GimmBO: Interactive Generative Image Model Merging via Bayesian Optimization
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917223808892928 |
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| author | Liu, Chenxi Ling, Selena Jacobson, Alec |
| author_facet | Liu, Chenxi Ling, Selena Jacobson, Alec |
| contents | Fine-tuning-based adaptation is widely used to customize diffusion-based image generation, leading to large collections of community-created adapters that capture diverse subjects and styles. Adapters derived from the same base model can be merged with weights, enabling the synthesis of new visual results within a vast and continuous design space. To explore this space, current workflows rely on manual slider-based tuning, an approach that scales poorly and makes weight selection difficult, even when the candidate set is limited to 20-30 adapters. We propose GimmBO to support interactive exploration of adapter merging for image generation through Preferential Bayesian Optimization (PBO). Motivated by observations from real-world usage, including sparsity and constrained weight ranges, we introduce a two-stage BO backend that improves sampling efficiency and convergence in high-dimensional spaces. We evaluate our approach with simulated users and a user study, demonstrating improved convergence, high success rates, and consistent gains over BO and line-search baselines, and further show the flexibility of the framework through several extensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18585 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GimmBO: Interactive Generative Image Model Merging via Bayesian Optimization Liu, Chenxi Ling, Selena Jacobson, Alec Computer Vision and Pattern Recognition Graphics I.3.6; I.4.9 Fine-tuning-based adaptation is widely used to customize diffusion-based image generation, leading to large collections of community-created adapters that capture diverse subjects and styles. Adapters derived from the same base model can be merged with weights, enabling the synthesis of new visual results within a vast and continuous design space. To explore this space, current workflows rely on manual slider-based tuning, an approach that scales poorly and makes weight selection difficult, even when the candidate set is limited to 20-30 adapters. We propose GimmBO to support interactive exploration of adapter merging for image generation through Preferential Bayesian Optimization (PBO). Motivated by observations from real-world usage, including sparsity and constrained weight ranges, we introduce a two-stage BO backend that improves sampling efficiency and convergence in high-dimensional spaces. We evaluate our approach with simulated users and a user study, demonstrating improved convergence, high success rates, and consistent gains over BO and line-search baselines, and further show the flexibility of the framework through several extensions. |
| title | GimmBO: Interactive Generative Image Model Merging via Bayesian Optimization |
| topic | Computer Vision and Pattern Recognition Graphics I.3.6; I.4.9 |
| url | https://arxiv.org/abs/2601.18585 |