NEW Open Source AI Video (Multi-Consistent Characters + 30 Second Videos + More)

AI Samson
4 May 202415:33

TLDRStory Diffusion, an innovative open-source AI video model, is revolutionizing the field with its ability to generate 30-second videos featuring consistent characters and realistic physics. The model excels in character consistency, including facial features, clothing, and body type, paving the way for AI-generated comics and videos. Despite minor imperfections, its impressive output, especially considering the significantly lower computational resources used compared to other models like Sora, showcases a leap in AI video generation capabilities.

Takeaways

  • 🌟 Story Diffusion is a new open-source AI video model that creates 30-second videos with high character consistency and realism.
  • 🐶 It overcomes issues like characters morphing or extra characters appearing out of nowhere, seen in previous models like Sora.
  • 🎨 The model excels in character consistency, including facial features, clothing, and body type, allowing for believable characters across scenes.
  • 📚 It can also generate AI comics by creating a sequence of images with consistent character features and animating them.
  • 🚴‍♀️ The videos produced are anatomically correct with minimal morphing, showcasing a significant step forward in AI video generation.
  • 🕒 The clips from Story Diffusion are notably longer than other models, with one example being 23 seconds long, maintaining character consistency.
  • 📏 Despite some minor jitteriness and square aspect ratio, the model shows impressive character clarity and consistency.
  • 😊 The model captures expressive facial animations, such as lip pursing in sync with music, enhancing the realism of the videos.
  • 📈 Compared to other models like Vidoo, Story Diffusion produces longer clips and is also from China, with ByteDance mentioned in its white paper.
  • 🔍 Although resolution details are not specified, the model's output can potentially be upscaled to higher definitions like 2K.
  • 🤖 Story Diffusion uses techniques like consistent self-attention and story splitting to ensure visual coherence and narrative sequence in its videos.

Q & A

  • What is Story Diffusion and what makes it unique among open source AI video models?

    -Story Diffusion is an open source AI video model that stands out for its ability to create videos up to 30 seconds long with a high level of character consistency, adherence to reality, and physics. It represents a significant advancement in character consistency, not just in facial features but also in clothing and body type, allowing for the creation of believable characters across different shots and scenes.

  • How does Story Diffusion handle character consistency in comparison to other models like Sora?

    -Story Diffusion excels in maintaining character consistency by ensuring that characters' facial features, clothing, and body types remain the same across different shots and scenes. This is a notable improvement over models like Sora, which have been known to struggle with character morphing and creating extra characters that appear out of nowhere.

  • What is the significance of Story Diffusion's ability to generate AI comics?

    -The ability to generate AI comics signifies that Story Diffusion can create a series of images that are consistent in terms of characters' faces and clothing. This opens up new creative possibilities for storytelling and allows for the animation of these images using a motion prediction model, enhancing the narrative experience.

  • How does the length of the video clips produced by Story Diffusion compare to other models?

    -Story Diffusion produces video clips that are significantly longer than those of other models. For instance, it can generate clips that are 23 seconds long, which is much longer than the 16-second clips produced by the Chinese model Vidoo, showcasing a substantial leap in AI video generation capabilities.

  • What is the resolution of the videos generated by Story Diffusion, and can it be improved?

    -The resolution of the videos generated by Story Diffusion is not explicitly mentioned in the white paper, but the previews on their website are rendered at 832 pixels by 832. This level of detail could potentially be upscaled to at least 2k definition using an AI upscaler, indicating that the quality of the videos has room for enhancement.

  • How does Story Diffusion handle the animation of obscured objects in its videos?

    -Story Diffusion faces challenges when animating obscured objects, as it needs to remember the object's appearance before it was obscured and then re-render it without predicting its new appearance. There are instances where the hand morphs unnaturally due to occlusion, indicating that this is an area where the model could be further improved.

  • What is the computational efficiency of training Story Diffusion compared to Sora?

    -Story Diffusion was trained using only eight GPUs, which is significantly more computationally efficient compared to Sora, which used 10,000 GPUs. This means that Story Diffusion achieved remarkable results with a fraction of the compute power, making it more accessible and cost-effective.

  • How does Story Diffusion ensure consistency across multiple characters in a scene?

    -Story Diffusion uses consistent self-attention and story splitting techniques to ensure that multiple characters in a scene remain consistent. It processes multiple text prompts simultaneously to produce images that depict the narrative in sequence, maintaining the continuity of characters' appearance and motion.

