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By mapping symbolic equations into a continuous latent space, GenSR enables gradient-based optimization for symbolic regression, sidestepping the noisy feedback that plagues discrete search methods.
Forget static LoRA fusion – this method dynamically blends subject and style, guided by feature divergence and CLIP/DINO metrics, for superior image synthesis.
