super_simple_stable_diffusion.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fladdict/stable-diffusion/blob/main/super_simple_stable_diffusion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SSSD, Super Simple Stable Diffusion\n",
        "\n",
        "Get latest version from [HERE](https://github.com/fladdict/stable-diffusion/blob/main/super_simple_stable_diffusion.ipynb).\n",
        "\n",
        "Lightwaight easy to use Stable Diffusion Kit.\n",
        "Just run all cells. \n",
        "\n",
        "Only you need is [Hugging Face's user setting page](https://huggingface.co/settings/tokens)."
      ],
      "metadata": {
        "id": "Mn4TYBCzPTE_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LICENSE\n",
        "[Stable Diffusion](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE)\n",
        "\n",
        "[Diffuser](https://github.com/huggingface/diffusers/blob/main/LICENSE)"
      ],
      "metadata": {
        "id": "m3yqqcoGqlVx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install Library"
      ],
      "metadata": {
        "id": "_hbawQKHpkrr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pyoxzbMxnBex"
      },
      "outputs": [],
      "source": [
        "#Install Lib\n",
        "!pip install diffusers==0.4.0 transformers scipy ftfy"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Login to Hugging Face\n",
        "\n",
        "**IMPORTANT**\n",
        "\n",
        "First you need go to Huggingface and accept [stable diffusion model's LICENCE ](https://huggingface.co/spaces/CompVis/stable-diffusion-license)\n",
        "\n",
        "Get ACCESS TOKEN from [Huggingface's user setting page](https://huggingface.co/settings/tokens).\n",
        "\n",
        "Run a cell below and fill ACCESS TOKEN (read) into the login form."
      ],
      "metadata": {
        "id": "seJw39zMpflA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Get access token from Hugging Face\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "4cb38968-cdf6-4162-c231-e468522267a2"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Function Setup"
      ],
      "metadata": {
        "id": "WYkj7P6kz98Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from diffusers import StableDiffusionPipeline\n",
        "from diffusers import StableDiffusionImg2ImgPipeline\n",
        "import numpy as np\n",
        "import PIL\n",
        "import gc\n",
        "import os\n",
        "\n",
        "def save_image(directory_path, file_name, img):\n",
        "  os.makedirs(directory_path, exist_ok = True)\n",
        "  img.save(f\"{directory_path}/{file_name}\")\n",
        "\n",
        "\n",
        "\n",
        "def clear_memory():\n",
        "  gc.collect()\n",
        "  torch.cuda.empty_cache()\n",
        "\n",
        "\n",
        "\n",
        "def get_txt2img_pipeline(model_path = \"CompVis/stable-diffusion-v1-4\"):\n",
        "  pipeline = StableDiffusionPipeline.from_pretrained(model_path, \n",
        "                                                     revision=\"fp16\", \n",
        "                                                     torch_dtype=torch.float16,\n",
        "                                                     use_auth_token=True)\n",
        "  pipeline.to(\"cuda\")\n",
        "  pipeline.enable_attention_slicing()\n",
        "  return pipeline\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def get_img2img_pipeline(model_path = \"CompVis/stable-diffusion-v1-4\"):\n",
        "  pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(model_path, \n",
        "                                                            revision=\"fp16\", \n",
        "                                                            torch_dtype=torch.float16,\n",
        "                                                            use_auth_token=True)\n",
        "  pipeline.to(\"cuda\")\n",
        "  pipeline.enable_attention_slicing()\n",
        "  return pipeline\n",
        "\n",
        "pipe = None\n",
        "last_model_path = None"
      ],
