We don’t have the answers but give you a tool to test them. We welcome feedback and contributions!.

SRM results show there’s no single best setup for every dataset or task:. *  Which noise schedule, parameterization, or diffusion formulation works best?.*  DiT, UNet, U-ViT, or something custom?.*  Train from scratch or fine-tune?.*  How much to sequentialize and which order?.

Just define the variables — elements of a sample that share the same noise level — and the package does the rest. Below are examples for image patches and video frames as variables.

Spatial Reasoners allow to bring (soft) autoregressive diffusion/flow models to a wider range of domains — aiming to facilitate research on denoising models for reasoning and generation tasks over multiple variables.

Poster: #CodeML Workshop · West Meeting Room 211-214 · 2:15 pm on Friday. Webpage: Github: Paper:

You can bring our Sudoku solving diffusion models to other domains!. If you are at interested and at #ICML2025, come see @bartek_pog and @ChrisWewer's 🌀 Spatial Reasoners package — now released in beta!. Here are some examples for images and videos. Links below.

@ChrisWewer @bartek_pog PS: 🌀We recently released spatial-reasoners, a general toolkit to apply SRMs to a wide range of different domains:

@ChrisWewer @bartek_pog We find that model hallucination can be drastically reduced by choosing the right configuration, allowing to significantly increase performance in complex reasoning tasks like solving visual Sudoku.

@ChrisWewer @bartek_pog Our Spatial Reasoning Models allow to explore the space between parallel and autoregressive diffusion models with different methods for choosing generation order. Project Website:

Can diffusion models solve visual Sudoku? . If you are at #ICML2025, come to our poster in the Wednesday morning poster session (Poster Session 3 East, Poster 3412) and find out!. @ChrisWewer @bartek_pog Bernt Schiele @janericlenssen

RT @XianghuiXie: 📢Is your multi-view generation (MVG) model 3D consistent? Do they produce high-quality and semantically correct novel view….

MET3R quantitatively measures 3D consistency between two images via DUSt3R reconstruction and feature comparison. It does not require camera poses. Code is available for plug-and-play use. We also provide an open source multi-view latent diffusion model for further research!.

Project page:

At #CVPR2025 and working on consistency in video and multi-view generative models?. Come and visit our poster on Friday afternoon, where I present 𝗠𝗘𝘁𝟯𝗥: 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗩𝗶𝗲𝘄 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗶𝗻 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗜𝗺𝗮𝗴𝗲𝘀

RT @Kumo_ai_team: Introducing KumoRFM – the world’s first Relational Foundation Model for enterprise data. Instantly generate accurate pred….

@ChrisWewer @bartek_pog We also show that good orders can be predicted by uncertainty, which is crucial for the Sudoku task to be solved well.

@ChrisWewer @bartek_pog It allows to explore the amount of (soft) sequentialization and the order of generation, both having significant impact on reasoning quality.

@ChrisWewer @bartek_pog Spatial Reasoning Models (SRMs) are a framework to propagate belief over a set of continuous variables (e.g. image patches) with generative denoising models.

Can image generators solve visual Sudoku? . Naively, no - with sequentialization and the correct order, they can!. Check out @ChrisWewer's and @bartek_pog's work, SRM, for details. Project: Paper: Code:

RT @haiyang73756134: Excited to see our paper "Tokenformer: Rethinking transformer scaling with tokenized model parameters" accepted as a s….