{ "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 __test\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": [ "656655b164d74bd5a01a70dbc0532845", "e1cae8971e0a41de98ee623d6fea9411", "209460fa783743c28500285f681738e3", "2979f86a5e6e4c9fa86640240734a090", "08f3eb41acf04be78e2185ddbc3b589a", "e39fdfa0cbaa4265881d27103ee3f85d", "70e39b7d792c488bb66b88f7c5da7089", "54189379cbc9448b99335d263fbbb41c", "e315cdda8ee4412e8b9a2a450a09f299", "6feaaa7cd4114c32b86fa532cda9eeae", "7032681441a54530a4fae01f16bfe89c" ] }, "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" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 697, "referenced_widgets": [ "f923db904b07419491332109e1a1d4de", "2874f292915543b582e01243245921a6", "c05e11e890fd4dfb8ae01dee3f861a35", "3dfed8d0e44140f1b5a29645260654e2", "5807bf7d9a5c4a3cad62685b79830986", "ef1c14b180c84ccb8380386732e2cceb", "5b321bacb07944fe8915bb47253b2e86", "539cd081dafe4182893265077fde98fb", "a8a9bef51c3545ebae622ab0bca90439", "9fc9ba7a63dd49eb92303f3f5f6340b6", "00cb4e795ff24b46b3c7fcc78111fb2e", "b8f95b21209b46f6a0762ddfc0e72b77", 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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", "[Citation placeholder]\n", "```" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "00cb4e795ff24b46b3c7fcc78111fb2e": { 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