Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior

Researchers have developed Diversity-inducing Initialization (DivIn), a method that addresses mode collapse in generative AI models by sampling initial noise from a guidance potential posterior rather than using standard Gaussian initialization. The technique uses Langevin dynamics to steer initial conditions toward diversity-rich regions while maintaining data validity, improving performance in both image and text-to-image generation tasks.

Mode collapse represents a persistent challenge in generative modeling where AI systems converge toward producing limited variations of outputs despite capable underlying architectures. This research identifies initialization as a critical but overlooked intervention point, challenging the assumption that standard Gaussian noise provides an agnostic starting point for generation. The authors demonstrate that guidance potential landscapes inherently contain structural information that early-stage generation processes fail to leverage, causing trajectories to collapse into dominant modes before meaningful diversity can emerge.

The proposed DivIn method fundamentally reframes the initialization problem by treating it as a posterior sampling challenge. Rather than accepting Gaussian randomness, the approach actively navigates initialization space using Langevin dynamics, a technique borrowed from statistical physics that allows efficient exploration of complex probability distributions. This positions the initial noise selection as a learned process that understands the relationship between input conditions and the broader output manifold.

The significance lies in DivIn's orthogonal compatibility with existing trajectory-based diversity methods. When combined, these approaches expand the achievable diversity-quality Pareto frontier beyond either method alone, suggesting genuine complementarity rather than incremental improvement. For practitioners deploying diffusion and flow matching models in production systems, this represents an inference-time enhancement requiring no model retraining.

The research validates DivIn across class-conditional and text-to-image scenarios, indicating robustness across different guidance modalities. Moving forward, the generalizability to other generative architectures and the computational overhead during inference warrant investigation, particularly for applications with strict latency requirements.

• →Standard Gaussian initialization causes mode collapse by ignoring guidance potential landscape structure, a critical oversight in current generative models
• →DivIn uses Langevin dynamics to sample from a guidance potential posterior, actively steering initial noise toward diversity-rich regions
• →The method works as inference-time enhancement compatible with diffusion and flow matching models without requiring retraining
• →Combining DivIn with trajectory-based diversity methods achieves superior diversity-quality trade-offs than either approach independently
• →Extensive experiments demonstrate performance gains in both class-conditional and text-to-image generation tasks