diffusers/ddim_noise_comparative_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DDIM Noise Comparative Analysis Pipeline\n",
    "\n",
    "The [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227) paper proposed an approach to answer the above question, which is their second contribution. The approach consists of the following steps:\n",
    "\n",
    "1. The input is an image x0.\n",
    "\n",
    "2. Perturb it to xt using a diffusion process q(xt|x0).\n",
    "\n",
    "3. `strength` is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.\n",
    "\n",
    "4. Reconstruct the image with the learned denoising process pθ(ˆx0|xt).\n",
    "\n",
    "5. Compare x0 and ˆx0 among various t to show how each step contributes to the sample. The authors used [openai/guided-diffusion](https://github.com/openai/guided-diffusion) model to denoise images in FFHQ dataset. This pipeline extends their second contribution by investigating DDIM on any input image. This script was contributed by [Anh Nguyen](https://github.com/ParagEkbote) and the notebook by [Parag Ekbote](https://github.com/ParagEkbote)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install diffusers transformers pillow torch datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from datasets import load_dataset\n",
    "from diffusers import DiffusionPipeline\n",
    "\n",
    "# Load the CelebA-HQ dataset\n",
    "ds = load_dataset(\"saitsharipov/CelebA-HQ\", split=\"train\")\n",
    "\n",
    "# Get the first image from the dataset\n",
    "image_data = ds[0][\"image\"]\n",
    "image_pil = Image.fromarray(np.array(image_data))  # Convert to PIL image\n",
    "image_name = \"first_image\"  # Use a descriptive name for saving files\n",
    "\n",
    "# Set device\n",
    "device = torch.device(\"cpu\" if not torch.cuda.is_available() else \"cuda\")\n",
    "\n",
    "# Load the diffusion pipeline\n",
    "pipe = DiffusionPipeline.from_pretrained(\n",
    "    \"google/ddpm-ema-celebahq-256\",\n",
    "    custom_pipeline=\"ddim_noise_comparative_analysis\",\n",
    ")\n",
    "pipe = pipe.to(device)\n",
    "\n",
    "# Perform noise comparative analysis\n",
    "for strength in np.linspace(0.1, 1, 25):\n",
    "    denoised_image, latent_timestep = pipe(\n",
    "        image_pil, strength=strength, return_dict=False\n",
    "    )\n",
    "    denoised_image = denoised_image[0]\n",
    "    denoised_image.save(\n",
    "        f\"noise_comparative_analysis_{image_name}_{latent_timestep}.png\"\n",
    "    )\n"
   ]
  }
 ],
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