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Hotshot – 4 Person Team Builds a State of the Art Video Model

Hacker News

Hotshot – 4 Person Team Builds a State of the Art Video Model

Hi HN! We're proud to share Hotshot, a large-scale diffusion transformer model for text-to-video generation that we built with just a 4-person team. You can try it today in beta at https://hotshot.co , with 2 free generations per day. The model generates 5 seconds of 720p video from text prompts. It excels at prompt alignment, and consistency. It also excels at generating people, animals, and nature. In blind tests with 100 users, Hotshot generations were preferred to Runway ML 60% of the time. Hotshot generations were preferred to Luma 80% of the time. Overall, users preferred Hotshot's results to other publicly available text-to-video models ~70% of the time. We built this model from scratch with a 4 person team in just 4 months. It is trained on 600 million video clips and 1 billion images. It uses a custom-trained video captioner for better temporal understanding and a custom autoencoder for efficient long sequence training. We've detailed more technical aspects of the journey in a blog post: https://hotshot.co/release Some technical highlights include A. scaling to thousands of GPUs, tackling infrastructure and optimization challenges. B. developing custom kernels and data parallelism techniques. C. Creating a Watchdog system to detect and respond to GPU process hangs. D. Optimizing data streaming and compression for efficient training. We believe that this model is just the beginning. In the next 12 months, entire YouTube videos will be AI generated by creators. Text to video models like this one lay the foundation for this and much more. Control over every aspect of generations, longer durations, higher resolutions, real time interactivity, and more modalities (like audio!) are just around the corner. We're here to answer any questions about the model, our training process, or our plans for the future. We're also always looking for talented individuals to join our team! We'd love for you to try our 2 free generations per day and let us know what you think. We're excited to see what the HN community will create with it!

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: efficient, users · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month, users · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time, audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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