arXiv:2606.11751v1 Announce Type: cross Abstract: Multi-turn image editing is essential for iterative design, yet current models often struggle with identity drift and error accumulation over successive steps. While existing research leverages video priors for consistency, their reliance on bidirectional attention is fundamentally misaligned with the causal, sequential nature of interactive editing. In this paper, we propose AnchorEdit, the first autoregressive (AR) diffusion-based framework designed specifically for high-resolution, long-term multi-turn editing. AnchorEdit bridges the gap bet
The rapid advancement in generative AI and diffusion models necessitates solutions for practical, iterative applications, pushing the frontier of stable and consistent image editing.
Improving multi-turn image editing means more efficient and scalable creative workflows, crucial for industries from entertainment to product design, accelerating AI's integration into complex visual tasks.
This development proposes a method to significantly reduce identity drift and error accumulation in sequential AI-driven image modifications, making long-term iterative creative processes more viable and reliable.
- · AI content creators
- · Creative industries (film, design)
- · AI model developers
- · Software companies
- · Platforms with inconsistent editing tools
- · Manual editing workflows
Iterative AI design processes become more efficient and produce higher quality outputs due to improved temporal consistency.
This efficiency could lead to a broader adoption of AI in complex visual content generation, reducing time-to-market for creative assets.
The enhanced capability for consistent long-term editing might enable the creation of AI-generated content indistinguishable from human-edited or real-world footage over extended sequences.
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Read at arXiv cs.AI