feat(demo): update reference

tools/inference/inference_pipeline.ipynb

@@ -1,515 +1,513 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/borisdayma/dalle-mini/blob/main/tools/inference/inference_pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "118UKH5bWCGa"
-      },
-      "source": [
-        "# DALL·E mini - Inference pipeline\n",
-        "\n",
-        "*Generate images from a text prompt*\n",
-        "\n",
-        "<img src=\"https://github.com/borisdayma/dalle-mini/blob/main/img/logo.png?raw=true\" width=\"200\">\n",
-        "\n",
-        "This notebook illustrates [DALL·E mini](https://github.com/borisdayma/dalle-mini) inference pipeline.\n",
-        "\n",
-        "Just want to play? Use [the demo](https://huggingface.co/spaces/flax-community/dalle-mini).\n",
-        "\n",
-        "For more understanding of the model, refer to [the report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dS8LbaonYm3a"
-      },
-      "source": [
-        "## 🛠️ Installation and set-up"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "uzjAM2GBYpZX"
-      },
-      "outputs": [],
-      "source": [
-        "# Install required libraries\n",
-        "!pip install -q transformers\n",
-        "!pip install -q git+https://github.com/patil-suraj/vqgan-jax.git\n",
-        "!pip install -q git+https://github.com/borisdayma/dalle-mini.git"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ozHzTkyv8cqU"
-      },
-      "source": [
-        "We load required models:\n",
-        "* dalle·mini for text to encoded images\n",
-        "* VQGAN for decoding images\n",
-        "* CLIP for scoring predictions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "K6CxW2o42f-w"
-      },
-      "outputs": [],
-      "source": [
-        "# Model references\n",
-        "\n",
-        "# dalle-mini\n",
-        "DALLE_MODEL = \"dalle-mini/dalle-mini/model-mehdx7dg:latest\"  # can be wandb artifact or 🤗 Hub or local folder\n",
-        "DALLE_COMMIT_ID = None\n",
-        "\n",
-        "# VQGAN model\n",
-        "VQGAN_REPO = \"dalle-mini/vqgan_imagenet_f16_16384\"\n",
-        "VQGAN_COMMIT_ID = \"e93a26e7707683d349bf5d5c41c5b0ef69b677a9\"\n",
-        "\n",
-        "# CLIP model\n",
-        "CLIP_REPO = \"openai/clip-vit-base-patch16\"\n",
-        "CLIP_COMMIT_ID = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Yv-aR3t4Oe5v"
-      },
-      "outputs": [],
-      "source": [
-        "import jax\n",
-        "import jax.numpy as jnp\n",
-        "\n",
-        "# check how many devices are available\n",
-        "jax.local_device_count()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "HWnQrQuXOe5w"
-      },
-      "outputs": [],
-      "source": [
-        "# type used for computation - use bfloat16 on TPU's\n",
-        "dtype = jnp.bfloat16 if jax.local_device_count() == 8 else jnp.float32\n",
-        "\n",
-        "# TODO: fix issue with bfloat16\n",
-        "dtype = jnp.float32"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "92zYmvsQ38vL"
-      },
-      "outputs": [],
-      "source": [
-        "# Load models & tokenizer\n",
-        "from dalle_mini.model import DalleBart, DalleBartTokenizer\n",
-        "from vqgan_jax.modeling_flax_vqgan import VQModel\n",
-        "from transformers import CLIPProcessor, FlaxCLIPModel\n",
-        "import wandb\n",
-        "\n",
-        "# Load dalle-mini\n",
-        "model = DalleBart.from_pretrained(\n",
-        "    DALLE_MODEL, revision=DALLE_COMMIT_ID, dtype=dtype, abstract_init=True\n",
-        ")\n",
-        "tokenizer = DalleBartTokenizer.from_pretrained(\n",
-        "    DALLE_MODEL, revision=DALLE_COMMIT_ID\n",
-        ")\n",
-        "\n",
-        "# Load VQGAN\n",
-        "vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
-        "\n",
-        "# Load CLIP\n",
-        "clip = FlaxCLIPModel.from_pretrained(CLIP_REPO, revision=CLIP_COMMIT_ID)\n",
-        "processor = CLIPProcessor.from_pretrained(CLIP_REPO, revision=CLIP_COMMIT_ID)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "o_vH2X1tDtzA"
-      },
-      "source": [
-        "Model parameters are replicated on each device for faster inference."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "wtvLoM48EeVw"
-      },
-      "outputs": [],
-      "source": [
-        "from flax.jax_utils import replicate\n",
-        "\n",
-        "# convert model parameters for inference if requested\n",
-        "if dtype == jnp.bfloat16:\n",
-        "    model.params = model.to_bf16(model.params)\n",
-        "\n",
