From 561f1f5ac29877f6a651a243b7de1659a7e6090f Mon Sep 17 00:00:00 2001 From: Francesco Scheffczyk Date: Thu, 27 Oct 2022 12:43:47 +0200 Subject: [PATCH 1/3] Add missing options to notebook --- stable_diffusion_videos.ipynb | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/stable_diffusion_videos.ipynb b/stable_diffusion_videos.ipynb index 94cdb8d..8a38a35 100644 --- a/stable_diffusion_videos.ipynb +++ b/stable_diffusion_videos.ipynb @@ -307,6 +307,10 @@ " 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", + " output_dir='dreams', # Where images/videos will be saved\n", + " name='animals_test', # Subdirectory of output_dir where images/videos will be saved\n", + " guidance_scale=8.5, # Higher adheres to prompt more, lower lets model take the wheel\n", + " num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good default\n", ")\n", "visualize_video_colab(video_path)" ] @@ -348,6 +352,8 @@ " 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", + " guidance_scale=7.5, # Higher adheres to prompt more, lower lets model take the wheel\n", + " num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good default\n", ")\n", "visualize_video_colab(video_path)" ] From ac711c90863c43015c6121ec716c177de75c69b5 Mon Sep 17 00:00:00 2001 From: Francesco Scheffczyk Date: Thu, 27 Oct 2022 12:47:56 +0200 Subject: [PATCH 2/3] Add missing options to README --- README.md | 24 +++++++++++++----------- stable_diffusion_videos.ipynb | 6 +++--- 2 files changed, 16 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index eb72c63..d1f03c7 100644 --- a/README.md +++ b/README.md @@ -17,14 +17,15 @@ You can either dream up different versions of the same prompt, or morph between The app is built with [Gradio](https://gradio.app/), which allows you to interact with the model in a web app. Here's how I suggest you use it: 1. Use the "Images" tab to generate images you like. - - Find two images you want to morph between - - These images should use the same settings (guidance scale, scheduler, height, width) - - Keep track of the seeds/settings you used so you can reproduce them + + - Find two images you want to morph between + - These images should use the same settings (guidance scale, scheduler, height, width) + - Keep track of the seeds/settings you used so you can reproduce them 2. Generate videos using the "Videos" tab - - Using the images you found from the step above, provide the prompts/seeds you recorded - - 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). - - You can set the `output_dir` to the directory you wish to save to + - Using the images you found from the step above, provide the prompts/seeds you recorded + - 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). + - You can set the `output_dir` to the directory you wish to save to ## Python Package @@ -57,6 +58,7 @@ pipeline = StableDiffusionWalkPipeline.from_pretrained( video_path = pipeline.walk( prompts=['a cat', 'a dog'], seeds=[42, 1337], + fps=5, # use 5 for testing, 25 or 30 for better quality num_interpolation_steps=3, height=512, # use multiples of 64 if > 512. Multiples of 8 if < 512. width=512, # use multiples of 64 if > 512. Multiples of 8 if < 512. @@ -69,7 +71,7 @@ video_path = pipeline.walk( ### Making Music Videos -*New!* Music can be added to the video by providing a path to an audio file. The audio will inform the rate of interpolation so the videos move to the beat 🎢 +_New!_ Music can be added to the video by providing a path to an audio file. The audio will inform the rate of interpolation so the videos move to the beat 🎢 ```python from stable_diffusion_videos import StableDiffusionWalkPipeline @@ -92,6 +94,7 @@ num_interpolation_steps = [(b-a) * fps for a, b in zip(audio_offsets, audio_offs video_path = pipeline.walk( prompts=['a cat', 'a dog'], seeds=[42, 1337], + fps=fps, # important to set yourself based on the num_interpolation_steps you defined num_interpolation_steps=num_interpolation_steps, audio_filepath='audio.mp3', audio_start_sec=audio_offsets[0], @@ -99,6 +102,8 @@ video_path = pipeline.walk( height=512, # use multiples of 64 if > 512. Multiples of 8 if < 512. width=512, # use multiples of 64 if > 512. Multiples of 8 if < 512. output_dir='dreams', # Where images/videos will be saved + name=None, # Subdir of output dir. will be timestamp by default + batch_size=1, # increase until you go out of memory guidance_scale=7.5, # Higher adheres to prompt more, lower lets model take the wheel num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good