machine-learning/depth2image-stable-diffusion/Img2imgGenerationStableDiffusion_Depth2img_PythonCodeTutorial.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHvuI4dpcxYM"
      },
      "source": [
        "# Part 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHttLFAGRCX0"
      },
      "source": [
        "Note: Before running the code, make sure you upload the images you want to edit to Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tEWYSTAiPUgA"
      },
      "outputs": [],
      "source": [
        "%pip install --quiet --upgrade diffusers transformers scipy ftfy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "krtAPL7tKA-J"
      },
      "outputs": [],
      "source": [
        "%pip install --quiet --upgrade accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U7eXBBRZPqel"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "from torch import autocast\n",
        "\n",
        "from transformers import CLIPTextModel, CLIPTokenizer\n",
        "from transformers import DPTForDepthEstimation, DPTFeatureExtractor\n",
        "\n",
        "from diffusers import AutoencoderKL, UNet2DConditionModel\n",
        "from diffusers.schedulers.scheduling_pndm import PNDMScheduler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0B1NCql5jkmo"
      },
      "source": [
        "## Model definition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fK0Cu4xnPe4E"
      },
      "outputs": [],
      "source": [
        "class DiffusionPipeline:\n",
        "\n",
        "    def __init__(self, \n",
        "                 vae, \n",
        "                 tokenizer, \n",
        "                 text_encoder, \n",
        "                 unet, \n",
        "                 scheduler):\n",
        "        \n",
        "        self.vae = vae\n",
        "        self.tokenizer = tokenizer\n",
        "        self.text_encoder = text_encoder\n",
        "        self.unet = unet\n",
        "        self.scheduler = scheduler\n",
        "        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "    \n",
        "    \n",
        "    def get_text_embeds(self, text):\n",
        "        # tokenize the text\n",
        "        text_input = self.tokenizer(text, \n",
        "                                    padding='max_length', \n",
        "                                    max_length=tokenizer.model_max_length, \n",
        "                                    truncation=True, \n",
        "                                    return_tensors='pt')\n",
        "        # embed the text\n",
        "        with torch.no_grad():\n",
        "            text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n",
        "        return text_embeds\n",
        "\n",
        "\n",
        "    def get_prompt_embeds(self, prompt):\n",
        "        if isinstance(prompt, str):\n",
        "            prompt = [prompt]\n",
        "        # get conditional prompt embeddings\n",
        "        cond_embeds = self.get_text_embeds(prompt)\n",
        "        # get unconditional prompt embeddings\n",
        "        uncond_embeds = self.get_text_embeds([''] * len(prompt))\n",
        "        # concatenate the above 2 embeds\n",
        "        prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n",
        "        return prompt_embeds\n",
        "\n",
        "\n",
        "\n",
        "    def decode_img_latents(self, img_latents):\n",
        "        img_latents = 1 / self.vae.config.scaling_factor * img_latents\n",
        "        with torch.no_grad():\n",
        "            img = self.vae.decode(img_latents).sample\n",
        "        \n",
        "        img = (img / 2 + 0.5).clamp(0, 1)\n",
        "        img = img.cpu().permute(0, 2, 3, 1).float().numpy()\n",
        "        return img\n",
        "\n",
        "\n",
        "\n",
        "    def transform_img(self, img):\n",
        "        # scale images to the range [0, 255] and convert to int\n",
        "        img = (img * 255).round().astype('uint8')\n",
        "        # convert to PIL Image objects\n",
        "        img = [Image.fromarray(i) for i in img]\n",
        "        return img\n",
        "\n",
        "\n",
        "    def encode_img_latents(self, img, latent_timestep):\n",
        "        if not isinstance(img, list):\n",
        "            img = [img]\n",
        "        \n",
        "        img = np.stack([np.array(i) for i in img], axis=0)\n",
        "        # scale images to the range [-1, 1]\n",
        "        img = 2 * ((img / 255.0) - 0.5)\n",
        "        img = torch.from_numpy(img).float().permute(0, 3, 1, 2)\n",
        "        img = img.to(self.device)\n",
        "\n",
        "        # encode images\n",
        "        img_latents_dist = self.vae.encode(img)\n",
        "        img_latents = img_latents_dist.latent_dist.sample()\n",
        "        \n",
        "        # scale images\n",
        "        img_latents = self.vae.config.scaling_factor * img_latents\n",
        "        \n",
        "        # add noise to the latents\n",
