Multimodal_Search_and_Conditional_Image_Generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jIOoUNfvenAk"
   },
   "source": [
    "# Multimodal Search and Conditional Image Generation\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/togethercomputer/together-cookbook/blob/main/Multimodal_Search_and_Conditional_Image_Generation.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iaz7Bw6WenAl"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook we will demonstrate how to implement text-to-image search and image-to-image search. This will allow you to retrieve semantically relevant images and then we will use the retrieved images to condition the generation of new images using diffusion models.\n",
    "\n",
    "We will cover:\n",
    "1. How we can use multimodal embedding models like JinaCLIP to perform multimodal search.\n",
    "2. How we can perform conditional image generation using the FLUX models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CPAb6tNWenAm"
   },
   "source": [
    "## Install relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KFt-8p5En6ow",
    "outputId": "f744001e-ddad-4ae9-9219-a71738df66fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/83.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m83.7/83.7 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install -Uqq duckduckgo_search together transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "qPXVQhl7vsyw"
   },
   "outputs": [],
   "source": [
    "from duckduckgo_search import DDGS\n",
    "\n",
    "def search_images(keywords, max_images=10):\n",
    "    \"\"\"\n",
    "    Search for images based on given keywords and return a list of image URLs.\n",
    "    Args:\n",
    "        keywords (str): The search terms to use for finding images.\n",
    "        max_images (int, optional): The maximum number of images to retrieve. Defaults to 10.\n",
    "    Returns:\n",
    "        list: A list of URLs of the images found based on the search keywords.\n",
    "    \"\"\"\n",
    "\n",
    "    results = DDGS().images(keywords, max_results=max_images)\n",
    "\n",
    "    return [item['image'] for item in results]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GucDsOYpv0Lm",
    "outputId": "fc7492e3-ead4-47e8-aafc-c58f82868e6a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['http://leapphotography.com/blog/wp-content/uploads/2017/04/family-portrait-studio-boise-idaho-003.jpg',\n",
       " 'http://3.bp.blogspot.com/_b_LWsdjxDUI/TKJuV4BG7EI/AAAAAAAAEkM/qcdcqXqtZLc/s1600/Family+PIctures_53.jpg',\n",
       " 'https://www.uniqueideas.site/wp-content/uploads/img_6349-1087x1600-pixels-family-group-photos-pinterest-1.jpg']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our function will allow us to search the web for images\n",
    "search_images('family picture', max_images=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ywe1-FSVm8Wk"
   },
   "source": [
    "We use the following code to obtain a variety of image links we can index.\n",
    "\n",
    "```python\n",
    "# Lets create a small dataset of 12 images containing diverse topics\n",
    "searches = 'forest', 'dog', 'strawberry field', 'family picture'\n",
    "\n",
    "from time import sleep\n",
    "\n",
    "links = []\n",
    "\n",
    "for o in searches:\n",
    "    links += search_images(o, max_images=3)\n",
    "    sleep(1)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Sbfl17DvfRxu"
   },
   "outputs": [],
   "source": [
    "# Below we just provide the links obtained when this code was ran to make the notebook reproducible in case web search results change\n",
    "\n",
    "links = ['https://get.pxhere.com/photo/tree-forest-path-plant-hiking-trail-meadow-sunlight-rustic-solitude-recreation-green-jungle-scenic-peaceful-usa-relaxing-trees-leaves-outdoors-woods-spruce-vegetation-rainforest-deciduous-ferns-grove-woodland-habitat-ecosystem-north-carolina-biome-old-growth-forest-natural-environment-geographical-feature-woody-plant-temperate-broadleaf-and-mixed-forest-temperate-coniferous-forest-riparian-forest-elk-knob-state-park-1172973.jpg',\n",
    " 'https://wallup.net/wp-content/uploads/2019/09/952492-forest-trees-nature-landscape-tree.jpg',\n",
    " 'https://get.pxhere.com/photo/tree-nature-forest-path-wilderness-plant-trail-sunlight-leaf-green-jungle-autumn-ridge-trees-outdoors-woods-rainforest-deciduous-woodland-habitat-ecosystem-biome-old-growth-forest-natural-environment-woody-plant-temperate-broadleaf-and-mixed-forest-temperate-coniferous-forest-1170198.jpg',\n",
    " 'https://get.pxhere.com/photo/puppy-dog-animal-canine-pet-young-mammal-friend-golden-retriever-happy-vertebrate-funny-domestic-adorable-cub-dog-breed-retriever-pup-doggy-puppies-doggie-pedigree-cute-dog-young-dog-yellow-dog-dog-face-lazy-dog-dog-nose-young-dogs-dog-like-mammal-dog-breed-group-dog-crossbreeds-norfolk-terrier-tibetan-spaniel-nova-scotia-duck-tolling-retriever-female-dog-whelp-funny-dogs-yellow-dogs-dogs-types-dog-photos-smile-dogs-dog-cute-dog-funny-dog-laughing-dog-girl-1387994.jpg',\n",
