From a0df62500c6100d575ebb3a4af4707b1cc72b824 Mon Sep 17 00:00:00 2001
From: Sayak Paul <spsayakpaul@gmail.com>
Date: Thu, 25 May 2023 12:23:40 +0530
Subject: [PATCH 1/4] add: colab notebook on unidiffuser

---
 diffusers/unidiffuser.ipynb | 11393 ++++++++++++++++++++++++++++++++++
 1 file changed, 11393 insertions(+)
 create mode 100644 diffusers/unidiffuser.ipynb

diff --git a/diffusers/unidiffuser.ipynb b/diffusers/unidiffuser.ipynb
new file mode 100644
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+++ b/diffusers/unidiffuser.ipynb
@@ -0,0 +1,11393 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "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!"
+      ],
+      "metadata": {
+        "id": "PEQHAzAqkYp2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ],
+      "metadata": {
+        "id": "pH5NByOqlJ39"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lIYdn1woOS1n",
+        "outputId": "c9c4b6d9-1d13-4f58-d2d0-1120747f339e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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",
+            "  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q git+https://github.com/dg845/diffusers\n",
+        "!pip install transformers accelerate -q"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Unconditional image and text generation"
+      ],
+      "metadata": {
+        "id": "P65FyJgBlRMQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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)"
+      ],
+      "metadata": {
+        "id": "oc4Wtedcem_z",
+        "outputId": "cac43f80-01b4-4d3b-eb89-431c9f089159",
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+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
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+            }
+          },
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+            }
+          },
+          "metadata": {}
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+        },
+        {
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+        },
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+        },
+        {
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+          },
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+        },
+        {
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+            }
+          },
+          "metadata": {}
+        },
+        {
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+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8f1c07d5f42244d490f8906de304ecce"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2da8b550852a47afba3c33e8947bc066"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "No inputs or latents have been supplied, and mode has not been manually set, defaulting to mode 'joint'.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b2b128f0aff146659f27904bc2329ee1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "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"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The interior of a fancy restaurant, chandeliers\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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):"
+      ],
+      "metadata": {
+        "id": "soX66TISlkNN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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]"
+      ],
+      "metadata": {
+        "id": "cSJ9bmJzltw6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To reset a mode, call: `pipe.reset_mode()`. "
+      ],
+      "metadata": {
+        "id": "egHRrlWnmOcL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "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. "
+      ],
+      "metadata": {
+        "id": "I5-8Yz96lwXf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe.set_text_to_image_mode()"
+      ],
+      "metadata": {
+        "id": "v3nkV9BAhRUo"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "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\")"
+      ],
+      "metadata": {
+        "id": "xfiS2K4biogu",
+        "outputId": "fe344adc-1ecb-431c-be62-1f287b63e103",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "ccadac87f1f042779ef2cc8a94d059d0",
+            "1b458f6126ad4b75b52e6d7a42f0d280",
+            "126fcac31c424972a171488288e7e3c8",
+            "c410271bc85942f8b4fa63305e2abc8f",
+            "825d15cd3fd1496faef8292b419fc02d",
+            "11eada94e41c4e61b88d5af354822537",
+            "7da331e664de4ad5b653eb57d9183bdb",
+            "e63a4f3fed4a467599f4891c4c6c0cf6",
+            "67a704e3b7dc413a81b22a2892669931",
+            "21d77f015e2b4f0e97f8a013245cb0b7",
+            "9e9a6cf44c984e3ab0467c2b36d98a39"
+          ]
+        }
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ccadac87f1f042779ef2cc8a94d059d0"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Image-to-text generation"
+      ],
+      "metadata": {
+        "id": "UutaSm0vmZox"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pipe.set_image_to_text_mode()"
+      ],
+      "metadata": {
+        "id": "nfHNyWlWisfd"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from diffusers 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(text)"
+      ],
+      "metadata": {
+        "id": "ULwD_y8pjHGO",
+        "outputId": "d4d69955-d904-493d-97a1-e0f2ca737318",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 67,
+          "referenced_widgets": [
+            "d983f8bd5aba4386bb041140974e5a48",
+            "fa87fbc43db74d8fb18a2527788b5934",
+            "3f43a00e61cc4edcbd8adbd687dde1e2",
+            "fb000355a85f4cb6ba4a7081505585db",
+            "a579107545fe4560b40b63a13559771d",
+            "10956525b8b1468cb6fc92488c80dd95",
+            "3ffab75e9e424608a0336c3d4da1210a",
+            "977fdf932bac4b9d8b930623cc6bd49d",
+            "e9ae8ff7f81c4177b068314489af6f54",
+            "5888c40bf8ab4d5e8914bf3c36f55d0d",
+            "cb1af83a64cf4801b6f93a532fe7833c"
+          ]
+        }
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d983f8bd5aba4386bb041140974e5a48"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The interior of a fancy restaurant, chandeliers\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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. "
+      ],
+      "metadata": {
+        "id": "6Ojwa3Y9mrCD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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\")"
+      ],
+      "metadata": {
+        "id": "VI7lo9SojT07",
+        "outputId": "1e904c37-1b18-4d7b-a5cf-08fca0b0b5f3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 99,
+          "referenced_widgets": [
+            "d2cb39eed9b8469c8ef60c7860ed4ca8",
+            "8a41c75d3cce4dd7a8c76f982892b1ea",
+            "9e56ade720c342638aba6c2972da4733",
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+            "9b169c44df5b43779a8ddfb8c0101553",
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+        }
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
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+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "An astronaut floating in effect                                                              \n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
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+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Text variation"
+      ],
+      "metadata": {
+        "id": "04wFXW23nfBW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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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+        "id": "4K0YqNuHkCOi",
+        "outputId": "bec46bd4-a917-4e0f-da78-daf036493dea",
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+      "execution_count": 10,
+      "outputs": [
+        {
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+              "  0%|          | 0/20 [00:00<?, ?it/s]"
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+          "text": [
+            "An elephant swimming in the ocean by an aquarium\n"
+          ]
+        }
+      ]
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\ No newline at end of file

From 26ebc67f60822a57c6cc3ff1c6a8912adf6961f2 Mon Sep 17 00:00:00 2001
From: Sayak Paul <spsayakpaul@gmail.com>
Date: Thu, 25 May 2023 13:35:16 +0530
Subject: [PATCH 2/4] minor updates.

