"LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation"

LoRA.rar: Instant style-content merging through hypernetworks for real-time image personalization.

LoRA.rar introduces a hypernetwork that instantly merges content and style LoRA parameters for personalized image generation, achieving 4000x speedup over existing methods.

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https://arxiv.org/abs/2412.05148

🎯 Original Problem:

Merging Low-Rank Adaptation (LoRA) parameters for subject and style personalization in image generation is computationally expensive and slow, making it impractical for real-time use on resource-constrained devices like smartphones.

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🔬 Solution in this Paper:

→ The paper introduces LoRA.rar, a 0.5M parameter hypernetwork that predicts merging coefficients for content and style LoRAs.

→ The hypernetwork is pre-trained on diverse content-style LoRA pairs to learn an efficient merging strategy.

→ It processes input LoRAs through separate layers matched to the generative model's dimensions, using a shared hidden layer.

→ The system applies hypernetwork-guided merging for query and output LoRAs while using simple averaging for key and value LoRAs.

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🧠 Key Insights:

→ Existing metrics like CLIP-I and DINO are inadequate for evaluating joint subject-style personalization

→ Multimodal LLMs provide more accurate assessment of generated image quality

→ Column-wise prediction of merging coefficients enables efficient parallel processing

→ Non-trivial adaptive merging strategy outperforms binary approaches

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📊 Results:

→ 4000x faster coefficient generation compared to ZipLoRA

→ 3x fewer parameters (0.49M vs 1.5M)

→ 71% average accuracy vs 58% for ZipLoRA in MARS2 metric

→ Zero additional memory overhead at test time