    " 'https://wallup.net/wp-content/uploads/2018/10/06/364377-puppies-puppy-baby-dog-dogs-41.jpg',\n",
    " 'https://get.pxhere.com/photo/puppy-dog-animal-cute-canine-pet-fur-mammal-hound-close-up-nose-snout-ears-vertebrate-beagle-resting-adorable-dog-breed-street-dog-dog-like-mammal-816169.jpg',\n",
    " 'https://images.pexels.com/photos/7534234/pexels-photo-7534234.jpeg?auto=compress&cs=tinysrgb&h=750&w=1260',\n",
    " 'https://i.pinimg.com/originals/9e/dd/56/9edd568dd03d58a00d50adb2ec040208.jpg',\n",
    " 'https://images.pexels.com/photos/7707012/pexels-photo-7707012.jpeg?auto=compress&cs=tinysrgb&h=750&w=1260',\n",
    " 'http://leapphotography.com/blog/wp-content/uploads/2017/04/family-photographer-boise-professional-portrait-photographers-002.jpg',\n",
    " 'https://icmedonline.com/blog/wp-content/uploads/2017/06/MensHealth.jpeg',\n",
    " 'https://simpleasthatblog.com/wp-content/uploads/2014/12/familyphotosIG.jpg']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yUOA_mHNenAn"
   },
   "source": [
    "## Embed Images using JinaCLIP\n",
    "\n",
    "JinaCLIP is an embedding model that we will use to generate vector embeddings for our 12 images above. It is trained using contrastive learning to unify text and image representations.\n",
    "\n",
    "During contrastive training text and image pairs that are semantically similar are pulled closer together in vector space and those that are dissimilar are pushed apart.\n",
    "\n",
    "This model allows us to perform retrieval over image and text modalities.\n",
    "\n",
    "<img src=\"images/CLIP.png\" width=\"1000\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sadHxeC-ttoS",
    "outputId": "0b35ca7e-f55a-4a52-8323-14ffb4e9d8dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 768)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoModel\n",
    "\n",
    "# Initialize the model\n",
    "model = AutoModel.from_pretrained('jinaai/jina-clip-v1', trust_remote_code=True)\n",
    "\n",
    "# Encode text and images\n",
    "image_embeddings = model.encode_image(links)  # also accepts PIL.image, local filenames, dataURI\n",
    "\n",
    "image_embeddings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "arcWhwoIenAo"
   },
   "source": [
    "`image_embeddings` is now a numpy array/vector index that contains vector representations for each of our 12 images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LpNnX9iYenAo"
   },
   "source": [
    "## Image Retrieval Function\n",
    "\n",
    "Below we implement a retrieval function that will embed an image or text query and return the most semantically relevant image.\n",
    "\n",
    "Since JinaCLIP is a multimodal model is can accept both text or images as input and thus our function will need to handle both text or image queries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "958yTntvubgF"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def retrieve_image(query, query_type, index):\n",
    "    \"\"\"\n",
    "    Retrieve the index of the most similar image based on a query.\n",
    "    Args:\n",
    "        query (str or PIL.Image or str): The query input, which can be a text string,\n",
    "                                         a PIL image, or a local filename.\n",
    "        query_type (str): The type of the query, either 'text' or 'image'.\n",
    "        index (int): The index of the image to be retrieved.\n",
    "    Returns:\n",
    "        int: The index of the most similar image based on the query.\n",
    "    Raises:\n",
    "        ValueError: If the query_type is not 'text' or 'image'.\n",
    "    \"\"\"\n",
    "\n",
    "    if query_type == 'text':\n",
    "        query_embedding = model.encode_text(query)\n",
    "    elif query_type == 'image':\n",
    "        query_embedding = model.encode_image(query) # Accepts PIL.image, local filenames, dataURI\n",
    "    else:\n",
    "        raise ValueError(\"query_type must be 'text' or 'image'\")\n",
    "\n",
    "    similarities = query_embedding @ index.T # We calculate the similaritry between the query embedding and all the image embeddings\n",
    "\n",
    "    return np.argmax(similarities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "chwFVZ4ienAo"
   },
   "source": [
    "Below we perform text2image retrieval:"
   ]
  },
  {
   "cell_type": "code",
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   ],
   "source": [
    "retrieved_image = links[retrieve_image(query = 'family pics', query_type = 'text', index = image_embeddings)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F6eEJHdmenAo"
   },
   "source": [
    "This image is the most semantically similar to the text query: `family pics`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "id": "dvqtBwmCzdco",
