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sid-the-kid

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Good question! Software gets democratized so fast that I am sure others will implement similar approaches soon. And, to be clear, some of our "speed upgrades" are pieced together from recent DiT papers. I do think getting everything running on a single GPU at this resolution and speed is totally new (as far as i have seen).

I think people will just copy it, and we just need to continue moving as fast as we can. I do think that a bit of a revolution is happening right now in real-time video diffusion models. There are so many great papers being published in that area in the last 6 months. My guess is that many DiT models will be real time within 1 year.

Our text control is good, especially for emotions. For example, you can add the text prompt: "a person talking. they are angry", and agent will have an angry expression.

You can also control background motions (like ocean waves, or a waterfall or car driving).

We are actively training a model that has better text control over hand motions.

hey HN! one of the founders here. as of today, we are seeing informational avatars + roleplaying for training as the most common use cases. The roleplaying use-case was surprising to us. Think a nurse training to triage with AI patients. Or, SDRs practicing lead qualification with different kinds of clients.

IMO, most videos models will be fully real time within 2 years. You will be able to pick a model, imagine any world and then be fully immersed in it. Walk around any city interacting with people, first person shooter games on any map with crazy monsters, or just let the model auto-pilot an adventure for you.

Nice find! I hand't seen this before (and will take a deeper look later). It looks like this is an approach to better utilize the GPU memory. And, we would probably benefit from this to get more of a speed-up, which would also help us get better video quality.

I do not think they are running in real time though. From the website: "Personal RTX 4090 generates at speed 2.5 seconds/frame (unoptimized) or 1.5 seconds/frame (teacache)." That means it would take them 37.5s to generate 1 second of video, which is fast for video but way slower than real time.

Good question. I guess depends on how many users we get. Each users gets their own dedicated GPU. Most video generations systems (and AI systems in general) can share GPUs during generation. Since we are real time, we don't do that. So, each user minute is a GPU minute. This is the biggest driver of the cost.