default ) @@ -122,8 +127,7 @@ interface.launch() ## Credits -This work built off of [a script](https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355 -) shared by [@karpathy](https://github.com/karpathy). The script was modified to [this gist](https://gist.github.com/nateraw/c989468b74c616ebbc6474aa8cdd9e53), which was then updated/modified to this repo. +This work built off of [a script](https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355) shared by [@karpathy](https://github.com/karpathy). The script was modified to [this gist](https://gist.github.com/nateraw/c989468b74c616ebbc6474aa8cdd9e53), which was then updated/modified to this repo. ## Contributing @@ -168,5 +172,3 @@ from stable_diffusion_videos import RealESRGANModel model = RealESRGANModel.from_pretrained('nateraw/real-esrgan') model.upsample_imagefolder('path/to/images/', 'path/to/output_dir') ``` - - diff --git a/stable_diffusion_videos.ipynb b/stable_diffusion_videos.ipynb index 8a38a35..5c94b9a 100644 --- a/stable_diffusion_videos.ipynb +++ b/stable_diffusion_videos.ipynb @@ -368,7 +368,7 @@ }, "gpuClass": "standard", "kernelspec": { - "display_name": "Python 3.9.12 ('base')", + "display_name": "Python 3.9.6 64-bit", "language": "python", "name": "python3" }, @@ -382,11 +382,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.6" }, "vscode": { "interpreter": { - "hash": "7d7b96a25c39fa7937ff3ab94e1dd8c63b93cb924b8f0093093c6266e25a78bc" + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "widgets": { From 3b5b74d02114c02293689632a57aa133be830bc9 Mon Sep 17 00:00:00 2001 From: Francesco Scheffczyk Date: Thu, 27 Oct 2022 12:57:07 +0200 Subject: [PATCH 3/3] Remove unwanted changes --- stable_diffusion_videos.ipynb | 3530 ++++++++++++++++----------------- 1 file changed, 1765 insertions(+), 1765 deletions(-) diff --git a/stable_diffusion_videos.ipynb b/stable_diffusion_videos.ipynb index 5c94b9a..ca7e504 100644 --- a/stable_diffusion_videos.ipynb +++ b/stable_diffusion_videos.ipynb @@ -1,1767 +1,1767 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "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€—" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dvdCBpWWOhW-" - }, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Xwfc0ej1L9A0" - }, - "outputs": [], - "source": [ - "%%capture\n", - "! pip install stable_diffusion_videos[realesrgan]\n", - "! git config --global credential.helper store" - ] - }, - { - "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).\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()" - ] - }, - { - "cell_type": "markdown", - "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." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bgSNS368L-DV" - }, - "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", - "#@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", - "πŸ’‘ **Pro tip** - Click the link that looks like `https://.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": [ - "\n", - "interface.launch(debug=True)" - ] - }, - { - "cell_type": "markdown", - "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..." - ] - }, - { - "cell_type": "markdown", - "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", - "\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", - " \n", - " \"\"\" % data_url)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vd_RzwkoPM7X" - }, - "source": [ - "Walk! πŸšΆβ€β™€οΈ" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 566, - "referenced_widgets": [ - "c0a9ba5379ee41a7b66ca404bd9af06a", - "351d261eac1b48c1b29e347af958ca8c", - "7d88b225f43d4cafa2e2faa7645d0269", - "1b3cd410066e4cbd8ba40b6aedd125f2", - "21c2311eedd34d59a5eb1bb95feab90c", - "6b53cd3dc801436ba1a2e835f42178d4", - "89971e32f46f490682144de5c61c31ed", - "6b523b6f25f1409dbc87824bb85aa47e", - "5fbab10e6bea42a783dd738b2cfb52c0", - "6e54cae424bf41f3a4ae09253ee61364", - "0b084230986847bb892449467e624ff0", - "bc5b56fb74df40de93a720b93093bae2", - "2c6dd2da63874487a9f66c96677b84d0", - "f00aef1e28b749f8acbc11a1fa18ebb5", - "9a4ba69d780c4f11b471a17be6055ecd", - "17c8578abac84d09b7fe879d977411ae", - "3e5aef538626404facae4cb11262cb36", - "85888b6204734768ac8452828fca423d", - "5ee9d3ef029b4b8b96b18072e40f82a8", - "8ce2521693184ba7a9d5511dd68a7deb", - "4ba5cb5cb0f449a3bbcbdfb472ffdd0b", - "f991b4b8bbb548dc8cb5f7fdad476a9e", - "dafcde0b927943a1b3fad22eb5345b7e", - "2a82518fe2ea4d0f97889089d784d1d6", - "03f7635b0e424047831ab281f19c1948", - "07bd449010c344c1852d9ba7e4e7d49e", - "c576a8e2c2d24f51bbf8f4853c37973b", - "0f6c0495e61841aa87ac0881189b606c", - "272e66eb9ed2425b84815b71706af4d5", - "f25ff2a7e6e742cbaa0580811b114113", - "cef9d02b7b5a40ac81d11fd4cd86b4b8", - "b93c6b2028d8436e9677fba7c0829367", - "417bbde6a0654e9486b36c02bad99b50", - "50a863d60b854eab8df7bedacd79c8f4", - "48b9afb6ada0490ebb09feef54c7df60", - "6eb90f2ad3174674a446927ae3da20a3", - "7264f48fe2e24adc91aa11400767fc33", - "26916633b7ee42a19ae08dd00c8048ea", - "e297fea7384e4d30ba7a5b76278fa64f", - "ad04c6e287e940e8b2a1bc23a5948ce3", - "d31612a49a9a4cc3b24449bdf5d83561", - "d47a63723e3d42ceaff9b56283fc9e78", - "ee4e0e22f2484ef795f7d5f274f90e05", - "2109f0b4569942759d366367321ff842" - ] - }, - "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", - " output_dir='dreams', # Where images/videos will be saved\n", - " name='animals_test', # Subdirectory of output_dir where images/videos will be saved\n", - " guidance_scale=8.5, # Higher adheres to prompt more, lower lets model take the wheel\n", - " num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good default\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", - 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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()" + ] + }, + { + "cell_type": "markdown", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bgSNS368L-DV" + }, + "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", + "#@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", + "πŸ’‘ **Pro tip** - Click the link that looks like `https://.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": [ + "\n", + "interface.launch(debug=True)" + ] + }, + { + "cell_type": "markdown", + "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..." + ] + }, + { + "cell_type": "markdown", + "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", + "\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", + " \n", + " \"\"\" % data_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vd_RzwkoPM7X" + }, + "source": [ + "Walk! πŸšΆβ€β™€οΈ" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 566, + "referenced_widgets": [ + "c0a9ba5379ee41a7b66ca404bd9af06a", + "351d261eac1b48c1b29e347af958ca8c", + "7d88b225f43d4cafa2e2faa7645d0269", + "1b3cd410066e4cbd8ba40b6aedd125f2", + "21c2311eedd34d59a5eb1bb95feab90c", + "6b53cd3dc801436ba1a2e835f42178d4", + "89971e32f46f490682144de5c61c31ed", + "6b523b6f25f1409dbc87824bb85aa47e", + "5fbab10e6bea42a783dd738b2cfb52c0", + "6e54cae424bf41f3a4ae09253ee61364", + "0b084230986847bb892449467e624ff0", + "bc5b56fb74df40de93a720b93093bae2", + "2c6dd2da63874487a9f66c96677b84d0", + "f00aef1e28b749f8acbc11a1fa18ebb5", + "9a4ba69d780c4f11b471a17be6055ecd", + "17c8578abac84d09b7fe879d977411ae", + "3e5aef538626404facae4cb11262cb36", + "85888b6204734768ac8452828fca423d", + "5ee9d3ef029b4b8b96b18072e40f82a8", + "8ce2521693184ba7a9d5511dd68a7deb", + "4ba5cb5cb0f449a3bbcbdfb472ffdd0b", + "f991b4b8bbb548dc8cb5f7fdad476a9e", + "dafcde0b927943a1b3fad22eb5345b7e", + "2a82518fe2ea4d0f97889089d784d1d6", + "03f7635b0e424047831ab281f19c1948", + "07bd449010c344c1852d9ba7e4e7d49e", + "c576a8e2c2d24f51bbf8f4853c37973b", + "0f6c0495e61841aa87ac0881189b606c", + "272e66eb9ed2425b84815b71706af4d5", + "f25ff2a7e6e742cbaa0580811b114113", + "cef9d02b7b5a40ac81d11fd4cd86b4b8", + "b93c6b2028d8436e9677fba7c0829367", + "417bbde6a0654e9486b36c02bad99b50", + "50a863d60b854eab8df7bedacd79c8f4", + "48b9afb6ada0490ebb09feef54c7df60", + "6eb90f2ad3174674a446927ae3da20a3", + "7264f48fe2e24adc91aa11400767fc33", + "26916633b7ee42a19ae08dd00c8048ea", + "e297fea7384e4d30ba7a5b76278fa64f", + "ad04c6e287e940e8b2a1bc23a5948ce3", + "d31612a49a9a4cc3b24449bdf5d83561", + "d47a63723e3d42ceaff9b56283fc9e78", + "ee4e0e22f2484ef795f7d5f274f90e05", + "2109f0b4569942759d366367321ff842" + ] + }, + "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", + " output_dir='dreams', # Where images/videos will be saved\n", + " name='animals_test', # Subdirectory of output_dir where images/videos will be saved\n", + " guidance_scale=8.5, # Higher adheres to prompt more, lower lets model take the wheel\n", + " num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good default\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", + " guidance_scale=7.5, # Higher adheres to prompt more, lower lets model take the wheel\n", + " num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good 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": 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