Merge pull request #140 from charlielito/feature/flax_tpu

Flax Implementation for TPU support

README.md

@@ -1,6 +1,7 @@
 # stable-diffusion-videos
 
 Try it yourself in Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nateraw/stable-diffusion-videos/blob/main/stable_diffusion_videos.ipynb)
+TPU version (~x6 faster than standard colab GPUs): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nateraw/stable-diffusion-videos/blob/main/flax_stable_diffusion_videos.ipynb)
 
 **Example** - morphing between "blueberry spaghetti" and "strawberry spaghetti"
 

flax_stable_diffusion_videos.ipynb

@@ -0,0 +1,389 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/charlielito/stable-diffusion-videos/blob/feature%2Fflax_tpu/flax_stable_diffusion_videos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z4GhhH25OdYq"
+      },
+      "source": [
+        "# Flax Stable Diffusion Videos\n",
+        "\n",
+        "This notebook allows you to generate videos by interpolating the latent space of [Stable Diffusion](https://github.com/CompVis/stable-diffusion) using TPU for faster inference.\n",
+        "\n",
+        "In comparison with standard Colab GPU, this runs ~6x faster after the first run. The first run is comparable to the GPU version because it compiles the code.\n",
+        "\n",
+        "You can either dream up different versions of the same prompt, or morph between different text prompts (with seeds set for each for reproducibility).\n",
+        "\n",
+        "If you like this notebook:\n",
+        "- consider giving the [repo a star](https://github.com/nateraw/stable-diffusion-videos) ⭐️\n",
+        "- consider following us on Github [@nateraw](https://github.com/nateraw) [@charlielito](https://github.com/charlielito)\n",
+        "\n",
+        "You can file any issues/feature requests [here](https://github.com/nateraw/stable-diffusion-videos/issues)\n",
+        "\n",
+        "Enjoy 🤗"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dvdCBpWWOhW-"
+      },
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Set up JAX\n",
+        "#@markdown If you see an error, make sure you are using a TPU backend. Select `Runtime` in the menu above, then select the option \"Change runtime type\" and then select `TPU` under the `Hardware accelerator` setting.\n",
+        "!pip install --upgrade jax jaxlib \n",
+        "\n",
+        "import jax.tools.colab_tpu\n",
+        "jax.tools.colab_tpu.setup_tpu('tpu_driver_20221011')\n",
+        "\n",
+        "!pip install flax diffusers transformers ftfy\n",
+        "jax.devices()"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "5EZdSq4HtmcE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Xwfc0ej1L9A0"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "! pip install stable_diffusion_videos"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H7UOKJhVOonb"
+      },
+      "source": [
+        "## Run the App 🚀"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g71hslP8OntM"
+      },
+      "source": [
+        "### Load the Interface\n",
+        "\n",
+        "This step will take a couple minutes the first time you run it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bgSNS368L-DV"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import jax\n",
+        "import jax.numpy as jnp\n",
+        "\n",
+        "from jax import pmap\n",
+        "from flax.jax_utils import replicate\n",
+        "from flax.training.common_utils import shard\n",
+        "from PIL import Image\n",
+        "\n",
+        "from stable_diffusion_videos import FlaxStableDiffusionWalkPipeline, Interface\n",
+        "\n",
+        "pipeline, params = FlaxStableDiffusionWalkPipeline.from_pretrained(\n",
+        "    \"CompVis/stable-diffusion-v1-4\", \n",
+        "    revision=\"bf16\", \n",
+        "    dtype=jnp.bfloat16\n",
+        ")\n",
+        "p_params = replicate(params)\n",
+        "\n",
+        "interface = Interface(pipeline, params=p_params)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "kidtsR3c2P9Z"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Connect to Google Drive to Save Outputs\n",
+        "\n",
+        "#@markdown If you want to connect Google Drive, click the checkbox below and run this cell. You'll be prompted to authenticate.\n",
+        "\n",
+        "#@markdown If you just want to save your outputs in this Colab session, don't worry about this cell\n",
+        "\n",
+        "connect_google_drive = True #@param {type:\"boolean\"}\n",
+        "\n",
+        "#@markdown Then, in the interface, use this path as the `output` in the Video tab to save your videos to Google Drive:\n",
+        "\n",
+        "#@markdown > /content/gdrive/MyDrive/stable_diffusion_videos\n",
+        "\n",
+        "\n",
+        "if connect_google_drive:\n",
+        "    from google.colab import drive\n",
+        "\n",
+        "    drive.mount('/content/gdrive')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VxjRVNnMOtgU"
+      },
+      "source": [
+        "### Launch\n",
+        "\n",
+        "This cell launches a Gradio Interface. Here's how I suggest you use it:\n",
+        "\n",
+        "1. Use the \"Images\" tab to generate images you like.\n",
+        "    - Find two images you want to morph between\n",
+        "    - These images should use the same settings (guidance scale, height, width)\n",
+        "    - Keep track of the seeds/settings you used so you can reproduce them\n",
+        "\n",
+        "2. Generate videos using the \"Videos\" tab\n",
