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"text": [
"sdxl log-variance: -0.105\n",
"AiArtLab_vae log-variance: -0.105\n"
]
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"text": [
"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==\"flux\":\n",
" vae = AutoencoderKL.from_pretrained(repo, 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\")"
]
},
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