Anchor Token Matching: Implicit Structure Locking for Training-free AR Image Editing

• URL: http://arxiv.org/abs/2504.10434v1
• Date: Mon, 14 Apr 2025 17:25:19 GMT
• Title: Anchor Token Matching: Implicit Structure Locking for Training-free AR Image Editing
• Authors: Taihang Hu, Linxuan Li, Kai Wang, Yaxing Wang, Jian Yang, Ming-Ming Cheng,
• Abstract summary: Implicit Structure Locking (ISLock) is the first training-free editing strategy for AR visual models.<n>Our method preserves structural blueprints by dynamically aligning self-attention patterns with reference images.<n>Our findings pioneer the way for efficient and flexible AR-based image editing, further bridging the performance gap between diffusion and autoregressive generative models.
• Score: 60.102602955261084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image generation has seen groundbreaking advancements with diffusion models, enabling high-fidelity synthesis and precise image editing through cross-attention manipulation. Recently, autoregressive (AR) models have re-emerged as powerful alternatives, leveraging next-token generation to match diffusion models. However, existing editing techniques designed for diffusion models fail to translate directly to AR models due to fundamental differences in structural control. Specifically, AR models suffer from spatial poverty of attention maps and sequential accumulation of structural errors during image editing, which disrupt object layouts and global consistency. In this work, we introduce Implicit Structure Locking (ISLock), the first training-free editing strategy for AR visual models. Rather than relying on explicit attention manipulation or fine-tuning, ISLock preserves structural blueprints by dynamically aligning self-attention patterns with reference images through the Anchor Token Matching (ATM) protocol. By implicitly enforcing structural consistency in latent space, our method ISLock enables structure-aware editing while maintaining generative autonomy. Extensive experiments demonstrate that ISLock achieves high-quality, structure-consistent edits without additional training and is superior or comparable to conventional editing techniques. Our findings pioneer the way for efficient and flexible AR-based image editing, further bridging the performance gap between diffusion and autoregressive generative models. The code will be publicly available at https://github.com/hutaiHang/ATM

Related papers

• EditAR: Unified Conditional Generation with Autoregressive Models [58.093860528672735]
  We propose EditAR, a single unified autoregressive framework for a variety of conditional image generation tasks.<n>The model takes both images and instructions as inputs, and predicts the edited images tokens in a vanilla next-token paradigm.<n>We evaluate its effectiveness across diverse tasks on established benchmarks, showing competitive performance to various state-of-the-art task-specific methods.
  arXiv  Detail & Related papers  (2025-01-08T18:59:35Z)
• Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing [66.48853049746123]
  We analyze reconstruction from a structural perspective and propose a novel approach that replaces traditional cross-attention with uniform attention maps.<n>Our method effectively minimizes distortions caused by varying text conditions during noise prediction.<n> Experimental results demonstrate that our approach not only excels in achieving high-fidelity image reconstruction but also performs robustly in real image composition and editing scenarios.
  arXiv  Detail & Related papers  (2024-11-29T12:11:28Z)
• Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing [60.730661748555214]
  We introduce textbfTask-textbfOriented textbfDiffusion textbfInversion (textbfTODInv), a novel framework that inverts and edits real images tailored to specific editing tasks. ToDInv seamlessly integrates inversion and editing through reciprocal optimization, ensuring both high fidelity and precise editability.
  arXiv  Detail & Related papers  (2024-08-23T22:16:34Z)
• LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation? [10.72249123249003]
  We revisit diffusion models, highlighting their capacity for holistic context modeling and parallel decoding. We introduce a novel architecture, LaDiC, which utilizes a split BERT to create a dedicated latent space for captions. LaDiC achieves state-of-the-art performance for diffusion-based methods on the MS dataset with 38.2 BLEU@4 and 126.2 CIDEr.
  arXiv  Detail & Related papers  (2024-04-16T17:47:16Z)
• VASE: Object-Centric Appearance and Shape Manipulation of Real Videos [108.60416277357712]
  In this work, we introduce a framework that is object-centric and is designed to control both the object's appearance and, notably, to execute precise and explicit structural modifications on the object. We build our framework on a pre-trained image-conditioned diffusion model, integrate layers to handle the temporal dimension, and propose training strategies and architectural modifications to enable shape control. We evaluate our method on the image-driven video editing task showing similar performance to the state-of-the-art, and showcasing novel shape-editing capabilities.
  arXiv  Detail & Related papers  (2024-01-04T18:59:24Z)
• Forgedit: Text Guided Image Editing via Learning and Forgetting [17.26772361532044]
  We design a novel text-guided image editing method, named as Forgedit. First, we propose a vision-language joint optimization framework capable of reconstructing the original image in 30 seconds. Then, we propose a novel vector projection mechanism in text embedding space of Diffusion Models.
  arXiv  Detail & Related papers  (2023-09-19T12:05:26Z)
• DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models [66.43179841884098]
  We propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Our method achieves various editing modes for the generated or real images, such as object moving, object resizing, object appearance replacement, and content dragging.
  arXiv  Detail & Related papers  (2023-07-05T16:43:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.