AI Generated Videos with Stable Video Diffusion and Vultr Cloud GPU

Introduction

Stable Video Diffusion (SVD) model is an image-to-video diffusion model developed by StabilityAI that is capable of generating high-quality videos from image prompts. The model works by taking an image as an input and generating a series of images that match the prompt, then, the generated images are merged and converted into a single video.

To generate images, the SVD model uses a diffusion process which iteratively adds noise and removes it until the desired image is generated. The model can generate 2 to 4 seconds of video length conditioned to the provided input.

This article explains how to generate AI videos with Stable Video Diffusion on a Vultr Cloud GPU server. You are to install the required Stable Diffusion dependencies, then, start a Streamlit server to infer the model for video generation using a web user interface. When deployed, you can also infer the model using a Command Line Interface (CLI).

Prerequisites

Before you begin:

• Deploy a fresh A100 Ubuntu 22.04 Vultr Cloud GPU server using the Vultr marketplace application with at least 80 GB GPU RAM.
• Access the server using SSH as a non-root user with sudo privileges.

Model Variants

The Stable Video Diffusion (SVD) model has undergone 3 layers of training, first, the model is trained on images, then the image model for video generation is extended by inserting temporal layers that are pretrained on a larger dataset. The final step involves fine-tuning the model with a smaller dataset of high-quality videos. Hence, the model has 4 variants that are which include the base SVD model, SVD XT, SVD Image Decoder, and SVD XT Image Decoder as described below:

• SVD: Generates short videos with 14 frames using a 576x1024 resolution. It uses an f8-decoder that decodes the compressed image using a lossless compression algorithm to achieve temporal consistency between the frames.

• SVD XT: The fine-tuned version of the base model capable of generating 25 frames at a 576x1024 resolution given a context frame of the same size. Similar to the base variant, it uses an f8-decoder

• SVD Image Decoder: This model is capable of generating short videos with 14 frames, but uses the image decoder instead of an f8-decoder used by the base variant.

• SVD XT Image Decoder: This model is capable of generating short videos with 25 frames and uses the image decoder instead of the f8-decoder.

Both the f8 and image decoders offer different functionalities, and to find the right approach to use the models, experiment with both methods to find the best implementation that fits your use case.

Set Up the Development Environment

To implement the Stable Video Diffusion model, clone the model repository, download the model checkpoints in a Safetensors format, install the required Python packages and the ffmpeg package to encode your videos as described in the steps below.

1. Switch to your user home directory.

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   $ cd
   

2. Clone the Stability AI generative-models repository.

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   $ git clone https://github.com/Stability-AI/generative-models
   

3. Switch to the new model directory.

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   $ cd generative-models/
   

4. Create a new checkpoints directory to store the model checkpoints.

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   $ mkdir checkpoints
   

5. Download the model checkpoints using the wget utility.

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   $ wget -O checkpoints/svd.safetensors 'https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/resolve/main/svd.safetensors?download=true'
   
   $ wget -O checkpoints/svd_image_decoder.safetensors 'https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/resolve/main/svd_image_decoder.safetensors?download=true'
   
   $ wget -O checkpoints/svd_xt.safetensors 'https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors?download=true'
   
   $ wget -O checkpoints/svd_xt_image_decoder.safetensors 'https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt_image_decoder.safetensors?download=true'
   

   The above commands download all Stable Video Diffusion model variant checkpoints in a Safetensors format. Safetensors is a fast and secure way to store tensors. The format is designed to be safe from code execution attacks and also compatible with multiple programming languages and frameworks for the efficient sharing of machine learning models.

6. Install the required Python packages using Pip.

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   $ pip install requirements/pt2.txt --ignore-installed
   

7. Install the ffmpeg package.

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   $ sudo apt install ffmpeg
   

   The Stable Video Diffusion model produces raw videos that consist of uncompressed video frames. This makes raw videos complex to store and share. ffmpeg converts the raw video produced by the model and encodes it in the H.264 format. This process removes any redundant information and reduces the video size for convenient storage and sharing.

Inference the Model using the Streamlit Web Interface

Streamlit is an open-source Python library that provides a convenient development environment for the creation of custom machine learning and data science web applications. You can build visualizations, develop interactive tools for model training and machine learning, then, share the final application with your target audience.

Follow the steps below to start a Streamlit server, access the web user interface, and generate videos using the Stable Diffusion Model files by providing custom image inputs.

Start the Streamlit Server

1. Create a new PYTHONPATH environment variable to store your Stable Video Diffusion files generative-models directory path.

