The Future of AI Video Has Arrived! (Stable Diffusion Video Tutorial/Walkthrough)

The video introduces Stable Diffusion Video, a new AI video model from Stability AI that generates short video clips from a single image. Despite initial concerns about needing a powerful GPU, the video offers solutions for various setups, including Chromebooks. It highlights the model's capabilities, such as generating 25 frames at a resolution of 576x1024 and the potential for stunning results in 2-3 seconds. The video also discusses the model's understanding of 3D space, which contributes to more coherent faces and characters. Several tools and platforms for running Stable Diffusion Video are mentioned, including Pinocchio, Hugging Face, and Replicate, each with its pros and cons. The video also touches on future improvements and the use of Final Frame for extending video clips by merging AI-generated images into a continuous video file. The presenter, Tim, encourages viewers to like, subscribe, and provide feedback to support the development of these AI video tools.

Takeaways

• 🚀 A new AI video model called Stable Diffusion has been released by Stability AI, which can generate short video clips from images.
• 💻 Initially, the model is not very demanding on hardware, contrary to common misconceptions about AI video models.
• 🖼️ Stable Diffusion Video is currently image-to-video, with text-to-video capabilities expected in the future.
• 📏 The model is trained to generate 25 frames at a resolution of 576x1024, with an alternative fine-tuned model running at 14 frames.
• 🎨 The quality of the generated videos is high, with examples shown by Steve Mills demonstrating impressive fidelity.
• 🔍 Upscaling and interpolation can enhance the video output, with tools like Topaz mentioned for this purpose.
• 📈 There's a comparison of Stable Diffusion Video with other image-to-video platforms, highlighting its strengths in action and motion.
• 🔄 The model has a good understanding of 3D space, which is crucial for coherent faces and characters in the generated videos.
• 📦 Users have several options to run Stable Diffusion Video, including locally via Pinocchio or online through Hugging Face and Replicate.
• 💡 Final Frame is a tool that can extend video clips and merge them into a continuous file, despite being an indie project with room for improvement.
• 🌟 There are ongoing improvements to the Stable Diffusion Video model, including text video, 3D mapping, and longer video outputs in the pipeline.

Q & A

What is the name of the new AI video model mentioned in the transcript?

The new AI video model mentioned is called Stable Diffusion Video.

What is the current limitation of the Stable Diffusion Video model in terms of frames and resolution?

The model is trained to generate 25 frames at a resolution of 576 by 1024. There is also a fine-tune model that runs at 14 frames.

How long do the generated video clips from Stable Diffusion Video typically run?

The generated video clips typically run for about 2 to 3 seconds.

What tool was used to upscale and interpolate Steve Mills' outputs from Stable Diffusion Video?

Topaz was used to upscale and interpolate Steve Mills' outputs.

What is the current status of camera controls in Stable Diffusion Video?

As of the time of the video, camera controls are not yet available in Stable Diffusion Video, but they will be coming soon via custom luras.

How does one use Stable Diffusion Video locally?

One can use Pinocchio for local use, which offers one-click installation once downloaded.

What platform can be used to try Stable Diffusion Video for free?

Hugging Face can be used to try Stable Diffusion Video for free.

What is the cost per output on Replicate for running Stable Diffusion Video?

The cost per output on Replicate is about 7 cents.

How can one extend the length of video clips generated by Stable Diffusion Video?

One can use a tool like Final Frame to merge and extend video clips generated by Stable Diffusion Video.

What is the current limitation of the Final Frame tool in terms of project management?

As of the time of the video, the save project, open project, and new project buttons in Final Frame do not work, so any unsaved work will be lost upon closing the browser.

What upcoming improvements are being made to the Stable Diffusion Video model?

Upcoming improvements include text-to-video, 3D mapping, and longer video outputs.

How does the understanding of 3D space in Stable Diffusion Video contribute to the model's capabilities?

The understanding of 3D space allows for more coherent faces and characters, and enables features like 360-degree turnarounds and consistent environmental rendering across separate shots.

Outlines

00:00

🚀 Introduction to Stable Diffusion Video

The video introduces a new AI video model from Stability AI, emphasizing its user-friendly nature despite common misconceptions about complex workflows or high GPU requirements. The model is capable of generating short video clips from images, with a current focus on image-to-video and an upcoming text-to-video feature. It's trained to produce 25 frames at a 576x1024 resolution, with an alternative fine-tuned model at 14 frames. The video showcases the impressive quality of the generated clips, which can last around 2 to 3 seconds, and mentions the use of Topaz for upscaling and interpolation. It also compares Stable Diffusion Video to other image-to-video platforms, highlighting its motion and action capabilities, and discusses the model's understanding of 3D space for coherent face and character animations.

05:02

💻 Running Stable Diffusion Video on Different Platforms

The paragraph discusses various methods to run Stable Diffusion Video, starting with Pinocchio, which offers a one-click installation process but currently only supports Nvidia GPUs. It also mentions the possibility of a Mac version being released soon. The video then explores the option of using Hugging Face for a free trial, but warns of potential user limit errors during peak times. Another alternative is Replicate, which allows for a number of free generations before a small fee is introduced. The paragraph details the customization options available on Replicate, such as frame selection, aspect ratio, frames per second, motion control, and conditional augmentation. It also suggests tools for video upscaling and interpolation without the need for Replicate, and teases upcoming improvements to the Stable Diffusion Video model, including text-to-video, 3D mapping, and longer video outputs.