        "        noise = torch.randn(img_latents.shape).to(self.device)\n",
        "        img_latents = self.scheduler.add_noise(img_latents, noise, latent_timestep)\n",
        "\n",
        "        return img_latents\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2uME1Fs7Vq8y"
      },
      "outputs": [],
      "source": [
        "class Depth2ImgPipeline(DiffusionPipeline):\n",
        "    def __init__(self, \n",
        "                 vae, \n",
        "                 tokenizer, \n",
        "                 text_encoder, \n",
        "                 unet, \n",
        "                 scheduler, \n",
        "                 depth_feature_extractor, \n",
        "                 depth_estimator):\n",
        "        \n",
        "        super().__init__(vae, tokenizer, text_encoder, unet, scheduler)\n",
        "\n",
        "        self.depth_feature_extractor = depth_feature_extractor\n",
        "        self.depth_estimator = depth_estimator\n",
        "\n",
        "\n",
        "    def get_depth_mask(self, img):\n",
        "        if not isinstance(img, list):\n",
        "            img = [img]\n",
        "\n",
        "        width, height = img[0].size\n",
        "        \n",
        "        # pre-process the input image and get its pixel values\n",
        "        pixel_values = self.depth_feature_extractor(img, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "        # use autocast for automatic mixed precision (AMP) inference\n",
        "        with autocast('cuda'):\n",
        "            depth_mask = self.depth_estimator(pixel_values).predicted_depth\n",
        "        \n",
        "        # get the depth mask\n",
        "        depth_mask = torch.nn.functional.interpolate(depth_mask.unsqueeze(1),\n",
        "                                                     size=(height//8, width//8),\n",
        "                                                     mode='bicubic',\n",
        "                                                     align_corners=False)\n",
        "        \n",
        "        # scale the mask to range [-1, 1]\n",
        "        depth_min = torch.amin(depth_mask, dim=[1, 2, 3], keepdim=True)\n",
        "        depth_max = torch.amax(depth_mask, dim=[1, 2, 3], keepdim=True)\n",
        "        depth_mask = 2.0 * (depth_mask - depth_min) / (depth_max - depth_min) - 1.0\n",
        "        depth_mask = depth_mask.to(self.device)\n",
        "\n",
        "        # replicate the mask for classifier free guidance \n",
        "        depth_mask = torch.cat([depth_mask] * 2)\n",
        "        return depth_mask\n",
        "\n",
        "\n",
        "\n",
        "    \n",
        "    def denoise_latents(self, \n",
        "                        img,\n",
        "                        prompt_embeds,\n",
        "                        depth_mask,\n",
        "                        strength,\n",
        "                        num_inference_steps=50,\n",
        "                        guidance_scale=7.5,\n",
        "                        height=512, width=512):\n",
        "        \n",
        "        # clip the value of strength to ensure strength lies in [0, 1]\n",
        "        strength = max(min(strength, 1), 0)\n",
        "\n",
        "        # compute timesteps\n",
        "        self.scheduler.set_timesteps(num_inference_steps)\n",
        "\n",
        "        init_timestep = int(num_inference_steps * strength)\n",
        "        t_start = num_inference_steps - init_timestep\n",
        "        \n",
        "        timesteps = self.scheduler.timesteps[t_start: ]\n",
        "        num_inference_steps = num_inference_steps - t_start\n",
        "\n",
        "        latent_timestep = timesteps[:1].repeat(1)\n",
        "\n",
        "        latents = self.encode_img_latents(img, latent_timestep)\n",
        "\n",
        "        # use autocast for automatic mixed precision (AMP) inference\n",
        "        with autocast('cuda'):\n",
        "            for i, t in tqdm(enumerate(timesteps)):\n",
        "                latent_model_input = torch.cat([latents] * 2)\n",
        "                latent_model_input = torch.cat([latent_model_input, depth_mask], dim=1)\n",
        "                \n",
        "                # predict noise residuals\n",
        "                with torch.no_grad():\n",
        "                    noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds)['sample']\n",
        "\n",
        "                # separate predictions for unconditional and conditional outputs\n",
        "                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n",
        "                \n",
        "                # perform guidance\n",
        "                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n",
        "\n",
        "                # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n",
        "                latents = self.scheduler.step(noise_pred, t, latents)['prev_sample']\n",
