In the rapidly advancing realm of artificial intelligence, one of the most thrilling breakthroughs is the development of text-to-video models. These state-of-the-art models have the potential to transform the way we create and engage with visual content. Text-to-video models are unlocking new possibilities for storytelling, education, marketing, and a host of other fields. This is done by converting written descriptions into dynamic videos.
Central to this innovation is the AI's ability to comprehend and interpret human language with remarkable sophistication, made possible by advancements in Natural Language Processing (NLP) models. Additionally, the content generation, encompassing both sound and images, is made possible by specialized generative AI models.
The existence and capabilities of contemporary text-to-video models are primarily supported by the development of two key technologies: Large Language Models (LLMs) and Diffusion Models. Large Language Models enable the nuanced understanding and processing of textual data. Diffusion Models facilitate the creation of high-quality visual and auditory content from these textual descriptions.
What Is the Importance of Large Language Models
Arguably, the most significant advancement in the field of AI recently is the adoption of Large Language Models. They seem to have become a part of every major AI application nowadays, and for good reason. These models are the first to reliably interpret written commands and execute operations based on them. To understand its grand importance, we need to provide a quick overview of Large Language Models.
Large Language Models, such as OpenAI's GPT-3, are AI systems trained on huge amounts of text data. These models use advanced techniques to understand and generate human-like text based on the input they receive. The "large" part refers to the size of these models. They have billions of parameters that provide them with an understanding of the intricacies of human language.
Before the development of these advanced models, interaction with AI using written text had significant limitations. Older models had difficulty understanding context, grasping meaning, as well as generating coherent responses. They were capable of basic tasks like keyword extraction and sentiment analysis. However, many of the tasks that we now take for granted with the introduction of ChatGPT and similar models were previously beyond their capabilities.
These models were revolutionary upon their introduction, and not only. They have continued to rapidly evolve. Initially, their primary function was to generate textual responses. However, their capabilities have expanded dramatically over time, giving the impression that there are no limits to what they can achieve. The latest versions of these models can now search the Internet, process files, engage in spoken conversations, create images, and even generate videos. The transition of these models from strictly language models to multimodal models made this remarkable evolution possible.
Multimodal models are designed to process and generate various types of data, including text, images, and audio. By combining standard Large Language Models with other technologies, these models gain the ability to understand and create complex multimedia content. This versatility surpasses that of their predecessors, establishing them as the future of AI from our current standpoint.
The diffusion model is one of the most crucial technologies enabling the transition from standard Large Language Models to multimodal models. Diffusion models are essential for generating visual content such as images and videos. They are a key component in the evolution of advanced LLMs into multimodal models.
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What Is the Importance of Diffusion Models
Diffusion models, as another groundbreaking advancement in the field of AI, stand out as the cornerstone of today's generative AI applications. This is particularly noticeable in the field of multimedia content creation.
Diffusion models are a type of generative model that creates data by refining random noise into a structured output, like an image or video. They work by gradual noise reduction, transforming it into a clear and meaningful result. This method allows them to produce high-quality visual content with impressive precision and detail.
Before their introduction, Generative Adversarial Networks (GANs) were thought to be the pinnacle of visual content creation. They were able to create completely new images of relatively high quality and variety. Nowadays, they are not used as much because diffusion models have proved to produce higher-quality visual content.
While GANs can be adapted to generate videos, they were primarily used for creating static images. Similarly, diffusion models initially focused on image creation but soon advanced to video generation. However, their development has taken a unique turn. Instead of evolving independently, diffusion models have begun to integrate with Large Language Models. As a result, they are forming comprehensive multimodal systems.
What Are Text-to-Image Models
Before exploring text-to-video models, we need to understand their predecessors: text-to-image models. These models operate straightforwardly. We provide a text prompt, and the model generates an image based on that prompt. Initially, the images produced by these models were of relatively low quality. However, as Large Language Models gained popularity, text-to-image diffusion models also advanced rapidly. They achieved a quality level that could potentially deceive art critics into believing that the generated images were created by human artists. The quality of the produced content was very high, to the point that certain pieces of "AI art" managed to win art competitions. This sparked quite a controversy in the community, but that is a topic for another time. The three most popular text-to-image models are:
• DALL-E 3
• Midjourney V5.2
• Stable Diffusion XL
• Adobe Firefly 2
The potential of text-to-image models did not go unnoticed by the developers of Large Language Models. The concept of integrating such models into Large Language Models rapidly gained momentum. The outcome of this was multimodal models that can go beyond processing text and generating text-based responses. In addition, these models can also create images based on user input.
