This work introduces LILAC, a framework that composes independently trained low-rank adapters at inference time: each subject is conditioned on the frozen composite of previously placed subjects, with exactly one adapter active at a time, therefore identities never interfere at the parameter level.
Abstract
Personalizing text-to-image diffusion models to render several specific subjects in a coherent image remains challenging: the model must preserve each subject's identity while keeping the scene spatially and visually coherent. Methods that fuse independently trained concept adapters in a shared weight space (via federated averaging, gradient fusion, or orthogonality constraints) suffer from identity confusion and style bleeding and require joint retraining. In this work, we show that composing concepts as separate image layers, instead of merging their adapters in a shared weight space, avoids parameter-level interference. We introduce LILAC, a framework that composes independently trained low-rank adapters at inference time: each subject is conditioned on the frozen composite of previously placed subjects, with exactly one adapter active at a time, therefore identities never interfere at the parameter level. LILAC composes the adapters without any joint training, scales linearly with the number of concepts, and is backbone-agnostic. Under the Orthogonal Adaptation protocol, LILAC applied on Qwen-Image-Edit reaches an ArcFace detection rate of 0.861, while Orthogonal Adaptation reports 0.745 in its original setting. Adaptation reports 0.745 in its original setting. Code is available at https://github.com/marianlupascu/LILAC.
Chimera, a hybrid visual diffusion backbone with a principled scaling recipe that combines Kimi Delta Attention for long-context state tracking with O(N) complexity, interleaved Multi-head Latent Attention (MLA) for direct global interaction, and modality-aware short convolutions for local spatiotemporal context is introduced.
Chongjian Ge, Hanwen Jiang, Tianyu Wang et al.· 1 citation
Chunked Muon (CMuon) is introduced, a simple yet highly effective strategy that partitions these matrices into independent sub-components prior to orthogonalization, effectively overcoming the late-stage convergence plateaus of vanilla Muon.
TASSO, a new paradigm that efficiently preserves the latent space geometry while ensuring network plasticity, is introduced with two complementary techniques: subspace learning and geometry-aware knowledge distillation.
Changming Sun, Francesco Barbato, Matteo Caligiuri et al.· 0 citations
Structural Parameter-free Affinity Regularization (SPARE), a regularizer that matches the pairwise affinities of intermediate tokens to those of the clean latents across images, is proposed, a regularizer that attains the lowest FID among parameter-free regularizers in every tested setting.
Zong-Wei Hong, Jinglun Li, Shen Zhang et al.· 0 citations
Text-to-image diffusion transformers learn about objects and scenes by learning to generate them, making them strong candidates for training-free zero-shot open-vocabulary semantic segmentation. State-of-the-art attribution methods score each pixel independently, comparing its features against a fixed text-derived class representation, whether as an output-space similarity or as a cross-attention weight. This discards structured signals the model itself exposes: the temporal structure of the generative trajectory, the visual appearance statistics of each concept, and the image's own pairwise feature geometry. We present MAVISEG, a training-free refinement layer that recovers these signals. Because its operators consume only a pixel-by-concept score field and a pixel feature space, MAVISEG is capture-agnostic rather than tied to one attribution method. Across six benchmarks it achieves the strongest overall results among training-free methods, including the best mIoU on every benchmark. Interestingly, gains are largest where the initial capture is weakest, and individual operators contribute depending on the noise in the field they refine. Our results indicate that diffusion transformers carry more concept-level information than current attribution methods recover, and that much of it is lost on the way to the mask rather than absent from the model.
Rajatsubhra Chakraborty, Xujun Che, Ritabrata Chakraborty et al.· 0 citations
Though zero-shot adapters excel in image personalization, they often encounter significant challenges in multi-reference personalized generation, specifically failing to precisely adhere to the spatial layouts described in text prompts and suffering from feature leakage between reference images. To address these two challenges, we propose RIG (Regional Image-prompt Generation), a novel training-free framework. For the first challenge, leveraging Multimodal Large Language Models (MLLMs), we introduce a layout planning binder. Leveraging Chain-of-Thought (CoT) reasoning, this module infers and generates precise global layouts from text prompts, while simultaneously binding reference images to their corresponding regions. For the second, we introduce a satially decoupled diffusion mechanism that isolates feature streams during attention computation. By injecting reference features exclusively into designated regions, this mechanism effectively prevents feature interference between reference images. Extensive experiments demonstrate that RIG significantly outperforms state-of-the-art adapter methods in terms of both personalization fidelity and text-layout alignment.
Junhao Feng· Poster Volume 0008 The 2026...· 0 citations