      "metadata": {
        "id": "PF_WVK8jnjkp"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Additional Feature\n",
        "# if you use UPPER CASE text in a prompt, it randomly swap the woard from this dictionary.\n",
        "\n",
        "decorate_dict = {\n",
        "    \"COLOR\":[\"red\", \"green\", \"blue\", \"yellow\", \"orange\", \"purple\", \"black\", \"white\", \"dark\", \"blight\", \"gold\", \"silver\", \"brown\"],\n",
        "    \"GENDER\":[\"male\",\"female\",\"unisex\"],\n",
        "    \"MODIFIER\":[\"UnrealEngine\",\"Octane Render\", \"8k\", \"High Qulity\", \"Beautiful\", \"Art Lens\", \"Award Wining\", \"Pixiv Trending\", \"Masterpiece\", \"Epic\"]\n",
        "}\n",
        "\n",
        "\n",
        "def decorate_prompt(prompt, dict):\n",
        "    for index, key in enumerate(dict):\n",
        "      while(prompt.find(key)!=-1):\n",
        "        words = dict[key];\n",
        "        word = words[random.randint(0,len(words)-1)];\n",
        "        prompt = prompt.replace(key, word, 1);\n",
        "    return prompt;"
      ],
      "metadata": {
        "id": "gWkQyLnXqQkl"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Diffuse!!!\n",
        "\n",
        "Once you setup, just run only this cell"
      ],
      "metadata": {
        "id": "-TGcoo26z5IY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import autocast\n",
        "from datetime import datetime\n",
        "import random\n",
        "from PIL import Image\n",
        "\n",
        "#@markdown ## General Settings\n",
        "prompt = \"\" #@param {type:\"string\"}\n",
        "negative_prompt = \"blur, unsharp, unclear, noise, rough sketch, speed painting, bad drawing\" #@param {type:\"string\"}\n",
        "width = 768 #@param {type:\"integer\"}\n",
        "height = 512 #@param {type:\"integer\"}\n",
        "\n",
        "guidance_scale = 7.5 #@param {type:\"number\"}\n",
        "num_inference_steps = 50 #@param {type:\"number\"}\n",
        "seed = None #@param\n",
        "num = 3 #@param {type:\"integer\"}\n",
        "\n",
        "#@markdown -\n",
        "#@markdown ## Init Image settings\n",
        "#@markdown File path used for base Image. \n",
        "init_image = \"\" #@param {type:\"string\"}\n",
        "strength = 0.9 #@param {type:\"number\"}\n",
        "\n",
        "#@markdown - \n",
        "#@markdown ## Loopback\n",
        "#@markdown If True, it takes time but high quality\n",
        "is_loopback = True #@param {type:\"boolean\"}\n",
        "loopback_num = 2 #@param {type:\"integer\"}\n",
        "\n",
        "#@markdown -\n",
        "#@markdown ## Model Settings\n",
        "#@markdown Select huggingface's model card model path\n",
        "\n",
        "model_path = \"CompVis/stable-diffusion-v1-4\" #@param {type:\"string\"} [\"CompVis/stable-diffusion-v1-4\", \"naclbit/trinart_stable_diffusion_v2\", \"hakurei/waifu-diffusion\"] {allow-input: true}\n",
        "\n",
        "#@markdown -\n",
        "#@markdown ## Export Settings\n",
        "\n",
        "save_directory_path = \"/content/output/test\" #@param {type:\"string\"}\n",
        "save_file_prefix = \"SD\" #@param {type:\"string\"}\n",
        "is_save_image = True #@param {type:\"boolean\"}\n",
        "is_display_image = True #@param {type:\"boolean\"}\n",
        "\n",
        "clear_memory()\n",
        "\n",
        "with autocast(\"cuda\"):\n",
        "  if init_image != None:\n",
        "    _init_image = Image.open(init_image)\n",
        "  for index in range(num):\n",
        "    _seed = random.randint(0, 2**32) if seed == None else seed\n",
        "    generator = torch.Generator(\"cuda\").manual_seed(_seed)\n",
        "\n",
        "    _prompt = decorate_prompt(prompt, decorate_dict)\n",
        "    _negative_prompt = decorate_prompt(negative_prompt, decorate_dict)\n",
        "\n",
        "    if init_image != None and init_image != \"\" :\n",
        "      if pipe == None or type(pipe) != StableDiffusionImg2ImgPipeline or model_path != last_model_path:\n",
