DiT: The Secret Sauce of OpenAI's Sora & Stable Diffusion 3

28 Mar 202408:26

TLDRThe transcript discusses the rapid advancements in AI image generation, highlighting the current state of technology being near the peak of progress with models like Sora and Stable Diffusion 3. It emphasizes the integration of attention mechanisms from large language models to improve detail generation in images. The potential of diffusion Transformers in both image and video generation is underscored, with Sora's realistic video generation capabilities being particularly noted. The summary also touches on the computational demands and the future implications of these technologies.


  • 📈 AI image generation is rapidly evolving, with significant progress in the last six months making it increasingly difficult to distinguish real from fake images.
  • 🔍 Despite advancements, AI image generation still has imperfections, such as issues with fingers and text, which are key to identifying AI-created images.
  • 🛠️ The current AI models require simplification and streamlining of the complex workflows involved in image generation.
  • 🔄 The fusion of AI chatbots with diffusion models, leveraging the attention mechanism from large language models, could improve the generation of fine details in images.
  • 🌟 The attention mechanism is crucial for understanding relations between elements in a sentence or an image, enhancing coherence in AI-generated content.
  • 🔮 Diffusion Transformers, which combine attention mechanisms with fusion models, are emerging as the state-of-the-art in AI image and video generation.
  • 📚 The concept of using Transformers in AI generation has been around for a while, but the investment in training these models has only recently paid off.
  • 🎨 Stable Diffusion 3, though not officially released, shows promising results in generating high-quality images with complex scenes and text.
  • 📝 Sora, a text-to-video AI model by OpenAI, demonstrates the potential of Diffusion Transformers in creating realistic and coherent video content.
  • 💻 The generation of high-fidelity and coherent videos like Sora's might be as much about scaling computational resources as it is about architectural advancements.
  • 🚫 The public may not be ready for the level of realism in AI-generated videos, and the computational demands might be a barrier to wider availability.

Q & A

  • What does the term 'sigmoid curve' refer to in the context of AI image generation progress?

    -The term 'sigmoid curve' in this context refers to the S-shaped curve that represents the rapid growth and eventual saturation in the development of AI image generation technologies. It suggests that we are currently experiencing a phase of significant advancements in this field.

  • Why is it still possible to identify AI-generated images despite the progress in AI image generation?

    -AI-generated images can still be identified because they sometimes have imperfections, such as incorrect details in fingers or text. These imperfections are easier to spot, and researchers are working on techniques like 'inpainting' to fix these issues after the initial image generation.

  • What is the role of the attention mechanism in AI chatbots and how is it beneficial for language modeling?

    -The attention mechanism in AI chatbots allows the model to focus on multiple parts of the input data when generating a response. This is crucial for understanding the relationships between words in a sentence, which helps in generating more coherent and contextually accurate responses.

  • How does the attention mechanism help in generating images with more details?

    -The attention mechanism can help in image generation by allowing the AI to focus on specific areas of the image, making it easier to synthesize small details consistently. This is important for creating coherent images with strong relational connections between elements.

  • What are diffusion models and how do they relate to the current state-of-the-art models like Stable Diffusion 3 and Sora?

    -Diffusion models are a type of generative model that works by learning the reverse process of diffusion, which gradually adds noise to an image. Stable Diffusion 3 and Sora are examples of models that incorporate diffusion Transformers, which combine the attention mechanisms of Transformers with the generative capabilities of diffusion models.

  • What is unique about the architecture of Sora, the text-to-video AI model by OpenAI?

    -Sora's architecture is unique in that it adds space-time relations between visual patches extracted from individual frames. This allows it to generate videos with high fidelity and coherency, making it a significant advancement in video generation technology.

  • Why might the general public not be ready for AI-generated videos like Sora?

    -The public might not be ready for AI-generated videos like Sora due to the highly realistic nature of the content it produces. This could lead to ethical and safety concerns, as well as the potential for misuse, such as creating deepfakes.

  • What is the significance of the multimodal capability of Stable Diffusion 3's diffusion model?

    -The multimodal capability of Stable Diffusion 3's diffusion model means that image generation can be directly conditioned on images, potentially eliminating the need for control networks. This simplifies the generation process and could lead to more efficient and versatile image creation.

  • How does Domo AI differ from other AI video generation services?

    -Domo AI is a Discord-based service that allows users to generate, edit, and animate videos and images easily. It stands out for its ability to generate videos in various styles, particularly animations, with minimal effort and a simple workflow.

  • What is the potential impact of diffusion Transformers on future media generation?

    -Diffusion Transformers have the potential to revolutionize media generation by improving the quality and efficiency of both image and video generation. Their success in models like Stable Diffusion 3 and Sora suggests that they could become a pivotal architecture for future developments in this field.



🧠 AI Image Generation Progress and Challenges

The script discusses the rapid progress in AI image generation, suggesting we are near the peak of the development curve. It highlights the difficulty in distinguishing between real and AI-generated images, while also noting areas for improvement such as generating fingers and text. The script explores the potential of combining AI chatbots with diffusion models and the importance of the attention mechanism in language models for image generation. It also touches on the evolution towards diffusion Transformers in state-of-the-art models like Stable Diffusion 3 and Sora, emphasizing the complexity and capabilities of these models in generating coherent and detailed images, including text and complex scenes.