  • What are the potential applications of Story Diffusion in the field of animation?

    -Story Diffusion has the potential to create effective and usable anime-style animations, opening up possibilities for generating full films with AI for animated and anime-style genres. Its ability to handle diverse scenes and animate elements naturally makes it a promising tool for the animation industry.

  • How does Story Diffusion handle the generation of realistic videos compared to other AI video generators?

    -Story Diffusion shows a significant evolution in generating realistic videos by maintaining the continuity in appearance and motion of the subjects, making the scenes look fluid and natural as if they were captured in real life. It outperforms other AI video generators in terms of realism, consistency, and fluidity.

  • What are the current limitations of Story Diffusion in terms of character and scene consistency?

    -While Story Diffusion has made great strides in character and scene consistency, there are still some limitations. For example, there can be slight inconsistencies in elements like the length of a tie or the markings on a character's face. These minor imperfections indicate that there is room for further refinement in the model's capabilities.

Outlines

00:00

🚀 Introduction to Story Diffusion AI Video Model

The script introduces Story Diffusion, an open-source AI video model that excels in creating character-consistent and reality-adherent videos up to 30 seconds long. It highlights the model's superior performance over previous models like Sora, which struggled with character morphing and unrealistic physics. The video showcases the model's ability to maintain character consistency in terms of facial features, clothing, and body type, enabling the generation of AI videos and comics. The script also notes the model's impressive clip lengths and its potential for upscaling to higher resolutions.

05:02

🎨 Story Diffusion's Advancements in Character Consistency and Animation

This paragraph delves into Story Diffusion's capabilities for character consistency, not just in facial features but also in clothing and body type, allowing for believable characters across different scenes. It discusses the model's process of generating a series of images for a sequence, ensuring consistency before animating them using a motion prediction model. The script also compares Story Diffusion with Sora, emphasizing the former's efficiency in training on fewer GPUs and its open-source accessibility, despite lacking a user-friendly interface. The paragraph also explores the model's application in comic generation and its ability to handle multiple characters consistently.

10:02

🤖 Technical Insights into Story Diffusion's AI Mechanisms

The script provides an in-depth look at the technical aspects of Story Diffusion, focusing on its use of consistent self-attention to enhance image coherence and story splitting to process multiple text prompts simultaneously. It explains how the model generates a series of images that depict a narrative in sequence, maintaining environmental and character consistency. The paragraph also discusses the model's motion prediction capabilities to create fluid animations between frames, highlighting its effectiveness in creating anime-style animations and handling diverse scenes with natural movements and camera effects.

15:03

🌟 Conclusion: The Future of AI Video and User Engagement

The final paragraph wraps up the discussion on Story Diffusion, emphasizing its significance in the advancement of AI video models. It invites viewers to explore another AI video model from China and reflects on the potential applications of AI video in various creative fields. The script concludes by encouraging feedback on Story Diffusion and its comparison with existing AI videos, pondering the future creations that might emerge from AI video technology.

Mindmap

Keywords

💡Story Diffusion

Story Diffusion is an open source AI video model that represents a significant advancement in the field of AI-generated videos. It is capable of creating videos up to 30 seconds long with a high degree of character consistency and adherence to reality and physics. The model's ability to maintain character consistency is particularly noteworthy, as it extends beyond facial features to include clothing and body type, allowing for the creation of believable characters across different scenes and shots. This is exemplified in the script where it is mentioned that Story Diffusion can generate AI comics and videos with minimal morphing or disfigurement.

💡Character Consistency

Character consistency refers to the ability of an AI model to maintain the same visual attributes of a character throughout a video or a series of images. In the context of the video script, Story Diffusion demonstrates this by ensuring that characters' faces, clothing, and body types remain consistent, which is crucial for creating believable narratives in AI-generated content. The script provides examples of this consistency in the generated comic and the video demo of a female riding a bike.

💡Reality and Physics

The term 'reality and physics' in the script highlights the AI model's capability to create videos that adhere to the laws of physics and mimic real-world scenarios. Story Diffusion's understanding of reality is showcased through its ability to depict objects and characters interacting in a natural and believable manner, such as a basketball passing through a hoop or a hand morphing when obscured by another object.

💡AI Video Generators

AI video generators are systems that use artificial intelligence to create videos. The script discusses the limitations of existing AI video generators, such as Sora, which have issues with character morphing and maintaining realism. It contrasts these with Story Diffusion, which is praised for its longer video clips and improved character consistency and expressiveness.

