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.
Abstract
Test-Time Tuning (TTT) on pretrained diffusion models has emerged as a powerful paradigm for video editing. However, there exists a foundational mismatch between the distribution-mapping nature of generative models and the single-point optimization of standard TTT. In this paper, we demonstrate that this mismatch triggers \textit{Prior Collapse}, a degenerate state where the model discards the text conditions and spatial latents, collapsing generations to the source video, or entangling the features of distinct regions. To resolve this, we propose \textbf{ElasticTTT}, a novel framework that preserves the prior generative distribution and rescues generative elasticity. Specifically, we propose \textit{Target Distribution Regularization} to prevent sharp memorization minima, \textit{Contrastive CFG} to guide inference away from source biases, and \textit{Asynchronous Noise Schedule} to preserve unedited regions. Extensive evaluations, supported by theoretical analysis, demonstrate that ElasticTTT successfully preserves the generative prior of the base model, achieving state-of-the-art performance on one-shot video editing.
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.· 0 citations
VISTA is the first gradient-based test-time alignment framework for next-scale autoregressive image generation, and introduces the mechanisms needed to make such optimization stable across scales, together with an extensible objective space that any differentiable constraint on cross-attention can plug into.
Hossein Shahabadi, Niki Sepasian, M. Baghshah· 0 citations
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.
Yu Yamamoto, Qiu Chen· International Conference on...· 0 citations
Backbone training-free video editing built on pre-trained text-to-image (T2I) diffusion models enables lightweight, prompt-driven edits without additional finetuning. A critical yet often overlooked factor is cross-frame latent selection during DDIM inversion, which largely determines spatiotemporal coherence in the subsequent denoising process. Existing pipelines typically rely on static, heuristic keyframe policies and temperature-softmax responsibilities, yielding unscalability i.e., numerical sensitivity and scale bias, that degrades generalization across diverse scenes. In this paper, we propose VIVID (Variational Inference for Video editing with Image Diffusion), an uncertainty-aware variational latent anchoring module that dynamically selects informative frames and compresses cross-frame latents into a compact set of semantic anchors. VIVID learns stable assignments via a variational objective with contrastive alignment and prior regularization, producing anchors that preserve spatial details while enforcing temporal continuity, and can be plugged into existing backbone training-free T2I-based video editing frameworks as a drop-in replacement for heuristic selection. Extensive experiments on standard benchmarks and in-the-wild videos demonstrate that VIVID achieves state-of-the-art inversion fidelity, editing quality, and temporal consistency, while reducing memory and runtime compared with prior backbone training-freebaselines. Code is released in: https://github.com/amasawa/VIVID.
Zhangkai Wu, Xuhui Fan, Zhongyuan Xie et al.· Proceedings of the 32nd ACM...· 0 citations
NEAF (Natural image Editing with Attention Fusion), a novel zero-shot, universal tuning-free framework for arbitrary pre-trained T2I models, obviating the need for dataset curation or retraining, is proposed.
The results show that careful tokenizer--backbone--system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family.
Xinjie Zhang, Peng Zhang, Shicheng Zheng et al.· 2 citations
