Recently, there has been a craze about Sora on the Internet. As the latest technology launched by OpenAI, Sora gives the magic of text-generated videos, and the results it demonstrates are impressive.
At present, the appeal of short videos has far exceeded traditional novels and graphic comics. Therefore, the advent of Sora may trigger a revolution in the field of video production.
The charm of Sora is that it can generate up to 60 seconds of video content based on text descriptions, which includes detailed scene settings, lifelike character expressions, and smooth camera transitions.
This technology enables the creation of diverse characters, specific actions, and a high degree of consistency with description in terms of themes and backgrounds. Sora not only accurately understands the user’s instructions, but also has deep insights into how these elements should appear in the real world.
Sora demonstrates a deep understanding of language to accurately capture user intent, creating video content that is both vivid and emotionally charged. It can even present multiple scenes in the same video while maintaining character coherence and visual style unity.
However, Sora is not flawless. It still needs to be improved in terms of simulating physical effects in complex scenarios and understanding specific cause-and-effect relationships. For example, a character in the video might take a bite of a cookie without leaving any noticeable mark on the cookie.
In addition, Sora may also show certain limitations when processing spatial details, such as distinguishing directions, or describing specific events over a period of time, such as the movement trajectory of a camera.
**To put it simply, Sora is a technology that can generate videos of up to 60 seconds using text. It can also be used to generate pictures, because pictures are essentially one frame of video. **
This article will start from Sora’s architecture, then Sora’s ecology, and finally how ordinary people or developers can use Sora to prepare for this AI wave~
Sora represents a major innovation in AI video generation technology. It is significantly different in architecture from previous diffusion model-based systems such as Runway and Stable Diffusion. The core point is that Sora uses the Diffusion Transformer model, which is an advanced architecture that combines the diffusion model and the Transformer model, bringing unprecedented flexibility and quality improvement to video generation.
Sora’s architecture enables it to use more data and computing resources during training, resulting in higher quality output. This method not only avoids the original composition loss problem that may be caused by video preprocessing, but also because it can receive any video as training input, Sora’s output will not be affected by poor composition of the training input. In addition, Sora demonstrates the ability to simulate complex physical phenomena such as liquid dynamics, thanks to the physical rules contained in the large amounts of video data it uses during training.
The development of Sora was inspired by two papers, “Scalable Diffusion Models with Transformers” and “Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution”. These studies came from Google and were published shortly after the Sora project was launched. . These studies provide the theoretical basis and technical details of the Sora architecture, laying a solid foundation for the development of Sora and future AI video generation technology.
By combining the diffusion model and the Transformer model, Sora not only achieved a technological breakthrough, but also opened up new possibilities for video production and AI applications, indicating that the future of AI in film and television production, content creation and other fields will be broader and deeper.
The emergence of Sora in the field of AI video generation marks an important milestone in technological progress. Compared with earlier AI video generation tools, Sora introduces a series of innovations and upgrades that not only improve the quality of video generation, but also greatly expand the possibilities for video creation. The following are the main upgrades and optimizations between Sora and previous AI video generation tools:
Sora’s technological advancements are primarily reflected in its ability to generate high-quality videos. Compared with previous tools, the video generated by Sora can be up to 60 seconds long, while supporting camera switching, ensuring the stability of the characters and background in the picture, and achieving high-quality output. These improvements mean videos generated using Sora are more realistic and provide a better viewing experience, providing users with richer and more dynamic visual content.
Sora is able to achieve the above advantages thanks to its innovative technology architecture based on the Diffusion Transformer model. This architecture combines the advantages of the diffusion model and the Transformer model, allowing Sora to not only generate text content, but also predict and generate so-called “spatio-temporal patches”. These spatio-temporal patches can be understood as a small segment in the video, containing several frames of video content. This method makes Sora not limited by video length and graphics card performance during the training process. The generation process is more flexible and diverse, and it can combine different spatiotemporal patches to create new video content.
Compared with tools such as Pika based on the Diffusion model or LLM and ChatGPT based on the Transformer model, Sora’s technical architecture gives it higher flexibility and diversity. Pika is limited by graphics card performance when processing video content, and its main modes focus on video expansion or style transfer based on image keyframes. Sora, through its unique model, can create richer and more varied video content without being limited to specific video resolution or length.
Before discussing the cost and computing power requirements of Sora, we need to understand that the cost and computing power requirements of AI video generation technology, especially advanced models like Sora, are determined by a variety of factors. These factors include, but are not limited to, the complexity of the model, the resolution of the generated content, the length of the video, and the required generation quality. The following is a professional and detailed analysis of the cost and computing power requirements of Sora.
Before estimating the cost of generating a 60-second video with Sora, we looked at the pricing models of existing AI generation technologies. For example, DALL-E 3’s HD image generation costs $0.08 per generation, while Runway Gen-2’s video generation service charges $0.05/second. These prices provide a general range of pricing for AI generation services.
