tools/inference/inference_pipeline.ipynb

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   "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"
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   "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"
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   "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\")"
   ]
  }
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