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

28 Mar 202408:26

TLDRThe video script discusses the rapid advancements in AI image generation, highlighting the current state where it's challenging to differentiate between real and AI-generated images. It emphasizes the need for further improvement, particularly in generating fine details. The script also explores the potential of combining AI chatbots' attention mechanisms with diffusion models to enhance language and image synthesis. It mentions the promising results from models like Stable Diffusion 3 and Sora, suggesting a future where media generation, including complex scene compositions and text within images, could be significantly improved. The video ends by suggesting Domo AI as an accessible platform for generating videos and images based on text prompts.


  • πŸ“ˆ AI image generation is rapidly progressing, with recent advancements outpacing previous years' improvements.
  • πŸ€– Despite significant progress, AI-generated images still have minor flaws, such as issues with fingers or text, which can be nitpicked to identify them.
  • πŸ’‘ There is a need for simpler and more effective solutions in AI image generation, potentially combining different AI technologies like chatbots and diffusion models.
  • 🌐 Attention mechanisms used in large language models could be key in improving AI's ability to generate detailed and coherent images.
  • πŸ”„ The combination of transformers with attention mechanisms and fusion models is becoming the state-of-the-art in AI image generation.
  • πŸ–ΌοΈ Stable Diffusion 3 and Sora are examples of models that integrate these advancements, showing impressive results in text-to-image and text-to-video generation.
  • πŸ“Š The success of Stable Diffusion 3 and Sora may indicate a shift towards diffusion transformers in future AI research and development.
  • πŸš€ The complexity and performance of these new models, like Stable Diffusion 3, are remarkable, even surpassing fine-tuned models and other pre-existing methods.
  • πŸŽ₯ Sora's ability to generate high-fidelity and coherent videos suggests that the compute power and possibly the architecture used are significant factors in its quality.
  • πŸ‘₯ Public release of advanced models like Sora may be limited due to concerns about societal preparedness and the immense compute required for inference.
  • 🌟 The potential of DIT (Diffusion Transformers) as a pivotal architecture for future media generations is highlighted by their success in image and video generation tasks.

Q & A

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

    -The term 'sigmoid curve' in the context of AI image generation development refers to a period of rapid progress and growth in the field. It indicates that we are nearing a point of saturation where significant advancements are becoming less frequent as the technology matures.

  • How has the progress in AI image generation changed over the last six months compared to previous periods?

    -Over the last six months, the progress in AI image generation has been more incremental and less transformative compared to previous periods, which used to experience more drastic and comparable changes.

  • What are the current challenges in AI image generation that researchers are trying to address?

    -The current challenges in AI image generation include perfecting details such as fingers and text within images. Researchers are working on improving these aspects to make AI-generated images indistinguishable from real ones.

  • What is the significance of the attention mechanism in large language models for AI chatbots?

    -The attention mechanism in large language models is crucial as it allows the model to focus on multiple locations when generating a word, encoding information about the relationships between words. This helps in understanding context and producing coherent language output.

  • How does the attention mechanism benefit AI image generation?

    -In AI image generation, the attention mechanism can help the AI pay attention to specific locations within an image, making it easier to consistently synthesize small details like text or fingers.

  • What is the role of fusion models in AI image generation?

    -Fusion models play a key role in AI image generation as they are currently the best AI architecture for generating images. They are essential for achieving high-quality image generation.

  • What are diffusion transformers and how do they relate to the future of AI image generation?

    -Diffusion transformers are a type of model that combines the attention mechanisms of transformers with diffusion models. They represent a pivot towards more advanced and sophisticated architectures that are expected to be the next state-of-the-art in AI image generation.

  • What are the capabilities of the Stable Diffusion 3 model according to the technical papers?

    -Stable Diffusion 3 is capable of generating high-quality images, especially complex scenes with text. It can also generate images in 1024 * 1024 resolution and has shown impressive results in generating cursive text with minor mistakes.

  • How does Sora, the text-to-video AI model, demonstrate the potential of the diffusion transformer architecture?

    -Sora has demonstrated the potential of the diffusion transformer architecture by generating highly realistic and coherent videos from text prompts, showcasing the ability of this architecture to handle complex scene compositions and synthesize media effectively.

  • What are the potential reasons for Sora not being available for public use?

    -The potential reasons for Sora not being available for public use include the significant amount of compute required for inference and concerns about safety issues, which might make it challenging for the general public to access and use at this time.

  • How does Domo AI function as an alternative for generating videos and images?

    -Domo AI is a Discord-based service that allows users to generate, edit, animate, and stylize videos and images using AI. It offers a range of customized models for different styles and simplifies the process, making it accessible for users to create content with less effort and technical knowledge.



πŸ€– Advancements in AI Image Generation

The paragraph discusses the rapid progress in AI image generation, particularly in the last six months, to the point where it's challenging to distinguish between real and AI-generated images. It acknowledges that while significant advancements have been made, there is still room for improvement, particularly in generating finer details like text and fingers. The paragraph suggests that combining different AI technologies, such as chatbots and diffusion models, might be the next step forward. It highlights the importance of the attention mechanism in large language models for understanding relationships between words, and posits that a similar mechanism could greatly improve image generation. The discussion includes specific examples of state-of-the-art models like Stable Diffusion 3 and Sora, noting their capabilities and potential for further development.


πŸŽ₯ Future of AI Video Generation and its Challenges

This paragraph delves into the potential and challenges of AI video generation, focusing on the fusion Transformers architecture and its role in creating high-fidelity and coherent videos. It discusses the technical aspects of generating videos with space-time relations between visual patches and the significant computational resources required for training models like Sora. The paragraph also touches on the implications of these advancements, including the potential for public reaction and the need for substantial computational power. It ends with a mention of Domo AI, a service that offers video and image generation capabilities, as an accessible alternative for those interested in exploring AI-generated media.



