{"slug": "continuous-angular-control-in-text-to-image-generation-via-geometric-token", "title": "Continuous Angular Control in Text-to-Image Generation via Geometric Token Encoding", "summary": "A geometric token encoding framework raised analog-clock accuracy to 63-78% Acc@6 degrees across four pretrained text-to-image diffusion models, up from below 1% for baselines, according to arXiv paper 2611.13172v1. The method encodes angular components as periodic sine-cosine features projected into role-specific token embeddings injected into the prompt before a frozen text encoder, and adds Stochastic Localization Relaxation (SLR) to spatially ground the tokens without localization input at inference. On circular-fraction generation the framework reached 89.5% Acc@0.10, holding 88.8% on unseen object categories and 84.9% on unseen target values, versus roughly 20% for pretrained baselines.", "body_md": "arXiv:2611.13172v1 Announce Type: new \nAbstract: Text-to-image diffusion models can render a photorealistic analog clock but cannot control the time it displays. Across four pretrained models, fewer than 1% of generated clocks place both hands within 6 degrees of the requested time; the models instead collapse onto visually dominant configurations such as the canonical 10:10 pose. The same failure appears when a specific fraction of a circular object is requested, where models return complete or conventionally sliced objects. We observe that these two failures share a geometric structure: both target quantities can be parameterized by angular variables.\n  We introduce a geometric token encoding framework that conditions generation directly on these angles. Each angular component is encoded as a periodic sine-cosine feature and projected into a role-specific token embedding that is injected into the prompt before a frozen text encoder. To spatially ground these tokens without requiring localization input at inference, we introduce Stochastic Localization Relaxation (SLR), which stochastically relaxes attention supervision from component-level to object-level to unconstrained localization during training. For clocks, we additionally introduce a hand-swap preference objective that discourages assigning the correct angles to the wrong hand roles. On analog clocks our method reaches 63-78% Acc@6 degrees across evaluation methods, compared with below 1% for the baselines. On circular-fraction generation, it achieves 89.5% Acc@0.10, while maintaining 88.8% on unseen object categories and 84.9% on unseen target values, compared with roughly 20% for pretrained baselines.", "url": "https://wpnews.pro/news/continuous-angular-control-in-text-to-image-generation-via-geometric-token", "canonical_source": "https://arxiv.org/abs/2611.13172", "published_at": "2026-11-11 05:00:00+00:00", "updated_at": "2026-11-11 05:24:21.541079+00:00", "lang": "en", "topics": ["generative-ai", "computer-vision", "ai-research", "artificial-intelligence"], "entities": ["arXiv", "Stochastic Localization Relaxation"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/continuous-angular-control-in-text-to-image-generation-via-geometric-token", "markdown": "https://wpnews.pro/news/continuous-angular-control-in-text-to-image-generation-via-geometric-token.md", "text": "https://wpnews.pro/news/continuous-angular-control-in-text-to-image-generation-via-geometric-token.txt", "jsonld": "https://wpnews.pro/news/continuous-angular-control-in-text-to-image-generation-via-geometric-token.jsonld"}}