---
 diffusers/unidiffuser.ipynb | 23436 ++++++++++++++++++----------------
 1 file changed, 12098 insertions(+), 11338 deletions(-)

diff --git a/diffusers/unidiffuser.ipynb b/diffusers/unidiffuser.ipynb
index 704a5a8e..4c99d56b 100644
--- a/diffusers/unidiffuser.ipynb
+++ b/diffusers/unidiffuser.ipynb
@@ -1,11393 +1,12153 @@
 {
-  "cells": [
+ "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!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pH5NByOqlJ39"
+   },
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lIYdn1woOS1n",
+    "outputId": "f265eb37-70a8-48fa-d6f6-3e147541593a"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "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!"
-      ],
-      "metadata": {
-        "id": "PEQHAzAqkYp2"
-      }
+     "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",
+      "  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+     ]
+    }
+   ],
+   "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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     },
+    "id": "oc4Wtedcem_z",
+    "outputId": "52f78a4a-18ad-4024-fbf8-022789c7ee70"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Setup"
-      ],
-      "metadata": {
-        "id": "pH5NByOqlJ39"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b407caff839049af8b5ae54d1f55d86d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)ain/model_index.json:   0%|          | 0.00/694 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lIYdn1woOS1n",
-        "outputId": "c9c4b6d9-1d13-4f58-d2d0-1120747f339e"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dd5478f769d4f48bed6a2114f5d3668",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "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",
-            "  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install -q git+https://github.com/dg845/diffusers\n",
-        "!pip install transformers accelerate -q"
+      "text/plain": [
+       "Fetching 22 files:   0%|          | 0/22 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Unconditional image and text generation"
-      ],
-      "metadata": {
-        "id": "P65FyJgBlRMQ"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1866cab958a14e868597ea9a514857a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)cheduler_config.json:   0%|          | 0.00/555 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "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)"
-      ],
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-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "display_data",
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-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "No inputs or latents have been supplied, and mode has not been manually set, defaulting to mode 'joint'.\n"
-          ]
-        },
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-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "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"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "The interior of a fancy restaurant, chandeliers\n"
-          ]
-        }
+      "text/plain": [
+       "Downloading (…)_encoder/config.json:   0%|          | 0.00/572 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "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):"
-      ],
-      "metadata": {
-        "id": "soX66TISlkNN"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "484d7063e20943f6aaa6755c268ba980",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)rocessor_config.json:   0%|          | 0.00/518 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "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]"
-      ],
-      "metadata": {
-        "id": "cSJ9bmJzltw6"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3cd2dde567264e1bb03921ac13a7cbdd",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": null,
-      "outputs": []
+      "text/plain": [
+       "Downloading (…)okenizer_config.json:   0%|          | 0.00/806 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "To reset a mode, call: `pipe.reset_mode()`. "
-      ],
-      "metadata": {
-        "id": "egHRrlWnmOcL"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c71826ce7dda40b3a40969f2b5d47a1c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "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. "
-      ],
-      "metadata": {
-        "id": "I5-8Yz96lwXf"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "48c7030152164b78929e5f41a8b9a09b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)tokenizer/vocab.json:   0%|          | 0.00/1.06M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "source": [
-        "pipe.set_text_to_image_mode()"
-      ],
-      "metadata": {
-        "id": "v3nkV9BAhRUo"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "123e21257dda423c96d3291e038fc64c",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": 4,
-      "outputs": []
+      "text/plain": [
+       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/472 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "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\")"
-      ],
-      "metadata": {
-        "id": "xfiS2K4biogu",
-        "outputId": "fe344adc-1ecb-431c-be62-1f287b63e103",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 49,
-          "referenced_widgets": [
-            "ccadac87f1f042779ef2cc8a94d059d0",
-            "1b458f6126ad4b75b52e6d7a42f0d280",
-            "126fcac31c424972a171488288e7e3c8",
-            "c410271bc85942f8b4fa63305e2abc8f",
-            "825d15cd3fd1496faef8292b419fc02d",
-            "11eada94e41c4e61b88d5af354822537",
-            "7da331e664de4ad5b653eb57d9183bdb",
-            "e63a4f3fed4a467599f4891c4c6c0cf6",
-            "67a704e3b7dc413a81b22a2892669931",
-            "21d77f015e2b4f0e97f8a013245cb0b7",
-            "9e9a6cf44c984e3ab0467c2b36d98a39"
-          ]
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8641c24e76124ee4b17953105c9f1c84",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
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-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ccadac87f1f042779ef2cc8a94d059d0"
-            }
-          },
-          "metadata": {}
-        }
+      "text/plain": [
+       "Downloading (…)_decoder/config.json:   0%|          | 0.00/565 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Image-to-text generation"
-      ],
-      "metadata": {
-        "id": "UutaSm0vmZox"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "953d7484988945889d017fd5ddf2eca5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "source": [
-        "pipe.set_image_to_text_mode()"
-      ],
-      "metadata": {
-        "id": "nfHNyWlWisfd"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df5713b123214070a97169fb9020c933",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": 7,
-      "outputs": []
+      "text/plain": [
+       "Downloading (…)er/added_tokens.json:   0%|          | 0.00/23.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "source": [
-        "from diffusers 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(text)"
-      ],
-      "metadata": {
-        "id": "ULwD_y8pjHGO",
-        "outputId": "d4d69955-d904-493d-97a1-e0f2ca737318",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 67,
-          "referenced_widgets": [