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   $ export PYTHONPATH=$PYTHONPATH:$HOME/generative-models
   

   The above command adds your Stable Video Diffusion files path generative-models to the PYTHONPATH environment variable. This enables the Streamlit script to import local directories as modules for execution in the correct format and indexing by the Python interpreter.

2. Allow incoming connections to the Streamlit port 8501 through the default server firewall.

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   $ sudo ufw allow 8501
   

3. Start the Streamlit web server.

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   $ streamlit run scripts/demo/video_sampling.py
   

   The above command starts a new Streamlit session using the PYTHONPATH variable and runs the video_sampling.py script to generate videos based on the input prompt.

4. Access the Streamlit interface using a web browser such as Firefox.

   http://SERVER-IP:8501

Generate Videos using Stable Video Diffusion

In this section, select a target model for your use case, and load the model to initialize its pipeline. Then, upload a base input image and start the image-to-video generation process.

1. Within the Streamlit interface, click the Model Version dropdown to select your target model from the list.

   

   For purposes of this article, select the SVD XT model variant that offers higher video frame rates.

2. Check the Load Model box to start the model and its pipeline.

   

3. Within the Input section, click Browse Files to upload the base image from your local computer directories.

   

   When the upload is complete, the processes your base image and displays the input and output frames. If the shape image dimensions are wrong, adjust the H and W parameters from the left sidebar.

   

4. Keep 14 selected as your decode frames, then click the Sample button to start the generation process.

   

   Verify that an image grid with all the generated frames displays together with the final H264-encoded video.

   

   Click the Play button to view the generated video, and verify it's exported to the default samples directory in your model directory path generative-models/outputs/demo/vid/$VERSION/samples/.

Inference Stable Video Diffusion via CLI

In addition to generating videos using the web interface, you can generate videos via CLI in your SSH session using the simple_video_sample.py script. The sample() function in the script contains the core logic of video generation that can be replicated in other applications with slight configuration changes. The function also comes with Fire CLI implementation which simplifies the creation of CLI applications.

Follow the steps in this section to infer the Stable Video Diffusion model via CLI using the simple_video_sampling.py script. Then, generate videos for singular and multiple images at once using additional parameters available in the script.

1. Create the PYTHONPATH environment variable that points to your model files directory.

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   $ export PYTHONPATH=$PYTHONPATH:$HOME/generative-models
   

2. Switch to the /scripts/sampling/ directory using your model files path.

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   $ cd $HOME/generative-models/scripts/sampling
   

3. To generate videos using Stable Video Diffusion, use the following options:

   • --input_path: Defines the path to the input image file or directory that contains the base input image files. The default value is assets/test_image.png.

   • --num_frames: An optional argument that specifies the number of video frames. The default value depends on your selected model version.

   • --num_steps: Specifies the number of inference steps.

   • --version: Specifies the model version to use. The default value is svd while other possible values include svd_xt, svd_image_decoder, and svd_xt_image_decoder.

   • --fps_id: Specifies the frames per second ID. When unspecified, the default value is set to 6.

   • --motion_bucket_id: Specifies the motion bucket ID. The default value is 127.

   • --cond_aug: Specifies the conditional augmentation value. The default value is 0.02.

   • --seed: Specifies the seed for the random number generator. The default value is 23.

   • --decoding_t: Specifies the number of frames decoded at a time. This parameter increases the amount of VRAM usage, reduce it when necessary depending on your GPU memory. By default, it's set to 14.

   • --device: Specifies the system device to use for computation tasks. The default value is cuda which represents NVIDIA GPU drivers.

   • --output_folder: Specifies the output folder to export generated videos to. The default value is generative-models/outputs/simple_video_sample/$VERSION/.

   For example:

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   $ python3 simple_video_sample.py --input_path="path_to_your_image" --num_frames=20 --version="svd_xt"
   

   The above command runs the simple_video_sample.py script with your specified source image, 20 frames, and the svd_xt model version while the rest of the parameters use the respective default values. To specify more values on each option, modify your command similar to the one below:

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   $ python3 simple_video_sample.py --input_path="path_to_your_image" --num_frames=20 --version="svd_xt" --fps_id=5 --motion_bucket_id=100 --cond_aug=0.01 --seed=42 --decoding_t=10 --device="cpu" --output_folder="path_to_your_output_folder"
   

   The above command runs the script with all the specified parameters. Replace "path_to_your_image" and "path_to_your_output_folder" with your actual path generation paths. When using multiple images, the script generates a sample for each image.

Conclusion

You have explored the different Stable Video Diffusion variants, and generated videos using the model. You performed inference steps using the Streamlit web interface and the Python CLI interpreter. For additional deployment options, visit the Stable Video Diffusion model page.