a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more
Introducing ⇆ Marigold-DC — our training-free zero-shot approach to monocular Depth Completion with guided diffusion! If you have ever wondered how else a long denoising diffusion schedule can be useful, we have an answer for you!
Depth Completion addresses sparse, incomplete, or noisy measurements from photogrammetry or sensors like LiDAR. Sparse points aren’t just hard for humans to interpret — they also hinder downstream tasks.
Traditionally, depth completion was framed as image-guided depth interpolation. We leverage Marigold, a diffusion-based monodepth model, to reframe it as sparse-depth-guided depth generation. How the turntables! Check out the paper anyway 👇
Team ETH Zürich: Massimiliano Viola (@mviola), Kevin Qu (@KevinQu7), Nando Metzger (@nandometzger), Bingxin Ke (@Bingxin), Alexander Becker, Konrad Schindler, and Anton Obukhov (@toshas). We thank Hugging Face for their continuous support.
- Pre-training code with nanotron - Evaluation suite with lighteval - Synthetic data generation using distilabel (powers our new SFT dataset HuggingFaceTB/smoltalk) - Post-training scripts with TRL & the alignment handbook - On-device tools with llama.cpp for summarization, rewriting & agents
Apache 2.0 licensed. V2 pre-training data mix coming soon!