app.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmsaT5lYSU3i"
      },
      "source": [
        "## Unpacking SDXL Turbo\n",
        "### Interpreting Text-to-Image Models with Sparse Autoencoders\n",
        "\n",
        "This colab prepares and launches the demo Gradio app for SAE's features discovery"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7linZOsoSU3j"
      },
      "source": [
        "### Cloning code and installing dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvEE-YYwSU3j",
        "outputId": "cc89f9fd-7c97-423c-d68c-b8b28714a68a"
      },
      "outputs": [],
      "source": [
        "# if we are on colab then we need to clone the repo\n",
        "import sys\n",
        "IN_COLAB = 'google.colab' in sys.modules\n",
        "if IN_COLAB:\n",
        "    !git clone https://github.com/surkovv/sdxl-unbox.git\n",
        "    %cd sdxl-unbox\n",
        "\n",
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pc-qlGl6SU3k"
      },
      "outputs": [],
      "source": [
        "%load_ext gradio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GXyWZLm1SU3k"
      },
      "source": [
        "### Loading SDXL Turbo and SAEs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
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        },
        "id": "D1l7FR7CSU3k",
        "outputId": "f3587f7c-10f8-44d7-ef1f-8b830bab400d"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import gradio as gr\n",
        "import torch\n",
        "from SDLens import HookedStableDiffusionXLPipeline\n",
        "from SAE import SparseAutoencoder\n",
        "from app import create_demo\n",
        "\n",
        "assert torch.cuda.is_available(), \"Your machine has no access to GPU. If you are using Colab, consider changing environment\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rV4L1HxJSU3l"
      },
      "outputs": [],
      "source": [
        "# The SAEs were trained to work with torch.float32, but they can also work with torch.float16\n",
        "# Change this value to torch.float32 if you have access to a GPU with >30GB of memory\n",
        "dtype=torch.float16"
      ]
    },
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        },
        "id": "QSAq2lvhSU3l",
        "outputId": "f308fd7a-67d1-49d1-ca24-42298795a4d2"
      },
      "outputs": [],
      "source": [
        "pipe = HookedStableDiffusionXLPipeline.from_pretrained(\n",
        "    'stabilityai/sdxl-turbo',\n",
        "    torch_dtype=dtype,\n",
        "    device_map=\"balanced\",\n",
        "    variant=(\"fp16\" if dtype==torch.float16 else None)\n",
        ")\n",
        "pipe.set_progress_bar_config(disable=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uClSRloSU3m",
        "outputId": "48332d4c-5ba8-40d4-c685-993767f01dbb"
      },
      "outputs": [],
      "source": [
        "path_to_checkpoints = './checkpoints/'\n",
        "\n",
        "code_to_block = {\n",
        "    \"down.2.1\": \"unet.down_blocks.2.attentions.1\",\n",
        "    \"mid.0\": \"unet.mid_block.attentions.0\",\n",
        "    \"up.0.1\": \"unet.up_blocks.0.attentions.1\",\n",
        "    \"up.0.0\": \"unet.up_blocks.0.attentions.0\"\n",
        "}\n",
        "\n",
        "saes_dict = {}\n",
        "means_dict = {}\n",
        "\n",
        "for code, block in code_to_block.items():\n",
        "    sae = SparseAutoencoder.load_from_disk(\n",
        "        os.path.join(path_to_checkpoints, f\"{block}_k10_hidden5120_auxk256_bs4096_lr0.0001\", \"final\"),\n",
        "    )\n",
        "    means = torch.load(\n",
        "        os.path.join(path_to_checkpoints, f\"{block}_k10_hidden5120_auxk256_bs4096_lr0.0001\", \"final\", \"mean.pt\"),\n",
        "        weights_only=True\n",
        "    )\n",
        "    saes_dict[code] = sae.to('cuda', dtype=dtype)\n",
        "    means_dict[code] = means.to('cuda', dtype=dtype)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWjyowm9SU3m"
      },
      "source": [
        "### Launching Demo\n",
        "Once demo is running, you can also use a pullic link in the output of the cell below.\n",
        "\n",
        "Interesting features to look at:\n",
        "- `down.2.1 #4998`: cartoon feature\n",
        "- `down.2.1 #230:`: \"fury\" feature\n",
        "- `down.2.1 #89`: \"muscleman\" feature\n",
        "- `down.2.1 #4074`: anime feature\n",
        "- `up.0.1   #4977`: tiger stripes\n",
        "- `up.0.1   #90`: fur\n",
        "- `up.0.1   #2165`: twilight blur"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3bCIVboiSU3m",
        "outputId": "0df71c75-2f65-4f00-f476-f66296263193"
      },
      "outputs": [],
      "source": [
        "demo = create_demo(pipe, saes_dict, means_dict)\n",
        "demo.launch(share=True, height=1200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OsviC9I3SU3m"
      },
      "outputs": [],
      "source": [
        "demo.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Citation\n",
        "\n",
        "If you find this notebook useful in your research, please cite our paper:\n",
        "\n",
        "```bibtex\n",
        "@misc{surkov2024unpackingsdxlturbointerpreting,\n",
        "      title={Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders},\n",
        "      author={Viacheslav Surkov and Chris Wendler and Mikhail Terekhov and Justin Deschenaux and Robert West and Caglar Gulcehre},\n",
        "      year={2024},\n",
        "      eprint={2410.22366},\n",
        "      archivePrefix={arXiv},\n",
        "      primaryClass={cs.LG},\n",
        "      url={https://arxiv.org/abs/2410.22366},\n",
        "}\n",
        "```"
      ]
    }
  ],
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