Memes come to life with stable video diffusion – mind-blowing! 🔥
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Stable Video Diffusion Introduction
Welcome to the world of Stable Video Diffusion, a groundbreaking innovation set to redefine the landscape of video creation. This introduction provides an in-depth look into how Stable Video Diffusion is leveraging advanced AI technology to transform static images into captivating, dynamic videos. Dive into the mechanics of this powerful video generator and explore its diverse applications across various industries. From artists and filmmakers to marketers and educators, learn how Stable Video Diffusion is empowering creators to bring their visions to life in ways never thought possible. Whether you are a tech enthusiast or a creative professional, this introduction to Stable Video Diffusion is your gateway to understanding and harnessing one of the most exciting advancements in digital media.
Have you ever looked at a picture and imagined what it would be like if it were a video? Stability AI has brought this imagination to life with something called Stable Video Diffusion! It's a bit like a magic trick, transforming still images into fascinating videos.
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Stable Video Diffusion Showcase: Unveiling the Future of Video Generation
Welcome to our Stable Video Diffusion Showcase, a cutting-edge exhibition where the marvels of the Stable Video Diffusion model are on full display. Here, we celebrate the transformative power of this advanced video generator, showcasing how it is revolutionizing the way we create and interact with digital videos.
Today, we are releasing Stable Video Diffusion, our first foundation model for generative AI video based on the image model, @StableDiffusion. As part of this research preview, the code, weights, and research paper are now available. Additionally, today you can sign up for our…
Bandit's Roost, 59½ Mulberry Street by Jacob Riis, 1888 Explore history with Stable Video Diffusion.
SDV (Stable Diffusion Image To Video) Google Colab available here for anyone who wants to play along at home. colab.research.google.com/github/mkshing… Generates 3 seconds of video in about 30 seconds using an A100 GPU on Colab+ No control of the actual video in any way at all (yet), but it…
AI generated video just got SCARY good! This uses Stable Video Diffusion, which released 2 days ago and Topaz Labs to interpolate 6fps to 24fps. Cinema quality.
【事例】最新の画像生成AI！Stable Video Diffusion活用事例10選 最新の動画生成AIモデル「Stable Video Diffusion」 RunwayやPikaLabs超えと話題だが、 一体どんな動画を作れるの？と気になる方も多いはず 今回は思わず試したくなる事例10個を厳選 >>
Stability just released Stable Diffusion Video this week. It's quite something. The text-to-video model is fully open source and let's you generate 14 or 25 frames at 576 x 1024. It can also do do multi-view generation, 3D scene understanding, and camera control via LoRA.…
Midjourney + Magnific AI + Stable Diffusion Video + Topaz AI = WOW !!🤯 !! 😃 !! 🎞️ The next generative video production workflows are rapidly taking shape.
Image to Video : - Stable Video Diffusion (SVD) - Runway - Pika Labs Images: Midjourney Notes: 1) I used Stable Video Diffusion (SVD) on Replicate. 2) I tried a few times in each of the platforms and picked the result I liked the most. 3) By experimenting with different…
最新の画像生成AI「Stable Video Diffusion」。 数日前の公開から、有志の方々による改善が入りまくり、現在こんなことになってます。 NovelAIが流行っていた１年前には、まさかAI動画を個人で自由に生成できるなんて夢にも思いませんでした。
Stable Video Diffusion using cc0 images. #stablevideo #img2vid
🎥 Workflow for PhotoAI.com image to video with people: - generate AI image of person - image2video w/ Stable Video Diffusion - faceswap with source img to fix face Not great yet, it's at the level that image gen was a year ago with deformities all over If that…
I did it! Stable Video Diffusion running on M2 Ultra! 03:37 10.87s/it Let me try on M3 Max...
*Stable Video Diffusion #comfyUI アニメ静止画からの動画生成をためす
All the artwork here was made from scratch on my PS5 in the 3d software "Dreams", upscaled and detailed using Magnific ai and finally animated using Stable Video Diffusion. The generations are limited to these short clips for now, but it probably won't be too long before we can…
Super excited to announce the release of Stable Video Diffusion (SVD) -- the first set of video models in the Stable Diffusion series. To start with, we release 14-frame (SVD) and 25-frame image-to-video (SVD-XT) models. The code/weights are already out! SVD:…
What is Stable Video Diffusion?
Stable Video Diffusion AI model can animate any still image. Stable Video Diffusion consists of two models: one that can produce image-to-video synthesis at 14 frames of length (called SVD), and another that generates 25 frames (called SVD-XT). They can operate at varying speeds from 3 to 30 frames per second, and they output short (typically 2-4 second-long) MP4 video clips at 576×1024 resolution.
