{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e76b6794",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/Qwen-Image-Edit-Angles\n"
     ]
    }
   ],
   "source": [
    "%cd /home/ubuntu/Qwen-Image-Edit-Angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0f4ce28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Skipping import of cpp extensions due to incompatible torch version 2.9.1+cu128 for torchao version 0.14.1             Please see https://github.com/pytorch/ao/issues/2919 for more info\n",
      "TMA benchmarks will be running without grid constant TMA descriptor.\n",
      "2025-11-19 13:40:07.009826: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-11-19 13:40:07.024313: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1763559607.043157 4020936 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1763559607.048744 4020936 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "W0000 00:00:1763559607.062035 4020936 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1763559607.062054 4020936 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1763559607.062056 4020936 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "W0000 00:00:1763559607.062057 4020936 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
      "2025-11-19 13:40:07.066186: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "/usr/lib/python3/dist-packages/sklearn/utils/fixes.py:25: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n",
      "  from pkg_resources import parse_version  # type: ignore\n",
      "Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 86864.28it/s]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from pathlib import Path\n",
    "from collections import defaultdict\n",
    "import statistics\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import lpips\n",
    "import torch\n",
    "import torchvision.transforms.v2 as T\n",
    "import torchvision.transforms.v2.functional as TF\n",
    "from pydantic import BaseModel\n",
    "\n",
    "from qwenimage.reporting.datamodels import ExperimentSet\n",
    "from qwenimage.reporting.visualize_barplot import compare_sets, compare_sets_with_timing\n",
    "from qwenimage.experiment import ExperimentConfig\n",
    "from qwenimage.experiments.experiments_qwen import ExperimentRegistry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "226af1b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_base</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.752080</td>\n",
       "      <td>0.038048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fa3</td>\n",
       "      <td>0.098819</td>\n",
       "      <td>0.050530</td>\n",
       "      <td>1.486262</td>\n",
       "      <td>0.024040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_sageattn_qk_int8_pv_fp8_cuda_sm90</td>\n",
       "      <td>0.183041</td>\n",
       "      <td>0.090641</td>\n",
       "      <td>1.550240</td>\n",
       "      <td>0.072654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               experiment  lpips_mean  lpips_std  time_mean  \\\n",
       "0                               qwen_base    0.000000   0.000000   1.752080   \n",
       "1                                qwen_fa3    0.098819   0.050530   1.486262   \n",
       "2  qwen_sageattn_qk_int8_pv_fp8_cuda_sm90    0.183041   0.090641   1.550240   \n",
       "\n",
       "   time_std  \n",
       "0  0.038048  \n",
       "1  0.024040  \n",
       "2  0.072654  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = compare_sets_with_timing(\n",
    "    ExperimentSet.create(\n",
    "        \"qwen_base\",\n",
    "        \"qwen_fa3\",\n",
    "        \"qwen_sageattn_qk_int8_pv_fp8_cuda_sm90\",\n",
    "    ),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=None\n",
    ")\n",
    "\n",
    "# Display the combined data\n",
    "df_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "477d7613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_base</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.752080</td>\n",
       "      <td>0.038048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fa3</td>\n",
       "      <td>0.098819</td>\n",
       "      <td>0.050530</td>\n",
       "      <td>1.486262</td>\n",
       "      <td>0.024040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_sage</td>\n",
       "      <td>0.839612</td>\n",