Soon after integrating text-to-image models, attention shifted towards the future. The aim was on the next big innovation: text-to-video models. This seemed like a logical progression considering that videos are essentially sequences of images. However, creating text-to-video models proved challenging initially. Several issues needed resolution before true text-to-video models could exist.
What Is the Difference Between Generating Images and Generating Videos
Videos are sequences of images displayed in rapid succession, and the number of images shown per second is measured as frames per second (FPS). FPS is a crucial metric in video production and directly impacts:
• consistency and coherence
• video quality and resolution
• computational resources
Ensuring smooth transitions between each frame is essential for creating a cohesive video. Without this consistency, the video may exhibit flickering or disjointed transitions. Each frame must align in terms of color, lighting, and positioning to maintain visual harmony throughout the sequence. Previously, achieving this natural and smooth motion between frames was challenging for diffusion models due to the difficulty in obtaining consistent outputs from the model.
Models have currently advanced to the point where this consistency and coherence can be accomplished. Nowadays models can grasp the context and meaning behind the text, as well as understand relationships between characters, objects, and settings in images. This enables us to achieve narrative coherence, as the model can understand that the relationships present in the first frame must continue to exist in the following frame.
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In regards to quality and resolution, it must be mentioned that it is a bigger challenge to produce high-quality videos that are visually detailed and clear, compared to generating static images. Making sure that each frame is rendered at a high resolution without losing detail is critical for creating professional and realistic videos. As the model generates multiple frames, it must preserve fine details across all frames. This is done to ensure that the video does not appear blurry or lose definition over time.
Finally, as a result of all of the aforementioned constraints, generating videos requires significantly more computational power compared to single images. Each frame needs to be processed. Moreover, maintaining consistency and coherence across potentially thousands of frames demands substantial computational resources. High-resolution videos are particularly problematic to create. This is because we must ensure a fine balance between processing speed and the amount of resources we are using to generate them. In addition, high-resolution videos also consume a significant amount of memory. Therefore, that is another limiting factor to be taken into account when creating videos using AI.
What Are the Most Popular Text to Video Models
The nature of text-to-video models is complex and its training is especially difficult and expensive. Consequently, only a few companies in the world have produced text-to-video models that generate excellent results. The best text-to-video models are:
• Sora
• Lumiere
• Emu
• Gen-2
Sora, the model created by OpenAI, is considered to be the pinnacle of text-to-video creation. It is a model capable of generating high-quality videos lasting up to a minute, ensuring visual coherence and detailed adherence to prompts. Sora stands out for its ability to manage various resolutions, durations, and aspect ratios, making it highly versatile. However, it is currently available only to select testers and has not yet been released to the public.
Lumiere, the model from Google, was regarded as the best model before the release of Sora. While it can also generate high-quality videos, it is more limited than Sora. It supports resolutions only up to 512x512 pixels, whereas Sora can generate videos up to 1920x1080 pixels. Additionally, the maximum length of a video generated by Lumiere is 5 seconds, while Sora can generate videos that are up to 60 seconds long. Finally, Lumiere cannot create videos with multiple shots. This is likely connected to the fact that it can only create videos that are up to 5 seconds long. Sora excels at creating multi-shot videos that display dynamic interactions between elements in multiple shots.
Emu, and Gen-2, the models from Meta and Runway, while deserving to be mentioned here, produce results that are highly inferior to Sora and Lumiere. Therefore, at this moment, Sora still stands as the best text-to-video model ever produced.
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Text-to-image models have reached a level of sophistication where AI-generated images are often indistinguishable from those created by human artists. While there is always room for improvement, these models have established a high standard in AI-generated visuals. In contrast, text-to-video models still have significant development ahead. Despite remarkable progress and promising examples, like OpenAI's Sora, current models do not yet present a threat to professional videographers. Yet, considering the rapid advancements in text-to-image technology over just a few years, it is likely that we will witness similarly sophisticated text-to-video models in the near future.