-        "model_params = replicate(model.params)\n",
-        "vqgan_params = replicate(vqgan.params)\n",
-        "clip_params = replicate(clip.params)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0A9AHQIgZ_qw"
-      },
-      "source": [
-        "Model functions are compiled and parallelized to take advantage of multiple devices."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "sOtoOmYsSYPz"
-      },
-      "outputs": [],
-      "source": [
-        "from functools import partial\n",
-        "\n",
-        "# model inference\n",
-        "@partial(jax.pmap, axis_name=\"batch\", static_broadcasted_argnums=(3, 4))\n",
-        "def p_generate(tokenized_prompt, key, params, top_k, top_p):\n",
-        "    return model.generate(\n",
-        "        **tokenized_prompt,\n",
-        "        do_sample=True,\n",
-        "        num_beams=1,\n",
-        "        prng_key=key,\n",
-        "        params=params,\n",
-        "        top_k=top_k,\n",
-        "        top_p=top_p,\n",
-        "        max_length=257\n",
-        "    )\n",
-        "\n",
-        "\n",
-        "# decode images\n",
-        "@partial(jax.pmap, axis_name=\"batch\")\n",
-        "def p_decode(indices, params):\n",
-        "    return vqgan.decode_code(indices, params=params)\n",
-        "\n",
-        "\n",
-        "# score images\n",
-        "@partial(jax.pmap, axis_name=\"batch\")\n",
-        "def p_clip(inputs, params):\n",
-        "    logits = clip(params=params, **inputs).logits_per_image\n",
-        "    return logits"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HmVN6IBwapBA"
-      },
-      "source": [
-        "Keys are passed to the model on each device to generate unique inference per device."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4CTXmlUkThhX"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "\n",
-        "# create a random key\n",
-        "seed = random.randint(0, 2**32 - 1)\n",
-        "key = jax.random.PRNGKey(seed)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BrnVyCo81pij"
-      },
-      "source": [
-        "## 🖍 Text Prompt"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rsmj0Aj5OQox"
-      },
-      "source": [
-        "Our model may require to normalize the prompt."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "YjjhUychOVxm"
-      },
-      "outputs": [],
-      "source": [
-        "from dalle_mini.text import TextNormalizer\n",
-        "\n",
-        "text_normalizer = TextNormalizer() if model.config.normalize_text else None"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BQ7fymSPyvF_"
-      },
-      "source": [
-        "Let's define a text prompt."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "x_0vI9ge1oKr"
-      },
-      "outputs": [],
-      "source": [
-        "prompt = \"a blue table\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "VKjEZGjtO49k"
-      },
-      "outputs": [],
-      "source": [
-        "processed_prompt = text_normalizer(prompt) if model.config.normalize_text else prompt\n",
-        "processed_prompt"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QUzYACWxOe5z"
-      },
-      "source": [
-        "We tokenize the prompt."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "n8e7MvGwOe5z"
-      },
-      "outputs": [],
-      "source": [
-        "tokenized_prompt = tokenizer(\n",
-        "    processed_prompt,\n",
-        "    return_tensors=\"jax\",\n",
-        "    padding=\"max_length\",\n",
-        "    truncation=True,\n",
-        "    max_length=128,\n",
-        ").data\n",
-        "tokenized_prompt"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_Y5dqFj7prMQ"
-      },
-      "source": [
-        "Notes:\n",
-        "\n",
-        "* `0`: BOS, special token representing the beginning of a sequence\n",
-        "* `2`: EOS, special token representing the end of a sequence\n",
-        "* `1`: special token representing the padding of a sequence when requesting a specific length"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "-CEJBnuJOe5z"
-      },
-      "source": [
-        "Finally we replicate it onto each device."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "lQePgju5Oe5z"
-      },
-      "outputs": [],
-      "source": [
-        "tokenized_prompt = replicate(tokenized_prompt)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "phQ9bhjRkgAZ"
-      },
-      "source": [
-        "## 🎨 Generate images\n",
-        "\n",
-        "We generate images using dalle-mini model and decode them with the VQGAN."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "d0wVkXpKqnHA"
