{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4447a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from typing import Iterable, List\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from IPython.display import display\n",
    "\n",
    "from vibe_blending import run_vibe_blend_not_safe\n",
    "from ipadapter_model import create_image_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f9e85da",
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFIG_PATH = Path('config.yaml')\n",
    "IMAGES_DIR = Path('images')\n",
    "\n",
    "POSITIVE_IMAGE_PATHS = [\n",
    "    IMAGES_DIR / 'playviolin_hr.png',\n",
    "    IMAGES_DIR / 'playguitar_hr.png',\n",
    "]\n",
    "\n",
    "EXTRA_IMAGE_PATHS = [\n",
    "    # ...\n",
    "]\n",
    "\n",
    "NEGATIVE_IMAGE_PATHS = [\n",
    "    # ...\n",
    "]\n",
    "\n",
    "def load_images(paths: Iterable[Path]) -> List[Image.Image]:\n",
    "    return [Image.open(path).convert('RGB') for path in paths]\n",
    "\n",
    "positive_images = load_images(POSITIVE_IMAGE_PATHS)\n",
    "extra_images = load_images(EXTRA_IMAGE_PATHS)\n",
    "negative_images = load_images(NEGATIVE_IMAGE_PATHS)\n",
    "\n",
    "print(f'Loaded {len(positive_images)} positive, {len(extra_images)} extra, {len(negative_images)} negative images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b079a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "GRID_PREVIEW_SIZE = (256, 256)\n",
    "GRID_BLEND_SIZE = (512, 512)\n",
    "\n",
    "def resize_for_grid(images: List[Image.Image], size: tuple[int, int]) -> List[Image.Image]:\n",
    "    return [img.resize(size, Image.Resampling.LANCZOS) for img in images]\n",
    "\n",
    "def show_row(title: str, images: List[Image.Image]):\n",
    "    if not images:\n",
    "        return\n",
    "    print(f\"{title} ({len(images)} images)\")\n",
    "    thumbs = resize_for_grid(images, GRID_PREVIEW_SIZE)\n",
    "    grid = create_image_grid(thumbs, rows=1, cols=len(thumbs))\n",
    "    display(grid)\n",
    "\n",
    "show_row('Positives', positive_images)\n",
    "show_row('Extra references', extra_images)\n",
    "show_row('Negatives (attributes to suppress)', negative_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d396972",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha_weights = np.linspace(0, 1, 10).tolist()\n",
    "print(f'α values: {alpha_weights}')\n",
    "\n",
    "blended_with_negatives = run_vibe_blend_not_safe(\n",
    "    image1=positive_images[0],\n",
    "    image2=positive_images[1],\n",
    "    extra_images=extra_images,\n",
    "    negative_images=negative_images,\n",
    "    config_path=str(CONFIG_PATH),\n",
    "    interpolation_weights=alpha_weights,\n",
    "    n_clusters=20,\n",
    ")\n",
    "\n",
    "sequence_with_neg = [positive_images[0], *blended_with_negatives, positive_images[1]]\n",
    "rows = int(np.ceil(len(sequence_with_neg) / 4))\n",
    "normalized_sequence = resize_for_grid(sequence_with_neg, GRID_BLEND_SIZE)\n",
    "neg_grid = create_image_grid(normalized_sequence, rows=rows, cols=4)\n",
    "display(neg_grid)"
   ]
  }
 ],
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