Exploring the Dual Models of Stable Video Diffusion: SVD and SVD-XT
Stable Video Diffusion, a groundbreaking advancement in AI-driven video generation, offers two distinct models: SVD and SVD-XT. These models are engineered to cater to different needs in the realm of image-to-video synthesis. The SVD model specializes in creating short video clips, typically lasting between 2 to 4 seconds, with a frame length of 14 frames. On the other hand, the SVD-XT variant extends this capability, producing videos with up to 25 frames. Both models exhibit versatility in operation, functioning across a range of frame rates from a slow 3 to a rapid 30 frames per second. The result is a series of crisp, high-quality MP4 video clips, each rendered at a resolution of 576×1024. This dual-model approach in Stable Video Diffusion not only showcases the flexibility of AI in video creation but also opens a plethora of possibilities for users seeking tailored image-to-video transformations.
Open-Source Technology
Stable Video Diffusion, available for research and testing, is known for its adaptability and efficiency. Being open-source, it's ideal for various uses in advertising, education, and entertainment. Researchers note that it performs better than traditional image-based methods while requiring less computing power. A key aspect of the technology is ensuring temporal consistency across video frames. This is achieved by conditioning the generation of each frame not only on a noise pattern but also on previous frames. This approach helps in maintaining stability and continuity in the generated video. he training process involves teaching the model to reverse a diffusion process, starting from random noise and gradually denoising it to form a stable video. This process is iterative and requires a large dataset of videos to learn from.
How Does It Work?
The paper introduces video diffusion models, an extension of image diffusion models, which are a class of generative models. These models work by gradually converting noise into a coherent video sequence, frame by frame.
Fields of Use
The model is adaptable to various video applications, including media, entertainment, education, and marketing. It is designed to transform text and image inputs into vivid scenes and cinematic creations
Performance Metrics
In terms of performance, Stable Video Diffusion has been found to surpass leading closed models in user preference studies. It has a video duration capability of 2-5 seconds and a processing time of 2 minutes or less
How to access source code
The code and weights for Stable Video Diffusion are available on GitHub and Hugging Face, respectively, making it accessible for research and experimentation
Licensing Information
Stable Video Diffusion is available under a non-commercial community license. This reflects Stability AI's commitment to making their research widely available for the benefit of humanity.
Competing in AI Video Generation
In the fast-paced world of AI video generation, Stable Video Diffusion emerges as a formidable contender, squaring off against the likes of innovative models from Pika Labs, Runway, and Meta. Each of these companies is making significant strides in the realm of AI-driven video creation, offering unique approaches and capabilities.
Meta's Emu Video: A Notable Contender
Meta's recent introduction of Emu Video, a model sharing Stable Video Diffusion's text-to-video functionality, is particularly noteworthy. Emu Video distinguishes itself with a specialized focus on image editing and video creation. However, it currently operates within the bounds of a 512x512 pixel resolution limit for videos. This presents a contrast to the broader scope and higher resolution capabilities offered by Stable Video Diffusion.
Ethical Considerations and Model Refinement
Stability AI, the force behind Stable Video Diffusion, is not only pushing the envelope in terms of technological prowess but is also conscientiously navigating through a myriad of challenges. A significant concern is the ethical use of copyrighted data in AI model training, a topic that has garnered much attention and debate in the AI community.
A Commitment to Responsible Innovation
Recognizing the sensitive nature of this issue, Stability AI has been proactive in addressing these concerns. The company clearly states that Stable Video Diffusion is currently not intended for real-world or commercial use. At this developmental stage, the focus remains firmly on refining the model through community feedback and a rigorous assessment of safety and ethical standards.
The Path Forward for Stable Video Diffusion
As Stable Video Diffusion continues to evolve, it stands as a testament to Stability AI's dedication to pushing the boundaries of AI technology, while remaining mindful of the broader implications of its use. The journey of Stable Video Diffusion in the competitive landscape of AI video generation is a blend of technological ambition and conscientious development, setting a precedent in the dynamic field of artificial intelligence.
Understanding the Limitations of Stable Video Diffusion (SVD)
Stable Video Diffusion (SVD) has several restrictions. It consists of two models that produce short clips with 14 to 25 frames at 576 x 1024 resolution. The frame rate varies between 3 to 30 FPS, with videos typically under 4 seconds. SVD cannot create realistic styles or complex camera movements, struggles with text and human features, but can add life to static images.