-      },
-      "outputs": [],
-      "source": [
-        "# number of predictions\n",
-        "n_predictions = 32\n",
-        "\n",
-        "# We can customize top_k/top_p used for generating samples\n",
-        "gen_top_k = None\n",
-        "gen_top_p = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "SDjEx9JxR3v8"
-      },
-      "outputs": [],
-      "source": [
-        "from flax.training.common_utils import shard_prng_key\n",
-        "import numpy as np\n",
-        "from PIL import Image\n",
-        "from tqdm.notebook import trange\n",
-        "\n",
-        "# generate images\n",
-        "images = []\n",
-        "for i in trange(n_predictions // jax.device_count()):\n",
-        "    # get a new key\n",
-        "    key, subkey = jax.random.split(key)\n",
-        "    # generate images\n",
-        "    encoded_images = p_generate(\n",
-        "        tokenized_prompt, shard_prng_key(subkey), model_params, gen_top_k, gen_top_p\n",
-        "    )\n",
-        "    # remove BOS\n",
-        "    encoded_images = encoded_images.sequences[..., 1:]\n",
-        "    # decode images\n",
-        "    decoded_images = p_decode(encoded_images, vqgan_params)\n",
-        "    decoded_images = decoded_images.clip(0.0, 1.0).reshape((-1, 256, 256, 3))\n",
-        "    for img in decoded_images:\n",
-        "        images.append(Image.fromarray(np.asarray(img * 255, dtype=np.uint8)))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tw02wG9zGmyB"
-      },
-      "source": [
-        "Let's calculate their score with CLIP."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "FoLXpjCmGpju"
-      },
-      "outputs": [],
-      "source": [
-        "from flax.training.common_utils import shard\n",
-        "\n",
-        "# get clip scores\n",
-        "clip_inputs = processor(\n",
-        "    text=[prompt] * jax.device_count(),\n",
-        "    images=images,\n",
-        "    return_tensors=\"np\",\n",
-        "    padding=\"max_length\",\n",
-        "    max_length=77,\n",
-        "    truncation=True,\n",
-        ").data\n",
-        "logits = p_clip(shard(clip_inputs), clip_params)\n",
-        "logits = logits.squeeze().flatten()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4AAWRm70LgED"
-      },
-      "source": [
-        "Let's display images ranked by CLIP score."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "zsgxxubLLkIu"
-      },
-      "outputs": [],
-      "source": [
-        "print(f\"Prompt: {prompt}\\n\")\n",
-        "for idx in logits.argsort()[::-1]:\n",
-        "    display(images[idx])\n",
-        "    print(f\"Score: {logits[idx]:.2f}\\n\")"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "collapsed_sections": [],
-      "machine_shape": "hm",
-      "name": "DALL·E mini - Inference pipeline.ipynb",
-      "provenance": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.7"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/borisdayma/dalle-mini/blob/main/tools/inference/inference_pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "118UKH5bWCGa"
+   },
+   "source": [
+    "# DALL·E mini - Inference pipeline\n",
+    "\n",
+    "*Generate images from a text prompt*\n",
+    "\n",
+    "<img src=\"https://github.com/borisdayma/dalle-mini/blob/main/img/logo.png?raw=true\" width=\"200\">\n",
+    "\n",
+    "This notebook illustrates [DALL·E mini](https://github.com/borisdayma/dalle-mini) inference pipeline.\n",
+    "\n",
+    "Just want to play? Use [the demo](https://huggingface.co/spaces/flax-community/dalle-mini).\n",
+    "\n",
+    "For more understanding of the model, refer to [the report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dS8LbaonYm3a"
+   },
+   "source": [
+    "## 🛠️ Installation and set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "uzjAM2GBYpZX"
+   },
+   "outputs": [],
+   "source": [
+    "# Install required libraries\n",
+    "!pip install -q transformers\n",
+    "!pip install -q git+https://github.com/patil-suraj/vqgan-jax.git\n",
+    "!pip install -q git+https://github.com/borisdayma/dalle-mini.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ozHzTkyv8cqU"
+   },
+   "source": [
+    "We load required models:\n",
+    "* dalle·mini for text to encoded images\n",
+    "* VQGAN for decoding images\n",
+    "* CLIP for scoring predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "K6CxW2o42f-w"
+   },
+   "outputs": [],
+   "source": [
+    "# Model references\n",
+    "\n",
+    "# dalle-mini\n",