🎥 The Future of AI in Video Generation

This paragraph delves into the complexities and potential of AI in video generation, focusing on the fusion Transformers and their role in adding space-time relations to visual patches. It questions the novelty of the technology, suggesting that the real advancement might be in the computational power used for training. The script mentions the capabilities of Sora in generating high-fidelity and coherent videos and the challenges of making such technology publicly available due to computational demands and safety concerns. It also speculates on the future of media generation with the rise of models like DIT and the potential of services like Domo AI, which allows users to generate and edit videos and images in various styles through a Discord-based service.



💡Sigmoid curve

The sigmoid curve is a mathematical function that resembles the letter 'S' and is often used to model the growth of biological populations or the progression of a process over time. In the context of the video, it refers to the rapid development phase of AI image generation technology, suggesting that we are at or near the peak of its growth curve where the rate of improvement is slowing down.

💡AI image generation

AI image generation is the process by which artificial intelligence algorithms create images from scratch or modify existing images. The script discusses the significant progress in this field, highlighting the difficulty in distinguishing between real and AI-generated images, which is central to the video's theme of advancements in AI.

💡Fingers and words

In the script, 'fingers' and 'words' are mentioned as elements that AI image generation still struggles to accurately depict. These details are often used as tell-tale signs to identify AI-generated images, as they are still areas where the technology needs refinement.


Highr is a technique mentioned in the script that is used to fix imperfections in AI-generated images. It is an example of post-processing methods that can be applied after the initial image generation to improve the quality and realism of the output.

💡Diffusion models

Diffusion models are a type of generative model used in AI to create new data samples that resemble a given dataset. In the video, they are discussed as a key component in the latest advancements of AI image generation, combining with other techniques to create more realistic images.

💡Attention mechanism

The attention mechanism is a feature within large language models that allows the model to focus on certain parts of the input data when generating an output. In the context of the video, it is highlighted as a crucial component for improving the generation of details in images, such as text or fingers, by helping the model understand and encode relationships within the image.

💡Convolutional neural network (CNN)

A convolutional neural network is a type of deep learning algorithm widely used in image recognition and processing. The script mentions CNNs in contrast to the attention mechanism, suggesting that the latter provides a stronger relational connection necessary for coherent image generation.

💡Diffusion Transformers

Diffusion Transformers are a specific type of transformer model that incorporates elements of diffusion models. The video discusses how these models are becoming pivotal in the state-of-the-art AI image and video generation, as seen in models like Stable Diffusion 3 and Sora.

💡Stable Diffusion 3

Stable Diffusion 3 is a text-to-image generation model mentioned in the script. It is noted for its high-quality output and the use of advanced techniques like bidirectional information flow and rectify flow, which contribute to its superior performance in generating detailed and coherent images.


Sora is a text-to-video AI model developed by OpenAI, which is capable of generating highly realistic videos from textual descriptions. The script highlights the impressive results produced by Sora, suggesting that it represents a significant leap in video generation technology.


In the context of AI, 'multimodal' refers to systems that can process and understand multiple types of data, such as text, images, and videos. The script mentions that Stable Diffusion 3's Diffusion Transformers are multimodal, meaning they can be conditioned on images for image generation, potentially reducing the need for additional control mechanisms.


Compute refers to the computational resources required to perform tasks, such as training AI models or generating images and videos. The script discusses the massive amounts of compute power needed for training models like Sora, which may be a factor in why such technologies are not yet widely available to the public.

💡Domo AI

Domo AI is a service mentioned in the script that allows users to generate and edit videos, images, and animations through a Discord-based platform. It is highlighted as an alternative for those interested in experimenting with AI-generated content, offering a user-friendly approach to creating videos and images in various styles.


AI image generation is rapidly evolving, with recent progress making it difficult to distinguish between real and fake images.

AI image generation still has room for improvement, particularly in generating details like fingers and text.

The current state of AI image generation is not yet perfect, with researchers seeking more efficient solutions.

The attention mechanism within large language models has proven useful for language modeling and may benefit image generation.

Attention mechanisms allow models to focus on multiple locations, aiding in the generation of coherent images.

Diffusion models and fusion Transformers are becoming pivotal in the state-of-the-art AI image generation models.

Stable Diffusion 3 and Sora, a text-to-video model, both utilize diffusion Transformers with slight modifications.

Stable Diffusion 3's performance has surpassed many fine-tuned models, even in its base form.

Stable Diffusion 3 introduces new techniques for generating text within images, improving detail generation.

Sora demonstrates the potential of diffusion Transformers in video generation, with impressively realistic results.

Sora's generation process is claimed to be efficient, taking only minutes to produce high-quality videos.

The compute requirements for generating videos with Sora may be a significant factor in its limited public availability.

Diffusion Transformers may represent the next pivotal architecture for media generation, including both images and videos.

DIT-based research like DeFIt from Nvidia and HDIT from Stability AI holds promise for the future of AI-generated media.

Domo AI, a Discord-based service, offers an alternative for generating videos and images conditioned on text.

Domo AI is efficient and user-friendly, requiring minimal effort to generate videos and images in various styles.

The image animate feature of Domo AI allows users to turn static images into moving sequences.