        "\n",
        "        return latents\n",
        "\n",
        "\n",
        "    def __call__(self, \n",
        "                 prompt, \n",
        "                 img, \n",
        "                 strength=0.8,\n",
        "                 num_inference_steps=50,\n",
        "                 guidance_scale=7.5,\n",
        "                 height=512, width=512):\n",
        "\n",
        "\n",
        "        prompt_embeds = self.get_prompt_embeds(prompt)\n",
        "\n",
        "        depth_mask = self.get_depth_mask(img)\n",
        "\n",
        "        latents = self.denoise_latents(img,\n",
        "                                       prompt_embeds,\n",
        "                                       depth_mask,\n",
        "                                       strength,\n",
        "                                       num_inference_steps,\n",
        "                                       guidance_scale,\n",
        "                                       height, width)\n",
        "\n",
        "        img = self.decode_img_latents(latents)\n",
        "\n",
        "        img = self.transform_img(img)\n",
        "        \n",
        "        return img\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQ0YObQCjbUU"
      },
      "source": [
        "## Create instance of the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SjVpjMXOPe6h"
      },
      "outputs": [],
      "source": [
        "device = 'cuda'\n",
        "\n",
        "# Load autoencoder\n",
        "vae = AutoencoderKL.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='vae').to(device)\n",
        "\n",
        "# Load tokenizer and the text encoder\n",
        "tokenizer = CLIPTokenizer.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='tokenizer')\n",
        "text_encoder = CLIPTextModel.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='text_encoder').to(device)\n",
        "\n",
        "# Load UNet model\n",
        "unet = UNet2DConditionModel.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='unet').to(device)\n",
        "\n",
        "# Load scheduler\n",
        "scheduler = PNDMScheduler(beta_start=0.00085, \n",
        "                          beta_end=0.012, \n",
        "                          beta_schedule='scaled_linear', \n",
        "                          num_train_timesteps=1000)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5FipQQOKbiS",
        "outputId": "ff6719d7-cc17-4e3b-ff60-9342fe911af1"
      },
      "outputs": [],
      "source": [
        "# Load DPT Depth Estimator\n",
        "depth_estimator = DPTForDepthEstimation.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='depth_estimator')\n",
        "\n",
        "# Load DPT Feature Extractor\n",
        "depth_feature_extractor = DPTFeatureExtractor.from_pretrained('stabilityai/stable-diffusion-2-depth', subfolder='feature_extractor')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IgWXvP7TPe8p"
      },
      "outputs": [],
      "source": [
        "depth2img = Depth2ImgPipeline(vae, \n",
        "                              tokenizer, \n",
        "                              text_encoder, \n",
        "                              unet, \n",
        "                              scheduler,\n",
        "                              depth_feature_extractor,\n",
        "                              depth_estimator)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3End173jf5D"
      },
      "source": [
        "## Examples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "PvdI6VVnPfAE",
        "outputId": "6ddb97f9-ef83-4430-eecb-3f156fe0c3dd"
      },
      "outputs": [],
      "source": [
        "import urllib.parse as parse\n",
        "import os\n",
        "import requests\n",
        "\n",
        "# a function to determine whether a string is a URL or not\n",
        "def is_url(string):\n",
        "    try:\n",
        "        result = parse.urlparse(string)\n",
        "        return all([result.scheme, result.netloc, result.path])\n",
        "    except:\n",
        "        return False\n",
        "\n",
        "\n",
        "# a function to load an image\n",
        "def load_image(image_path):\n",
        "    if is_url(image_path):\n",
        "        return Image.open(requests.get(image_path, stream=True).raw)\n",
        "    elif os.path.exists(image_path):\n",
        "        return Image.open(image_path)\n",
        "\n",
        "\n",
        "url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
        "img = load_image(url)\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "B1FRHDHtFZ2x",
        "outputId": "b10b39ee-4ba5-4769-920f-1ea71c5de282"
      },
      "outputs": [],
      "source": [
        "depth2img(\"two tigers\", img)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "WMLYkwDoGKUk",
        "outputId": "8e5d015f-187c-49b1-f863-4fba0abd2f45"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image16.png\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "bjBvAVZVGKWr",
