GoKawiilRoad to Linum v3 · Issue 01 Since shipping Linum v2 in January, we've been heads down working on our next open-weight generative video model. For Linum v3, we're targeting three key improvements: better prompt adherence, training/inference acceleration, and consistent physics. In this series of blogs, we'll be breaking down both the engineering work and fundamental research that goes into training text-to-video models from scratch.
Image and video models have gotten a lot better over the last few years, even though the internals of these models haven't changed much since Stable Diffusion 3.Of course, there have been small variants like the auto-regressive diffusion that GPT-Image popularized. But at a high level, it's pretty much all flow matching with a transformer backbone and a v-prediction objective. In our experience, most of the gains are directly attributable to 3 flavors of data improvements:RL has driven some improvement, but it's only started truly working for image and video in the past few months.
Data Filtering & Rebalancing: Remove noisy data and resample your data strategically so your model learns more effectively Data Annotation: Gather better annotations like richer captions, bounding boxes, and font details so that it's easier for your model to disambiguate visual concepts LLMs have gotten infinitely better at image captioning in the last 12 months. Less so for video, but that's for another time. Synthetic Data Generation: Finetune an ensemble of existing generative models to create training data for which there is little-to-no naturally occurring data (e.g. image editing / reference-conditioning for Nano-Banana style models) Often a collection of brittle LoRAs trained for very specific tasks and an LLM finetuned to filter out bad synthetic data.
A couple of years ago, the prevailing wisdom across all generative models (be it text, image, audio) was to aggregate as much data as humanly possible for pre-training. Luckily, the field has gotten a lot smarter about this. If you throw a bunch of low-quality data (e.g. heavily compressed JPEGs) into pre-training, your model is going to waste a significant amount of its capacity learning how to mimic this slice of data. If you filter your dataset well, your model will have a lot easier time learning what you want it to learn.
We know this sounds obvious, but it's a lot harder to do in practice.Then again, all good advice should seem obvious in retrospect.
Today we're going to walk you through how our approach to data filtering has evolved since 2024. And, hopefully we'll save you from a couple of headaches if you end up training your own generative models down the line.
The evolution of our data filtration stack 2024 Old-school CV on CPUs CPU Early 2025 Finetuned LLMs on GPUs GPU Late 2025 Reinforcement Learning GPU kept
kept thrown out
thrown out thrown out by mistake
thrown out by mistake kept but should be thrown out
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