-            "d983f8bd5aba4386bb041140974e5a48",
-            "fa87fbc43db74d8fb18a2527788b5934",
-            "3f43a00e61cc4edcbd8adbd687dde1e2",
-            "fb000355a85f4cb6ba4a7081505585db",
-            "a579107545fe4560b40b63a13559771d",
-            "10956525b8b1468cb6fc92488c80dd95",
-            "3ffab75e9e424608a0336c3d4da1210a",
-            "977fdf932bac4b9d8b930623cc6bd49d",
-            "e9ae8ff7f81c4177b068314489af6f54",
-            "5888c40bf8ab4d5e8914bf3c36f55d0d",
-            "cb1af83a64cf4801b6f93a532fe7833c"
-          ]
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "720f32130f374f5d9074154751516884",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
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-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "d983f8bd5aba4386bb041140974e5a48"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "The interior of a fancy restaurant, chandeliers\n"
-          ]
-        }
+      "text/plain": [
+       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/319 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "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. "
-      ],
-      "metadata": {
-        "id": "6Ojwa3Y9mrCD"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cd54dc940db4ef5a3f4b7875f96f3ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)tokenizer/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "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\")"
-      ],
-      "metadata": {
-        "id": "VI7lo9SojT07",
-        "outputId": "1e904c37-1b18-4d7b-a5cf-08fca0b0b5f3",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 99,
-          "referenced_widgets": [
-            "d2cb39eed9b8469c8ef60c7860ed4ca8",
-            "8a41c75d3cce4dd7a8c76f982892b1ea",
-            "9e56ade720c342638aba6c2972da4733",
-            "955c7cafe73a4563b049d6035a3b17e5",
-            "0144436b84e54d8cbf0fb36435a72e20",
-            "cbb3c09831544b4ca00adc9b30ca4c3c",
-            "ee530ab7da6f4b53be615f36e6fe0a6b",
-            "225d921d1c6c4430925dd2b1685653d8",
-            "1b66d334be9d4baf8feb47a2fbf5d3e7",
-            "634154cb63c64462a50434bd94e1e2e1",
-            "532a227aafec496e80d347ff6ea73f19",
-            "9b169c44df5b43779a8ddfb8c0101553",
-            "6ee2b986a51a4c45834edc357a51bee3",
-            "5628216d302b44d49f7e2ec0cccd2265",
-            "73dcfbb9e8684dd9b2c42178d43899ef",
-            "f40ef34ada2a4a26864b9985f18466b7",
-            "a40c0c1461b64ab68fd7a67ce6756c3f",
-            "e4a7237d00954809b7106f93ca86988a",
-            "763889933ee840f292d133239b08c1eb",
-            "4b932eb2bc044ab8a4be3857552aeb97",
-            "1e26fe94d93d4505b5373449b2995bc7",
-            "00300caca0a84d09a110ca89229b59f3"
-          ]
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa5e22ec2d484e968a145c2a2ccec6e1",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
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-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "d2cb39eed9b8469c8ef60c7860ed4ca8"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "An astronaut floating in effect                                                              \n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
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-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "9b169c44df5b43779a8ddfb8c0101553"
-            }
-          },
-          "metadata": {}
-        }
+      "text/plain": [
+       "Downloading (…)okenizer_config.json:   0%|          | 0.00/748 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Text variation"
-      ],
-      "metadata": {
-        "id": "04wFXW23nfBW"
-      }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bdf35e50b144bc493cd31d625f6b0f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading pytorch_model.bin:   0%|          | 0.00/351M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "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)"
-      ],
-      "metadata": {
-        "id": "4K0YqNuHkCOi",
-        "outputId": "bec46bd4-a917-4e0f-da78-daf036493dea",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 99,
-          "referenced_widgets": [
-            "aa0313ab91b5473ea65991efb1594317",
-            "6c6ad66bd515447080eabafa332fe6d3",
-            "cb8f909a9479492b9544cbf43180f254",
-            "22b80b56586b4a41bc18eaaa52f46b2f",
-            "3ab2d26e942f4b3d91739b5b3b795123",
-            "6cba3fa70b4341c69c4aa24683cb475d",
-            "09d57f92a7dc4dc8a46abfa6a8fc6f22",
-            "519120bfd09e40bf85602fb7a8637af9",
-            "7aabd4cc5f3543539132b0b01f329e60",
-            "565fd36422014b15b119bfbb56ad3e6d",
-            "9eee43f3239d4259a6d541331578a2bf",
-            "67847fd2ba124ec7b17058779077b7a8",
-            "ce415736821140da8cf6311dad5a033e",
-            "128067100be949868d2ec5effa5e2f70",
-            "65d2c3e376dd4727939410c122aed43d",
-            "fdd063b0c08048819f00499690c1132f",
-            "7072dc20291542a0916b00c31af4f631",
-            "fd73d1cda10943629fa9d897b2527fa0",
-            "3e5980cc33c147e987832e4f1d764016",
-            "a5ba01cd4f4c4351a9ae31c341991cd1",
-            "82f4f72bf59540dc9e9fea09dfa3977c",
-            "7c70290688ab41909d3f7330feecd67c"
-          ]
-        }
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd9476beeb8c4d5fb57de4cee457877b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)16e/unet/config.json:   0%|          | 0.00/839 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a659145de1ad475caff14327217528fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)tokenizer/vocab.json:   0%|          | 0.00/999k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2a87d6c2db854a77905c60434fc1a61a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/511M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "363ec3aec34e46698b476d42fff6a36c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading pytorch_model.bin:   0%|          | 0.00/492M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be7e923df5ad4379998a7abbe7598db1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)216e/vae/config.json:   0%|          | 0.00/582 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "27cdd81a42da482792f530fc9c06ab72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/3.81G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3ae046f1dfd74278a729dacf5d0195df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/335M [00:00<?, ?B/s]"
+      ]
+     },
+     "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": "4647b3622d1c4bea9b20a42d1015e4b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "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 stream in of the middle stream in forest\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": [
+      "f279bebd3a0f46a7ac439d5f4afb1689",
+      "16759b87a5c94b048c279522c04c11b0",
+      "97f56ca2c5b942b49331cbcf9b1cf48c",
+      "2832d17fdb7c43fda88e6905e6d68b68",
+      "eb1b5341e0cb49dd92fa52ad1b04adaa",
+      "8de98c04c7404142857551341ddbe33e",
+      "4325f1508f534ecc9f31e543b3a06a16",
+      "71d9424a1c08488f890d8da5880f9dbd",
+      "47c76dbd09ed45dcbe8db014a434878b",
+      "3fb4e12622bf4974a92b59b58df50866",
+      "e91d7c7bce9e4c078ebecaf35d8cdfcb",
+      "80dda2c33fec47f08bf6c741e7790489",
+      "84b4f6cf84db40e598f27863c04cde7e",
+      "737fb4fb753b4a4b9b45790842530fc7",
