Diffusion.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers diffusers ftfy accelerate"
      ],
      "metadata": {
        "id": "lUoGyKdZLVbe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\"/content/drive/MyDrive/Visual Story Telling/Story_Gen_Model/DistilGPT2\")\n",
        "base_tokenizer = AutoTokenizer.from_pretrained('distilgpt2')\n",
        "base_tokenizer.pad_token = base_tokenizer.eos_token\n",
        "\"\"\"\n",
        "Format of the input\n",
        "Title: ADD_TITLE. Genre: ADD_GENRES. Characters: ADD_CHARS. Relations: Neutral: <>. Positive: <>. Negative: <>.  Plot: ADD_PLOT\n",
        "\"\"\"\n",
        "\n",
        "from transformers import pipeline\n",
        "\n",
        "input = \"Title: Saiyan Warrior. Genre: Action, Adventure, Drama. Characters: Goku, Vegeta, Beerus. Relations: Neutral: <Beerus, Vegeta>. Positive: <Goku, Vegeta>. Negative: <Goku, Beerus>. Plot: Beerus is a god of destruction, who came to Earth to destroy it. Goku and Vegeta in order to protect their family fight and defeat him.\"\n",
        "generator = pipeline(\"text-generation\", model=base_model, tokenizer = base_tokenizer)\n",
        "do_sample = False\n",
        "num_beams = 1\n",
        "top_p = 0.9\n",
        "story = generator(input, num_beams = num_beams, top_p = top_p, do_sample = do_sample, min_length = 150, max_length = 250)[0]['generated_text'][len(input)+10:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxVO1k4nROj1",
        "outputId": "86e298ff-d687-4745-a392-3b91135f1435"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "story"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "aWrVKQFTWwg3",
        "outputId": "08e3430f-1643-4533-e608-fa0774594b44"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The film begins with Goku and Vegeta in a battle with Bulma, Goku, Vegeta, and a giant monster. Goku is then shown defeating Bulma in a fight with Goku and Vegeta. Goku and Vegeta then go to Earth to destroy the monster. Goku and Vegeta are shown in a flashback as they are shown to be in the beginning of the film. Goku and Vegeta are shown in a spaceship traveling to the center of the universe. They are shown to be in the form of a giant monster, referred to as a \"Pluto\", and are shown to be in the form of a woman. They are shown to be in the form of a woman, who is shown to be in the form of a woman. The woman is shown to be in the form of a woman. The woman is shown to be in the form'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "each_sent = story.split(\".\")\n",
        "first_sent = each_sent[0]"
      ],
      "metadata": {
        "id": "_XGZBEudRfMO"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn-ofL5MJQeu",
        "outputId": "70cd7f40-4bf5-4e38-9005-f041aa32d565"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"id2label\"]` will be overriden.\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from diffusers import DiffusionPipeline\n",
        "\n",
        "generator = DiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generator.to(\"cuda\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phtgIBOIJfau",
        "outputId": "12b33279-b569-4f2c-a60e-f3c067d9caec"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StableDiffusionPipeline {\n",
              "  \"_class_name\": \"StableDiffusionPipeline\",\n",
              "  \"_diffusers_version\": \"0.17.1\",\n",
              "  \"feature_extractor\": [\n",
              "    \"transformers\",\n",
              "    \"CLIPImageProcessor\"\n",
              "  ],\n",
              "  \"requires_safety_checker\": true,\n",
              "  \"safety_checker\": [\n",
              "    \"stable_diffusion\",\n",
              "    \"StableDiffusionSafetyChecker\"\n",
              "  ],\n",
              "  \"scheduler\": [\n",
              "    \"diffusers\",\n",
              "    \"PNDMScheduler\"\n",
              "  ],\n",
              "  \"text_encoder\": [\n",
              "    \"transformers\",\n",
              "    \"CLIPTextModel\"\n",
              "  ],\n",
              "  \"tokenizer\": [\n",
              "    \"transformers\",\n",
              "    \"CLIPTokenizer\"\n",
              "  ],\n",
              "  \"unet\": [\n",
              "    \"diffusers\",\n",
              "    \"UNet2DConditionModel\"\n",
              "  ],\n",
              "  \"vae\": [\n",
              "    \"diffusers\",\n",
              "    \"AutoencoderKL\"\n",
              "  ]\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
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        "image = generator(first_sent).images[0]"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.mkdir(\"Story\")"
      ],
      "metadata": {
        "id": "fdHpAlUNTTBq"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image.save(\"Story/sent_0.png\")"
      ],
      "metadata": {
        "id": "RTMF7WjzR3DX"
      },
      "execution_count": 8,
      "outputs": []
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        "for i in range(1,len(each_sent)):\n",
        "  image = generator(each_sent[i]).images[0]\n",
        "  image.save(f\"Story/sent_{i}.png\")"
      ],
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          "text": [
            "Potential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed.\n"
          ]
        },
        {
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Potential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from PIL import Image\n",
        "from diffusers import StableDiffusionImg2ImgPipeline"
      ],
      "metadata": {
        "id": "fl7W-sXdKm3L"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del generator\n",
        "del base_tokenizer\n",
        "del base_model\n",
        "import gc\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "QiZKBg8eRGQN"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\"\n",
        "pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\"nitrosocke/Ghibli-Diffusion\", torch_dtype=torch.float16).to(\n",
        "    device\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CH7I7FAaLd1B",
        "outputId": "d64c7de6-8a1b-4464-ccda-85fab8aab048"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "vae/diffusion_pytorch_model.safetensors not found\n",
            "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"id2label\"]` will be overriden.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1,len(each_sent)):\n",
        "  init_image = Image.open(f\"Story/sent_{i-1}.png\").convert(\"RGB\")\n",
        "  prompt = f\"ghibli style, {each_sent[i]}\"\n",
        "  generator = torch.Generator(device=device).manual_seed(1024)\n",
        "  image = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, generator=generator).images[0]\n",
        "  image.save(f\"Story/sent_{i}.png\")"
      ],
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}