On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering

Inference-time steering enables pretrained diffusion/flow models to be adapted to new tasks without retraining. A widely used approach is the ratio-of-densities method, which defines a time-indexed target path by reweighting probability-density trajectories from multiple models with positive, or in some cases, negative exponents. This construction, however, harbors a critical and previously unformalized failure mode: Marginal Path Collapse, where intermediate densities become non-normalizable even though endpoints remain valid. Collapse arises systematically when composing heterogeneous models trained on different noise schedules or datasets, including a common setting in molecular design where de-novo, conformer, and pocket-conditioned models must be combined for tasks such as flexible-pose scaffold decoration. We provide a novel and complete solution for the problem. First, we derive a simple path existence criterion that predicts exactly when collapse occurs from noise schedules and exponents alone. Second, we introduce Adaptive path Correction with Exponents (ACE), which extends Feynman-Kac steering to time-varying exponents and guarantees a valid probability path. On a synthetic 2D benchmark and on flexible-pose scaffold decoration, ACE eliminates collapse and enables high-guidance compositional generation, improving distributional and docking metrics over constant-exponent baselines and even specialized task-specific scaffold decoration models. Our work turns ratio-of-densities steering with heterogeneous experts from an unstable heuristic into a reliable tool for controllable generation.

ISAC: Training-Free Instance-to-Semantic Attention Control for Improving Multi-Instance Generation

Text-to-image diffusion models excel at generating single-instance scenes but struggle with multi-instance scenarios, often merging or omitting objects. Unlike previous training-free approaches that rely solely on semantic-level guidance without addressing instance individuation, our training-free method, Instance-to-Semantic Attention Control (ISAC), explicitly resolves incomplete instance formation and semantic entanglement through an instance-first modeling approach. This enables ISAC to effectively leverage a hierarchical, tree-structured prompt mechanism, disentangling multiple object instances and individually aligning them with their corresponding semantic labels. Without employing any external models, ISAC achieves up to 52% average multi-class accuracy and 83% average multi-instance accuracy by effectively forming disentangled instances. The code will be made available upon publication.

DiffEGG: Diffusion-Driven Edge Generation as a Pixel-Annotation-Free Alternative for Instance Annotation

Achieving precise panoptic segmentation relies on pixel-wise instance annotations, but obtaining such datasets is costly. Unsupervised instance segmentation (UIS) eliminates annotation requirements but struggles with adjacent instance merging and single-instance fragmentation, largely due to the limitations of DINO-based backbones which lack strong instance separation cues. Weakly-supervised panoptic segmentation (WPS) reduces annotation costs using sparse labels (e.g., points, boxes), yet these annotations remain expensive and introduce human bias and boundary errors. To address these challenges, we propose DiffEGG (Diffusion-Driven EdGe Generation), a fully annotation-free method that extracts instance-aware features from pretrained diffusion models to generate precise instance edge maps. Unlike DINO-based UIS methods, diffusion models inherently capture fine-grained, instance-aware features, enabling more precise boundary delineation. For WPS, DiffEGG eliminates annotation costs and human bias by operating without any form of manual supervision, addressing the key limitations of prior best methods. Additionally, we introduce RIP, a post-processing technique that fuses DiffEGG's edge maps with segmentation masks in a task-agnostic manner. RIP allows DiffEGG to be seamlessly integrated into various segmentation frameworks. When applied to UIS, DiffEGG and RIP achieve an average $+4.4\text{ AP}$ improvement over prior best UIS methods. When combined with weakly-supervised semantic segmentation (WSS), DiffEGG enables WPS without instance annotations, outperforming prior best point-supervised WPS methods by $+1.7\text{ PQ}$. These results demonstrate that DiffEGG's edge maps serve as a cost-effective, annotation-free alternative to instance annotations, significantly improving segmentation without human intervention. Code is available at https://github.com/shjo-april/DiffEGG.