        "outputId": "ad13f40d-de3a-480c-843c-b4e481a8b7a2"
      },
      "outputs": [],
      "source": [
        "prompt = \"A boulder with gemstones falling down a hill\"\n",
        "depth2img(prompt, img)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "6eTFNAewGKZJ",
        "outputId": "4f906958-c76a-4cfe-dc2a-ca60272d1d08"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image11.png\").resize((512, 512))\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DlJv2-1eN0da",
        "outputId": "a879b4b6-9c03-47be-8834-494365cb58b3"
      },
      "outputs": [],
      "source": [
        "import gc\n",
        "import torch\n",
        "\n",
        "# Run this cell if you get OOM - Out of Memory - errors\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhvSACo4N6g2",
        "outputId": "6292f90c-aaf7-4c73-9384-d5e59bf59b5b"
      },
      "outputs": [],
      "source": [
        "# just to check GPU memory\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "Uy0ziM_xGKbP",
        "outputId": "aa03b031-d5f7-48fa-ad48-22e7edcee10a"
      },
      "outputs": [],
      "source": [
        "prompt = \"A futuristic city on the edge of space, a robotic bionic singularity portal, sci fi, utopian, tim hildebrandt, wayne barlowe, bruce pennington, donato giancola, larry elmore\"\n",
        "depth2img(prompt, img)[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RryrZSIucvee"
      },
      "source": [
        "# Part 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "37BGMrgkbgOX"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import requests\n",
        "from PIL import Image\n",
        "from diffusers import StableDiffusionDepth2ImgPipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1jqWIRGBbgR8"
      },
      "outputs": [],
      "source": [
        "pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(\n",
        "    \"stabilityai/stable-diffusion-2-depth\",\n",
        "    torch_dtype=torch.float16,\n",
        ").to(\"cuda\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a11H5bbvfjs1"
      },
      "source": [
        "## Impact of negative prompt example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "7_HoqLyqrlta",
        "outputId": "26b03e01-d07d-4a6f-e9f5-642ddbb299f2"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"https://images.pexels.com/photos/406152/pexels-photo-406152.jpeg?auto=compress&cs=tinysrgb&w=600\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409,
          "referenced_widgets": [
            "b168918d31b34653897402751e9bbecc",
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            "95ebc05ee7f640af86a54aadcd40ddbe",
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        },
        "id": "mBngqY_Q7zRb",
        "outputId": "8c7a6992-514c-4bbf-dcb3-4b6f5774d311"
      },
      "outputs": [],
      "source": [
        "prompt = \"A salad with tomatoes and guanas chips mixed with ketchup and mustard and bay leaf and guacamole and onions and ketchup and luscious patty with sesame seeds and cashews and onions and ketchup, ethereal,\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409,
          "referenced_widgets": [
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            "5066ac8781ec4f00b27ec6be27eb8506",
            "a3a361916b864a3cadb650a563842b16",
            "279e8805d1a8468496bf091b2854683e"
          ]
        },
        "id": "aj197V-_RYWl",
        "outputId": "038b263c-01d7-4e7d-f49b-5b26d3661152"
      },
      "outputs": [],
      "source": [
        "prompt = \"A salad with tomatoes and guanas chips mixed with ketchup and mustard and bay leaf and guacamole and onions and ketchup and luscious patty with sesame seeds and cashews and onions and ketchup, ethereal,\"\n",
        "n_prompt = \"ugly, deformed, not detailed, bad architectures, blurred, too much blurred, motion blur\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "qnPTkMvOQeM0",
        "outputId": "ce7d32c7-fac1-4df7-b9e2-ea9cedd12b5a"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image15.png\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "3f2e54c7185e4251962269369e30a9ec",
            "f037d2a0f68e4647ab8d1e3c278aac8f",
            "77bdcf219f2e4aa8ba1725329011f2bc",
            "bf5ffbefc35449659cc9aae956e9a292",
            "6186ca097ee64f289f1becdd180cf1aa",
            "f50555e1470b4a8e95ea430871af77cf",
            "9f6a32585a514034975b71b2954b639f",
            "41c7ad82fa3f4a24a13a09f384e8fd51",
            "3cd041ccf57c46ff91b714b7c572f581",
            "7d60b538a6e248369d2df053e9548a14",
            "a2bc87dd43e14ee0abba026d57cb349f"
          ]
        },
        "id": "8thR1RQ08HPI",
        "outputId": "5b9c661a-e291-4d68-8fde-5bb6f081d8f9"
      },
      "outputs": [],
      "source": [
        "prompt = \"Last remaining old man on earth\"\n",
        "n_prompt = \"bad anatomy, ugly, wrinkles\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_HH2z2bXfaYV"
      },
      "source": [
