Prompt2Effect: Training-Free Image-to-Video Model Specialization via LoRA Generation
Quick Answer
Prompt2Effect introduces a weight-driven hypernetwork for image-to-video (I2V) diffusion models, enabling effect-specific LoRA weight synthesis in just 3.3 seconds, drastically reducing training costs from 56 GPU hours.
Quick Take
Prompt2Effect introduces a weight-driven hypernetwork for image-to-video (I2V) diffusion models, enabling effect-specific LoRA weight synthesis in just 3.3 seconds, drastically reducing training costs from 56 GPU hours. It achieves comparable or superior video quality and effect alignment compared to traditional LoRA fine-tuning, enhancing performance and speeding up optimization by 10x when used for fine-tuning.
Key Points
- Prompt2Effect synthesizes effect-specific LoRA weights in a single forward pass.
- Reduces training time from 56 GPU hours to just 3.3 seconds.
- Achieves comparable or superior video quality to conventional LoRA fine-tuning.
- Introduces SVD-canonicalized parameterization for stable weight synthesis.
- Improves optimization speed by approximately 10x when used for fine-tuning.
Paper Resources
Article Content
From source RSS / original summaryarXiv: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.
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