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+{
+ "cells": [
+  {
+   "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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+    "outputId": "8b8f260f-a236-42b0-df90-342db791225b"
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+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2623bf67e4ac4aef8ea577c79e5cab5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
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+       "version_major": 2,
+       "version_minor": 0
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+      ]
+     },
+     "metadata": {},
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    },
+    {
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+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90279c703b2644afb9f3b253e2ec6ed1",
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
+     "data": {
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+       "version_minor": 0
+      },
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b6b544a0ac04ddea7cfb401357326b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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",
+       "version_major": 2,
+       "version_minor": 0
+      },
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+      "41c9d40eed774a42bb0980ec92205287",
+      "f7a4d53dc6c84e4bbc0d1a1d5b056d8d",
+      "5ebdb3f9f5a8409cb320d76f1859af0f",
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+      "d0b99577789e416b84ed91d76c26b051",
+      "7f45605e621543d1a4e1e609f04d8b00",
+      "e433487e0bf0403c93d3ca5e65056f73",
+      "d2032c0f0b6c43fda8bfc593fa6175f2",
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+      "2a570c9016454531aa4961cc94dd8d6f",
+      "509b4d13b8ba4507b4e996253e192d87",
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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,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "509b4d13b8ba4507b4e996253e192d87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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": [
+      "7659d991715740aaa84ffd2b2ef7196e",
+      "95982e716d294f1bb15c301eeca3c106",
+      "035f9afb7eb240ca908254bcafee4451",
+      "6d12d9685cba4f90aa18218a0be92666",
+      "5f3d656f354d4568a3e18a4ce1a1b494",
+      "76777f6468864707a74438a8653091a6",
+      "ec915693e12745d583996d2af5848c6c",
+      "fa62b2fa268e46c39ca6da44fadf4c54",
+      "752aded6aecf4fc9b01d266f316cbd5a",
+      "96c1ae6e0b754897984e0c44bfc14c79",
+      "1bf2d33f8c39437fb212419f3cb54c14"
+     ]
+    },
+    "id": "xfiS2K4biogu",
+    "outputId": "67da2d87-463a-4b69-aa7d-2b05f7409e0c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7659d991715740aaa84ffd2b2ef7196e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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": [
+      "ef3dc41655fe4327bdd64c5ba9f839ca",
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+    "outputId": "f0f54d93-8530-4fb4-87ca-093109bd274f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef3dc41655fe4327bdd64c5ba9f839ca",
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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": {
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+    },
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+    "outputId": "cff95448-6ea1-4329-9d00-aa8101614c25"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36ac820f9c4c4fa782e10f6de46a91c3",
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+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "An astronaut floating in                                                               \n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b0854b4a25b4d5495171d592562cf96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 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\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "04wFXW23nfBW"
+   },
+   "source": [
+    "## Text variation\n",
+    "\n",
+    "The same round-trip methodology can be applied here. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
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+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0cbf710250884de19d8743087845fe2a",
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+      },
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+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d07e5f86e8f41ac9460eb6b9da079ba",
+       "version_major": 2,
+       "version_minor": 0
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+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A baby elephant in a aquarium with a fish\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 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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+  "accelerator": "GPU",
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