[HN Gopher] Show HN: Text-to-video model from scratch (2 brother...
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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 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!
Author : schopra909
Score : 28 points
Date : 2026-01-22 16:31 UTC (6 hours ago)
(HTM) web link (huggingface.co)
(TXT) w3m dump (huggingface.co)
| streamer45 wrote:
| Rad! huggingface link gives 404 on my side though.
| schopra909 wrote:
| Oh 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-v2-360p
| schopra909 wrote:
| Should be fixed now! Thanks again for the heads up
| streamer45 wrote:
| All good, cheers!
| schopra909 wrote:
| Per the RAM comment, you may able to get it run locally
| with two tweaks:
|
| 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
| streamer45 wrote:
| Looks like 20GB VRAM isn't enough for the 360p demo :( need
| to bump my specs :sweat_smile:
| E-Reverance wrote:
| Post it on r/StableDiffusion
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