+      "6f49a8fb4b05498b9b60ccb43183beb9",
+      "5f90f3b0993543feb950614bf292cb25",
+      "80e1ce7ac8b441499f7a22b83f6442f6",
+      "9adabc6f79a64490bd8ce5dd51ac7a00",
+      "c24c1cf1bd6b4209a55e5d47856bdebd",
+      "758b779034794018aa08268dd4fe37ba",
+      "15727260a9ef4805b70bde2c33105ab6",
+      "4bd4c39e0599417686d3213aefb98308"
+     ]
+    },
+    "id": "cSJ9bmJzltw6",
+    "outputId": "aabff12d-8f33-4dcd-c60d-5335302d5023"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f279bebd3a0f46a7ac439d5f4afb1689",
+       "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": "80dda2c33fec47f08bf6c741e7790489",
+       "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": [
+      "625a745f7fa74c3fb594441d94007c8d",
+      "85d599c80eae4c97a93ad2b169dfec14",
+      "57cc9a55457a4ddca74ffbdbf000f31e",
+      "50af1f06bf5f446188ade525b2394434",
+      "671f078788874c6880bedcab2fe5fc86",
+      "84940128f53846f999af1a0a6e323483",
+      "dec01177db914bbc99e5feb8b41c4223",
+      "a54c0bd6dad344e9833e5dc15c5f31bd",
+      "a3f924333d3a4416951e63e751437ae7",
+      "051e9463f91b4eb99be08a48f061ecc7",
+      "291107b04b88491fbc7efe2a0974eb84"
+     ]
+    },
+    "id": "xfiS2K4biogu",
+    "outputId": "113c6034-9ccf-4da5-8b5e-468d32a6abb9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "625a745f7fa74c3fb594441d94007c8d",
+       "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": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 67,
+     "referenced_widgets": [
+      "60254b14e51b42cda6bc96e4718d3e35",
+      "a607c30cea0942f3aafbf39db6b4e0b9",
+      "7f0976151eab4c0290e37c8eed73a6e1",
+      "58f5a09ff1c644e895a7eaa173ea0fd1",
+      "9f43724297464625a9f048a5f63e2dbd",
+      "0d383b3064a84449aa3c8bc05fb80566",
+      "ecbf4dc1589844ddb56f87b29b0cff21",
+      "75b7a4d1e8264b6cb7b5774f4e4a9919",
+      "fa277c639bdf46bbaeab3533593b0999",
+      "99a08c7382f04d04b2d84ff12db896fd",
+      "5a46ed6269f7497fa90297a7bb659cff"
+     ]
+    },
+    "id": "ULwD_y8pjHGO",
+    "outputId": "3ca448fc-9826-49c9-e617-d4214f4da984"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60254b14e51b42cda6bc96e4718d3e35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A stream in of the middle stream in forest\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(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": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
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       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "An astronaut floating in flight above in Earth\n"
+     ]
+    },
+    {
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+   "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"
+   ]
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+   "outputs": [
+    {
+     "data": {
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+       "model_id": "c591f855f8394b799113b3c057abaf3f",
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+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "data": {
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+     "output_type": "stream",
+     "text": [
+      "An elephant floating in the ocean with 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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+   "machine_shape": "hm",
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+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
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From a72cd166add1e73d0e8ec2bbaeee00e9b26d8f1c Mon Sep 17 00:00:00 2001
From: Sayak Paul <spsayakpaul@gmail.com>
Date: Thu, 25 May 2023 14:38:52 +0530
Subject: [PATCH 3/4] minor updates.

---
 diffusers/unidiffuser.ipynb | 24159 +++++++++++++++++-----------------
 1 file changed, 12083 insertions(+), 12076 deletions(-)

diff --git a/diffusers/unidiffuser.ipynb b/diffusers/unidiffuser.ipynb
index 4c99d56b..ead8c396 100644
--- a/diffusers/unidiffuser.ipynb
+++ b/diffusers/unidiffuser.ipynb
@@ -1,12153 +1,12160 @@
 {
- "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!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pH5NByOqlJ39"
-   },
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "lIYdn1woOS1n",
-    "outputId": "f265eb37-70a8-48fa-d6f6-3e147541593a"
-   },
-   "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",
-      "  Building wheel for diffusers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
-     ]
-    }
-   ],
-   "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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-    "id": "oc4Wtedcem_z",
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-      ]
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-      ]
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+  "cells": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8641c24e76124ee4b17953105c9f1c84",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PEQHAzAqkYp2"
       },
-      "text/plain": [
-       "Downloading (…)_decoder/config.json:   0%|          | 0.00/565 [00:00<?, ?B/s]"
+      "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!"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "953d7484988945889d017fd5ddf2eca5",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pH5NByOqlJ39"
       },
-      "text/plain": [
-       "Downloading (…)_encoder/config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
+      "source": [
+        "## Setup"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "df5713b123214070a97169fb9020c933",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lIYdn1woOS1n",
+        "outputId": "23733809-fa5b-4d10-8c56-80014c5be4ce"
       },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
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-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "720f32130f374f5d9074154751516884",
-       "version_major": 2,
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-      },
-      "text/plain": [
-       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/319 [00:00<?, ?B/s]"
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cd54dc940db4ef5a3f4b7875f96f3ba",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P65FyJgBlRMQ"
       },
-      "text/plain": [
-       "Downloading (…)tokenizer/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+      "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)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
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-       "version_minor": 0
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+            "546836834e7843bfa9e33c10cbe3a6f9",
+            "111c471b394b4ecc97cfb2804743075a"
+          ]
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+        "outputId": "8b8f260f-a236-42b0-df90-342db791225b"
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+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            }
+          },
+          "metadata": {}
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+        {
+          "output_type": "display_data",
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
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+            }
+          },
+          "metadata": {}
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+        {
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+          "metadata": {}
+        },
