(www.linum.ai)
Really happy to see a small shop like yours doing this sort of work!
Are pictures easier than English, or did the methods advance so much?
Edit: I guess I should have said, are pictures plus English easier than English!
>The text representation comes from a frozen Qwen3.5-4B model, which we read at layers 7, 15 and 27 (its three full-attention layers). We concatenate and project these representations into the text refiner.
Still, as a layperson I would have expected text to be simpler than images, so I'm still amazed by this.
I'm sure Moore's Law for GPUs helped a little, but I'm really curious about the savings on the methodology side. (The repo mentions "hyperparameters")
Have you thought about setting up auto-research on this for smaller experiments? What's the minimum-viable budget for these types of workloads?
Also I'm a little intrigued on looking into efficiency techniques for video gen. What are current research directions in that field and promising directions you are particularly interested in? Any pointers for beginners?
CVPR 2026 paper list (after filtering on video generation) might be a good start - asking LLMs to take a look and filter relevant papers sound even better.
[1] https://arxiv.org/abs/2609.15810 [2] https://arxiv.org/abs/2602.13515 [3] https://arxiv.org/abs/2605.23381
Any chance you'll release your training recipe as well? I know the model code is public, but I'd love to dive into the full process.
Reminds me of this great paper where they trained a LLM that beats LLaMA2-13B from scratch with < $0.1M: https://arxiv.org/abs/2404.07413
If you swap the text encoder for something like T5-small or CLIP, this could be <2.5Gb at INT4; doable on a lot of machines.