GoKawiilLinum previews v3 roadmap, betting on data quality over raw scale for video AI
Linum has begun detailing its work on Linum v3, the successor to its open-weight text-to-video model released in January, in a new blog series. The company says its focus is on three goals: improving how well outputs follow prompts, speeding up training and inference, and making generated physics more consistent, while arguing that most recent progress in image and video generation comes from better data practices rather than new model architectures.
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Linum's framing challenges the industry's earlier assumption that simply scaling up training data produces better generative models, pointing instead to curated filtering, richer annotations, and synthetic data as the real drivers of quality. For an open-weight competitor to closed systems from larger labs, transparently sharing this engineering process could help it attract developers and researchers looking to understand or replicate state-of-the-art video generation techniques.
- Linum v3 targets better prompt adherence, faster training/inference, and more consistent physics simulation.
- The company attributes recent video/image model gains mainly to data filtering, richer annotation, and synthetic data generation rather than architecture changes.
- Linum is publishing a blog series detailing both engineering and research behind training text-to-video models from scratch.
NVIDIA GeForce RTX 5090 Graphics Card — If reading about training and running text-to-video diffusion models makes you want to try generating your own clips locally, a GeForce RTX 5090 is the card that makes open-weight video models practical at home. Its large VRAM pool and CUDA ecosystem support the same diffusion-transformer workflows the article describes, from inference to fine-tuning experiments.
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Published there as: “Getting video models to learn better, faster”
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