    "outputId": "f8bcb279-abb4-4a36-fb65-198293f2924b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://simpleasthatblog.com/wp-content/uploads/2014/12/familyphotosIG.jpg\" width=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython.display as display\n",
    "\n",
    "display.Image(url=retrieved_image, width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zddSB3XyenAo"
   },
   "source": [
    "## Conditional Image Generation Using Diffusion Models\n",
    "\n",
    "We will use the retrieved image above to generate a holiday card cartoon version of the image above!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "VW76cIqOxkDR"
   },
   "outputs": [],
   "source": [
    "from together import Together\n",
    "\n",
    "client = Together(api_key = 'TOGETHER_API_KEY')\n",
    "\n",
    "def generate_image(image_prompt, retrieved_image, model = \"black-forest-labs/FLUX.1-depth\"):\n",
    "\n",
    "    imageCompletion = client.images.generate(\n",
    "        model = model,\n",
    "        width=1024,\n",
    "        height=768,\n",
    "        steps=28,\n",
    "        prompt = image_prompt,\n",
    "        image_url = retrieved_image,\n",
    "    )\n",
    "\n",
    "    return imageCompletion.data[0].url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "tb-cxswByl2G"
   },
   "outputs": [],
   "source": [
    "generated_image = generate_image(\"Create a cute holiday cartoon version of this image.\", retrieved_image = retrieved_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
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     "height": 396
    },
    "id": "3Zax0B6Dy5Go",
    "outputId": "0eadf142-90a3-40b2-9df1-f561867de118"
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       "<img src=\"https://api.together.ai/imgproxy/Qf_1G6VLR7BaIsSyBT0-204LJU28r8K7ObU3NR7MNso/format:jpeg/aHR0cHM6Ly90b2dldGhlci1haS1iZmwtaW1hZ2VzLXByb2QuczMudXMtd2VzdC0yLmFtYXpvbmF3cy5jb20vaW1hZ2VzLzEwMjIwZTY4NzJmNWMyOTcyZDBlOGI0N2NkN2YwNTMwNDJlM2FmYTY4ZjM1MmM5NzZlNDEyZDhlZTMwNDNmYmY_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\" width=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display.Image(url=generated_image, width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZOGFfLT9enAp"
   },
   "source": [
    "## Image to Image Search and Conditional Generation\n",
    "\n",
    "Next we will demonstrate using an image as a query and then used the semantically relevant retrieved image to generate another holiday cartoon generated image!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "id": "mhem9NXwzFzS",
    "outputId": "469c3b32-9b62-44fd-f55c-6e265f6112d9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://www.dogsbestfriendtraining.com/wp-content/uploads/2021/04/iStock-1248789778-1-2048x1407.jpg\" width=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search the internet for a new image\n",
    "new_image = search_images('cute pet dog running', max_images=1)[0]\n",
    "\n",
    "display.Image(url=new_image, width=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 648
    },
    "id": "wg3uiDQy1WsC",
    "outputId": "02f06b3e-c905-4df2-d63b-1eaa589fed99"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://get.pxhere.com/photo/puppy-dog-animal-canine-pet-young-mammal-friend-golden-retriever-happy-vertebrate-funny-domestic-adorable-cub-dog-breed-retriever-pup-doggy-puppies-doggie-pedigree-cute-dog-young-dog-yellow-dog-dog-face-lazy-dog-dog-nose-young-dogs-dog-like-mammal-dog-breed-group-dog-crossbreeds-norfolk-terrier-tibetan-spaniel-nova-scotia-duck-tolling-retriever-female-dog-whelp-funny-dogs-yellow-dogs-dogs-types-dog-photos-smile-dogs-dog-cute-dog-funny-dog-laughing-dog-girl-1387994.jpg\" width=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
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   ],
   "source": [
    "# Use the image above as a query to retrieve the most similar image from our dataset of 12 images\n",
    "image_2_image = links[retrieve_image(query=new_image, query_type='image', index=image_embeddings)]\n",
    "\n",
    "display.Image(url=image_2_image, width=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "zVjb0O8u2KPf"
   },
   "outputs": [],
   "source": [
    "# Generate a holiday cartoon version of the retrieved image\n",
    "generated_image_2 = generate_image(image_prompt=\"Create a cute holiday cartoon version of this image.\", retrieved_image = image_2_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "ZDbNGPu92yDa",
    "outputId": "955f6ffb-6adc-43a4-e0a0-290c0d2b48fa"
   },
   "outputs": [
    {
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       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "display.Image(url=generated_image_2, width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check our how you can generated images conditioned on input images [here](https://www.together.ai/blog/flux-tools-models-together-apis-canny-depth-image-generation)!"
   ]
  }
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