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+        },
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+          "metadata": {}
+        },
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+          "metadata": {}
+        },
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+        },
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+          "metadata": {}
+        },
+        {
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/3.81G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a324f4a4b6c64558a1ebc7a7480db9e3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9047410dc4a54e51be22d2d4735db2ec"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
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+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2b6b544a0ac04ddea7cfb401357326b1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "No inputs or latents have been supplied, and mode has not been manually set, defaulting to mode 'joint'.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "690fbd8adadc476b9a8dc5bc2c536569"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "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"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bdf35e50b144bc493cd31d625f6b0f1",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "soX66TISlkNN"
       },
-      "text/plain": [
-       "Downloading pytorch_model.bin:   0%|          | 0.00/351M [00:00<?, ?B/s]"
+      "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):"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bd9476beeb8c4d5fb57de4cee457877b",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "41c9d40eed774a42bb0980ec92205287",
+            "f7a4d53dc6c84e4bbc0d1a1d5b056d8d",
+            "5ebdb3f9f5a8409cb320d76f1859af0f",
+            "722a8a60c2744d9d82892a4a184dcb8a",
+            "c774541c5ab24679a52aa071f3e1d7e6",
+            "d0b99577789e416b84ed91d76c26b051",
+            "7f45605e621543d1a4e1e609f04d8b00",
+            "e433487e0bf0403c93d3ca5e65056f73",
+            "d2032c0f0b6c43fda8bfc593fa6175f2",
+            "e466c487167a4556b88f03cf9351f6be",
+            "2a570c9016454531aa4961cc94dd8d6f",
+            "509b4d13b8ba4507b4e996253e192d87",
+            "61bcfe911d4a404f98d258eeeb1d08b3",
+            "d0e34d1e425a4641b7ac1f12c366774f",
+            "51d285e2f5914e558aa3526de2f058b8",
+            "1ac7d28415864454855991752e47123d",
+            "b35d8329bd354b43bd5977e8dc1753f4",
+            "f3438022bcf841bd813ee7e28b42f2b4",
+            "09ecaf14c7974b24b0860229d96738cf",
+            "62fa99183f404d04bfa6b2e15e5dc852",
+            "2297b01a591541069a53c1eec3e05803",
+            "574c126f31404caebe06904f9886b55a"
+          ]
+        },
+        "id": "cSJ9bmJzltw6",
+        "outputId": "3df922d0-2f35-468e-915b-6b6cbd441145"
       },
-      "text/plain": [
-       "Downloading (…)16e/unet/config.json:   0%|          | 0.00/839 [00:00<?, ?B/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "41c9d40eed774a42bb0980ec92205287"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "509b4d13b8ba4507b4e996253e192d87"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "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]"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a659145de1ad475caff14327217528fe",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "egHRrlWnmOcL"
       },
-      "text/plain": [
-       "Downloading (…)tokenizer/vocab.json:   0%|          | 0.00/999k [00:00<?, ?B/s]"
+      "source": [
+        "To reset a mode, call: `pipe.reset_mode()`. "
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2a87d6c2db854a77905c60434fc1a61a",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I5-8Yz96lwXf"
       },
-      "text/plain": [
-       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/511M [00:00<?, ?B/s]"
+      "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. "
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "363ec3aec34e46698b476d42fff6a36c",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "v3nkV9BAhRUo"
       },
-      "text/plain": [
-       "Downloading pytorch_model.bin:   0%|          | 0.00/492M [00:00<?, ?B/s]"
+      "outputs": [],
+      "source": [
+        "pipe.set_text_to_image_mode()"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be7e923df5ad4379998a7abbe7598db1",
-       "version_major": 2,
-       "version_minor": 0
+      "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"
       },
-      "text/plain": [
-       "Downloading (…)216e/vae/config.json:   0%|          | 0.00/582 [00:00<?, ?B/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7659d991715740aaa84ffd2b2ef7196e"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "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\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "27cdd81a42da482792f530fc9c06ab72",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UutaSm0vmZox"
       },
-      "text/plain": [
-       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/3.81G [00:00<?, ?B/s]"
+      "source": [
+        "## Image-to-text generation"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3ae046f1dfd74278a729dacf5d0195df",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "nfHNyWlWisfd"
       },
-      "text/plain": [
-       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/335M [00:00<?, ?B/s]"
+      "outputs": [],
+      "source": [
+        "pipe.set_image_to_text_mode()"
       ]
-     },
-     "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": "4647b3622d1c4bea9b20a42d1015e4b2",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 67,
+          "referenced_widgets": [
+            "ef3dc41655fe4327bdd64c5ba9f839ca",
+            "109ea54b8a9b4163a8af410e82bd4d36",
+            "4ad4508c72304fbf98bea4419e8ac891",
+            "391da011da024c0281129d130a0abd19",
+            "1e33445a628c4fb98d76cb9fafb03800",
+            "4c32d824502b4dfeaf6a2a0814570f33",
+            "4453039feb814ddf94402c7d0bc8cce7",
+            "13ecf80db5fb40b3808acce2a7b79165",
+            "8ac480adcecd46598f6c7141bde7dd5c",
+            "46c5ab9ce8bd4aa1b4cd4bf9e4c47a1c",
+            "1eeb558a427a4011acf44b78c0a0aea0"
+          ]
+        },
+        "id": "ULwD_y8pjHGO",
+        "outputId": "f0f54d93-8530-4fb4-87ca-093109bd274f"
       },
-      "text/plain": [
-       "  0%|          | 0/20 [00:00<?, ?it/s]"
-      ]
-     },
-     "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 stream in of the middle stream in forest\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": [
-      "f279bebd3a0f46a7ac439d5f4afb1689",
-      "16759b87a5c94b048c279522c04c11b0",
-      "97f56ca2c5b942b49331cbcf9b1cf48c",
-      "2832d17fdb7c43fda88e6905e6d68b68",
-      "eb1b5341e0cb49dd92fa52ad1b04adaa",
-      "8de98c04c7404142857551341ddbe33e",
-      "4325f1508f534ecc9f31e543b3a06a16",
-      "71d9424a1c08488f890d8da5880f9dbd",
-      "47c76dbd09ed45dcbe8db014a434878b",
-      "3fb4e12622bf4974a92b59b58df50866",
-      "e91d7c7bce9e4c078ebecaf35d8cdfcb",
-      "80dda2c33fec47f08bf6c741e7790489",
-      "84b4f6cf84db40e598f27863c04cde7e",
-      "737fb4fb753b4a4b9b45790842530fc7",
-      "6f49a8fb4b05498b9b60ccb43183beb9",
-      "5f90f3b0993543feb950614bf292cb25",
-      "80e1ce7ac8b441499f7a22b83f6442f6",
-      "9adabc6f79a64490bd8ce5dd51ac7a00",
-      "c24c1cf1bd6b4209a55e5d47856bdebd",
