扩散探针：使用 CNN 探针预测生成图像结果
Diffusion Probe: Generated Image Result Prediction Using CNN Probes
利用扩散模型早期去噪步骤的交叉注意力图统计特征，训练轻量级 CNN 探针预测最终图像质量，可用于提示词优化、种子选择与加速 RL 训练等需要早期质量引导的 T2I 工作流。
原文依据 we first reveal a strong correlation between the attention distribution in the early diffusion process and the final image quality
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论文本到图像（T2I）扩散模型目前缺乏一种高效的早期质量评估机制，在需要多次生成的场景（例如迭代提示词、基于智能体的图像生成、flow-grpo）中，这迫使用户进行代价高昂的随机试错。为解决这一问题，我们首先揭示了扩散过程早期的注意力分布与最终图像质量之间存在强相关性。基于这一发现，我们提出了扩散探针（Diffusion Probe），这是一个开创性框架，利用模型内部的交叉注意力图作为预测信号。我们提出一个轻量级预测器，训练它建立从这些早期交叉注意力分布的统计特性（从初始去噪步骤中提取）到最终图像综合质量的直接映射。这使得我们的探针能够在完整合成完成之前很久，就准确预测图像质量的各个方面，且不依赖于特定的真实质量指标。我们通过扩散探针在广泛条件下始终强劲的预测准确性，实证验证了其可靠性和泛化能力。在多种 T2I 模型（如 SDXL、FLUX、Qwen-Image）上，在广泛的早期去噪窗口内，跨各种分辨率，并使用不同的质量指标，它均实现了高相关性（PCC > 0.7）和分类性能（AUC-ROC > 0.9）。这种内在可靠性还在实践中得到进一步证明：它成功优化了受益于早期质量引导决策的 T2I 工作流，例如提示词优化、种子选择和加速强化学习训练。在这些应用中，探针的早期信号能够实现更有针对性的采样策略，从而避免在低潜力路径上进行代价高昂的计算。这带来了双重好处：显著降低计算开销，同时提升最终结果质量，使扩散探针成为一个模型无关且广泛适用的工具，有望彻底改变 T2I 的效率。
英文原文摘要 2027 字符
Text-to-image (T2I) diffusion models currently lack an efficient mechanism for early quality assessment, forcing costly random trial-and-error in scenarios requiring multiple generations (e.g., iterating on prompts, agent-based image generation, flow-grpo). To address this, we first reveal a strong correlation between the attention distribution in the early diffusion process and the final image quality. Building upon this insight, we introduce Diffusion Probe, a pioneering framework that leverages the model's internal cross-attention maps as a predictive signal. We propose a lightweight predictor, trained to establish a direct mapping from statistical properties of these nascent cross-attention distributions--extracted from the initial denoising steps--to the final image's comprehensive quality. This allows our probe to accurately forecast various aspects of image quality, regardless of the specific ground-truth quality metric, long before full synthesis is complete. We empirically validate the reliability and generalizability of Diffusion Probe through its consistently strong predictive accuracy across a wide spectrum of conditions. On diverse T2I models (e.g., SDXL, FLUX, Qwen-Image), throughout broad early-denoising windows, across various resolutions, and with different quality metrics, it achieves high correlation (PCC > 0.7) and classification performance (AUC-ROC > 0.9). This intrinsic reliability is further demonstrated in practice by successfully optimizing T2I workflows that benefit from early, quality-guided decisions, such as Prompt Optimization, Seed Selection, and Accelerated RL Training. In these applications, the probe's early signal enables more targeted sampling strategies, preempting costly computations on low-potential paths. This yields a dual benefit: a significant reduction in computational overhead and a simultaneous improvement in final outcome quality, establishing Diffusion Probe as a model-agnostic and broadly applicable tool poised to revolutionize T2I efficiency.