diff --git a/stable_diffusion_videos.ipynb b/stable_diffusion_videos.ipynb index fa07a39..94cdb8d 100644 --- a/stable_diffusion_videos.ipynb +++ b/stable_diffusion_videos.ipynb @@ -1,1434 +1,44 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "collapsed_sections": [], - "include_colab_link": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + { + "cell_type": "markdown", + "metadata": { + "id": "z4GhhH25OdYq" + }, + "source": [ + "# 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).\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 me on Github [@nateraw](https://github.com/nateraw) \n", + "\n", + "You can file any issues/feature requests [here](https://github.com/nateraw/stable-diffusion-videos/issues)\n", + "\n", + "Enjoy πŸ€—" + ] }, - 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"\n", - "You can file any issues/feature requests [here](https://github.com/nateraw/stable-diffusion-videos/issues)\n", - "\n", - "Enjoy πŸ€—" - ], - "metadata": { - "id": "z4GhhH25OdYq" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Setup" - ], - "metadata": { - "id": "dvdCBpWWOhW-" - } + { + "cell_type": "markdown", + "metadata": { + "id": "dvdCBpWWOhW-" + }, + "source": [ + "## Setup" + ] }, { "cell_type": "code", @@ -1445,61 +55,79 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "dR5iVGYbOky5" + }, "source": [ "## Authenticate with Hugging Face Hub\n", "\n", - "You have to be a registered user in πŸ€— Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens)." - ], - "metadata": { - "id": "dR5iVGYbOky5" - } + "You have to be a registered user in πŸ€— Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).\n", + "\n", + " > ⚠️ **Important**: You must also go to the [model repository](https://huggingface.co/CompVis/stable-diffusion-v1-4) and click \"Access Repository\" so you can download the model." + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GmejIGhFMTXG" + }, + "outputs": [], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" - ], - "metadata": { - "id": "GmejIGhFMTXG" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## Run the App πŸš€" - ], "metadata": { "id": "H7UOKJhVOonb" - } + }, + "source": [ + "## Run the App πŸš€" + ] }, { "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." - ], - "metadata": { - "id": "g71hslP8OntM" - } + ] }, { "cell_type": "code", - "source": [ - "from stable_diffusion_videos import interface" - ], + "execution_count": null, "metadata": { "id": "bgSNS368L-DV" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "import torch\n", + "\n", + "from stable_diffusion_videos import StableDiffusionWalkPipeline, Interface\n", + "\n", + "pipeline = StableDiffusionWalkPipeline.from_pretrained(\n", + " \"CompVis/stable-diffusion-v1-4\",\n", + " torch_dtype=torch.float16,\n", + " revision=\"fp16\",\n", + ").to(\"cuda\")\n", + "\n", + "interface = Interface(pipeline)" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "kidtsR3c2P9Z" + }, + "outputs": [], "source": [ "#@title Connect to Google Drive to Save Outputs\n", "\n", @@ -1516,18 +144,15 @@ "\n", "if connect_google_drive:\n", " from google.colab import drive\n", - "\n", - " drive.mount('/content/gdrive')" - ], - "metadata": { - "id": "kidtsR3c2P9Z", - "cellView": "form" - }, - "execution_count": null, - "outputs": [] + "\n", + " drive.mount('/content/gdrive')" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "VxjRVNnMOtgU" + }, "source": [ "### Launch\n", "\n", @@ -1543,55 +168,56 @@ " - 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", "πŸ’‘ **Pro tip** - Click the link that looks like `https://.gradio.app` below , and you'll be able to view it in full screen." - ], - "metadata": { - "id": "VxjRVNnMOtgU" - } + ] }, { "cell_type": "code", - "source": [ - "interface.launch(debug=True)" - ], + "execution_count": null, "metadata": { "id": "8es3_onUOL3J" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "\n", + "interface.launch(debug=True)" + ] }, { "cell_type": "markdown", - "source": [ - "---" - ], "metadata": { "id": "mFCoTvlnPi4u" - } + }, + "source": [ + "---" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "SjTQLCiLOWeo" + }, "source": [ "## Use `walk` programatically\n", "\n", - "The other option is to not use the interface, and instead use `walk` programatically. Here's how you would do that...