DALL-E 3
DALL-E 3 is the latest generation of AI image generation model developed by OpenAI, which is a subsequent version of the DALL-E series. This AI uses deep learning to generate high-resolution images. Users only need to provide short text descriptions, and DALL-E 3 can create corresponding images based on these descriptions. This model demonstrates impressive creativity and understanding, able to handle complex concepts and abstract thinking, generating images in a variety of styles and themes. DALL-E 3 has wide application potential in many fields such as art creation, design exploration, education and entertainment.
Runway Gen-2
Runway Gen-2 is an AI video generation tool launched by RunwayML, which enables users to easily create and edit video content through AI technology. Runway Gen-2 provides a series of AI-based video editing functions, such as real-time video synthesis, style conversion, content generation, etc. These tools allow users to convert text descriptions into video scenes, or to stylize and edit existing video footage. Runway Gen-2 is designed to simplify the video creation process and lower the threshold for producing high-quality video content. It is suitable for film and television production, advertising creativity, digital art and other fields.
Sora’s technical documents or promotional materials have not clearly disclosed its computing power requirements. However, based on the technical architecture it adopts - combining the diffusion model and the Transformer model - we can reasonably speculate that Sora’s demand for computing power is relatively high. Assume that Sora requires about 8 NVIDIA A100 GPUs for inference, which are some of the most high-end computing cards in the industry and are designed for deep learning and AI tasks.
According to the assumption, if Sora’s inference requires approximately 8 A100 GPUs, we can estimate it by referring to the GPU rental cost of cloud computing services. Assuming a cloud rental cost of $3 per hour per A100 GPU (this is an assumption and actual costs may vary by vendor and region), the Sora runtime costs approximately $24 per hour.
If Sora takes one minute to generate a one-minute video, the direct computing power cost per minute of video is approximately $0.4. However, this does not include other potential costs such as software usage fees, data storage and transfer fees, and any additional processing time.
In summary, if software usage fees and other operating costs are taken into account, we can speculate that the cost of Sora generating a 60-second video may be higher than the direct computing power cost. If we estimate that half an hour costs about $10 (which is a very rough estimate), the video cost per second is about $0.33. This price may be adjusted based on the actual resources used and the service pricing strategy.
Currently, DALL-E 3 and Runway Gen-2 mainly focus on visual content generation of images and videos. Although they have not yet been directly applied to music (audio) generation, there are several problems that may be faced in realizing this function in the future:
Similar to ChatGPT, it is expected that users do not need to deploy and set up in the local environment, but can access and use the service in the following two convenient ways:
Due to the high cost and long processing time of video generation, you may encounter the following limitations when using this service:
It is expected that the availability and functionality of this service will be gradually released within the next three months to six months.
The market size will be huge, triggering a new wave of AI~
As the length of video generation increases, the demand for video memory also increases. However, considering the rapid progress of current technology development, we can optimistically predict that within a year, the technology will be able to support the generation of videos up to 5 to 10 minutes long. For longer videos, such as 30 minutes or 60 minutes, this is expected to be implemented within the next 3 years.
Video generation and the resulting copyright ownership issues are hot topics in today’s technical and legal discussions. When a video is generated based on an image or text, copyright is generally considered to belong to the original content creator who created the video. However, this principle applies only if the resulting work itself does not infringe the copyright of others.
In practice, determining the copyright ownership of AI-generated works may encounter a series of challenges, especially when the original input materials or generation algorithms involve the rights of multiple parties. In addition, different countries and regions may have different legal interpretations and practices regarding the copyright ownership of AI-generated works, which brings additional complexity to creators and users.
I personally speculate that copyright issues will be a big direction in the future.
With the development of AI technology, especially advanced video generation tools like Sora, we are faced with the problem of increasingly blurred boundaries between virtual content and real content. This is not only about how to distinguish which videos were shot for real and which were produced using tools like Sora, but also about the nature of authenticity in the future and how we deal with the potential risks posed by deepfakes.
As the quality of AI-generated videos gets higher and higher, it becomes more difficult to distinguish which content was actually shot and which was AI-generated. However, technological advancements also mean that more accurate detection tools will be developed to identify AI-generated videos. Currently, video content is often embedded with watermarks to identify its source, and it is expected that more advanced tagging and verification technologies will be available in the future to help distinguish virtual and real content.
The development of deepfake technology makes fake content easier to produce, thereby increasing the risk of being defrauded. However, just like photography and film and television production techniques throughout history, the public’s ability to discern such content continues to improve. Although the current AI technology may not be perfect in some details, such as the generated ants with only four legs, or errors such as deformation of the character’s hands, these illogical places provide clues to identify the content generated by the AI.
Faced with the problem of deep forgery, the game between forgery and anti-counterfeiting will be a long-term process. In addition to developing more accurate detection tools, educating the public on how to identify fake content and improving their media literacy are key to meeting this challenge. In addition, as technology develops and laws and regulations improve, we may see more standards and protocols for content authenticity verification being established, aiming to protect consumers from the potential harm of deepfake content.
