NEW Open Source AI Video is BEST Yet! (Multiple Consistent Characters + More)
TLDRThe latest open-source AI video model, Story Diffusion, is making waves for its ability to create videos up to 30 seconds long with remarkable character consistency and adherence to reality and physics. Unlike previous models that struggled with morphing and creating extra characters, Story Diffusion demonstrates a significant leap in character consistency, including facial features, clothing, and body type. This advancement opens up new possibilities for AI video and comic creation, with the model generating believable characters that maintain consistency across different shots and scenes. The model also handles a diverse range of scenes, from realistic videos to anime-style animations, and even incorporates multiple characters in a consistent manner. Despite minor imperfections in occlusion handling and a lack of a user-friendly interface, Story Diffusion's efficiency in training with only eight GPUs compared to Sora's 10,000 is a game-changer. It presents a novel method for generating consistent images for storytelling and transitioning these into fluid, natural-looking videos, indicating a promising future for AI in the realm of video generation.
Takeaways
- ๐ Story Diffusion is an open-source AI video model that stands out for its character consistency and adherence to reality and physics.
- ๐ถ The model has improved upon previous AIs by reducing issues like characters morphing or objects passing through each other unrealistically.
- ๐ It focuses on more than just facial consistency; it also maintains consistency in clothing and body types across different shots and scenes.
- ๐จ The technology enables the creation of believable characters and has potential applications in generating AI Comics with a sequence of consistent images.
- ๐ดโโ๏ธ Videos generated are up to 30 seconds long, showcasing characters that maintain their identity without significant morphing or disfigurement.
- ๐ Story Diffusion produces square videos, and while there's a slight jitteriness, the character clarity and consistency are notably improved.
- ๐ญ The expressiveness of the characters' faces, especially their emotions, is impressively captured towards the end of the generated videos.
- ๐ The model has been trained on significantly fewer resources than its predecessors, making it more accessible and cost-effective.
- ๐ Story Diffusion is capable of including multiple characters consistently within a scene, overcoming a significant challenge in AI video generation.
- ๐ Despite the overall high quality, close inspection reveals minor inconsistencies, such as changes in the length of a tie or slight variations in facial markings.
- ๐ค The AI uses consistent self-attention and story splitting to ensure visual coherence and narrative flow in the generated images and videos.
- ๐ Story Diffusion represents a significant step forward in AI video generation, offering a cost-effective and accessible tool for creating realistic and consistent character animations.
Q & A
What is the name of the new open-source AI video model mentioned in the transcript?
-The new open-source AI video model mentioned is called 'Story Diffusion'.
What is the significance of Story Diffusion in terms of character consistency?
-Story Diffusion is significant because it not only maintains facial consistency but also ensures consistency in clothing and body type across different shots and scenes, which is crucial for creating believable characters.
How does Story Diffusion handle the creation of AI comics?
-Story Diffusion creates AI comics by generating a series of images for a sequence, ensuring consistency in terms of face and clothing, then predicting the movement between those images and animating them using a motion prediction model.
What is the typical length of the videos created by Story Diffusion?
-The videos created by Story Diffusion can be up to 30 seconds long, with a high level of character consistency and adherence to reality and physics.
How does Story Diffusion compare to Sora in terms of video resolution?
-There is no specific information on the resolution of videos generated by Story Diffusion in the transcript. However, the previews on their website are rendered at 832 pixels by 832, which could be upscaled to at least 2K definition with an AI upscaler.
What is the main advantage of Story Diffusion in terms of computational resources compared to Sora?
-Story Diffusion used only eight GPUs for training its model, whereas Sora used 10,000 GPUs, which is 1250 times more computational power. This makes Story Diffusion significantly more efficient in terms of training and running costs.
How does Story Diffusion ensure consistency across different images?
-Story Diffusion uses consistent self-attention, which enhances the consistency of different generated images by ensuring that each one shares certain attributes or themes, making them visually coherent when viewed as a series.
What is the process of 'story splitting' as used by Story Diffusion?
-Story splitting involves breaking down a story into multiple text prompts, each describing a part of the story. These prompts are processed simultaneously to produce images that depict the narrative in sequence, ensuring continuity and coherence in the generated images.
How does Story Diffusion handle the animation of diverse scenes?
-Story Diffusion effectively animates diverse scenes by correctly identifying and animating moving objects and characters while leaving static objects inanimate. It also applies camera movements and understands zooming functions to create a natural and fluid animation.
What are the limitations of Story Diffusion when it comes to character consistency?
-While Story Diffusion performs well in maintaining character consistency, there are still minor inconsistencies that can be noticed upon close inspection, such as changes in the length of a character's tie or slight variations in facial markings.
How does Story Diffusion's approach to AI video generation compare to existing models?
