用动态束搜索生成连贯的视觉序列，让视频故事更自然。
Latent Beam Diffusion Models for Generating Visual Sequences
- 通过动态采样历史隐变量，优化序列生成路径
- 在文本与视觉上下文中实现更高一致性，提升画面连贯性
- 适合需要长序列视觉生成的应用，如叙事视频
尽管扩散模型在文本到图像生成上表现优异，但在生成图像序列时难以保持视觉一致性。现有方法独立生成每张图像，导致叙事断裂，非线性叙事中问题更严重。本文提出一种新的潜空间束搜索策略，实现条件化生成完整图像序列。不同于依赖固定潜先验的方法，本方法动态采样过去潜变量，搜索最优潜表示序列，确保视觉过渡连贯。在潜空间去噪过程中，利用交叉注意力机制对搜索路径进行评分并剪枝，高效优先考虑与文本提示和视觉上下文的一致性。人类评估与自动评测均表明，BeamDiffusion优于基线方法，在序列连贯性、视觉连续性和文本对齐方面均有显著提升。
原文摘要 · Abstract (English)
While diffusion models excel at generating high-quality images from text prompts, they struggle with visual consistency when generating image sequences. Existing methods generate each image independently, leading to disjointed narratives - a challenge further exacerbated in non-linear storytelling, where scenes must connect beyond adjacent images. We introduce a novel beam search strategy for latent space exploration, enabling conditional generation of full image sequences with beam search decoding. In contrast to earlier methods that rely on fixed latent priors, our method dynamically samples past latents to search for an optimal sequence of latent representations, ensuring coherent visual transitions. As the latent denoising space is explored, the beam search graph is pruned with a cross-attention mechanism that efficiently scores search paths, prioritizing alignment with both textual prompts and visual context. Human and automatic evaluations confirm that BeamDiffusion outperforms other baseline methods, producing full sequences with superior coherence, visual continuity, and textual alignment.
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