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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c15deb04-94a0-4073-a174-adcd22af10b8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The config attributes {'block_out_channels': [128, 128, 256, 512, 512], 'force_upcast': False} were passed to AsymmetricAutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Создана новая модель: <class 'diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL'>\n",
"\n",
"--- Перенос весов ---\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 326/326 [00:00<00:00, 54186.54it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"✅ Перенос завершён.\n",
"Статистика:\n",
" перенесено: 227\n",
" дублировано: 0\n",
" пропущено: 0\n",
"AutoencoderKL(\n",
" (encoder): Encoder(\n",
" (conv_in): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (down_blocks): ModuleList(\n",
" (0): DownEncoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0-2): 3 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (downsamplers): ModuleList(\n",
" (0): Downsample2D(\n",
" (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2))\n",
" )\n",
" )\n",
" )\n",
" (1): DownEncoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1-2): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (downsamplers): ModuleList(\n",
" (0): Downsample2D(\n",
" (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))\n",
" )\n",
" )\n",
" )\n",
" (2): DownEncoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1-2): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (downsamplers): ModuleList(\n",
" (0): Downsample2D(\n",
" (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))\n",
" )\n",
" )\n",
" )\n",
" (3): DownEncoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1-2): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (downsamplers): ModuleList(\n",
" (0): Downsample2D(\n",
" (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))\n",
" )\n",
" )\n",
" )\n",
" (4): DownEncoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0-2): 3 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (mid_block): UNetMidBlock2D(\n",
" (attentions): ModuleList(\n",
" (0): Attention(\n",
" (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
" (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
" (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=512, out_features=512, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0-1): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" )\n",
" (conv_norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (conv_act): SiLU()\n",
" (conv_out): Conv2d(512, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" (decoder): Decoder(\n",
" (conv_in): Conv2d(16, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (up_blocks): ModuleList(\n",
" (0-1): 2 x UpDecoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0-3): 4 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (upsamplers): ModuleList(\n",
" (0): Upsample2D(\n",
" (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (2): UpDecoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1-3): 3 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (upsamplers): ModuleList(\n",
" (0): Upsample2D(\n",
" (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (3): UpDecoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1-3): 3 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" (upsamplers): ModuleList(\n",
" (0): Upsample2D(\n",
" (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (4): UpDecoderBlock2D(\n",
" (resnets): ModuleList(\n",
" (0): ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" (conv_shortcut): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (1-3): 3 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (mid_block): UNetMidBlock2D(\n",
" (attentions): ModuleList(\n",
" (0): Attention(\n",
" (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
" (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
" (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
" (to_out): ModuleList(\n",
" (0): Linear(in_features=512, out_features=512, bias=True)\n",
" (1): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (resnets): ModuleList(\n",