+        "    - Using the images you found from the step above, provide the prompts/seeds you recorded\n",
+        "    - Set the `num_interpolation_steps` - for testing you can use a small number like 3 or 5, but to get great results you'll want to use something larger (60-200 steps). \n",
+        "\n",
+        "💡 **Pro tip** - Click the link that looks like `https://<id-number>.gradio.app` below , and you'll be able to view it in full screen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8es3_onUOL3J"
+      },
+      "outputs": [],
+      "source": [
+        "interface.launch(debug=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mFCoTvlnPi4u"
+      },
+      "source": [
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SjTQLCiLOWeo"
+      },
+      "source": [
+        "## Use `walk` programmatically\n",
+        "\n",
+        "The other option is to not use the interface, and instead use `walk` programmatically. Here's how you would do that..."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fGQPClGwOR9R"
+      },
+      "source": [
+        "First we define a helper fn for visualizing videos in colab"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GqTWc8ZhNeLU"
+      },
+      "outputs": [],
+      "source": [
+        "from IPython.display import HTML\n",
+        "from base64 import b64encode\n",
+        "\n",
+        "def visualize_video_colab(video_path):\n",
+        "    mp4 = open(video_path,'rb').read()\n",
+        "    data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
+        "    return HTML(\"\"\"\n",
+        "    <video width=400 controls>\n",
+        "        <source src=\"%s\" type=\"video/mp4\">\n",
+        "    </video>\n",
+        "    \"\"\" % data_url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Vd_RzwkoPM7X"
+      },
+      "source": [
+        "Walk! 🚶‍♀️"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hv2wBZXXMQ-I"
+      },
+      "outputs": [],
+      "source": [
+        "video_path = pipeline.walk(\n",
+        "    p_params,\n",
+        "    ['a cat', 'a dog'],\n",
+        "    [42, 1337],\n",
+        "    fps=5,                      # use 5 for testing, 25 or 30 for better quality\n",
+        "    num_interpolation_steps=30,  # use 3-5 for testing, 30 or more for better results\n",
+        "    height=512,                 # use multiples of 64 if > 512. Multiples of 8 if < 512.\n",
+        "    width=512,                  # use multiples of 64 if > 512. Multiples of 8 if < 512.\n",
+        "    jit=True                    # To use all TPU cores\n",
+        ")\n",
+        "visualize_video_colab(video_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oLXULBMwSDnY"
+      },
+      "source": [
+        "### Bonus! Music videos\n",
+        "\n",
+        "First, we'll need to install `youtube-dl`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%%capture\n",
+        "! pip install youtube-dl"
+      ],
+      "metadata": {
+        "id": "302zMC44aiC6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Then, we can download an example music file. Here we download one from my soundcloud:"
+      ],
+      "metadata": {
+        "id": "Q3gCLCkLanzO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! youtube-dl -f bestaudio --extract-audio --audio-format mp3 --audio-quality 0 -o \"music/thoughts.%(ext)s\" https://soundcloud.com/nateraw/thoughts"
+      ],
+      "metadata": {
+        "id": "rEsTe_ujagE5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import Audio\n",
+        "\n",
+        "Audio(filename='music/thoughts.mp3')"
+      ],
+      "metadata": {
+        "id": "RIKA-l5la28j"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DsIxXFTKSG5j"
+      },
+      "outputs": [],
+      "source": [
+        "# Seconds in the song\n",
+        "audio_offsets = [7, 9]\n",
+        "fps = 8\n",
+        "\n",
+        "# Convert seconds to frames\n",
+        "num_interpolation_steps = [(b-a) * fps for a, b in zip(audio_offsets, audio_offsets[1:])]\n",
+        "\n",
+        "video_path = pipeline.walk(\n",
+        "    p_params,\n",
+        "    prompts=['blueberry spaghetti', 'strawberry spaghetti'],\n",
+        "    seeds=[42, 1337],\n",
+        "    num_interpolation_steps=num_interpolation_steps,\n",
+        "    height=512,                            # use multiples of 64\n",
+        "    width=512,                             # use multiples of 64\n",
+        "    audio_filepath='music/thoughts.mp3',   # Use your own file\n",
+        "    audio_start_sec=audio_offsets[0],      # Start second of the provided audio\n",
+        "    fps=fps,                               # important to set yourself based on the num_interpolation_steps you defined\n",
+        "    batch_size=2,                          # in TPU-v2 typically maximum of 3 for 512x512\n",
+        "    output_dir='./dreams',                 # Where images will be saved\n",
+        "    name=None,                             # Subdir of output dir. will be timestamp by default\n",
+        "    jit=True                               # To use all TPU cores\n",
+        ")\n",
+        "visualize_video_colab(video_path)"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "TPU",
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3.9.12 ('base')",
+      "language": "python",
+      "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": "3.9.12"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "7d7b96a25c39fa7937ff3ab94e1dd8c63b93cb924b8f0093093c6266e25a78bc"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}