5 ms·
Show HN: Text-to-video model from scratch (2 brothers, 2 years, 2B params)
Writeup (includes good/bad sample generations): https://www.linum.ai/field-notes/launch-linum-v2 https://www.linum.ai/field-notes/launch-linum-v2
We're Sahil and Manu, two brothers who spent the last 2 years training text-to-video models from scratch. Today we're releasing them under Apache 2.0.
These are 2B param models capable of generating 2-5 seconds of footage at either 360p or 720p. In terms of model size, the closest comparison is Alibaba's Wan 2.1 1.3B. From our testing, we get significantly better motion capture and aesthetics.
We're not claiming to have reached the frontier. For us, this is a stepping stone towards SOTA - proof we can train these models end-to-end ourselves.
Why train a model from scratch?
We shipped our first model in January 2024 (pre-Sora) as a 180p, 1-second GIF bot, bootstrapped off Stable Diffusion XL. Image VAEs don't understand temporal coherence, and without the original training data, you can't smoothly transition between image and video distributions. At some point you're better off starting over.
For v2, we use T5 for text encoding, Wan 2.1 VAE for compression, and a DiT-variant backbone trained with flow matching. We built our own temporal VAE but Wan's was smaller with equivalent performance, so we used it to save on embedding costs. (We'll open-source our VAE shortly.)
The bulk of development time went into building curation pipelines that actually work (e.g., hand-labeling aesthetic properties and fine-tuning VLMs to filter at scale).
What works: Cartoon/animated styles, food and nature scenes, simple character motion. What doesn't: Complex physics, fast motion (e.g., gymnastics, dancing), consistent text.
Why build this when Veo/Sora exist? Products are extensions of the underlying model's capabilities. If users want a feature the model doesn't support (character consistency, camera controls, editing, style mapping, etc.), you're stuck. To build the product we want, we need to update the model itself. That means owning the development process. It's a bet that will take time (and a lot of GPU compute) to pay off, but we think it's the right one.
What’s next?
- Post-training for physics/deformations
- Distillation for speed
- Audio capabilities
- Model scaling
We kept a “lab notebook” of all our experiments in Notion. Happy to answer questions about building a model from 0 → 1. Comments and feedback welcome!
- streamer45 9mo agoRad! huggingface link gives 404 on my side though.
- schopra909 9mo agoOh damn! Thanks for catching that -- going to ping the HF folks to see what they can do to fix the collection link. In the meantime here's the individual links to the models: https://huggingface.co/Linum-AI/linum-v2-720p https://huggingface.co/Linum-AI/linum-v2-720p https://huggingface.co/Linum-AI/linum-v2-360p https://huggingface.co/Linum-AI/linum-v2-360p
- schopra909 9mo agoShould be fixed now! Thanks again for the heads up
- streamer45 9mo agoAll good, cheers!
- schopra909 9mo agoPer the RAM comment, you may able to get it run locally with two tweaks: https://github.com/Linum-AI/linum-v2/blob/298b1bb9186b5b9ff60331ee44de746734a79075/linum_v2/models/text2video.py#L285 https://github.com/Linum-AI/linum-v2/blob/298b1bb9186b5b9ff6... 1) Free up the t5 as soon as the text is encoded, so you reclaim GPU RAM 2) Manual Layer Offloading; move layers off GPU once they're done being used to free up space for the remaining layers + activations
- dsrtslnd23 9mo agoAny idea on the minimum VRAM footprint with those tweaks? 20GB seems high for a 2B model. I guess the T5 encoder is responsible for that.
- schopra909 9mo agoT5 Encoder is ~5B parameters so back of the envelope would be ~10GB of VRAM (it's in bfloat16). So, for 360p should take ~15 GB RAM (+/- a few GB based on the duration of video generated). We can update the code over the next day or two to provide the option for delete VAE after the text encoding is computed (to save on RAM). And then report back the GB consumed for 360p, 720p 2-5 seconds on GitHub so there are more accurate numbers. Beyond the 10 GB from the T5, there's just a lot of VRAM taken up by the context window of 720p video (even though the model itself is 2B parameters).
- E-Reverance 9mo agoPost it on r/StableDiffusion
- WhitneyLand 9mo agoGreat work. How many GPU hours to train?
- throwaway314155 9mo agoHow much compute was ultimately required to get this done?
- popalchemist 9mo agoIncredibly impressive, dudes. Well done.
- Jack_a11y 9mo ago[dead]
- convivialdingo 9mo agoThat’s amazing effort - I am impressed. Awesome to see more small teams making impressive leaps.
- taherchhabra 9mo agoI want to build my own video model, just for learning purposes, is there any course which can teach end to end
- schopra909 9mo agoI think YC just release video on the basics of diffusion, but honestly I don’t have a good end to end guide. We’re going to write up going 0->1 on a video model (all the steps) over the coming months. But it likely won’t be a class or anything like that. https://www.linum.ai/field-notes https://www.linum.ai/field-notes We want to share our learnings with folks who are curious about the space - but don’t have time to make it a full class experience. Hopefully karpathy does that with his courses in the future!
- mandeepj 9mo ago> I want to build my own video model, just for learning purposes Sorry, it might sound like a cliche, but try that as a prompt to a deep thinking and learning model, and see what comes out. An expensive option: Look at Project #5 at https://bytebyteai.com/ https://bytebyteai.com/
- whywhywhywhy 9mo ago> We kept a “lab notebook” of all our experiments in Notion Couldn't find a link to this, is this public?
- schopra909 9mo agoNot public yet — we’re going to clean it up so it’s readable and release it as blog posts. First one will be everything you need to know on building a VAE for image and video. Should be out in a few weeks. We’re figuring out the write balance between spending time writing and all the work we have on our plate for the next model. If you’re interested in this stuff, keep an eye on field notes (our blog).
- schopra909 9mo agohttps://www.linum.ai/field-notes https://www.linum.ai/field-notes
- tariqshams 9mo agoVery cool, especially given that it’s a two person team. I will be checking this out on the weekend. Also I’m super curious on how you’re attempting to have more realistic physics with post training.
- hackomorespacko 9mo ago[flagged]
- glohbalrob 9mo agoNice work. Are you guys on X?