       "      <td>0.055112</td>\n",
       "      <td>1.300836</td>\n",
       "      <td>0.067245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qwen_fa3_aot</td>\n",
       "      <td>0.117448</td>\n",
       "      <td>0.056472</td>\n",
       "      <td>1.143772</td>\n",
       "      <td>0.020417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qwen_sage_aot</td>\n",
       "      <td>0.796404</td>\n",
       "      <td>0.044936</td>\n",
       "      <td>0.931531</td>\n",
       "      <td>0.069647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>qwen_fa3_aot_fp8</td>\n",
       "      <td>0.406982</td>\n",
       "      <td>0.092241</td>\n",
       "      <td>1.066962</td>\n",
       "      <td>0.023784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>qwen_sage_aot_fp8</td>\n",
       "      <td>0.808424</td>\n",
       "      <td>0.048056</td>\n",
       "      <td>0.848567</td>\n",
       "      <td>0.071020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>qwen_fa3_aot_int8</td>\n",
       "      <td>0.189754</td>\n",
       "      <td>0.083396</td>\n",
       "      <td>1.125457</td>\n",
       "      <td>0.021317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>qwen_sage_aot_int8</td>\n",
       "      <td>0.796001</td>\n",
       "      <td>0.045817</td>\n",
       "      <td>0.901470</td>\n",
       "      <td>0.068409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>qwen_sage_aot_int8da</td>\n",
       "      <td>0.817488</td>\n",
       "      <td>0.049491</td>\n",
       "      <td>0.846157</td>\n",
       "      <td>0.113203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>qwen_sagefp8cuda_aot_int8wo</td>\n",
       "      <td>0.233688</td>\n",
       "      <td>0.100033</td>\n",
       "      <td>1.428728</td>\n",
       "      <td>0.109006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>qwen_sagefp8cuda_aot_int8da</td>\n",
       "      <td>0.817488</td>\n",
       "      <td>0.049491</td>\n",
       "      <td>0.830875</td>\n",
       "      <td>0.075003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     experiment  lpips_mean  lpips_std  time_mean  time_std\n",
       "0                     qwen_base    0.000000   0.000000   1.752080  0.038048\n",
       "1                      qwen_fa3    0.098819   0.050530   1.486262  0.024040\n",
       "2                     qwen_sage    0.839612   0.055112   1.300836  0.067245\n",
       "3                  qwen_fa3_aot    0.117448   0.056472   1.143772  0.020417\n",
       "4                 qwen_sage_aot    0.796404   0.044936   0.931531  0.069647\n",
       "5              qwen_fa3_aot_fp8    0.406982   0.092241   1.066962  0.023784\n",
       "6             qwen_sage_aot_fp8    0.808424   0.048056   0.848567  0.071020\n",
       "7             qwen_fa3_aot_int8    0.189754   0.083396   1.125457  0.021317\n",
       "8            qwen_sage_aot_int8    0.796001   0.045817   0.901470  0.068409\n",
       "9          qwen_sage_aot_int8da    0.817488   0.049491   0.846157  0.113203\n",
       "10  qwen_sagefp8cuda_aot_int8wo    0.233688   0.100033   1.428728  0.109006\n",
       "11  qwen_sagefp8cuda_aot_int8da    0.817488   0.049491   0.830875  0.075003"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "experiment_names = [\n",
    "    \"qwen_base\",\n",
    "    \"qwen_fa3\",\n",
    "    \"qwen_sage\",\n",
    "\n",
    "    \"qwen_fa3_aot\",\n",
    "    \"qwen_sage_aot\",\n",
    "\n",
    "    \"qwen_fa3_aot_fp8\",\n",
    "    \"qwen_sage_aot_fp8\",\n",
    "\n",
    "    \"qwen_fa3_aot_int8\",\n",
    "    \"qwen_sage_aot_int8\",\n",
    "\n",
    "    \"qwen_sage_aot_int8da\",\n",
    "\n",
    "    \"qwen_sagefp8cuda_aot_int8wo\",\n",
    "    \"qwen_sagefp8cuda_aot_int8da\",\n",
    "]\n",
    "\n",
    "df_all = compare_sets_with_timing(\n",
    "    ExperimentSet.create(*experiment_names),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=None\n",
    ")\n",
    "\n",
    "# Display the combined data\n",
    "df_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e99efc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/ubuntu/.local/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from: /home/ubuntu/.local/lib/python3.10/site-packages/lpips/weights/v0.1/alex.pth\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>lpips_mean</th>\n",