+    "DALLE_MODEL = \"dalle-mini/dalle-mini/model-1reghx5l:latest\"  # can be wandb artifact or 🤗 Hub or local folder\n",
+    "DALLE_COMMIT_ID = None\n",
+    "\n",
+    "# VQGAN model\n",
+    "VQGAN_REPO = \"dalle-mini/vqgan_imagenet_f16_16384\"\n",
+    "VQGAN_COMMIT_ID = \"e93a26e7707683d349bf5d5c41c5b0ef69b677a9\"\n",
+    "\n",
+    "# CLIP model\n",
+    "CLIP_REPO = \"openai/clip-vit-base-patch16\"\n",
+    "CLIP_COMMIT_ID = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Yv-aR3t4Oe5v"
+   },
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "\n",
+    "# check how many devices are available\n",
+    "jax.local_device_count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "HWnQrQuXOe5w"
+   },
+   "outputs": [],
+   "source": [
+    "# type used for computation - use bfloat16 on TPU's\n",
+    "dtype = jnp.bfloat16 if jax.local_device_count() == 8 else jnp.float32\n",
+    "\n",
+    "# TODO: fix issue with bfloat16\n",
+    "dtype = jnp.float32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "92zYmvsQ38vL"
+   },
+   "outputs": [],
+   "source": [
+    "# Load models & tokenizer\n",
+    "from dalle_mini.model import DalleBart, DalleBartTokenizer\n",
+    "from vqgan_jax.modeling_flax_vqgan import VQModel\n",
+    "from transformers import CLIPProcessor, FlaxCLIPModel\n",
+    "import wandb\n",
+    "\n",
+    "# Load dalle-mini\n",
+    "model = DalleBart.from_pretrained(\n",
+    "    DALLE_MODEL, revision=DALLE_COMMIT_ID, dtype=dtype, abstract_init=True\n",
+    ")\n",
+    "tokenizer = DalleBartTokenizer.from_pretrained(DALLE_MODEL, revision=DALLE_COMMIT_ID)\n",
+    "\n",
+    "# Load VQGAN\n",
+    "vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
+    "\n",
+    "# Load CLIP\n",
+    "clip = FlaxCLIPModel.from_pretrained(CLIP_REPO, revision=CLIP_COMMIT_ID)\n",
+    "processor = CLIPProcessor.from_pretrained(CLIP_REPO, revision=CLIP_COMMIT_ID)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o_vH2X1tDtzA"
+   },
+   "source": [
+    "Model parameters are replicated on each device for faster inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "wtvLoM48EeVw"
+   },
+   "outputs": [],
+   "source": [
+    "from flax.jax_utils import replicate\n",
+    "\n",
+    "# convert model parameters for inference if requested\n",
+    "if dtype == jnp.bfloat16:\n",
+    "    model.params = model.to_bf16(model.params)\n",
+    "\n",
+    "model_params = replicate(model.params)\n",
+    "vqgan_params = replicate(vqgan.params)\n",
+    "clip_params = replicate(clip.params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0A9AHQIgZ_qw"
+   },
+   "source": [
+    "Model functions are compiled and parallelized to take advantage of multiple devices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "sOtoOmYsSYPz"
+   },
+   "outputs": [],
+   "source": [
+    "from functools import partial\n",
+    "\n",
+    "# model inference\n",
+    "@partial(jax.pmap, axis_name=\"batch\", static_broadcasted_argnums=(3, 4))\n",
+    "def p_generate(tokenized_prompt, key, params, top_k, top_p):\n",
+    "    return model.generate(\n",
+    "        **tokenized_prompt,\n",
+    "        do_sample=True,\n",
+    "        num_beams=1,\n",
+    "        prng_key=key,\n",
+    "        params=params,\n",
+    "        top_k=top_k,\n",
+    "        top_p=top_p,\n",
+    "        max_length=257\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# decode images\n",
+    "@partial(jax.pmap, axis_name=\"batch\")\n",
+    "def p_decode(indices, params):\n",
+    "    return vqgan.decode_code(indices, params=params)\n",
+    "\n",
+    "\n",
+    "# score images\n",
+    "@partial(jax.pmap, axis_name=\"batch\")\n",
+    "def p_clip(inputs, params):\n",
+    "    logits = clip(params=params, **inputs).logits_per_image\n",
+    "    return logits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HmVN6IBwapBA"
+   },
+   "source": [
+    "Keys are passed to the model on each device to generate unique inference per device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4CTXmlUkThhX"
+   },
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "# create a random key\n",
+    "seed = random.randint(0, 2 ** 32 - 1)\n",
+    "key = jax.random.PRNGKey(seed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BrnVyCo81pij"
+   },
+   "source": [
+    "## 🖍 Text Prompt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rsmj0Aj5OQox"
+   },
+   "source": [
+    "Our model may require to normalize the prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "YjjhUychOVxm"
+   },
+   "outputs": [],
+   "source": [
+    "from dalle_mini.text import TextNormalizer\n",
