Prompt2Effect: Training-Free LoRA Synthesis for Controllable Video Effects

While personalizing Image-to-Video (I2V) diffusion modelswith specific visual effects is increasingly demanded for high-end genera-tion, current practice requires training a separate Low-Rank Adaptation(LoRA) module for each effect, incurring substantial data curation anditerative optimization costs that hinder interactive control. We presentPrompt2Effect, a weight-driven hypernetwork that amortizes per-effecttraining by directly synthesizing effect-specific LoRA weights in a singleforward pass. Unlike prior hypernetworks that regress adapter weightspurely from semantics, Prompt2Effect is explicitly conditioned on thefrozen base model weights, grounding prediction in the structural geom-etry of each layer. Furthermore, instead of predicting raw LoRA matri-ces, we introduce an SVD-canonicalized parameterization that resolvesfactorization ambiguity and stabilizes large-scale synthesis. Extensive ex-periments demonstrate that Prompt2Effect achieves on-par or superiorvideo quality and effect alignment compared to conventional LoRA fine-tuning, while reducing the computational cost from 56 GPU traininghours to 3.3 seconds of hypernetwork inference. When used as initializa-tion for subsequent fine-tuning, our predicted weights further improvefinal performance and accelerate optimization by approximately 10×.