Adrian Wälchli

Julien Abadji, Marah Abdin, Connor Adams et al.

We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has $225.8$B total parameters ($23.4$B activated per token) and XS.2 has $33.4$B total ($3$B activated). Both models were trained from scratch end-to-end inside the same internal system that we refer to as our Model Factory: a tightly-integrated stack of versioned data, training, evaluation, and inference components that turn model development into an industrial process. We describe the principles and design choices of the Model Factory and also detail the end-to-end training process of our models, throughout pre-training data and architecture, post-training stages, evaluation, and quantization. On agentic software engineering and terminal benchmarks (SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro, and Terminal-Bench 2.0) M.1 and XS.2 are competitive with state-of-the-art open models in their respective weight classes. Laguna XS.2 weights are released under Apache~2.0 at https://huggingface.co/collections/poolside/laguna-xs2.

Adrian Wälchli, Paolo Favaro

The estimation of optical flow is an ambiguous task due to the lack of correspondence at occlusions, shadows, reflections, lack of texture and changes in illumination over time. Thus, unsupervised methods face major challenges as they need to tune complex cost functions with several terms designed to handle each of these sources of ambiguity. In contrast, supervised methods avoid these challenges altogether by relying on explicit ground truth optical flow obtained directly from synthetic or real data. In the case of synthetic data, the ground truth provides an exact and explicit description of what optical flow to assign to a given scene. However, the domain gap between synthetic data and real data often limits the ability of a trained network to generalize. In the case of real data, the ground truth is obtained through multiple sensors and additional data processing, which might introduce persistent errors and contaminate it. As a solution to these issues, we introduce a novel method to build a training set of pseudo-real images that can be used to train optical flow in a supervised manner. Our dataset uses two unpaired frames from real data and creates pairs of frames by simulating random warps, occlusions with super-pixels, shadows and illumination changes, and associates them to their corresponding exact optical flow. We thus obtain the benefit of directly training on real data while having access to an exact ground truth. Training with our datasets on the Sintel and KITTI benchmarks is straightforward and yields models on par or with state of the art performance compared to much more sophisticated training approaches.

Qiyang Hu, Adrian Wälchli, Tiziano Portenier et al.

We present a method to generate a video sequence given a single image. Because items in an image can be animated in arbitrarily many different ways, we introduce as control signal a sequence of motion strokes. Such control signal can be automatically transferred from other videos, e.g., via bounding box tracking. Each motion stroke provides the direction to the moving object in the input image and we aim to train a network to generate an animation following a sequence of such directions. To address this task we design a novel recurrent architecture, which can be trained easily and effectively thanks to an explicit separation of past, future and current states. As we demonstrate in the experiments, our proposed architecture is capable of generating an arbitrary number of frames from a single image and a sequence of motion strokes. Key components of our architecture are an autoencoding constraint to ensure consistency with the past and a generative adversarial scheme to ensure that images look realistic and are temporally smooth. We demonstrate the effectiveness of our approach on the MNIST, KTH, Human3.6M, Push and Weizmann datasets.