        "## Changing strength - Futuristic city example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cNIgur9_91-8"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image11.png\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "95b5dce9d5e140e78849c5aae374f27c",
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            "a83bca5872324f19a6a024c536141795",
            "0f79744697af4a53835dee31a1b4f748",
            "7dd5be64da7a41f8875765186bdd0b10",
            "8dccd6692afc41caa28072f35667bfac",
            "9e67bf17f1d64768954b47e3e1001b41",
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            "650ea058ba304da9b8febd6a72e10260",
            "f3d5fc14999740b2bb7e4c21d1413b37",
            "b274cfa1678e49938e163e2d17d7f179"
          ]
        },
        "id": "aQBJTWJG92Mr",
        "outputId": "d612403b-593c-4796-af26-bbd61e1c1ee6"
      },
      "outputs": [],
      "source": [
        "prompt = \"A futuristic city\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=None, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "c33aa07ac5a741adbef140e57b9702e2",
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            "e7da8d3d63f740968550b2852880ccc1",
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          ]
        },
        "id": "bbgJ1lQw92PO",
        "outputId": "c35afeb3-e07f-406e-9088-2b032292e405"
      },
      "outputs": [],
      "source": [
        "prompt = \"Futuristic city, modern, highly detailed, aesthetic, octane render, 8K, UHD, photoshopped\"\n",
        "n_prompt = \"ugly, deformed, not detailed, bad architectures, blurred, too much blurred, motion blur\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.7).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "cdcc3550ed8340ebb2e8f0f903500c9c",
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            "b86219c801bb4d2e8395ce17518fa432",
            "3ff6eda7d146478880b0d26670caa23c",
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            "2cc5cba3a49842fab50519d9968a4e53"
          ]
        },
        "id": "kjubi4RD92Sn",
        "outputId": "3717a51a-63b1-45a9-df8a-f096aa533ded"
      },
      "outputs": [],
      "source": [
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.1).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
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            "6c4f2c3e26614dce84bc1bf63b1e263f"
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        },
        "id": "fHqLC7NGALi9",
        "outputId": "96b0fda0-40a3-4d7a-f702-19c48230c2c9"
      },
      "outputs": [],
      "source": [
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.5).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
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          ]
        },
        "id": "dqMjMcJMARNr",
        "outputId": "b07157f3-ece3-428b-b595-53f2a4304898"
      },
      "outputs": [],
      "source": [
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.9).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "7174c6d2b8844dde9ab72574b22b9767",
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            "708184f7d3b240a38f17656f8bbfd8ae",
            "a9651ab87eae4180a9729b7e28f11376",
            "061de01186f440bea0491134ff347c86",
            "15b2d929ce0a4c70a156302db442e1a9",
            "4c55892c8dd3450a9904bce9e9b80057",
            "02dae045c1e7433cbdd872206d35e2f4",
            "d75aad33f36b432c8175e076ce4cc99e",
            "a3073940c26743b79c7477cbc92cb8e0"
          ]
        },
        "id": "HRkexFAEALls",
        "outputId": "0905b122-6f11-41c2-8982-6167973e70fd"
      },
      "outputs": [],
      "source": [
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=1).images[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXvOug64fM6H"
      },
      "source": [
        "## Article beginning examples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "HhA7oF_OA6SY",
        "outputId": "66afe568-50ff-4bc1-fe07-ca1807fec2bc"
      },
      "outputs": [],
      "source": [
        "img = load_image(\"image12.png\")\n",
        "img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
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        },
        "id": "T58XQSFpDEfZ",
        "outputId": "a6f4a271-6a91-4578-e7f0-89800ed684f8"
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      "outputs": [],
      "source": [
        "prompt = \"World war, aesthetic\"\n",
        "n_prompt = \"bad looking, deformed, wholesome\"\n",
        "pipe(prompt=prompt, image=img, negative_prompt=n_prompt, strength=0.9).images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
      "outputs": [],
      "source": [
        "img = load_image(\"image3.png\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "outputId": "15685051-9943-4f05-cd5b-71b7830870d1"
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      "outputs": [],
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        "prompt = \"Beautiful anime landscape\"\n",
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    {
      "cell_type": "code",
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