       "      <th>lpips_std</th>\n",
       "      <th>time_mean</th>\n",
       "      <th>time_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qwen_base</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.752080</td>\n",
       "      <td>0.038048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qwen_fa3</td>\n",
       "      <td>0.098819</td>\n",
       "      <td>0.050530</td>\n",
       "      <td>1.486262</td>\n",
       "      <td>0.024040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>qwen_sagefp8cuda_aot_int8wo</td>\n",
       "      <td>0.233688</td>\n",
       "      <td>0.100033</td>\n",
       "      <td>1.428728</td>\n",
       "      <td>0.109006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qwen_sage</td>\n",
       "      <td>0.839612</td>\n",
       "      <td>0.055112</td>\n",
       "      <td>1.300836</td>\n",
       "      <td>0.067245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qwen_fa3_aot</td>\n",
       "      <td>0.117448</td>\n",
       "      <td>0.056472</td>\n",
       "      <td>1.143772</td>\n",
       "      <td>0.020417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>qwen_fa3_aot_int8</td>\n",
       "      <td>0.189754</td>\n",
       "      <td>0.083396</td>\n",
       "      <td>1.125457</td>\n",
       "      <td>0.021317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>qwen_fa3_aot_fp8</td>\n",
       "      <td>0.406982</td>\n",
       "      <td>0.092241</td>\n",
       "      <td>1.066962</td>\n",
       "      <td>0.023784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qwen_sage_aot</td>\n",
       "      <td>0.796404</td>\n",
       "      <td>0.044936</td>\n",
       "      <td>0.931531</td>\n",
       "      <td>0.069647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>qwen_sage_aot_int8</td>\n",
       "      <td>0.796001</td>\n",
       "      <td>0.045817</td>\n",
       "      <td>0.901470</td>\n",
       "      <td>0.068409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>qwen_sage_aot_fp8</td>\n",
       "      <td>0.808424</td>\n",
       "      <td>0.048056</td>\n",
       "      <td>0.848567</td>\n",
       "      <td>0.071020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>qwen_sage_aot_int8da</td>\n",
       "      <td>0.817488</td>\n",
       "      <td>0.049491</td>\n",
       "      <td>0.846157</td>\n",
       "      <td>0.113203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>qwen_sagefp8cuda_aot_int8da</td>\n",
       "      <td>0.817488</td>\n",
       "      <td>0.049491</td>\n",
       "      <td>0.830875</td>\n",
       "      <td>0.075003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     experiment  lpips_mean  lpips_std  time_mean  time_std\n",
       "0                     qwen_base    0.000000   0.000000   1.752080  0.038048\n",
       "1                      qwen_fa3    0.098819   0.050530   1.486262  0.024040\n",
       "10  qwen_sagefp8cuda_aot_int8wo    0.233688   0.100033   1.428728  0.109006\n",
       "2                     qwen_sage    0.839612   0.055112   1.300836  0.067245\n",
       "3                  qwen_fa3_aot    0.117448   0.056472   1.143772  0.020417\n",
       "7             qwen_fa3_aot_int8    0.189754   0.083396   1.125457  0.021317\n",
       "5              qwen_fa3_aot_fp8    0.406982   0.092241   1.066962  0.023784\n",
       "4                 qwen_sage_aot    0.796404   0.044936   0.931531  0.069647\n",
       "8            qwen_sage_aot_int8    0.796001   0.045817   0.901470  0.068409\n",
       "6             qwen_sage_aot_fp8    0.808424   0.048056   0.848567  0.071020\n",
       "9          qwen_sage_aot_int8da    0.817488   0.049491   0.846157  0.113203\n",
       "11  qwen_sagefp8cuda_aot_int8da    0.817488   0.049491   0.830875  0.075003"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "compare_sets_with_timing(\n",
    "    ExperimentSet.create(\n",
    "       *experiment_names\n",
    "    ),\n",
    "    profile_target=\"loop\",\n",
    "    sort_by=\"time\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06c65a7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31dea8be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4efef8a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b6d974",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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