diffusers/unidiffuser.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PEQHAzAqkYp2"
   },
   "source": [
    "# Generating images and text with UniDiffuser\n",
    "\n",
    "UniDiffuser was introduced in [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://arxiv.org/abs/2303.06555).\n",
    "\n",
    "In this notebook, we will show how the [UniDiffuser pipeline](https://huggingface.co/docs/diffusers/api/pipelines/unidiffuser) in 🧨 diffusers can be used for:\n",
    "\n",
    "* Unconditional image generation\n",
    "* Unconditional text generation\n",
    "* Text-to-image generation\n",
    "* Image-to-text generation\n",
    "* Image variation\n",
    "* Text variation\n",
    "\n",
    "One pipeline to rule six use cases 🤯\n",
    "\n",
    "Let's start!\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/unidiffuser.gif\" width=350/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pH5NByOqlJ39"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lIYdn1woOS1n",
    "outputId": "23733809-fa5b-4d10-8c56-80014c5be4ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m104.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m219.1/219.1 kB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m105.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install -q git+https://github.com/huggingface/diffusers\n",
    "!pip install transformers accelerate -q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P65FyJgBlRMQ"
   },
   "source": [
    "## Unconditional image and text generation\n",
    "\n",
    "Throughout this notebook, we'll be using the [\"thu-ml/unidiffuser-v1\"](https://huggingface.co/thu-ml/unidiffuser-v1) checkpoint. UniDiffuser comes with two checkpoints:\n",
    "\n",
    "* [\"thu-ml/unidiffuser-v1\"](https://huggingface.co/thu-ml/unidiffuser-v1)\n",
    "* [\"thu-ml/unidiffuser-v0\"](https://huggingface.co/thu-ml/unidiffuser-v0)"
   ]
  },
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No inputs or latents have been supplied, and mode has not been manually set, defaulting to mode 'joint'.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "690fbd8adadc476b9a8dc5bc2c536569",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/models/gpt2/modeling_gpt2.py:202: UserWarning: where received a uint8 condition tensor. This behavior is deprecated and will be removed in a future version of PyTorch. Use a boolean condition instead. (Triggered internally at ../aten/src/ATen/native/TensorCompare.cpp:493.)\n",
      "  attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A small white car parked up in parking lot\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from diffusers import UniDiffuserPipeline\n",
    "\n",
    "device = \"cuda\"\n",
    "model_id_or_path = \"thu-ml/unidiffuser-v1\"\n",
    "pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n",
    "pipe.to(device)\n",
    "\n",
    "# Unconditional image and text generation. The generation task is automatically inferred.\n",
    "sample = pipe(num_inference_steps=20, guidance_scale=8.0)\n",
    "image = sample.images[0]\n",
    "text = sample.text[0]\n",
    "image.save(\"unidiffuser_joint_sample_image.png\")\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "soX66TISlkNN"
   },
   "source": [
    "You can also generate only an image or only text (which the UniDiffuser paper calls “marginal” generation since we sample from the marginal distribution of images and text, respectively):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
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    "id": "cSJ9bmJzltw6",
    "outputId": "3df922d0-2f35-468e-915b-6b6cbd441145"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41c9d40eed774a42bb0980ec92205287",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Unlike other generation tasks, image-only and text-only generation don't use classifier-free guidance\n",
    "\n",
    "# Image-only generation\n",
    "pipe.set_image_mode()\n",
    "sample_image = pipe(num_inference_steps=20).images[0]\n",
    "\n",
    "# Text-only generation\n",
    "pipe.set_text_mode()\n",
    "sample_text = pipe(num_inference_steps=20).text[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "egHRrlWnmOcL"
   },
   "source": [
    "To reset a mode, call: `pipe.reset_mode()`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I5-8Yz96lwXf"
   },
   "source": [
    "## Text-to-image generation\n",
    "\n",
    "The `UniDiffuserPipeline` can infer the right mode of execution from provided inputs to the pipeline called. Since we started with the joint unconditional mode (`set_joint_mode()`), the subsequent calls will be executed in this model. Now, we want to generate images from text. So, we set the model accordingly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "v3nkV9BAhRUo"
