# Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation

arXiv: https://arxiv.org/abs/2606.13971 · HTML: https://mlsanity.com/paper/2606.13971/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 28 of its 34 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models](https://mlsanity.com/paper/2404.02948/) (2024)
- [HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models](https://mlsanity.com/paper/2307.06949/) (2023)
- [Text-to-LoRA: Instant Transformer Adaption](https://mlsanity.com/paper/2506.06105/) (2025)
- [StyleMaster: Stylize Your Video with Artistic Generation and Translation](https://mlsanity.com/paper/2412.07744/) (2024)
- [Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions](https://mlsanity.com/paper/2401.01827/) (2024)
- [Optimal CUR Matrix Decompositions](https://mlsanity.com/paper/1405.7910/) (2014)
- [Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation](https://mlsanity.com/paper/2508.07981/) (2025)
- [Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment](https://mlsanity.com/paper/2502.16894/) (2025)
- [Dynamic Concepts Personalization from Single Videos](https://mlsanity.com/paper/2502.14844/) (2025)
- [Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights](https://mlsanity.com/paper/2506.16406/) (2025)
- [VFX Creator: Animated Visual Effect Generation with Controllable Diffusion Transformer](https://mlsanity.com/paper/2502.05979/) (2025)
- [Video-As-Prompt: Unified Semantic Control for Video Generation](https://mlsanity.com/paper/2510.20888/) (2025)
- [DiffLoRA: Generating Personalized Low-Rank Adaptation Weights with Diffusion](https://mlsanity.com/paper/2408.06740/) (2024)
- [Zero-Shot Dynamic Concept Personalization with Grid-Based LoRA](https://mlsanity.com/paper/2507.17963/) (2025)
- [ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion](https://mlsanity.com/paper/2503.24354/) (2025)
- [EasyVFX: Frequency-Driven Decoupling for Resource-Efficient VFX Generation](https://mlsanity.com/paper/2605.22051/) (2026)
- [LoFA: Learning to Predict Personalized Priors for Fast Adaptation of Visual Generative Models](https://mlsanity.com/paper/2512.08785/) (2025)

## Abstract (arXiv)

While personalizing Image-to-Video (I2V) diffusion models with specific visual effects is increasingly demanded for high-end 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 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 synthesis. 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.