💡Resolution

Resolution in the context of the script refers to the pixel dimensions of the videos generated by the AI model. While the white paper does not specify the resolution of Story Diffusion's output, the script mentions that the previews on the website are rendered at 832 pixels by 832, suggesting a square aspect ratio. The potential for upscaling these videos to higher resolutions like 2K using AI upscaling techniques is also discussed.

💡Motion Prediction Model

The motion prediction model is a component of the AI system that predicts and animates the movement between a series of images. In the script, it is mentioned that after ensuring consistency in a sequence of images, the motion prediction model is used to animate the characters, as seen in the comic generated using Story Diffusion.

💡Occlusion

Occlusion in the script refers to a situation where an object or character is obscured by another, challenging the AI to remember and accurately render the obscured part when it reappears. The script points out that this is one of the most difficult aspects for AI to comprehend, as seen in the example where a hand morphs unnaturally when it becomes unobscured.

💡Anime Style Animations

Anime style animations are a specific type of animated content that often features exaggerated expressions and movements. The script notes that Story Diffusion is particularly effective at creating anime-style animations, which opens up the possibility for full films to be generated using AI, with examples of characters and background elements animated in a natural and coherent way.

💡Consistent Self-Attention

Consistent self-attention is a technique used by the AI model to enhance the visual coherence of a series of generated images by ensuring they share certain attributes or themes. The script explains that this method involves noting down features that should be consistent across images and then using this information to guide the AI in generating new images that maintain these attributes.

💡Story Splitting

Story splitting is a process where a narrative is divided into multiple text prompts, each describing a part of the story. These prompts are then processed by the AI to produce a sequence of images that depict the story in an orderly fashion. The script provides an example of a story being split into five prompts and generated in a batch to maintain narrative and visual consistency.

💡AI Upscaling

AI upscaling refers to the process of increasing the resolution of an image or video using artificial intelligence. The script suggests that the videos generated by Story Diffusion, which are initially rendered at 832 pixels by 832, could be upscaled to at least 2K definition using an AI upscaler, indicating the potential for higher quality output from the model.

Highlights

Story Diffusion is a new open source AI video model that generates 30-second videos with high character consistency and adherence to reality and physics.

Sora, another AI model, has struggled with character consistency, such as objects appearing out of thin air or unrealistic basketball movements.

Story Diffusion demonstrates a deeper understanding of reality, improving on character consistency, including facial features, clothing, and body type.

The model allows for the creation of believable characters with perfect consistency across shots and scenes, enabling AI video and comic generation.

A comic generated by Story Diffusion showcases the model's ability to maintain character consistency in a sequence of images.

Story Diffusion's video demo features a female character on a bike with anatomical correctness and minimal morphing.

The model produces clips up to 23 seconds long, maintaining character consistency throughout the video.

Story Diffusion's videos show minor jitteriness and square format, but with significant improvements in consistency and character clarity.

The model's expressiveness is highlighted by the detailed facial animations, such as lip pursing in time with music.

Compared to other AI video generators, Story Diffusion produces significantly longer video clips, outperforming the 16-second clips of Vidoo.

Story Diffusion is developed by ByteDance, the company behind TikTok, and has been trained on just eight GPUs, compared to Sora's 10,000.

Despite the lower computational power used for training, Story Diffusion shows remarkable performance in realism, consistency, and fluidity.

The model's training process is open source, but it currently lacks a user-friendly interface and requires self-installation or cloud server use.

Story Diffusion introduces a method for including multiple characters consistently in scenes, overcoming a significant barrier in AI video generation.

The model's ability to generate consistent images for storytelling and transition them into fluid, natural videos is a significant advancement in AI video technology.

Story Diffusion uses consistent self-attention to enhance the visual coherence of generated images, ensuring shared attributes or themes.

The model employs story splitting, processing multiple text prompts simultaneously to produce a sequence of images that depict a narrative.

Story Diffusion's motion predictor model is used to animate images, creating fluid transitions between frames.

The model is capable of creating effective and usable anime-style animations, opening up possibilities for full AI-generated films in the genre.

Story Diffusion handles a diverse range of scenes, including realistic animations of objects and characters in various environments.

The model's ability to maintain continuity in appearance and motion of subjects results in videos that look as if they were captured in real life.

Turn Your Ideas to Images for Free

Generate professional visuals with text or images in seconds.

Turn Your Ideas to Images for Free Try It Free