-Story Diffusion shows a significant evolution in character consistency and the ability to create scenes that make realistic and cohesive sense. It is considered a step forward in AI video generation, offering more natural and fluid animations compared to existing models.
Outlines
๐ฌ Introduction to Story Diffusion: A New Open-Source Video Model
This paragraph introduces the new open-source AI video model called Story Diffusion. It is praised as the best open-source video model for creating videos up to 30 seconds long with impressive character consistency and adherence to reality and physics. The paragraph also highlights the limitations of previous models like Sora, which struggled with not morphing and creating extra characters. Story Diffusion is presented as a significant step forward in character consistency, not just in facial features but also in clothing and body type.
๐ค Character Consistency and Application to Animation and Comics
This paragraph delves into the character consistency of Story Diffusion, emphasizing its ability to maintain consistency in clothing and body type across shots and scenes. It allows for the creation of believable characters and opens up opportunities for generating AI comics. The paragraph also discusses the impressive length of clips produced by Story Diffusion, the expressiveness of the characters, and its ability to work with animation. However, it also points out some minor issues with hand animations and the need for a more forgiving approach to reality in animations.
๐ Training Efficiency and Multi-Character Consistency
This paragraph highlights the efficiency of Story Diffusion, which was trained using only eight GPUs compared to Sora's 10,000 GPUs. It emphasizes the cost-effectiveness of Story Diffusion, which is open-source but lacks a user-friendly interface. The paragraph also discusses the model's ability to include multiple characters consistently in scenes, a significant challenge in AI video generation. Examples are provided to demonstrate this capability, along with its application to comic generation. However, some limitations are noted, such as inconsistencies in certain details like tie lengths and facial markings.
๐ Technical Approach and Future of AI Video Generation
This paragraph provides an in-depth look at the technical approach of Story Diffusion, including its use of consistent self-attention and story splitting. It explains how the model ensures visual coherence across images by noting down consistent attributes and using motion prediction to animate frames. The paragraph also showcases the model's ability to handle diverse scenes and create effective anime-style animations. It concludes by noting the rapid advancements in AI video generation and the potential for creating full films with AI in the future.
๐ Conclusion and Invitation to Explore Other AI Video Models
In the concluding paragraph, the speaker invites viewers to check out another AI video model called Vidu that has emerged from China. They ask for the viewers' thoughts on Story Diffusion and how it compares to existing AI video models. The speaker also encourages viewers to explore the possibilities of AI video generation and thanks them for watching.
Mindmap
Keywords
๐กOpen Source AI Video Model
๐กCharacter Consistency
๐กReality and Physics Adherence
๐กAI Comics
๐กMotion Prediction Model
๐กResolution
๐กConsistent Self-Attention
๐กStory Splitting
๐กAnimation
๐กTraining-Free Generation
๐กAI Video Generators
Highlights
Story Diffusion is a new open-source AI video model that creates videos up to 30 seconds long with high character consistency and realism.
The model demonstrates an understanding of reality, avoiding common AI video issues like objects appearing out of nowhere or passing through solid objects.
Story Diffusion achieves character consistency not only in facial features but also in clothing and body type.
The model allows for the creation of believable characters that maintain consistency across different shots and scenes.
Story Diffusion's method can be used to generate AI Comics by ensuring consistency in a sequence of images.
Videos generated by the model feature anatomically correct characters with minimal morphing or disfigurement.
The model produces clips of impressive length, maintaining character consistency throughout.
Despite minor jitteriness and square format, the model shows significant improvements in consistency and character clarity.
Story Diffusion's expressiveness is notable, particularly in the detailed facial animations.
The model outperforms other AI video generators in terms of video length and consistency, even with less computational power.
Story Diffusion was trained on only eight GPUs compared to Sora's 10,000, making it significantly more efficient.
The model is open-source but lacks a user-friendly interface, requiring self-installation or cloud server access.
Story Diffusion presents a method for including multiple characters consistently in scenes, overcoming a significant barrier in AI video.
The model can generate a comic strip with consistent characters and scenarios, enhancing the potential for AI in comic creation.
Despite some inconsistencies in details, the model demonstrates the ability to create believable and engaging comic strips.
Story Diffusion uses consistent self-attention to ensure visual coherence between generated images in a series.
The model employs story splitting, breaking down a story into text prompts that are processed simultaneously to produce a sequence of images.
Story Diffusion's motion predictor model animates images by predicting movement between frames, creating fluid animations.
The model is capable of creating effective and usable anime-style animations, opening possibilities for full AI-generated films.
Story Diffusion handles a diverse range of scenes, from realistic tourist footage to simple animations, with impressive coherence.
The model ensures that videos generated from a series of images look fluid and natural, maintaining continuity in appearance and motion.