Jul 2026· International Conference on Image, Video and Signal Processing· Vol 14268, pp. 1426806 - 1426806-10· 0 citations· 13 references
Engineering
TL;DR
This study proposes an automated discrete "Latent Space Exploration" framework utilizing random search to optimize seed selection and concludes that discrete seed optimization offers a robust and practical solution for personalized generation.
Abstract
Text-to-Image diffusion models are highly effective but remain heavily sensitive to the initial noise. This sensitivity causes significant instability in personalization tasks, where maintaining a specific subject's identity is crucial. While inference-based methods like the W+ Adapter offer efficient alternatives to costly fine-tuning, they suffer from structural conflicts between identity preservation and prompt consistency depending on this noise. In this study, we address this issue by proposing an automated discrete "Latent Space Exploration" framework utilizing random search to optimize seed selection. We compare our discrete seed optimization approach against "Initial Noise Selection," a continuous optimization method that modifies the noise tensor directly via gradient descent. We define a multi-objective scoring function integrating text consistency (CLIP), identity preservation (ArcFace), and structural validity (MTCNN). Quantitative experiments reveal a critical trade-off: while continuous optimization preserves identity competitively, it frequently degrades text consistency by ignoring prompt contexts like clothing or backgrounds. In contrast, our discrete exploration achieves a superior balance, ensuring a 100% face detection rate while maximizing both identity fidelity and text alignment. Furthermore, a subjective evaluation with 151 participants confirms that our method yields significantly higher overall visual quality and prompt fidelity. We conclude that discrete seed optimization offers a robust and practical solution for personalized generation.
AnchorSteer is proposed, a training-free framework that exerts fine-grained control over both initialization and denoising trajectory that consistently outperforms existing baselines in text--image alignment while preserving high visual quality.
Xinyi Wang, Yuyang Huang, Yalin Su et al.· arXiv.org· 0 citations
This paper proposes ElasticTTT, a novel framework that preserves the prior generative distribution and rescues generative elasticity in standard TTT, achieving state-of-the-art performance on one-shot video editing.
Yueyi Liu, Chi Zhang, Sen Cui et al.· arXiv.org· 1 citation
Diffusion models have recently advanced text-to-video (T2V) generation, yet they still struggle with fine-grained compositional alignment, such as attribute binding, spatial relations, and object interactions. While reward-based fine-tuning improves alignment, it is susceptible to reward hacking and adapts poorly to new prompt distributions. In this work, we propose NoisEasier, a test-time scaling framework that improves T2V generation through differentiable reward-guided noise optimization without modifying the underlying model. By combining efficient short-step generators with a multi-objective reward formulation, NoisEasier enables stable and practical test-time optimization under realistic inference budgets. Our key insight is that jointly optimizing the entire stochastic trajectory accelerates reward convergence and improves compositional alignment over optimizing only the initial latent, with negligible additional computational and time cost. Experiments on VBench and T2V-CompBench demonstrate consistent improvements across multiple backbones, achieving over 10% average gains on challenging dimensions such as attribute binding, object interaction, and numeracy. Overall, NoisEasier serves as both a flexible alternative and a complementary enhancement to reward-based fine-tuning, establishing test-time scaling as an effective paradigm for controllable text-to-video generation.
A training-free Dual-path Attention Modulation (DAR) framework that decouples semantic edits while preserving source image structure is proposed and Adaptive Self-Attention (ASA) and Adaptive Cross-Attention (ACA) modules that dynamically regulate attention replacement are introduced.
Tong Cui, Jie Yang, Kairu Li et al.· International Conference on...· 0 citations
Generating and editing a person's face demands high precision, as even minor modifications can significantly alter a subject's perceived identity. Current personalization and editing methods built on general-purpose text-to-image models, however, often lack the precision required for fine-grained facial edits. We present a method for fine-grained identity tuning in text-to-image personalization models. Unlike standard image editing, which operates on a given image, identity tuning modifies the latent representation of a specific identity, enabling the generation of diverse images that consistently depict the same edited identity. To enable fine-grained latent identity tuning, we explore the latent space of a pre-trained, frozen encoder for text-to-image personalization. Our approach requires no additional training. Instead, it leverages the existing architecture of a frozen encoder to uncover latent semantic directions. This space consists of a set of latent tokens that play distinct roles in capturing different aspects of an identity and often correspond to specific spatial or semantic facial regions. We show that meaningful directions can be identified within this space and within subspaces defined by selected tokens, enabling localized, fine-grained, and semantically coherent edits. We validate our approach through qualitative and quantitative experiments that demonstrate diverse localized facial edits while preserving cross-image identity consistency. Project page at: https://garibida.github.io/IdentityTuning/
Daniel Garibi, Ronen Kamenetsky, Hadar Averbuch-Elor et al.· arXiv.org· 0 citations
Text-to-image diffusion models have achieved remarkable success in generating high-quality images from a given text prompt. Subject-driven generation aims to synthesize customized images to mimic the appearance of subjects in given reference images within different visual contexts specified by the text prompts. The central challenge here is that, when the reference image changes, the diffusion model cannot efficiently adapt to different visual contexts while consistently maintaining the subject identity. Existing methods either train the model with a large domain-specific dataset or fine-tune the model using the reference image for hundreds of iterations before actual image generation. In this work, we explore a new approach, called \textit{In-Loop Model Adaptation} (IMA), which adapts the core diffusion model at each generation step during the actual process of image generation, without being trained on the reference image before the generation process. To this end, we establish a DDIM inversion chain that maps the reference image to a sequence of latent, as well as a text-to-image generation chain which generates the image from the text prompt only. We then introduce a masked latent consistency loss and a noise regularization loss to characterize the latent-noise difference between the diffusion model and these two chains at each generation step. This coupled latent-noise loss is used to guide the in-loop model adaptation to preserve the subject identity specified by the reference image while maintaining accurate alignment with the text prompt, resulting in high-fidelity text-to-image generation. Our extensive experiments demonstrate that our proposed IMA method significantly improves the performance of subject-driven text-to-image generation.
Yushun Tang, Weiming Chen, Siyi Liu et al.· IEEE transactions on multime...· 0 citations
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