TBNAI
Controllable Diverse Sampling through Pullback Directions
Publication: cs.AI updates on arXiv.org
Author: Sylvain Dehayem Kenfouo, Aaron Zweig, Mingxuan Zhang, Elham Azizi
Source published: 14/10/2026, 04:00:00 UTC
Briefing created: 25/10/2026, 19:16:30 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 14/10/2026, 12:19:27 UTC.
Briefings
A new training-free method for diverse image generation uses local pullback geometry to identify sampling directions that maximize output sensitivity. Experiments on MS-COCO and BrushNet demonstrate improved diversity and consistency in prompt-conditioned image generation and inpainting tasks.
Key points
- The method leverages local pullback geometry induced by the mapping from conditioning-embedding space to image space to identify directions of maximal output sensitivity.
- Steering sampling trajectories along these directions encourages distinct generations while maintaining consistency with the conditioning signal.
Context
Conditional diffusion models generate high-quality images aligned with prompts but often produce visually similar samples from different seeds. This method offers a training-free approach to enhance diversity without additional optimization overhead.
Limitations
Results are limited to MS-COCO and BrushNet datasets. No additional background context is supplied beyond the abstract; preprint status and uncertainty remain.
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