{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Plug and Play"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "!apt-get update && apt-get install git-lfs\n",
    "!git-lfs clone https://huggingface.co/spaces/hysts/PnP-diffusion-features\n",
    "%cd PnP-diffusion-features\n",
    "!pip install -r requirements.txt\n",
    "!git clone https://github.com/MichalGeyer/plug-and-play\n",
    "!pip install clip taming-transformers-rom1504 \n",
    "!pip install git+https://github.com/CompVis/taming-transformers\n",
    "!pip install -U transformers"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/notebooks/PnP-diffusion-features\n"
     ]
    }
   ],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T01:17:25.956120Z",
     "iopub.status.busy": "2023-03-03T01:17:25.955418Z",
     "iopub.status.idle": "2023-03-03T01:17:25.961046Z",
     "shell.execute_reply": "2023-03-03T01:17:25.960498Z",
     "shell.execute_reply.started": "2023-03-03T01:17:25.956089Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "import sys\n",
    "import fileinput\n",
    "\n",
    "# with is like your try .. finally block in this case\n",
    "with open('app.py', 'r') as file:\n",
    "    # read a list of lines into data\n",
    "    data = file.readlines()\n",
    "\n",
    "# now change the 2nd line, note that you have to add a newline\n",
    "data[-1] = '\\ndemo.launch(share=True)'\n",
    "\n",
    "# and write everything back\n",
    "with open('app.py', 'w') as file:\n",
    "    file.writelines( data )\n",
    "\n",
    "    \n",
    "\n"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T01:25:04.928147Z",
     "iopub.status.busy": "2023-03-03T01:25:04.927479Z",
     "iopub.status.idle": "2023-03-03T01:25:04.933165Z",
     "shell.execute_reply": "2023-03-03T01:25:04.932581Z",
     "shell.execute_reply.started": "2023-03-03T01:25:04.928116Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!python app.py --share "
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T01:25:37.986838Z",
     "iopub.status.busy": "2023-03-03T01:25:37.986259Z",
     "iopub.status.idle": "2023-03-03T01:30:55.551780Z",
     "shell.execute_reply": "2023-03-03T01:30:55.551040Z",
     "shell.execute_reply.started": "2023-03-03T01:25:37.986811Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Self Attention Guidance"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!apt-get update && apt-get install git-lfs\n",
    "!git-lfs clone https://huggingface.co/spaces/susunghong/Self-Attention-Guidance\n",
    "%cd Self-Attention-Guidance\n",
    "!pip install -r requirements.txt\n",
    "!pip install -U diffusers\n",
    "!pip install gradio\n",
    "!pip install -U transformers "
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T01:33:39.538102Z",
     "iopub.status.busy": "2023-03-03T01:33:39.537496Z",
     "iopub.status.idle": "2023-03-03T01:33:42.779249Z",
     "shell.execute_reply": "2023-03-03T01:33:42.778304Z",
     "shell.execute_reply.started": "2023-03-03T01:33:39.538077Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "import sys\n",
    "import fileinput\n",
    "\n",
    "# with is like your try .. finally block in this case\n",
    "with open('app.py', 'r') as file:\n",
    "    # read a list of lines into data\n",
    "    data = file.readlines()\n",
    "\n",
    "# now change the 2nd line, note that you have to add a newline\n",
    "data[-5] = '\\n    demo.launch(share=True)'\n",
    "\n",
    "# and write everything back\n",
    "with open('app.py', 'w') as file:\n",
    "    file.writelines( data )\n"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T01:32:54.407719Z",
     "iopub.status.busy": "2023-03-03T01:32:54.407409Z",
     "iopub.status.idle": "2023-03-03T01:32:54.417801Z",
     "shell.execute_reply": "2023-03-03T01:32:54.416842Z",
     "shell.execute_reply.started": "2023-03-03T01:32:54.407694Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!python app.py --share"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T01:33:44.067013Z",
     "iopub.status.busy": "2023-03-03T01:33:44.066139Z",
     "iopub.status.idle": "2023-03-03T01:40:12.873547Z",
     "shell.execute_reply": "2023-03-03T01:40:12.872797Z",
     "shell.execute_reply.started": "2023-03-03T01:33:44.066975Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Universal Guided Diffusion"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "%cd ~/../notebooks\n",