-      "758b779034794018aa08268dd4fe37ba",
-      "15727260a9ef4805b70bde2c33105ab6",
-      "4bd4c39e0599417686d3213aefb98308"
-     ]
-    },
-    "id": "cSJ9bmJzltw6",
-    "outputId": "aabff12d-8f33-4dcd-c60d-5335302d5023"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f279bebd3a0f46a7ac439d5f4afb1689",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ef3dc41655fe4327bdd64c5ba9f839ca"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80dda2c33fec47f08bf6c741e7790489",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6Ojwa3Y9mrCD"
       },
-      "text/plain": [
-       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      "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. "
       ]
-     },
-     "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": [
-      "625a745f7fa74c3fb594441d94007c8d",
-      "85d599c80eae4c97a93ad2b169dfec14",
-      "57cc9a55457a4ddca74ffbdbf000f31e",
-      "50af1f06bf5f446188ade525b2394434",
-      "671f078788874c6880bedcab2fe5fc86",
-      "84940128f53846f999af1a0a6e323483",
-      "dec01177db914bbc99e5feb8b41c4223",
-      "a54c0bd6dad344e9833e5dc15c5f31bd",
-      "a3f924333d3a4416951e63e751437ae7",
-      "051e9463f91b4eb99be08a48f061ecc7",
-      "291107b04b88491fbc7efe2a0974eb84"
-     ]
     },
-    "id": "xfiS2K4biogu",
-    "outputId": "113c6034-9ccf-4da5-8b5e-468d32a6abb9"
-   },
-   "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "625a745f7fa74c3fb594441d94007c8d",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 99,
+          "referenced_widgets": [
+            "36ac820f9c4c4fa782e10f6de46a91c3",
+            "dbf7df2a28b84c068492bbb948450021",
+            "724988285a6a498ebabdc4cc8d44ecf3",
+            "4190af9a8cb94c5292fec26478d7c009",
+            "9226fbb6906646a59b6db307a5bbc296",
+            "2691c7dfca114445a66757f985bd92ae",
+            "063477fcedb24d9fa9c3af0bd3590abd",
+            "9a15c1a460bf437883a0bdcab0b220ce",
+            "6b4fb1bf209f444f84d5b772e130784c",
+            "c6df1c2f79ea4948bf5cac1578139312",
+            "85eeb5f18a74479baaec5b98f40276d1",
+            "6b0854b4a25b4d5495171d592562cf96",
+            "3be5490dc0e24d8b917ba28cb5cd8e74",
+            "dc0a3507b35d4602827e9256b5965654",
+            "04912b918c654517832e4750528c1142",
+            "aff351a3cabe446c99e704e942518c57",
+            "26dde5cc5aee4f5dad2f9fb085409178",
+            "3bf69c1d365840a69538f0109c44665f",
+            "d1a67162bec541248202f338d4ad4fe0",
+            "76c8c23568924595920bd07a5f4cb0ac",
+            "45a61b996e6f496f8a4e424edb8455e8",
+            "aaebfbd1eca54cec81f28b001289b3e6"
+          ]
+        },
+        "id": "VI7lo9SojT07",
+        "outputId": "cff95448-6ea1-4329-9d00-aa8101614c25"
       },
-      "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": 8,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 67,
-     "referenced_widgets": [
-      "60254b14e51b42cda6bc96e4718d3e35",
-      "a607c30cea0942f3aafbf39db6b4e0b9",
-      "7f0976151eab4c0290e37c8eed73a6e1",
-      "58f5a09ff1c644e895a7eaa173ea0fd1",
-      "9f43724297464625a9f048a5f63e2dbd",
-      "0d383b3064a84449aa3c8bc05fb80566",
-      "ecbf4dc1589844ddb56f87b29b0cff21",
-      "75b7a4d1e8264b6cb7b5774f4e4a9919",
-      "fa277c639bdf46bbaeab3533593b0999",
-      "99a08c7382f04d04b2d84ff12db896fd",
-      "5a46ed6269f7497fa90297a7bb659cff"
-     ]
-    },
-    "id": "ULwD_y8pjHGO",
-    "outputId": "3ca448fc-9826-49c9-e617-d4214f4da984"
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60254b14e51b42cda6bc96e4718d3e35",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "36ac820f9c4c4fa782e10f6de46a91c3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "An astronaut floating in                                                               \n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6b0854b4a25b4d5495171d592562cf96"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "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\")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A stream in of the middle stream in forest\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(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": 9,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 99,
-     "referenced_widgets": [
-      "e5db476f2fea4e28b3248de0bdaeab4b",
-      "9f519a5130f1453d8ba31570d6a0401a",
-      "9dc5ccafa3794dac81ef9d0ad09d15a0",
-      "02dca025fada49c9a54b6ff4b0f1f7d7",
-      "02c625b3ae15429f82ffded701262a9a",
-      "8d055eab5de24bd38ae9d537f5f2dc14",
-      "ecc624d9bba84bfa915ff0b19afce1dd",
-      "1c6f06acdb644688a83d4ce57e0fb291",
-      "123697ddb288482b88f690061e062c7e",
-      "1c99c30c8e8a4f70b63f2273a6596262",
-      "050394cd1a7845b8a2c8440417dc9417",
-      "af9e872888ef4d558bb820ec28f09717",
-      "038b158318034e3e92f3bb159e614263",
-      "501dc6a3e80543ff8da2ecbbb706cddb",
-      "44bf2ef7439340cfb91b633a5d898a07",
-      "b611c028258d4f81bd7cf16d11cb5a23",
-      "f46ba1b0207449b6924c10da8fdfb0e1",
-      "c1d4912c8f2e4f559b1e17d472a87402",
-      "29a4d2139b0740b78e055dfc180b2f11",
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-      "text/plain": [
-       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      "source": [
+        "## Text variation\n",
+        "\n",
+        "The same round-trip methodology can be applied here. "
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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       },
-      "text/plain": [
-       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
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+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/20 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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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-     "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\")"
-   ]
-  },
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-    "id": "04wFXW23nfBW"
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-   "source": [
-    "## Text variation"
-   ]
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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",
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+}
\ No newline at end of file

From 6616adba742728f425c3c602ba9dc1c3e4cb063d Mon Sep 17 00:00:00 2001
From: Sayak Paul <spsayakpaul@gmail.com>
Date: Thu, 25 May 2023 14:53:47 +0530
Subject: [PATCH 4/4] add unidiffuser gif

---
 diffusers/unidiffuser.ipynb | 24170 +++++++++++++++++-----------------
 1 file changed, 12087 insertions(+), 12083 deletions(-)

diff --git a/diffusers/unidiffuser.ipynb b/diffusers/unidiffuser.ipynb
index ead8c396..c01d85d9 100644
--- a/diffusers/unidiffuser.ipynb
+++ b/diffusers/unidiffuser.ipynb
@@ -1,12160 +1,12164 @@
 {
-  "cells": [
+ "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
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+     "height": 876,
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+     ]
+    },
+    "id": "oc4Wtedcem_z",
+    "outputId": "8b8f260f-a236-42b0-df90-342db791225b"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PEQHAzAqkYp2"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2623bf67e4ac4aef8ea577c79e5cab5c",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "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!"
