Generative AI has been a driving force in the AI ​​community for some time, and the advances made in the field of generative image modeling, especially with the use of diffusion models, have helped generative video models make significant progress, not only in the field of research, but also in the field of research. real world applications. Conventionally, generative video models are either trained from scratch or refined in whole or in part based on pre-trained image models with additional temporal layers, on a combination of image and video datasets.

Continuing the progress in generative video models, in this article we will talk about the Stable video distribution model, a latent video diffusion model capable of generating state-of-the-art high-resolution image-to-video and text-to-video content. We’ll talk about how latent diffusion models trained to synthesize 2D images have improved the capabilities and efficiency of generative video models by adding temporal layers and fine-tuning the models on small datasets consisting of high-quality videos. We will delve deeper into the architecture and operation of the Stable Video Diffusion Model, and evaluate its performance on various metrics and compare it to current state-of-the-art video generation frameworks. So let’s get started.

Thanks to its virtually limitless potential, generative AI has been the main research topic for AI and ML practitioners for some time, and recent years have seen rapid advancements in both the efficiency and performance of generative image models. The lessons learned from generative image models have enabled researchers and developers to make advances in the field of generative video models, leading to improved usability and real-world applications. However, the majority of research attempting to improve the capabilities of generative video models focuses primarily on the exact arrangement of temporal and spatial layers, with little attention paid to examining the influence of selecting the right data on the outcome of these generative models.

Thanks to the advances made by generative image models, researchers have found that the impact of training data distribution on the performance of generative models is indeed significant and undisputed. Furthermore, researchers have also observed that pre-training a generative image model on a large and diverse data set, followed by refining it on a smaller data set with better quality, often results in a significant improvement in performance. Traditionally, generative video models implement the lessons learned from successful generative image models, and researchers have yet to study the effect of data, and training strategies have yet to be studied. The Stable Video Diffusion Model is an attempt to improve the capabilities of generative video models by venturing into previously unknown areas, paying special attention to data selection.

Related: 12 Best Stable Diffusion Websites to Generate AI Images (Free & Paid)

Recent generative video models rely on diffusion models and text conditioning or image conditioning approaches to synthesize multiple consistent video or image frames. Diffusion models are known for their ability to learn how to gradually remove the noise of a sample from its normal distribution by implementing an iterative refinement process, and they have produced desirable results in high-resolution video and text-to-image synthesis. Using the same principle at its core, the Stable Video Diffusion Model trains a latent video diffusion model on its video dataset, along with the use of Generative Adversarial Networks, or GANs, and even autoregressive models to some extent.

The Stable Video Diffusion Model follows a unique strategy that has never been implemented by any generative video model, as it relies on latent video diffusion baselines with a fixed architecture and a fixed training strategy followed by assessing the effect of managing the data. The Stable Video Diffusion Model aims to make the following contributions to the field of generative video modeling.

1. Presenting a systematic and effective data curation workflow in an attempt to convert a large collection of uncurated video clips into high-quality datasets that are then used by the generative video models.
2. To train advanced image-to-video and text-to-video models that outperform existing frameworks.
3. Conducting domain-specific experiments to investigate 3D understanding, and strong prior motion of the model.

Now the Stable Video Diffusion Model implements the lessons of Latent Video Diffusion Models and data curation techniques as the core of its foundation.

Latent video diffusion models

Latent video diffusion models or video LDMs follow the approach of training the primary generative model in a latent space with reduced computational complexity, and most video LDMs implement a pre-trained text-to-image model in combination with the addition of temporal mixing layers in the pre-training. architecture. As a result, most video latent diffusion models only train temporary layers, or skip the training process altogether, unlike the stable video diffusion model that fine-tunes the entire framework. Furthermore, the Stable Video Diffusion Model conditions itself to synthesize text to video data directly at a text prompt, and the results indicate that the resulting framework can be easily refined into a multi-view synthesis or an image-to-video model.

Data curation

Data curation is an essential part not only of the Stable Video Diffusion Model, but for generative models as a whole, as it is essential to pre-train large models on large-scale datasets to improve performance on various tasks, including language modeling or discrimination generation between text and images. , and much more. Data Curation has been successfully implemented on generative image models by exploiting the capabilities of efficient language-image representations, although such discussions have never been central to the development of generative video models. There are several hurdles developers face when managing data for generative video models. To address these challenges, the Stable Video Diffusion Model implements a three-phase training strategy, resulting in improved results and a significant performance boost.

Data management for high-quality video synthesis

As discussed in the previous section, the Stable Video Diffusion Model implements a three-phase training strategy, resulting in better results and a significant performance improvement. Phase I is one image prior education phase that uses a 2D text-to-image diffusion model. Phase II is for video pre-training in which the framework trains on a large amount of video data. Finally, we have Stage III for video fine tuning in which the model is refined on a small subset of high-quality, high-resolution videos.

However, before the Stable Video Diffusion Model implements these three phases, it is essential to process and annotate the data as it serves as the basis for Phase II or the video pre-training phase and plays a crucial role in ensuring the optimal output. To ensure maximum efficiency, the framework first implements a cascaded pipeline for cut detection at 3 different FPS or Frames Per Second levels. The need for this pipeline is demonstrated in the following figure.

