef943"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `num_train_timesteps` directly via 'DDPMScheduler' object attribute is deprecated. Please access 'num_train_timesteps' over 'DDPMScheduler's config object instead, e.g. 'scheduler.config.num_train_timesteps'.\n",
            "  deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:5, loss: 0.11574860149994493\n",
            "Epoch:10, loss: 0.08401046157814562\n",
            "Epoch:15, loss: 0.06906332424841821\n",
            "Epoch:20, loss: 0.061762701254338026\n",
            "Epoch:25, loss: 0.04751908907201141\n",
            "Epoch:30, loss: 0.048894847044721246\n"
          ]
        }
      ]
    },
    {
      "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": {
        "id": "A1vF6dtUUb3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "02aae304-569d-4af9-a9d6-2e68640eceb9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generujemy obrazy"
      ],
      "metadata": {
        "id": "E1Mj7QmpUbIl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from diffusers import DDPMPipeline\n",
        "\n",
        "# Stwórz pipeline dla modeli dyfuzyjnych z naszym modelem i schedulere\n",
        "image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler) # TODO\n",
        "pipeline_output = image_pipe(batch_size=1)\n",
        "generated_image = pipeline_output.images[0]"
      ],
      "metadata": {
        "id": "-NpN6nvYUhvG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b70f93975f99440a838842dc06f2e135",
            "913376c2231e479ca64ba9837bc498ed",
            "726b541483fc4092b9e612afa314703b",
            "72ee599676e14169ba681081a57344b3",
            "0c6c88f264dc4db0b3a0b3fb07f9f84a",
            "bbe82204fa164965a163c41054dae423",
            "a59447d6091040e3889de6036b1548f3",
            "4952520af4ff4e6b9d197946e81df53a",
            "a19210e636544c82af900fc9b33d5645",
            "a1b28d2ccfd544b38250f38300dbed22",
            "fdd008e57bd746a4ab03a37ae4f5a19a"
          ]
        },
        "outputId": "dd5cc8f2-1b20-41d4-e71b-fcb930e916e1"
      },
      "execution_count": 8,
      "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": "b70f93975f99440a838842dc06f2e135"
            }
          },
          "metadata": {}
        }
      ]
    }
  ]
}