With the rapid development of artificial intelligence technology, Sora, as a cutting-edge AI video generation tool, has full of expectations for its future development prospects and evolution trends. The following are some imaginations and predictions for Sora’s next development:
With algorithm optimization and hardware advancement, the cost of generating videos with Sora is expected to be significantly reduced, while the generation speed will be significantly accelerated. This means that the production of high-quality videos will become faster and more economical, providing small and medium-sized enterprises and even individual creators with previously unimaginable video production capabilities. This revolution in cost and efficiency will further democratize the creation of video content, inspiring more innovation and creative expression.
In the future, Sora will not only improve the image quality and video duration, but also achieve a qualitative leap in lens switching, scene consistency, and compliance with physical laws. AI will be able to more accurately understand and simulate the physical laws of the real world, making the generated video content almost indistinguishable from real-life content. In addition, this ability of AI will be further expanded to simulate subtle human expressions and complex natural phenomena, providing audiences with an unprecedented visual experience.
We can foresee that it will not be limited to the generation of visual content. Combined with advanced sound synthesis technology, Sora will be able to generate sound effects and background music that perfectly match the video, and even achieve natural flow of character dialogue. Furthermore, the deep integration with text generation models such as GPT will unlock complete multi-modal interaction capabilities and realize all-round content generation from text description to visual, auditory and even more sensory dimensions. This multi-modal integration will greatly expand the application prospects of AI in education, entertainment, virtual reality and other fields.
Sora’s application scenarios and practicality cover a wide range of fields, and its commercial application value cannot be underestimated. The following is a comprehensive analysis of Sora’s value and applications:
Sora is like a comprehensive expression tool that greatly expands one’s creative and expressive abilities. Just as cars expand people’s mobility, ChatGPT expands people’s writing and communication abilities, Sora expands people’s visual and emotional expression capabilities through the medium of video. It allows ordinary people without professional writing, painting, photography, or video editing skills to express their thoughts and emotions like never before, resulting in richer, more intuitive communication.
As a low-cost video generation tool, Sora provides great value to video creators. It lowers the threshold for video production, allowing more people to produce high-quality video content at a lower cost. This is not only beneficial for individual creators, but also provides small businesses and educational institutions with the possibility to produce professional-grade videos, thus broadening the application field in many aspects such as marketing, teaching and content creation.
Sora opens up a new human-computer interaction model, especially showing great potential in dynamic video content generation. It can generate game plots, tasks and scenes in real time according to user instructions, providing unlimited content and experience for games and virtual reality. In addition, Sora can also dynamically convert news and articles into videos, providing a more intuitive and attractive form for information consumption, which is of great significance for improving the efficiency and effect of information reception.
Sora has unique value in emotional connection and memory retention.
By generating videos of deceased loved ones, it provides a new way for people to honor and preserve the memory of their loved ones.
As a digital companion, Sora can create avatars with personalized characteristics, provide users with emotional support and companionship, and open up a new dimension of interaction with the digital world.
Sora’s future market is very large, involving every industry and every field
Sora’s name is likely derived from the opening song of the anime “Tengen Breakthrough”, “Sora Shiro”, reflecting the project team’s pursuit of creativity and breaking through limitations.
Sora’s popularity is not only due to the conceptual hype of financing and stock price. It is indeed a technology with practical value and can already be applied to generate high-quality short video content, such as OpenAI’s display on TikTok accounts.
Sora has strong competitiveness on a global scale, and OpenAI’s technology and model advantages are significant. Although China is developing rapidly in this field, it is currently mainly led by large enterprises. The gap between China and Europe and the United States mainly lies in the in-depth application of computing power and AI technology.
The emergence of Sora is considered an epoch-making technology in the field of text-to-video generation, heralding the possibility of a new round of industrial revolution. Although there have been many highly sought-after technologies in history, such as web3, blockchain, etc., Sora’s practicality and innovation make people optimistic about its epoch-making definition.
Sora has received positive reviews in Silicon Valley and the industry. Although this may lead to more cautious investments in certain directions, it also encourages entrepreneurs and developers to explore new application directions and innovative models.
With the development of video generation technology, the demand for computing power continues to grow, which is expected to promote more companies to participate in the development and production of graphics cards, promote the diversification of computing resources and improve performance.
Sora’s discussion and analysis reflect its far-reaching potential in technological innovation, commercial applications and social impact, and also remind the industry of the importance of continuous observation and rational evaluation of emerging technologies.
Welcome to SoraEase, we are an open source community dedicated to simplifying the application of Sora AI video generation technology. SoraEase aims to provide a fast and efficient usage and development platform for Sora enthusiasts and developers to help everyone easily master Sora technology, inspire innovation, and jointly promote the development and application of video generation technology.
At SoraEase we offer:
We believe that through the power of the community, Sora technology can be made more accessible and easier to use, allowing everyone to create stunning AI video content.
sora to join the group. In our WeChat community, you can get Sora’s latest consultation and technology sharing, and it is also a communication platform for Sora enthusiasts and developers.We look forward to your joining and exploring the infinite possibilities of Sora technology!
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。