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Multi-LoRA Composition for Image Generation
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Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images. Nonetheless, existing methods face challenges in effectively composing multiple LoRAs, especially as the number of LoRAs to be integrated grows, thus hindering the creation of complex imagery. In this paper, we study multi-LoRA composition through a decoding-centric perspective. We present two training-free methods: LoRA Switch, which alternates between different LoRAs at each denoising step, and LoRA Composite, which simultaneously incorporates all LoRAs to guide more cohesive image synthesis. To evaluate the proposed approaches, we establish ComposLoRA, a new comprehensive testbed as part of this research. It features a diverse range of LoRA categories with 480 composition sets. Utilizing an evaluation framework based on GPT-4V, our findings demonstrate a clear improvement in performance with our methods over the prevalent baseline, particularly evident when increasing the number of LoRAs in a composition. The code, benchmarks, LoRA weights, and all evaluation details are available on our project website: https://maszhongming.github.io/Multi-LoRA-Composition.
Forward citations
Cited by 7 Pith papers
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Human Preference-Aligned Concept Customization Benchmark via Decomposed Evaluation
D-GPTScore, which averages GPT-4o's per-aspect ratings of concept-customized images, correlates with human preference at 0.78 Pearson on the new CC-AlignBench, beating prior metrics.
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LobRA: Multi-tenant Fine-tuning over Heterogeneous Data
LobRA reduces GPU seconds for multi-tenant LoRA fine-tuning by 45.03%-60.67% through heterogeneous FT replicas and per-step workload-balanced dispatching.
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FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization
A method for multi-subject image personalization that fuses independently trained LoRA modules at inference time on visual autoregressive models.
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MultLFG: Training-free Multi-LoRA composition using Frequency-domain Guidance
MultLFG merges multiple LoRA adapters by adaptively weighting them in wavelet frequency subbands per denoising timestep, improving multi-concept composition on the ComposLoRA benchmark compared to prior training-free methods.
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EmoStyle: Affective Conditioning of Style-Specialist Experts for Emotional Image Generation
EmoStyle injects LLM-inferred valence-arousal and emotion labels into Z-Image via AdaLN-style residual modulation over style-bucket LoRA experts, plus VLM candidate ranking, and ranked first on AffectiveArt Track 1.
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LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion
LiON-LoRA adds a learned scaling token to video-diffusion LoRA adapters, enabling linear and independent control of camera trajectory and object motion strength.
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QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation
QR-LoRA freezes the QR-decomposed basis of pretrained weights, trains only a residual matrix, and reports halved trainable parameters with improved content-style disentanglement in diffusion models.
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