+      "text/plain": [
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       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "pH5NByOqlJ39"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1f10f9e73b69440392315c9ece5fee61",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Setup"
+      "text/plain": [
+       "Fetching 22 files:   0%|          | 0/22 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "lIYdn1woOS1n",
-        "outputId": "23733809-fa5b-4d10-8c56-80014c5be4ce"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fbd63399230475791bb5c860e31f334",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "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"
+      "text/plain": [
+       "Downloading pytorch_model.bin:   0%|          | 0.00/351M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "P65FyJgBlRMQ"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5018d1d03df3403684afd87d5c80c41d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "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)"
+      "text/plain": [
+       "Downloading (…)okenizer_config.json:   0%|          | 0.00/806 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
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-      "outputs": [
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-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "No inputs or latents have been supplied, and mode has not been manually set, defaulting to mode 'joint'.\n"
-          ]
-        },
-        {
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-              "version_major": 2,
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-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "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"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "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)"
+      "text/plain": [
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       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "soX66TISlkNN"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41c1b0a602c54a7baa5b5f69f422f3b8",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "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):"
+      "text/plain": [
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       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 81,
-          "referenced_widgets": [
-            "41c9d40eed774a42bb0980ec92205287",
-            "f7a4d53dc6c84e4bbc0d1a1d5b056d8d",
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-            "722a8a60c2744d9d82892a4a184dcb8a",
-            "c774541c5ab24679a52aa071f3e1d7e6",
-            "d0b99577789e416b84ed91d76c26b051",
-            "7f45605e621543d1a4e1e609f04d8b00",
-            "e433487e0bf0403c93d3ca5e65056f73",
-            "d2032c0f0b6c43fda8bfc593fa6175f2",
-            "e466c487167a4556b88f03cf9351f6be",
-            "2a570c9016454531aa4961cc94dd8d6f",
-            "509b4d13b8ba4507b4e996253e192d87",
-            "61bcfe911d4a404f98d258eeeb1d08b3",
-            "d0e34d1e425a4641b7ac1f12c366774f",
-            "51d285e2f5914e558aa3526de2f058b8",
-            "1ac7d28415864454855991752e47123d",
-            "b35d8329bd354b43bd5977e8dc1753f4",
-            "f3438022bcf841bd813ee7e28b42f2b4",
-            "09ecaf14c7974b24b0860229d96738cf",
-            "62fa99183f404d04bfa6b2e15e5dc852",
-            "2297b01a591541069a53c1eec3e05803",
-            "574c126f31404caebe06904f9886b55a"
-          ]
-        },
-        "id": "cSJ9bmJzltw6",
-        "outputId": "3df922d0-2f35-468e-915b-6b6cbd441145"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e469c728ed9b4557b12ae1200954579a",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
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-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
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-              "model_id": "41c9d40eed774a42bb0980ec92205287"
-            }
-          },
-          "metadata": {}
-        },
-        {
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-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
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-              "model_id": "509b4d13b8ba4507b4e996253e192d87"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "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]"
+      "text/plain": [
+       "Downloading (…)cheduler_config.json:   0%|          | 0.00/555 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "egHRrlWnmOcL"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0f69e12e8554175b21d4858a65df43a",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "To reset a mode, call: `pipe.reset_mode()`. "
+      "text/plain": [
+       "Downloading (…)rocessor_config.json:   0%|          | 0.00/518 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "I5-8Yz96lwXf"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa121526f2f44ea2924790f63a070b8e",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "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. "
+      "text/plain": [
+       "Downloading (…)tokenizer/vocab.json:   0%|          | 0.00/1.06M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "v3nkV9BAhRUo"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4c89de4fcf07487e843b9519a0c59b3d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "pipe.set_text_to_image_mode()"
+      "text/plain": [
+       "Downloading (…)tokenizer/merges.txt:   0%|          | 0.00/525k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "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"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "297ccf531b3f4f31b1da61e9423ad71d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
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-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "7659d991715740aaa84ffd2b2ef7196e"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "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\")"
+      "text/plain": [
+       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/511M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UutaSm0vmZox"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61c8de67b49849328dde6ab809090327",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Image-to-text generation"
+      "text/plain": [
+       "Downloading pytorch_model.bin:   0%|          | 0.00/492M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "id": "nfHNyWlWisfd"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "608ceb9800d74ca9afca31d8616ce43f",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "pipe.set_image_to_text_mode()"
+      "text/plain": [
+       "Downloading (…)_encoder/config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 67,
-          "referenced_widgets": [
-            "ef3dc41655fe4327bdd64c5ba9f839ca",
-            "109ea54b8a9b4163a8af410e82bd4d36",
-            "4ad4508c72304fbf98bea4419e8ac891",
-            "391da011da024c0281129d130a0abd19",
-            "1e33445a628c4fb98d76cb9fafb03800",
-            "4c32d824502b4dfeaf6a2a0814570f33",
-            "4453039feb814ddf94402c7d0bc8cce7",
