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   "source": [
    "# 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",
    "\n",
    "import torch\n",
    "import random\n",
    "from PIL import Image\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_real\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",
    "# source_image = Image.open(\"images/cat.jpg\").resize((1024, 1024))\n",
    "# (prompt_src, prompt_tgt), subject_token = [\"A photo of a cat\", \"A photo of a cat wearing a scarf\"], \"scarf\"\n",
    "\n",
    "source_image = Image.open(\"images/bed_dark_room.jpg\").resize((1024, 1024))\n",
    "(prompt_src, prompt_tgt), subject_token = [\"A photo of a bed in a dark room\", \"A photo of a dog lying on a bed in a dark room\"], \"dog\"\n",
    "\n",
    "random.seed(0)\n",
    "seed_src = random.randint(0, 10000)\n",
    "seeds_obj = [0,1,2]\n",
    "\n",
    "for seed_obj in seeds_obj:\n",
    "    images_list = add_object_real(pipe, source_image=source_image, prompt_source=prompt_src, prompt_object=prompt_tgt, \n",
    "                            subject_token=subject_token, seed_src=seed_src, seed_obj=seed_obj, \n",
    "                            extended_scale =1.1, structure_transfer_step=4, blend_steps = [18], #localization_model=\"attention\",\n",
    "                            use_offset=False, show_attention=True, use_inversion=True, 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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