\n", - "\n", - "**You may need to reset runtime before running these cells if you used the interface above**" - ], - "metadata": { - "id": "SjTQLCiLOWeo" - } + "The other option is to not use the interface, and instead use `walk` programatically. Here's how you would do that..." + ] }, { "cell_type": "markdown", - "source": [ - "First we define a helper fn for visualizing videos in colab" - ], "metadata": { "id": "fGQPClGwOR9R" - } + }, + "source": [ + "First we define a helper fn for visualizing videos in colab" + ] }, { "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "GqTWc8ZhNeLU" + }, + "outputs": [], "source": [ "from IPython.display import HTML\n", "from base64 import b64encode\n", @@ -1604,50 +230,21 @@ " \n", " \n", " \"\"\" % data_url)" - ], - "metadata": { - "id": "GqTWc8ZhNeLU" - }, - "execution_count": 3, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "Walk! πŸšΆβ€β™€οΈ" - ], "metadata": { "id": "Vd_RzwkoPM7X" - } + }, + "source": [ + "Walk! πŸšΆβ€β™€οΈ" + ] }, { "cell_type": "code", - "source": [ - "from stable_diffusion_videos import StableDiffusionWalkPipeline\n", - "from diffusers.schedulers import LMSDiscreteScheduler\n", - "import torch\n", - "\n", - "pipeline = StableDiffusionWalkPipeline.from_pretrained(\n", - " \"CompVis/stable-diffusion-v1-4\",\n", - " use_auth_token=True,\n", - " torch_dtype=torch.float16,\n", - " revision=\"fp16\",\n", - " scheduler=LMSDiscreteScheduler(\n", - " beta_start=0.00085, beta_end=0.012, beta_schedule=\"scaled_linear\"\n", - " )\n", - ").to(\"cuda\")\n", - "\n", - "video_path = pipeline.walk(\n", - " ['a cat', 'a dog'],\n", - " [42, 1337],\n", - " num_interpolation_steps=3,\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", - ")\n", - "visualize_video_colab(video_path)" - ], + "execution_count": null, "metadata": { - "id": "Hv2wBZXXMQ-I", "colab": { "base_uri": "https://localhost:8080/", "height": 566, @@ -1698,132 +295,1467 @@ "2109f0b4569942759d366367321ff842" ] }, - "outputId": "a3c23c63-2c54-451e-d467-92d0cae90a45" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Fetching 16 files: 0%| | 0/16 [00:00 512. Multiples of 8 if < 512.\n", + " width=512, # use multiples of 64 if > 512. Multiples of 8 if < 512.\n", + ")\n", + "visualize_video_colab(video_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oLXULBMwSDnY" + }, + "source": [ + "### Bonus! Music videos... upload an audio file and give it a try" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DsIxXFTKSG5j" + }, + "outputs": [], + "source": [ + "# Seconds in the song\n", + "audio_offsets = [146, 148]\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", + "\n", + "video_path = pipeline.walk(\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='kanye_west_fade.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=1, # increase until you go out of memory\n", + " output_dir='./dreams', # Where images will be saved\n", + " name=None, # Subdir of output dir. will be timestamp by default\n", + ")\n", + "visualize_video_colab(video_path)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "include_colab_link": true, + "provenance": [] + }, + "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" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "03f7635b0e424047831ab281f19c1948": { + "model_module": "@jupyter-widgets/controls", + 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"markdown", - "source": [ - "### Bonus! Music videos... upload an audio file and give it a try" - ], - "metadata": { - "id": "oLXULBMwSDnY" } - }, - { - "cell_type": "code", - "source": [ - "# Seconds in the song\n", - "audio_offsets = [146, 148]\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", - "\n", - "video_path = pipeline.walk(\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='kanye_west_fade.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=1, # increase until you go out of memory\n", - " output_dir='./dreams', # Where images will be saved\n", - " name=None, # Subdir of output dir. will be timestamp by default\n", - ")\n", - "visualize_video_colab(video_path)" - ], - "metadata": { - "id": "DsIxXFTKSG5j" - }, - "execution_count": null, - "outputs": [] } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +}