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 @@
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"
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+ "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) βοΈ\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 π€"
+ ]
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- "cells": [
- {
- "cell_type": "markdown",
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- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- "
"
- ]
- },
- {
- "cell_type": "markdown",
- "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) βοΈ\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 π€"
- ],
- "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 π€ 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 π€ 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",
+ " > β οΈ **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 π"
- ],
"metadata": {
"id": "H7UOKJhVOonb"
- }
+ },
+ "source": [
+ "## Run the App π"
+ ]
},
{
"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",
"π‘ **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! πΆββοΈ"
- ],
"metadata": {
"id": "Vd_RzwkoPM7X"
- }
+ },
+ "source": [
+ "Walk! πΆββοΈ"
+ ]
},
{
"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, ?it/s]"
+ "id": "Hv2wBZXXMQ-I",
+ "outputId": "a3c23c63-2c54-451e-d467-92d0cae90a45"
+ },
+ "outputs": [],
+ "source": [
+ "video_path = pipeline.walk(\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=5, # 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",
+ ")\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",
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- "\n",
- "video_path = pipeline.walk(\n",
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- " 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",
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}
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