    "!git clone https://github.com/arpitbansal297/Universal-Guided-Diffusion\n",
    "%cd Universal-Guided-Diffusion/stable-diffusion-guided\n",
    "!pip install -e .\n",
    "!pip install GPUtil\n",
    "!pip install blobfile\n",
    "!pip install facenet-pytorch\n",
    "!pip install invisible-watermark diffusers clip kornia deepface\n",
    "!pip install -U transformers\n",
    "!git clone https://github.com/CompVis/taming-transformers\n",
    "!cp -r taming-transformers/taming ./"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T02:06:44.401522Z",
     "iopub.status.busy": "2023-03-03T02:06:44.401241Z",
     "iopub.status.idle": "2023-03-03T02:07:04.535073Z",
     "shell.execute_reply": "2023-03-03T02:07:04.534263Z",
     "shell.execute_reply.started": "2023-03-03T02:06:44.401500Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Face recognition"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!mkdir test_face\n",
    "!cp scripts/* ./\n",
    "!python face_detection.py --indexes 0 --text \"Headshot of a person with blonde hair with space background\" --optim_forward_guidance --fr_crop --optim_num_steps 2 --optim_forward_guidance_wt 20000 --optim_original_conditioning --ddim_steps 5 --optim_folder ./test_face/text_type_4/ --ckpt ../../../datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt\n",
    "!python face_detection.py --indexes 0 --text \"A headshot of a woman looking like a lara croft\" --optim_forward_guidance --fr_crop --optim_num_steps 2 --optim_forward_guidance_wt 20000 --optim_original_conditioning --ddim_steps 500 --optim_folder ./test_face/text_type_11/ --ckpt ../../../datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt\n"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T02:09:22.772652Z",
     "iopub.status.busy": "2023-03-03T02:09:22.772063Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!python face_top_k.py --folder ./test_face/text_type_4/ --img_index 0 --img_saved 20 --top_k 5\n",
    "!python face_top_k.py --folder ./test_face/text_type_11/ --img_index 0 --img_saved 20 --top_k 5"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-03T02:02:54.886820Z",
     "iopub.status.busy": "2023-03-03T02:02:54.886068Z",
     "iopub.status.idle": "2023-03-03T02:03:18.634801Z",
     "shell.execute_reply": "2023-03-03T02:03:18.634183Z",
     "shell.execute_reply.started": "2023-03-03T02:02:54.886792Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Segmentation\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!mkdir test_segmentation\n",
    "!python segmentation.py --indexes 1 --text \"Walker hound, Walker foxhound on snow\" --scale 1.5 --optim_forward_guidance --optim_num_steps 10 --optim_forward_guidance_wt 400 --optim_original_conditioning --ddim_steps 500 --optim_folder ./test_segmentation/text_type_4/ --ckpt ../../../datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt\n",
    "!python segmentation.py --indexes 1 --text \"Walker hound, Walker foxhound as an oil painting\" --scale 2.0 --optim_forward_guidance --optim_num_steps 10 --optim_forward_guidance_wt 400 --optim_original_conditioning --ddim_steps 500 --optim_folder ./test_segmentation/text_type_3/ --ckpt ../../../datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt\n"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-02T23:43:13.869062Z",
     "iopub.status.busy": "2023-03-02T23:43:13.868461Z",
     "iopub.status.idle": "2023-03-02T23:44:25.706654Z",
     "shell.execute_reply": "2023-03-02T23:44:25.705960Z",
     "shell.execute_reply.started": "2023-03-02T23:43:13.869029Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Object Detection"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!mkdir test_od\n",
    "!python segmentation.py --indexes 0 --text \"a headshot of a woman with a dog\" --scale 1.5 --optim_forward_guidance --optim_num_steps 5 --optim_forward_guidance_wt 100 --optim_original_conditioning --ddim_steps 250 --optim_folder ./test_od/ --ckpt ../../../datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt\n",
    "!python segmentation.py --indexes 0 --text \"a headshot of a woman with a dog on beach\" --scale 1.5 --optim_forward_guidance --optim_num_steps 5 --optim_forward_guidance_wt 100 --optim_original_conditioning --ddim_steps 250 --optim_folder ./test_od/ --ckpt ../../../datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-02T23:44:58.714404Z",
     "iopub.status.busy": "2023-03-02T23:44:58.714115Z",
     "iopub.status.idle": "2023-03-02T23:45:49.154756Z",
     "shell.execute_reply": "2023-03-02T23:45:49.154020Z",
     "shell.execute_reply.started": "2023-03-02T23:44:58.714379Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Style transfer"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!mkdir test_style\n",
    "!python style_transfer.py --indexes 0 --text \"A colorful photo of a eiffel tower\" --scale 3.0 --optim_forward_guidance --optim_num_steps 6 --optim_forward_guidance_wt 6 --optim_original_conditioning --ddim_steps 500 --optim_folder ./test_style/text_type_1/ --ckpt ../../../datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt\n",
    "!python style_transfer.py --indexes 0 --text \"A fantasy photo of volcanoes\" --scale 3.0 --optim_forward_guidance --optim_num_steps 6 --optim_forward_guidance_wt 6 --optim_original_conditioning --ddim_steps 500 --optim_folder ./test_style/text_type_2/ --ckpt ../../../datasets/stable-diffusion-classic/v1-5-pruned-emaonly.ckpt\n"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-02T23:47:18.620122Z",
     "iopub.status.busy": "2023-03-02T23:47:18.619322Z",
     "iopub.status.idle": "2023-03-02T23:47:36.387742Z",
     "shell.execute_reply": "2023-03-02T23:47:36.387005Z",
     "shell.execute_reply.started": "2023-03-02T23:47:18.620084Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Cross Domain Composting\n",
    "\n",
    "> This is a work in progress section. "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "!wget https://repo.anaconda.com/miniconda/Miniconda3-py39_23.1.0-1-Linux-x86_64.sh"
   ],
   "outputs": [],
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-02T21:07:35.383569Z",
     "iopub.status.busy": "2023-03-02T21:07:35.382959Z",
     "iopub.status.idle": "2023-03-02T21:07:41.906451Z",
     "shell.execute_reply": "2023-03-02T21:07:41.905485Z",
     "shell.execute_reply.started": "2023-03-02T21:07:35.383546Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Now go into the terminal, and paste the following:\n",
    "\n",
    ">bash Miniconda Miniconda3-py39_23.1.0-1-Linux-x86_64.sh\n",
    "\n",
    "### Then follow the instructions to set up. Agree to their license, and run conda init at the end.\n",
    "\n",
    "### Afterwards, in the terminal paste the following\n",
    "\n",
    ">cd ~/../notebooks/\n",
    ">\n",
    ">git clone --recursive https://github.com/cross-domain-compositing/cross-domain-compositing.git \n",
    ">\n",
    ">cd cross-domain-compositing/ \n",
    ">\n",
    ">conda env create -f environment.yaml \n",
    ">\n",
    ">conda activate ldm \n",
    ">\n",
    ">wget -P models/ldm/stable-diffusion-v1 https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt \n",
    ">\n",
    ">wget -P models/ldm/stable-diffusion-v1 https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt \n",
    "\n",
    "### From there, you can run the demo. \n",
    "\n",
    ">python \"scripts_cdc/img2img.py\n",
    ">    --config configs/stable-diffusion/v1-inference.yaml \\\n",
    ">    --ckpt models/ldm/stable-diffusion-v1/sd-v1-4.ckpt \\\n",
    ">    --init_img examples/scribbles/images/ \\\n",
    ">    --mask examples/scribbles/masks/ \\\n",
    ">    --from_file examples/scribbles/prompts.txt \\\n",
    ">    --batch_size 1 \\\n",
    ">    --n_samples 1 \\\n",
    ">    --outdir outputs/scribbles \\\n",
    ">    --ddim_steps 50 \\\n",
    ">    --strength 1.0 \\\n",
    ">    --T_out 1.0 \\\n",
    ">    --T_in 0.0 0.2 0.4 0.6 0.8 \\\n",
    ">    --down_N_out 1 \\\n",
    ">    --down_N_in 1 2 4 \\\n",
    ">    --seed 42 \\\n",
    ">    --repaint_start 0 0.2 0.4 0.6 0.8 \\\n",
    ">    --skip_grid\"\n",
    "\n",
    "or to paste:\n",
    "\n",
    "python scripts_cdc/img2img.py --config configs/stable-diffusion/v1-inference.yaml --ckpt models/ldm/stable-diffusion-v1/sd-v1-4.ckpt --init_img examples/scribbles/images/ --mask examples/scribbles/masks/ --from_file examples/scribbles/prompts.txt --batch_size 1 --n_samples 1 --outdir outputs/scribbles --ddim_steps 50 --strength 1.0 --T_out 1.0 --T_in 0.0 0.2 0.4 0.6 0.8 --down_N_out 1 --down_N_in 1 2 4 --seed 42 --repaint_start 0 0.2 0.4 0.6 0.8 --skip_grid\n",
    "\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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