        "        pipe = get_img2img_pipeline(model_path)\n",
        "        last_model_path = model_path\n",
        "        \n",
        "      result = pipe(\n",
        "          init_image = _init_image,\n",
        "          prompt = _prompt,\n",
        "          negative_prompt = _negative_prompt,\n",
        "          width = width,\n",
        "          height = height,\n",
        "          strength = strength,\n",
        "          guidance_scale = guidance_scale,\n",
        "          num_inference_steps = num_inference_steps,\n",
        "          generator = generator\n",
        "      )\n",
        "    else:\n",
        "      if pipe == None or type(pipe) != StableDiffusionPipeline or model_path != last_model_path:\n",
        "        pipe = get_txt2img_pipeline(model_path)\n",
        "        last_model_path = model_path\n",
        "      result = pipe(\n",
        "          prompt = _prompt,\n",
        "          negative_prompt = _negative_prompt,\n",
        "          width = width,\n",
        "          height = height,\n",
        "          strength = strength,\n",
        "          guidance_scale = guidance_scale,\n",
        "          num_inference_steps = num_inference_steps,\n",
        "          generator = generator\n",
        "      )\n",
        "\n",
        "    image = result[\"sample\"][0]\n",
        "    is_nswf = result[\"nsfw_content_detected\"][0]\n",
        "\n",
        "    if is_display_image == True:\n",
        "      display(image)\n",
        "\n",
        "    if is_save_image == True and is_nswf==False:\n",
        "      file_id = datetime.today().strftime('%Y-%m-%d-%H-%M-%S')\n",
        "      save_image(save_directory_path, f\"{save_file_prefix}-{file_id}.png\", image)\n",
        "\n"
      ],
      "metadata": {
        "id": "OZI3YNWWHIKP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cheat Sheet\n",
        "\n"
      ],
      "metadata": {
        "id": "yeemCWQkJZ6L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prompt for beginners\n",
        "\n",
        "\n",
        "```\n",
        "<MEDIUM> of <THEME> by <ARTIST> in the style of <STYLE>, <MODIFIER>, <MODIFIER>\n",
        "```\n",
        "\n",
        "```\n",
        "Oil painting of landscape by Rembrandt in the style of impressionism, high quality, masterpiece\n",
        "```"
      ],
      "metadata": {
        "id": "UqvXgM8LnHY4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Style\n",
        "* Ancient Egyptian Art\n",
        "* Ancient Greek Art\n",
        "* Byzantine Art\n",
        "* Gothic\n",
        "* Medieval Art\n",
        "* Renaissance\n",
        "* Mannerism\n",
        "* High Renaissance\n",
        "* Baroque\n",
        "* Rococo\n",
        "* Classicism\n",
        "* Neo Classicism\n",
        "* Academicism\n",
        "* Romanticism\n",
        "* Orientalism\n",
        "* Realism\n",
        "* Primitivism\n",
        "* Surrealism\n",
        "* Impressionism\n",
        "* Symbolism\n",
        "* Pointillism\n",
        "* Abstract Art\n",
        "* Japonism\n",
        "* Art Deco\n",
        "* Fauvism\n",
        "* Art Nouveau\n",
        "* Expressionism\n",
        "* Cubism\n",
        "* Futurism\n",
        "* Dada\n",
        "* Constructivism\n",
        "* Outsider art\n",
        "* Concretism\n",
        "* Neo Expressionism\n",
        "* Pop Art\n",
        "* Modernism\n",
        "* Minimalism\n",
        "* Photorealism\n",
        "* Hyper-Realism"
      ],
      "metadata": {
        "id": "tDhq6CBBm7VM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Artist\n",
        "* Rembrandt\n"
      ],
      "metadata": {
        "id": "DFkpvIbRnAmr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modifier\n",
        "* Masterpiece of\n",
        "* High quality\n",
        "* Photorealistic\n",
        "* Unrealengine\n",
        "* Octane Render\n",
        "* 8k\n",
        "* Insane detailing\n",
        "* Epic"
      ],
      "metadata": {
        "id": "0Sr7nyfomuRY"
      }
    }
  ]
}