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My brother and I run a 2-person lab that trains our own generative video models from scratch. The ultimate goal is a set of creative tools that make it easier for anyone to make animation.Video models today are too slow&#x2F;expensive and lack sufficient controls. Creativity is an iterative process. You make, you tweak, you get better. In our opinion, they&#x27;re not usable yet for storytelling.When we set out to build Linum v3 (our next video model) earlier this year, the primary goal was to bring the cost envelope of inference down significantly. If our model is fast and cheap, it&#x27;s the first step (of many) towards useful creative tools.Typically, you see &quot;latent diffusion models&quot;. A VAE (Variational Autoencoder) compresses the input data into a smaller &quot;latent space&quot; and the diffusion model learns how to generate within this subspace.Over the past few months, we&#x27;ve been publishing our work on efficient pixel-space diffusion models. These throw away the VAE, having the diffusion model learn a latent-space optimized for its exact task.This serves 3 purposes:
1) Simplify Architecture (no 2-stage model training)
2) Faster Convergence (11x faster convergence vs. our latent diffusion counterpart)
3) Cheaper Inference (4x more aggressive token reduction in attention context-windows, without significant loss in quality)This past month we were pressure testing our latest architecture (Pyramid-JiT) by training a text-to-image model longer than the typical one-off experiment. Specifically, we trained it for ~$20K on 660M samples.We were surprised by the quality of results, so we thought we&#x27;d release it as open weights for the community (Apache 2.0). The research itself is already public, on our website.For us, it&#x27;s a big stepping stone towards cheaper video models, since pretty much all video models start as image models. We like to say these models have to learn nouns before they learn verbs.

Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)

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Hi HN, we have been developing with Firebase for a long time, and the only way Firebase developers can build apps without directly costing them is using the official Firebase Emulator Suite.However, the issue with this suite is, it&#x27;s dependent and running on Java with a old Console UI (not updated in years), that was not built to support tons and millions of data and has pretty bad developer experience. For a long time, we were hitting Out of Memory on even high specs laptop while developing and the worst part is while this happens all the persists data keep getting lost.We were honestly frustrated with this, and thought what if we built the Firebase Emulator Suite the right way, as it always should have been.Introducing Firenook, a faster drop-in replacement for Firebase Emulator Suite for Firebase developers. It&#x27;s 40x faster writes and needs 10x less RAM to run it. We took the right approach of first building it Rust as a single engine, so that there will be no Java dependencies and built from scratch a new Console UI using modern web stacks.The result is, now we were able to run 2 millions datas loaded to Firenook with only taking around 2GB ram compared to 22GB of the official suite. Things are way faster and rebuilt Console UI gives best developer experience from logging Firebase Functions, to editing Firestore documents in JSON and many more.We developed this throughly for around 2 months now and ran this internally for a month before we decided it&#x27;s time to release it to public so that other devs can make use of Firenook and also contribute it to make it better.Firenook is still new and I&#x27;m sure it misses things. So please try it our on your project and let us if there are any issues.Thank you.

Show HN: Firenook – Firebase emulators in Rust: 40× faster writes, 10× less RAM

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