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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiple distributions found for package optimum. Picked distribution: optimum\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sdxl log-variance: -0.105\n"
     ]
    },
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      "text/plain": [
       "config.json:   0%|          | 0.00/805 [00:00<?, ?B/s]"
      ]
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       "version_minor": 0
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      "text/plain": [
       "vae/diffusion_pytorch_model.safetensors:   0%|          | 0.00/167M [00:00<?, ?B/s]"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AiArtLab_vae log-variance: -0.105\n",
      "EQ-SDXL-VAE log-variance: -3.922\n",
      "Готово\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from PIL import Image\n",
    "from diffusers import AutoencoderKL\n",
    "from torchvision.transforms.functional import to_pil_image\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from torchvision.transforms import ToTensor, Normalize, CenterCrop\n",
    "\n",
    "# путь к вашей картинке\n",
    "IMG_PATH = \"123456789.jpg\"\n",
    "OUT_DIR  = \"vaetest\"\n",
    "device   = \"cuda\"\n",
    "dtype    = torch.float32          # ← единый float32\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "# список VAE\n",
    "VAES = {\n",
    "    \"sdxl\": \"madebyollin/sdxl-vae-fp16-fix\",\n",
    "    \"AiArtLab_vae\": \"AiArtLab/sdxl_vae\",\n",
    "    \"EQ-SDXL-VAE\": \"KBlueLeaf/EQ-SDXL-VAE\"\n",
    "}\n",
    "\n",
    "def load_image(path):\n",
    "    img = Image.open(path).convert('RGB')\n",
    "    # обрезаем до кратности 8\n",
    "    w, h = img.size\n",
    "    img = CenterCrop((h // 8 * 8, w // 8 * 8))(img)\n",
    "    tensor = ToTensor()(img).unsqueeze(0)          # [0,1]\n",
    "    tensor = Normalize(mean=[0.5]*3, std=[0.5]*3)(tensor)  # [-1,1]\n",
    "    return img, tensor.to(device, dtype=dtype)\n",
    "\n",
    "# обратно в PIL\n",
    "def tensor_to_img(t):\n",
    "    t = (t * 0.5 + 0.5).clamp(0, 1)\n",
    "    return to_pil_image(t[0])\n",
    "\n",
    "def logvariance(latents):\n",
    "    \"\"\"Возвращает лог-дисперсию по всем элементам.\"\"\"\n",
    "    return torch.log(latents.var() + 1e-8).item()\n",
    "\n",
    "def plot_latent_distribution(latents, title, save_path):\n",
    "    \"\"\"Гистограмма + QQ-plot.\"\"\"\n",
    "    lat = latents.detach().cpu().numpy().flatten()\n",
    "    plt.figure(figsize=(10, 4))\n",
    "\n",
    "    # гистограмма\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.hist(lat, bins=100, density=True, alpha=0.7, color='steelblue')\n",
    "    plt.title(f\"{title} histogram\")\n",
    "    plt.xlabel(\"latent value\")\n",
    "    plt.ylabel(\"density\")\n",
    "\n",
    "    # QQ-plot\n",
    "    from scipy.stats import probplot\n",
    "    plt.subplot(1, 2, 2)\n",
    "    probplot(lat, dist=\"norm\", plot=plt)\n",
    "    plt.title(f\"{title} QQ-plot\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(save_path)\n",
    "    plt.close()\n",
    "\n",
    "for name, repo in VAES.items():\n",
    "    if name==\"AiArtLab_vae\":\n",
    "        vae = AutoencoderKL.from_pretrained(repo, subfolder=\"vae\", torch_dtype=dtype).to(device)\n",
    "    else:\n",
    "        vae = AutoencoderKL.from_pretrained(repo, torch_dtype=dtype).to(device)#, subfolder=\"vae\", variant=\"fp16\"\n",
    "\n",
    "    cfg   = vae.config\n",
    "    scale = getattr(cfg, \"scaling_factor\", 1.)\n",
    "    shift = getattr(cfg, \"shift_factor\", 0.0)\n",
    "    mean  = getattr(cfg, \"latents_mean\", None)\n",
    "    std  = getattr(cfg, \"latents_std\",  None)\n",
    "\n",
    "    C = 4  # 4 для SDXL\n",
    "    if mean is not None:\n",
    "        mean = torch.tensor(mean, device=device, dtype=dtype).view(1, C, 1, 1)\n",
    "    if std is not None:\n",
    "        std  = torch.tensor(std,  device=device, dtype=dtype).view(1, C, 1, 1)\n",
    "    if shift is not None:\n",
    "        shift = torch.tensor(shift, device=device, dtype=dtype)\n",
    "    else:\n",
    "        shift = 0.0 \n",
    "\n",
    "    scale = torch.tensor(scale, device=device, dtype=dtype)\n",
    "\n",
    "    img, x = load_image(IMG_PATH)\n",
    "    img.save(os.path.join(OUT_DIR, f\"original.jpg\"))\n",
    "\n",
    "    with torch.no_grad():\n",
    "        # encode\n",
    "        latents = vae.encode(x).latent_dist.sample().to(dtype)\n",
    "        if mean is not None and std is not None:\n",
    "            latents = (latents - mean) / std\n",
    "        latents = latents * scale + shift\n",
    "\n",
    "        lv = logvariance(latents)\n",
    "        print(f\"{name} log-variance: {lv:.3f}\")\n",
    "\n",
    "        # график\n",
    "        plot_latent_distribution(latents, f\"{name}_latents\",\n",
    "                                 os.path.join(OUT_DIR, f\"dist_{name}.png\"))\n",
    "\n",
    "        # decode\n",
    "        latents = (latents - shift) / scale\n",
    "        if mean is not None and std is not None:\n",
    "            latents = latents * std + mean\n",
    "        rec = vae.decode(latents).sample\n",
    "\n",
    "    tensor_to_img(rec).save(os.path.join(OUT_DIR, f\"decoded_{name}.png\"))\n",
    "\n",
    "print(\"Готово\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "200b72ab-1978-4d71-9aba-b1ef97cf0b27",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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