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    "# Copyright (C) 2025 NVIDIA Corporation.  All rights reserved.\n",
    "#\n",
    "# This work is licensed under the LICENSE file\n",
    "# located at the root directory.\n",
    "import torch\n",
    "import random\n",
    "\n",
    "from visualization_utils import show_images\n",
    "from addit_flux_pipeline import AdditFluxPipeline\n",
    "from addit_flux_transformer import AdditFluxTransformer2DModel\n",
    "from addit_scheduler import AdditFlowMatchEulerDiscreteScheduler\n",
    "from addit_methods import add_object_generated\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "my_transformer  = AdditFluxTransformer2DModel.from_pretrained(\"black-forest-labs/FLUX.1-dev\", subfolder=\"transformer\", torch_dtype=torch.bfloat16)\n",
    "\n",
    "pipe = AdditFluxPipeline.from_pretrained(\"black-forest-labs/FLUX.1-dev\", \n",
    "                                      transformer=my_transformer,\n",
    "                                      torch_dtype=torch.bfloat16).to(device)\n",
    "pipe.scheduler = AdditFlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset the GPU memory tracking\n",
    "torch.cuda.reset_max_memory_allocated(0)\n",
    "\n",
    "(prompt1, prompt2), subject_token = [\"A photo of a man sitting on a bench\", \"A photo of a man sitting on a bench with a dog\"], \"dog\"\n",
    "\n",
    "\n",
    "random.seed(0)\n",
    "seeds_src = [663]\n",
    "seeds_obj = [0,1,2]\n",
    "\n",
    "for seed_src in seeds_src:\n",
    "    for seed_obj in seeds_obj:\n",
    "        src_image, edited_image = add_object_generated(pipe, prompt1, prompt2, subject_token, seed_src, seed_obj, show_attention=True, \n",
    "                                        extended_scale=1.05, structure_transfer_step=2, blend_steps=[15], \n",
    "                                        localization_model=\"attention_points_sam\", display_output=True)\n",
    "\n",
    "# Report maximum GPU memory usage in GB\n",
    "max_memory_used = torch.cuda.max_memory_allocated(0) / (1024**3)  # Convert to GB\n",
    "print(f\"Maximum GPU memory used: {max_memory_used:.2f} GB\")"
   ]
  }
 ],
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