+    "\n",
+    "text_normalizer = TextNormalizer() if model.config.normalize_text else None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BQ7fymSPyvF_"
+   },
+   "source": [
+    "Let's define a text prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "x_0vI9ge1oKr"
+   },
+   "outputs": [],
+   "source": [
+    "prompt = \"a blue table\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "VKjEZGjtO49k"
+   },
+   "outputs": [],
+   "source": [
+    "processed_prompt = text_normalizer(prompt) if model.config.normalize_text else prompt\n",
+    "processed_prompt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QUzYACWxOe5z"
+   },
+   "source": [
+    "We tokenize the prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "n8e7MvGwOe5z"
+   },
+   "outputs": [],
+   "source": [
+    "tokenized_prompt = tokenizer(\n",
+    "    processed_prompt,\n",
+    "    return_tensors=\"jax\",\n",
+    "    padding=\"max_length\",\n",
+    "    truncation=True,\n",
+    "    max_length=128,\n",
+    ").data\n",
+    "tokenized_prompt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_Y5dqFj7prMQ"
+   },
+   "source": [
+    "Notes:\n",
+    "\n",
+    "* `0`: BOS, special token representing the beginning of a sequence\n",
+    "* `2`: EOS, special token representing the end of a sequence\n",
+    "* `1`: special token representing the padding of a sequence when requesting a specific length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-CEJBnuJOe5z"
+   },
+   "source": [
+    "Finally we replicate it onto each device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lQePgju5Oe5z"
+   },
+   "outputs": [],
+   "source": [
+    "tokenized_prompt = replicate(tokenized_prompt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "phQ9bhjRkgAZ"
+   },
+   "source": [
+    "## 🎨 Generate images\n",
+    "\n",
+    "We generate images using dalle-mini model and decode them with the VQGAN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "d0wVkXpKqnHA"
+   },
+   "outputs": [],
+   "source": [
+    "# number of predictions\n",
+    "n_predictions = 32\n",
+    "\n",
+    "# We can customize top_k/top_p used for generating samples\n",
+    "gen_top_k = None\n",
+    "gen_top_p = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SDjEx9JxR3v8"
+   },
+   "outputs": [],
+   "source": [
+    "from flax.training.common_utils import shard_prng_key\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "from tqdm.notebook import trange\n",
+    "\n",
+    "# generate images\n",
+    "images = []\n",
+    "for i in trange(n_predictions // jax.device_count()):\n",
+    "    # get a new key\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    # generate images\n",
+    "    encoded_images = p_generate(\n",
+    "        tokenized_prompt, shard_prng_key(subkey), model_params, gen_top_k, gen_top_p\n",
+    "    )\n",
+    "    # remove BOS\n",
+    "    encoded_images = encoded_images.sequences[..., 1:]\n",
+    "    # decode images\n",
+    "    decoded_images = p_decode(encoded_images, vqgan_params)\n",
+    "    decoded_images = decoded_images.clip(0.0, 1.0).reshape((-1, 256, 256, 3))\n",
+    "    for img in decoded_images:\n",
+    "        images.append(Image.fromarray(np.asarray(img * 255, dtype=np.uint8)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tw02wG9zGmyB"
+   },
+   "source": [
+    "Let's calculate their score with CLIP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "FoLXpjCmGpju"
+   },
+   "outputs": [],
+   "source": [
+    "from flax.training.common_utils import shard\n",
+    "\n",
+    "# get clip scores\n",
+    "clip_inputs = processor(\n",
+    "    text=[prompt] * jax.device_count(),\n",
+    "    images=images,\n",
+    "    return_tensors=\"np\",\n",
+    "    padding=\"max_length\",\n",
+    "    max_length=77,\n",
+    "    truncation=True,\n",
+    ").data\n",
+    "logits = p_clip(shard(clip_inputs), clip_params)\n",
+    "logits = logits.squeeze().flatten()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4AAWRm70LgED"
+   },
+   "source": [
+    "Let's display images ranked by CLIP score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "zsgxxubLLkIu"
+   },
+   "outputs": [],
+   "source": [
+    "print(f\"Prompt: {prompt}\\n\")\n",
+    "for idx in logits.argsort()[::-1]:\n",
+    "    display(images[idx])\n",
+    "    print(f\"Score: {logits[idx]:.2f}\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "machine_shape": "hm",
+   "name": "DALL·E mini - Inference pipeline.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}