arXiv:2606.13971v1 Announce Type: new 
Abstract: Personalizing Image-to-Video (I2V) diffusion models with specific visual effects is increasingly demanded for high-end video generation. Current practice requires training a separate Low-Rank Adaptation (LoRA) module for each effect, incurring substantial data curation and iterative optimization costs that hinder interactive control. We present Prompt2Effect, a weight-driven hypernetwork that amortizes per-effect training by directly synthesizing effect-specific LoRA weights in a single forward pass. Unlike prior hypernetworks that regress adapter weights purely from semantics, Prompt2Effect is explicitly conditioned on the frozen base model weights, grounding weight prediction in the structural geometry of each layer. Furthermore, instead of predicting raw LoRA matrices, we introduce an SVD-canonicalized parameterization that resolves factorization ambiguity and stabilizes large-scale weight synthesis. Together, these design principles enable accurate and scalable LoRA prediction for high-dimensional I2V diffusion models. Extensive experiments demonstrate that Prompt2Effect achieves on-par or superior video quality and effect alignment compared to conventional LoRA fine-tuning, while reducing the computational cost from 56 GPU training hours to 3.3 seconds of hypernetwork inference. When used as initialization for subsequent fine-tuning, our predicted weights further improve final performance and accelerate optimization by approximately 10x.

تم تقديم نموذج جديد يسمى Prompt2Effect، والذي يسمح بتخصص نماذج الانتشار من الصورة إلى الفيديو دون الحاجة إلى تدريب مكثف. يستخدم هذا النموذج شبكة هايبر مدفوعة بالوزن لتوليد أوزان التكيف منخفضة الرتبة (LoRA) المحددة للتأثيرات في تمريرة واحدة، مما يقلل بشكل كبير من التكاليف المرتبطة بتنظيم البيانات وتحسينها.

Se ha presentado un nuevo modelo llamado Prompt2Effect, que permite la especialización de modelos de difusión de imagen a video sin necesidad de un entrenamiento extenso. Este modelo utiliza una hiperred neuronal impulsada por pesos para generar pesos de Adaptación de Bajo Rango (LoRA) específicos para efectos en una sola pasada, reduciendo significativamente los costos asociados con la curación de datos y la optimización.

Un nouveau modèle nommé Prompt2Effect a été introduit, permettant la spécialisation des modèles de diffusion image-à-vidéo sans nécessiter d'entraînement intensif. Ce modèle utilise un hyperréseau piloté par le poids pour générer des poids d'adaptation à faible rang (LoRA) spécifiques aux effets en une seule passe avant, réduisant ainsi considérablement les coûts associés à la curation de données et à l'optimisation.

A new model named Prompt2Effect has been introduced, which allows for the specialization of image-to-video diffusion models without the need for extensive training. This model utilizes a weight-driven hypernetwork to generate effect-specific Low-Rank Adaptation (LoRA) weights in a single forward pass, significantly reducing the costs associated with data curation and optimization.