Tootfinder

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Haoyu Wu, Diankun Wu, Tianyu He, Junliang Guo, Yang Ye, Yueqi Duan, Jiang Bian
https://arxiv.org/abs/2507.07982

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our ke…

🇺🇦 Auf radioeins läuft...
Joy Division:
🎵 Atmosphere
#NowPlaying #JoyDivision
https://mrlckmshp.bandcamp.com/track/innocence-division-beautiful-athmosphere-joy-division-atmosphere-vs-innocence-mission-beautiful-change
https://open.spotify.com/track/0NBgmP6Yg5xZYsnIZ0pGdo

DevCon (https://events.nvisia.com/devcon) is usually in August and I am yet to hear anything about a 2025 date. I think that it might be dead. 🥺

Discrete Diffusion Models for Language Generation
Ashen Weligalle
https://arxiv.org/abs/2507.07050 https://arxiv.org/pdf/2507.07050…

Discrete Diffusion Models for Language Generation
Diffusion models have emerged as a powerful class of generative models, achieving state-of-the-art results in continuous data domains such as image and video generation. Their core mechanism involves a forward diffusion process that gradually transforms structured data into a Gaussian-like distribution, followed by a learned reverse process to reconstruct the data. While successful in continuous modalities, applying this framework to discrete data-particularly natural language-remains challengi…

J.K. Dobbins Remains In AFC West – The Division Of Running Backs Gets Deeper https://raiderramble.com/2025/06/10/j-k-dobbins-remains-afc-west-division-of-rbs-gets-deeper/

This https://arxiv.org/abs/2506.05960 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

AQUATIC-Diff: Additive Quantization for Truly Tiny Compressed Diffusion Models
Significant investments have been made towards the commodification of diffusion models for generation of diverse media. Their mass-market adoption is however still hobbled by the intense hardware resource requirements of diffusion model inference. Model quantization strategies tailored specifically towards diffusion models have been useful in easing this burden, yet have generally explored the Uniform Scalar Quantization (USQ) family of quantization methods. In contrast, Vector Quantization (VQ…

This https://arxiv.org/abs/2409.08906 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Gaussian is All You Need: A Unified Framework for Solving Inverse Problems via Diffusion Posterior Sampling
Diffusion models can generate a variety of high-quality images by modeling complex data distributions. Trained diffusion models can also be very effective image priors for solving inverse problems. Most of the existing diffusion-based methods integrate data consistency steps by approximating the likelihood function within the diffusion reverse sampling process. In this paper, we show that the existing approximations are either insufficient or computationally inefficient. To address these issues…

Diffusion Models for Safety Validation of Autonomous Driving Systems
Juanran Wang, Marc R. Schlichting, Harrison Delecki, Mykel J. Kochenderfer
https://arxiv.org/abs/2506.08459

Diffusion Models for Safety Validation of Autonomous Driving Systems
Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising diffusion model to generate potential failure cases of an autonomous vehicle given any initial traffic state. Experiments on a four-way intersection problem show that in a variety of scenarios, the diffusion model can generate realistic failure samples while capt…

Concept Unlearning by Modeling Key Steps of Diffusion Process
Chaoshuo Zhang, Chenhao Lin, Zhengyu Zhao, Le Yang, Qian Wang, Chao Shen
https://arxiv.org/abs/2507.06526

Concept Unlearning by Modeling Key Steps of Diffusion Process
Text-to-image diffusion models (T2I DMs), represented by Stable Diffusion, which generate highly realistic images based on textual input, have been widely used. However, their misuse poses serious security risks. While existing concept unlearning methods aim to mitigate these risks, they struggle to balance unlearning effectiveness with generative retainability.To overcome this limitation, we innovatively propose the Key Step Concept Unlearning (KSCU) method, which ingeniously capitalizes on th…

Diffusion Dataset Condensation: Training Your Diffusion Model Faster with Less Data
Rui Huang, Shitong Shao, Zikai Zhou, Pukun Zhao, Hangyu Guo, Tian Ye, Lichen Bai, Shuo Yang, Zeke Xie
https://arxiv.org/abs/2507.05914

Diffusion Dataset Condensation: Training Your Diffusion Model Faster with Less Data
Diffusion models have achieved remarkable success in various generative tasks, but training them remains highly resource-intensive, often requiring millions of images and many days of GPU computation. From a data-centric perspective addressing this limitation, we study diffusion dataset condensation as a new and challenging problem setting. The goal is to construct a "synthetic" sub-dataset with significantly fewer samples than the original dataset, enabling high-quality diffusion model trainin…