10:16

🔍 Extending Video Clips with Final Frame

The final paragraph introduces Final Frame, a tool for extending video clips generated by Stable Diffusion Video. It highlights the recent addition of an AI image-to-video tab by the tool's creator, Benjamin Deer. The process involves uploading an image, processing it, and then merging it with other video clips in the project preview tab. The user can rearrange clips on a timeline, although editing features are currently limited. The paragraph cautions that saving and reopening projects is not yet functional, so work will be lost upon closing the browser. It encourages viewers to provide feedback to improve Final Frame and appreciates the community-driven nature of such tools.

Keywords

💡Stable Diffusion Video

Stable Diffusion Video refers to an AI video model developed by Stability AI that generates short video clips from image conditioning. It's capable of producing high-fidelity and high-quality videos, as demonstrated in the script with examples from Steve Mills. This technology represents a significant advancement in the field of AI-generated content, particularly for creating dynamic visual sequences from static images.

💡Frame Rate

The frame rate in the context of the video refers to the number of frames produced per second of video. The Stable Diffusion Video model is mentioned to generate 25 frames at a resolution of 576 by 1024, which is a key aspect of the video's quality and motion dynamics. The script also discusses how manipulating the frame rate can affect the output length and motion intensity of the generated videos.

💡Topaz

Topaz is a software mentioned in the script that is used for upscaling and interpolating video frames to improve their resolution and smoothness. It's noted that outputs from Stable Diffusion Video were enhanced using Topaz, showcasing the potential for further refinement of AI-generated videos through post-processing with such tools.

💡Motion Control

Motion control in the video script pertains to the adjustments made to the level of motion within the generated video clips. The script discusses how different motion settings, such as motion 50, 180, or 255, can drastically change the dynamics and speed of the video, allowing for a range of creative possibilities.

💡3D Space Understanding

The understanding of 3D space by the Stable Diffusion Video model is highlighted as a key feature that enables more coherent faces and characters in the generated videos. The script provides an example where a 360-degree turnaround of a sunflower is created, demonstrating the model's ability to maintain consistency in the environment across different shots.

💡Pinocchio

Pinocchio is a tool mentioned for running Stable Diffusion Video locally. It offers one-click installation and is currently compatible with Nvidia GPUs. The script suggests that Pinocchio is a user-friendly option for those looking to generate videos without relying on cloud-based services.

💡Hugging Face

Hugging Face is an online platform where users can try out Stable Diffusion Video for free. The script mentions that users can upload an image and generate a video directly on the platform, although it warns of potential user limits due to the popularity of the service.

💡Replicate

Replicate is another platform mentioned in the script for generating Stable Diffusion Videos. It allows users to run a number of generations for free before asking for payment. The script outlines the process of using Replicate, including selecting frame options and adjusting settings for video output.

💡Video Upscaling and Interpolation

Video upscaling and interpolation are techniques used to enhance the quality of video output. The script discusses using tools like R Video Interpolation for this purpose, noting that these processes can be done outside of the Replicate platform to further refine the AI-generated videos.

💡Final Frame

Final Frame is a tool highlighted in the script for extending video clips generated by Stable Diffusion Video. It allows users to merge multiple clips into one continuous file, offering a way to create longer, more complex videos. The script also mentions that Final Frame is an indie project developed by a single person from the community.

💡AI Image to Video

AI Image to Video is a process described in the script where AI technology is used to animate static images, creating video content. The Stable Diffusion Video model is part of this process, and the script discusses how it can be combined with other tools like Final Frame to produce more extended and complex video sequences.

Highlights

A new AI video model has been released by Stability, offering a fantastic tool for video generation.

Contrary to common misconceptions, Stable Diffusion does not require a complicated workflow or a powerful GPU to run.

Stable Diffusion Video is currently an image-to-video model, with text-to-video capabilities coming soon.

The model generates short video clips at 25 frames with a resolution of 576 by 1024 pixels.

Steve Mills' example demonstrates the high fidelity and quality of Stable Diffusion Video outputs.

Upscaling and interpolation can be achieved without expensive tools like Topaz, with alternative suggestions provided.

Stable Diffusion Video has shown impressive action and motion capabilities when compared to other platforms.

The model is expected to improve in understanding 3D space, leading to more coherent faces and characters.

Local running of Stable Diffusion Video can be done using Pinocchio, which is a one-click install.

Hugging Face offers a free trial for Stable Diffusion Video, though it may be subject to high user traffic.

Replicate provides a non-local option for running Stable Diffusion Video with a reasonable pay-per-output model.

Users can control the level of motion, aspect ratio, and frames per second in the output video.

Conditional augmentation controls the amount of noise added to the initial input image for varied results.

Final Frame, a tool discussed in the past, has been updated with new features and a roadmap for future enhancements.

Final Frame allows users to extend video clips by merging multiple AI image-to-video outputs into one continuous file.

Despite its current limitations, Final Frame is an indie project developed by a single person from the community.

The creator of Final Frame is seeking suggestions and feedback for further improvements to the tool.

Stable Diffusion Video is in active development with improvements like text video, 3D mapping, and longer video outputs on the horizon.