Then, the Stable Video Diffusion Model annotates each video clip using three different synthetic subtitling methods. The following table compares the data sets used in the Stable Diffusion Framework before and after the filtration process.

Phase I: Image pretraining

The first stage in the three-stage pipeline implemented in the Stable Video Diffusion Model is image pretraining. To achieve this, the initial Stable Video Diffusion Model framework is based on a pre-trained image diffusion model, namely the Stable distribution 2.1 model that equips it with stronger visual representations.

Phase II: video preliminary training

The second phase is the Video Pre-Training phase and builds on the findings that using data curation in multimodal generative image models often results in better results and improved efficiency, along with powerful discriminative image generation. However, due to the lack of similarly powerful standard representations to filter out unwanted samples for generative video models, the Stable Video Diffusion Model relies on human preferences as input signals to create an appropriate dataset used to pre-train the framework. The following figure demonstrates the positive effect of pretraining the framework on a composite dataset that helps improve overall performance for video pretraining on smaller datasets.

To be more specific, the framework uses several methods to manage subsets of latent video diffusion, and takes into account the ranking of LVD models trained on these datasets. Furthermore, the Stable Video Diffusion framework also finds that using curated datasets to train the frameworks helps improve the performance of the framework, and of diffusion models in general. Moreover, the data curation strategy also works on larger, more relevant and very practical data sets. The following figure shows the positive effect of pretraining the framework on a composite dataset, which helps improve overall performance for video pretraining on smaller datasets.

Phase III: High-end fine-tuning

Until phase II, the Stable Video Diffusion framework focuses on improving performance prior to video pretraining, and in the third phase, the framework emphasizes optimizing or further improving the performance of the framework after matching videos of high quality, and how the transition from Phase II to Phase III is achieved in the framework. In phase III, the framework uses training techniques derived from latent image propagation models and increases the resolution of the training examples. To analyze the effectiveness of this approach, the framework compares it with three identical models that differ only in their initialization. The first identical model has its weights initialized and skips the video training process, while the remaining two identical models are initialized with the weights borrowed from other latent video models.

Results and findings

It’s time to see how the Stable Video Diffusion framework performs on real-world tasks, and how it compares to current state-of-the-art frameworks. The Stable Video Diffusion framework first uses the optimal data approach to train a basic model and then performs refinement to generate several advanced models, with each model performing a specific task.

The above image represents the high-resolution image for video samples generated by the framework, while the following image demonstrates the framework’s ability to generate high-quality text to video samples.

Pre-trained basic Model

As previously discussed, the Stable Video Diffusion model is built on the Stable Diffusion 2.1 framework, and based on recent findings, it was critical for developers to adopt the noise scheme and increase the noise to produce better resolution images obtainable when training image distribution models. This approach enables the Stable Video Diffusion base model to learn powerful motion representations and outperform the base models for text to video generation in a zero shot setting, and the results are shown in the following table.

Frame interpolation and multi-view generation

The Stable Video Diffusion framework refines the image-to-video model on multi-view datasets to obtain multiple new views of an object, and this model is known as SVD-MV or Stable Video Diffusion-Multi View model. The original SVD model was refined using two datasets such that the framework inputs a single image and returns a set of multi-view images as output.

As shown in the following images, the Stable Video Diffusion Multi View framework delivers high performance comparable to the state-of-the-art Scratch Multi View framework, and the results are a clear demonstration of SVD-MV’s ability to take advantage of the insights gained from the original SVD framework for multi-view image generation. Moreover, the results also indicate that running the model for a relatively smaller number of iterations helps in delivering optimal results, as is the case with most models refined from the SVD framework.

In the image above, the metrics are indicated on the left and as you can see, the Stable Video Diffusion Multi View framework outperforms the Scratch-MV and SD2.1 Multi-View framework by a fair margin. The second figure demonstrates the effect of the number of training iterations on the overall performance of the framework in terms of Clip Score, and the SVD-MV frameworks deliver sustainable results.

Frequently Asked Questions – FAQs

Final thoughts

In this article we talked about Stable Video Diffusion, a latent video diffusion model capable of generating state-of-the-art high-resolution image-to-video and text-to-video content. The Stable Video Diffusion Model follows a unique strategy that has never been implemented by any generative video model, as it relies on latent video diffusion baselines with a fixed architecture and a fixed training strategy followed by assessing the effect of managing the data.

We talked about how latent diffusion models trained to synthesize 2D images have improved the capabilities and efficiency of generative video models by adding temporal layers and refining the models on small datasets consisting of high-quality videos. To collect the pre-training data, the framework conducts scaling studies and follows systematic data collection practices, and finally proposes a method to manage a large amount of video data, and converts noisy videos into input data suitable for generative video models.

Additionally, the Stable Video Diffusion framework uses three different video model training phases that are analyzed independently to assess their impact on the framework’s performance. The framework ultimately produces a video representation powerful enough to refine the models for optimal video synthesis, and the results are comparable to state-of-the-art video generation models already in use.

More on this topic: How to Make Stunning QR Codes with AI using Stable Diffusion and ControlNet

Credit for gathering the information in this article goes to Kunal Kejriwal at Unite.ai