" (0-1): 2 x ResnetBlock2D(\n",
" (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (nonlinearity): SiLU()\n",
" )\n",
" )\n",
" )\n",
" (conv_norm_out): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
" (conv_act): SiLU()\n",
" (conv_out): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
")\n"
]
}
],
"source": [
"from diffusers.models import AsymmetricAutoencoderKL, AutoencoderKL\n",
"import torch\n",
"from tqdm import tqdm\n",
"\n",
"# ---- Конфиг новой модели ----\n",
"config = {\n",
" \"_class_name\": \"AutoencoderKL\",\n",
" \"act_fn\": \"silu\",\n",
" \"in_channels\": 3,\n",
" \"out_channels\": 3,\n",
" \"scaling_factor\": 1.0,\n",
" \"norm_num_groups\": 32,\n",
" \"block_out_channels\": [64, 128, 256, 512, 512],\n",
" \"down_block_types\": [\n",
" \"DownEncoderBlock2D\",\n",
" \"DownEncoderBlock2D\",\n",
" \"DownEncoderBlock2D\",\n",
" \"DownEncoderBlock2D\",\n",
" \"DownEncoderBlock2D\",\n",
" ],\n",
" \"latent_channels\": 16,\n",
" \"up_block_types\": [\n",
" \"UpDecoderBlock2D\",\n",
" \"UpDecoderBlock2D\",\n",
" \"UpDecoderBlock2D\",\n",
" \"UpDecoderBlock2D\",\n",
" \"UpDecoderBlock2D\",\n",
" ],\n",
"}\n",
"\n",
"# ---- Создание пустой асимметричной модели ----\n",
"vae = AutoencoderKL(\n",
" act_fn=config[\"act_fn\"],\n",
" block_out_channels=config[\"block_out_channels\"],\n",
" down_block_types=config[\"down_block_types\"],\n",
" latent_channels=config[\"latent_channels\"],\n",
" up_block_types=config[\"up_block_types\"],\n",
" in_channels=config[\"in_channels\"],\n",
" out_channels=config[\"out_channels\"],\n",
" scaling_factor=config[\"scaling_factor\"],\n",
" norm_num_groups=config[\"norm_num_groups\"],\n",
" layers_per_block=3,\n",
" sample_size=1024,\n",
" use_post_quant_conv = False,\n",
" use_quant_conv = False,\n",
")\n",
"\n",
"vae.save_pretrained(\"vae_empty\")\n",
"print(\"✅ Создана новая модель:\", type(vae))\n",
"\n",
"# ---- Функция переноса весов старого VAE ----\n",
"def transfer_weights(old_path, new_path, save_path=\"asymmetric_vae\", device=\"cuda\", dtype=torch.float16):\n",
" old_vae = AsymmetricAutoencoderKL.from_pretrained(old_path).to(device, dtype=dtype)\n",
" new_vae = AutoencoderKL.from_pretrained(new_path).to(device, dtype=dtype)\n",
"\n",
" old_sd = old_vae.state_dict()\n",
" new_sd = new_vae.state_dict()\n",
"\n",
" transferred_keys = set()\n",
" transfer_stats = {\"перенесено\": 0, \"дублировано\": 0, \"пропущено\": 0}\n",
"\n",
" print(\"\\n--- Перенос весов ---\")\n",
" for k, v in tqdm(old_sd.items()):\n",
" # Копирование энкодера и прочих совпадающих ключей\n",
" if (\"encoder\" in k) or (\"quant_conv\" in k) or (\"post_quant_conv\" in k):\n",
" if k in new_sd and new_sd[k].shape == v.shape:\n",
" new_sd[k] = v.clone()\n",
" transferred_keys.add(k)\n",
" transfer_stats[\"перенесено\"] += 1\n",
" continue\n",
"\n",
" # Копирование декодера (без сдвига)\n",
" if \"decoder.up_blocks\" in k:\n",
" if k in new_sd and new_sd[k].shape == v.shape:\n",
" new_sd[k] = v.clone()\n",
" transferred_keys.add(k)\n",
" transfer_stats[\"перенесено\"] += 1\n",
" continue\n",
"\n",
" # Дублирование весов старого первого 512→512 блока в новый блок 64→128 для апскейла\n",
" #ref_prefix = \"encoder.down_blocks.1\"\n",
" #new_prefix = \"encoder.down_blocks.0\"\n",
" #for k, v in old_sd.items():\n",
" # if k.startswith(ref_prefix) and new_prefix + k[len(ref_prefix):] in new_sd:\n",
" # new_k = k.replace(ref_prefix, new_prefix)\n",
" # if new_sd[new_k].shape == v.shape:\n",
" # new_sd[new_k] = v.clone()\n",
" # transferred_keys.add(new_k)\n",
" # transfer_stats[\"дублировано\"] += 1\n",
"\n",
" # Загрузка и сохранение\n",
" new_vae.load_state_dict(new_sd, strict=False)\n",
" new_vae.save_pretrained(save_path)\n",
"\n",
" print(\"\\n✅ Перенос завершён.\")\n",
" print(\"Статистика:\")\n",
" for k, v in transfer_stats.items():\n",
" print(f\" {k}: {v}\")\n",
" print(new_vae)\n",
"\n",
"# ---- Запуск переноса ----\n",
"transfer_weights(\"vae16\", \"vae_empty\", save_path=\"vae17\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59fcafb9-6d89-49b4-8362-b4891f591687",
"metadata": {},
"outputs": [],
"source": []
}
],
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|