πŸ’‘Sigmoid curve

The sigmoid curve is a mathematical function that represents the 'S'-shaped growth pattern often observed in various processes, including the development of technologies. In the context of the video, it refers to the rapid progress in AI image generation, suggesting that we are nearing the peak of this growth curve where advancements may start to slow down. The script mentions that while significant progress has been made, there is still room for improvement, such as refining details in generated images.

πŸ’‘AI image generation

AI image generation refers to the process by which artificial intelligence systems create visual content, such as photographs or illustrations, without human intervention. This technology has seen significant advancements, to the point where it becomes challenging to distinguish between real and AI-generated images. The video discusses the challenges and improvements in this field, including the integration of attention mechanisms and the use of diffusion models.

πŸ’‘Attention mechanism

The attention mechanism is a feature in large language models that allows the model to focus on specific parts of the input data when generating a response. This is crucial for understanding the context and relationships between words in a sentence. In the video, it is suggested that applying a similar mechanism to AI image generation could improve the consistency and accuracy of details within generated images, such as text or fingers.

πŸ’‘Diffusion models

Diffusion models are a type of generative model used in AI to create new data samples, such as images or videos, by learning the patterns and structures of existing data. These models have been pivotal in advancing AI image generation, as they can synthesize high-quality and complex visual content. The video highlights the potential of combining diffusion models with other AI technologies to further enhance image generation capabilities.

πŸ’‘Fusion Transformers

Fusion Transformers are a class of AI models that combine the strengths of different architectural approaches, such as transformers and convolutional neural networks, to improve performance in various tasks. In the context of the video, they are considered the best architecture for generating images, despite the need for further refinement. The script suggests that while new solutions are being explored, the importance of fusion models in AI image generation remains undeniable.

πŸ’‘Stable Diffusion 3

Stable Diffusion 3 is a state-of-the-art model for AI image generation that is mentioned in the video as being highly effective, even in its base form. It represents a significant leap in technology, surpassing the performance of many fine-tuned models and pre-existing generation methods. The model is noted for its ability to generate detailed and complex images, including text and intricate scene compositions.

πŸ’‘Multimodal DIT

Multimodal DIT, or Diffusion Models with multimodal capabilities, refers to the ability of a model to generate or process data across different types of content, such as images and text. In the video, it is mentioned that Stable Diffusion 3's DIT can be conditioned directly on images, which suggests a potential shift away from the need for control nets and towards more versatile and efficient generation methods.


Sora is a text-to-video AI model developed by OpenAI, which is capable of generating highly realistic videos based on textual descriptions. The video script describes Sora as an impressive achievement that demonstrates the potential of AI in video generation. Despite the model's capabilities, it is not yet available to the public, possibly due to concerns about the impact of such technology on society.

πŸ’‘DIT architecture

The DIT (Diffusion Models with Transformers) architecture is a type of neural network structure that has been gaining attention for its potential in media generation, including both images and videos. The video suggests that this architecture might be the next pivotal step in the evolution of AI-generated content, as it has shown promising results in various applications, such as Sora and other DIT-based research.

πŸ’‘Domo AI

Domo AI is a Discord-based service that enables users to generate videos, edit images, animate images, and stylize images using AI. It offers a range of customized models for different styles and is particularly adept at creating animations. The service streamlines the process of generating media, making it accessible and user-friendly by reducing the need for complex workflows.


In the context of AI and machine learning, compute refers to the processing power required to train and run models. The video script suggests that the quality of AI-generated media, such as videos from Sora, is significantly influenced by the amount of compute available. High compute resources enable more complex and detailed generation tasks but can also be a limiting factor in terms of accessibility and cost.


AI image generation is rapidly progressing, with recent advancements making it difficult to distinguish between real and AI-generated images.

Despite significant progress, AI image generation still has areas to improve, such as generating detailed elements like fingers and text.

The current state of AI image generation is not yet at the peak of the technological progress curve, indicating potential for further improvements.

Researchers are exploring simpler solutions to improve AI image generation, considering the vast number of existing workflows and workarounds.

Combining AI chatbots with diffusion models might offer a new approach to enhancing AI image generation.

The attention mechanism within large language models is crucial for understanding relationships between words, which could be applied to image generation for improved detail synthesis.

Diffusion Transformers, which incorporate attention mechanisms, are emerging as the next state-of-the-art models for AI image generation.

Stable Diffusion 3, a new model, has shown exceptional performance in generating images, even surpassing fine-tuned methods.

Stable Diffusion 3 introduces new techniques like bidirectional information flow and rectify flow to enhance text generation within images.

The architecture of Stable Diffusion 3 is complex, but it excels at generating detailed images, particularly for complex scenes.

Stable Diffusion 3's ability to understand complex scene compositions is a significant advancement in AI image generation.

Sora, a text-to-video AI model, has generated highly realistic videos, showcasing the potential of the dit architecture.

The development of Sora involved adding space-time relations to visual patches, enhancing its capability to generate coherent videos.

Compute resources play a significant role in the quality of AI-generated media, as seen with the high fidelity of Sora's output.

The potential of dit architecture extends beyond images to video generation, making it a pivotal technology for future media generations.

Domo AI, a Discord-based service, offers an accessible platform for generating and editing videos and images, simplifying the creative process.

Domo AI excels in creating animations and stylized content, providing users with a range of customization options.