HimmBO introduces a two‑stage Bayesian optimization framework for merging large collections of lightweight adapters in text‑to‑image models. The screening stage identifies an active subset of adapters via level‑set estimation on pre‑generated images, avoiding costly image generation, while the refinement stage optimizes merge coefficients within this reduced subspace using a style‑aware kernel. Experiments show HimmBO outperforms existing methods in similarity and efficiency, maintaining performance even as the adapter collection scales to 1,080 items.
By Jinyuan Mao, Zhihao Shen, Chenxi Liu
arXiv:2610.09015v1 Announce Type: new
Abstract: Diffusion models can generate high-quality images, yet aligning their outputs with individual user preferences remains challenging. A key bottleneck is...
By Jiamu Bai, Jiaming Hu, Yanhong Wu, Zellux Wang
Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis. However, these models are computationally expensive in runtime, and their cost scales severely with the number of input references.
Recent text-to-image models such as DALLE-3 excel at following diverse prompts yet remain blind to individual aesthetic preferences. We study personalized image generation, where models must align outputs with a user's implicit visual preferences based on a few historically preferred images and a short prompt.
The paper introduces InterIL, a joint generative model that simultaneously produces a background image and a layout of foreground elements for graphic design templates, addressing the limitations of sequential generation approaches. InterIL connects pretrained image and layout diffusion backbones via a learnable communication module, freezing the backbones to preserve prior knowledge while training only the interaction module. The model also offers a test‑time guidance strategy, enabling users to impose preferences without retraining, and demonstrates superior image, layout, and harmonization quality compared to previous methods.
By Shirong Yang, Bo Yang, Ying Cao
Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows. Their inherent stochasticity causes minor variations in textual prompts or hyperparameters to yield drastically different outputs often necessitating inefficient, brute-force trial-and-error processes.