-            "13ecf80db5fb40b3808acce2a7b79165",
-            "8ac480adcecd46598f6c7141bde7dd5c",
-            "46c5ab9ce8bd4aa1b4cd4bf9e4c47a1c",
-            "1eeb558a427a4011acf44b78c0a0aea0"
-          ]
-        },
-        "id": "ULwD_y8pjHGO",
-        "outputId": "f0f54d93-8530-4fb4-87ca-093109bd274f"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "34da22dcbc534e31bb0ade4ff7554cf4",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "  0%|          | 0/20 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ef3dc41655fe4327bdd64c5ba9f839ca"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "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)"
+      "text/plain": [
+       "Downloading (…)_decoder/config.json:   0%|          | 0.00/565 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6Ojwa3Y9mrCD"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5366bfcceaa44969b0e767e3955464eb",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "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. "
+      "text/plain": [
+       "Downloading (…)er/added_tokens.json:   0%|          | 0.00/23.0 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 99,
-          "referenced_widgets": [
-            "36ac820f9c4c4fa782e10f6de46a91c3",
-            "dbf7df2a28b84c068492bbb948450021",
-            "724988285a6a498ebabdc4cc8d44ecf3",
-            "4190af9a8cb94c5292fec26478d7c009",
-            "9226fbb6906646a59b6db307a5bbc296",
-            "2691c7dfca114445a66757f985bd92ae",
-            "063477fcedb24d9fa9c3af0bd3590abd",
-            "9a15c1a460bf437883a0bdcab0b220ce",
-            "6b4fb1bf209f444f84d5b772e130784c",
-            "c6df1c2f79ea4948bf5cac1578139312",
-            "85eeb5f18a74479baaec5b98f40276d1",
-            "6b0854b4a25b4d5495171d592562cf96",
-            "3be5490dc0e24d8b917ba28cb5cd8e74",
-            "dc0a3507b35d4602827e9256b5965654",
-            "04912b918c654517832e4750528c1142",
-            "aff351a3cabe446c99e704e942518c57",
-            "26dde5cc5aee4f5dad2f9fb085409178",
-            "3bf69c1d365840a69538f0109c44665f",
-            "d1a67162bec541248202f338d4ad4fe0",
-            "76c8c23568924595920bd07a5f4cb0ac",
-            "45a61b996e6f496f8a4e424edb8455e8",
-            "aaebfbd1eca54cec81f28b001289b3e6"
-          ]
-        },
-        "id": "VI7lo9SojT07",
-        "outputId": "cff95448-6ea1-4329-9d00-aa8101614c25"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0755018085b4c3b9a5cd0ef78304d15",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "  0%|          | 0/20 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "36ac820f9c4c4fa782e10f6de46a91c3"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "An astronaut floating in                                                               \n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "  0%|          | 0/20 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "6b0854b4a25b4d5495171d592562cf96"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "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\")"
+      "text/plain": [
+       "Downloading (…)okenizer_config.json:   0%|          | 0.00/748 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "04wFXW23nfBW"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f1ee951d03844578f18173a31ee3703",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "## Text variation\n",
-        "\n",
-        "The same round-trip methodology can be applied here. "
+      "text/plain": [
+       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/319 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 99,
-          "referenced_widgets": [
-            "0cbf710250884de19d8743087845fe2a",
-            "12852e28de684848b1b8fd8e4acc0185",
-            "66880e7a87bf429e93e97852a2a1f4c5",
-            "ed2ff0cea7a84717a32abf7be7ab668a",
-            "baedb51d3d07428292a645f1a626343e",
-            "34f00c55d4d6442e9e47c8f011ae31d4",
-            "695df781b53f474cb0ac8c6848f00baf",
-            "c125ee1f4a274d4f8ca2fcb1979d36cb",
-            "dc5037443fd641a5a411eb942ac4f097",
-            "938d62178362434e86e982b750a54683",
-            "e5c823598f104d398b5018e37861df6a",
-            "3d07e5f86e8f41ac9460eb6b9da079ba",
-            "a2917d8c22a74058924b8f76e319d248",
-            "dd9a406b5f6d4bdd9af1e0c967e7dca4",
-            "2e0f075cab6e4b32a4e28bcfbc0343d6",
-            "c3b1e37598be44bc8dd340ff841a810f",
-            "93e699db4fac44b4a5a84d05654e86b5",
-            "316bdf8da5e0444e9fa4ceaba782192c",
-            "96cebdf3d37f49898c6a75f16870ec56",
-            "51db591b7c2b47abad111c7f6208a71d",
-            "fb37b85904494831930f8d3b7bcab922",
-            "6e9d5e84466743759a13171936b6c153"
-          ]
-        },
-        "id": "4K0YqNuHkCOi",
-        "outputId": "a3b5b5f3-0c6d-48bd-e0e1-fd8d676d7e70"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d061a5eb8f443fba871973841012129",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "  0%|          | 0/20 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "0cbf710250884de19d8743087845fe2a"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "  0%|          | 0/20 [00:00<?, ?it/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "3d07e5f86e8f41ac9460eb6b9da079ba"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "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)"
+      "text/plain": [
+       "Downloading (…)tokenizer/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bbaed02c2ef44e692f524c3a1b0c83f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)tokenizer/vocab.json:   0%|          | 0.00/999k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "90279c703b2644afb9f3b253e2ec6ed1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)16e/unet/config.json:   0%|          | 0.00/839 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a324f4a4b6c64558a1ebc7a7480db9e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/3.81G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9047410dc4a54e51be22d2d4735db2ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/335M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b6b544a0ac04ddea7cfb401357326b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)216e/vae/config.json:   0%|          | 0.00/582 [00:00<?, ?B/s]"
+      ]
+     },
+     "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
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
       ]
+     },
+     "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"
+     ]
     }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
+   ],
+   "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,
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+      "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()"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
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+      "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": [
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+    "id": "ULwD_y8pjHGO",
+    "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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