   },
   "outputs": [],
   "source": [
    "pipe.set_text_to_image_mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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    },
    "id": "xfiS2K4biogu",
    "outputId": "67da2d87-463a-4b69-aa7d-2b05f7409e0c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7659d991715740aaa84ffd2b2ef7196e",
       "version_major": 2,
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Text-to-image generation\n",
    "prompt = \"an elephant under the sea\"\n",
    "\n",
    "sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)\n",
    "t2i_image = sample.images[0]\n",
    "t2i_image.save(\"unidiffuser_text2img_sample_image.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UutaSm0vmZox"
   },
   "source": [
    "## Image-to-text generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "nfHNyWlWisfd"
   },
   "outputs": [],
   "source": [
    "pipe.set_image_to_text_mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
     "referenced_widgets": [
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      "4ad4508c72304fbf98bea4419e8ac891",
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      "13ecf80db5fb40b3808acce2a7b79165",
      "8ac480adcecd46598f6c7141bde7dd5c",
      "46c5ab9ce8bd4aa1b4cd4bf9e4c47a1c",
      "1eeb558a427a4011acf44b78c0a0aea0"
     ]
    },
    "id": "ULwD_y8pjHGO",
    "outputId": "f0f54d93-8530-4fb4-87ca-093109bd274f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef3dc41655fe4327bdd64c5ba9f839ca",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "An image of an astronaut flying over the Earth\n"
     ]
    }
   ],
   "source": [
    "from diffusers.utils import load_image\n",
    "\n",
    "# Image-to-text generation\n",
    "image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg\"\n",
    "init_image = load_image(image_url).resize((512, 512))\n",
    "\n",
    "sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)\n",
    "i2t_text = sample.text[0]\n",
    "print(i2t_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Ojwa3Y9mrCD"
   },
   "source": [
    "## Image variation\n",
    "\n",
    "For image variation, we follow a \"round-trip\" method as suggested in the paper. We first generate a caption from a given image. And then use the caption to generate a image from it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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     "text": [
      "An astronaut floating in                                                               \n"
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    "# Image variation can be performed with a image-to-text generation followed by a text-to-image generation:\n",
    "# 1. Image-to-text generation\n",
    "image_url = \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg\"\n",
    "init_image = load_image(image_url).resize((512, 512))\n",
    "\n",
    "pipe.set_image_to_text_mode()\n",
    "sample = pipe(image=init_image, num_inference_steps=20, guidance_scale=8.0)\n",
    "i2t_text = sample.text[0]\n",
    "print(i2t_text)\n",
    "\n",
    "# 2. Text-to-image generation\n",
    "pipe.set_text_to_image_mode()\n",
    "sample = pipe(prompt=i2t_text, num_inference_steps=20, guidance_scale=8.0)\n",
    "final_image = sample.images[0]\n",
    "final_image.save(\"unidiffuser_image_variation_sample.png\")"
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    "## Text variation\n",
    "\n",
    "The same round-trip methodology can be applied here. "
   ]
  },
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     "text": [
      "A baby elephant in a aquarium with a fish\n"
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    "# Text variation can be performed with a text-to-image generation followed by a image-to-text generation:\n",
    "# 1. Text-to-image generation\n",
    "prompt = \"an elephant under the sea\"\n",
    "\n",
    "pipe.set_text_to_image_mode()\n",
    "sample = pipe(prompt=prompt, num_inference_steps=20, guidance_scale=8.0)\n",
    "t2i_image = sample.images[0]\n",
    "t2i_image.save(\"unidiffuser_text2img_sample_image.png\")\n",
    "\n",
    "# 2. Image-to-text generation\n",
    "pipe.set_image_to_text_mode()\n",
    "sample = pipe(image=t2i_image, num_inference_steps=20, guidance_scale=8.0)\n",
    "final_prompt = sample.text[0]\n",
    "print(final_prompt)"
   ]
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