# Diffusion Probe: Generated Image Result Prediction Using CNN Probes

arXiv: https://arxiv.org/abs/2602.23783 · HTML: https://mlsanity.com/paper/2602.23783/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 27 of its 40 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2304.05977/) (2023)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Training Diffusion Models with Reinforcement Learning](https://mlsanity.com/paper/2305.13301/) (2023)
- [The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets](https://mlsanity.com/paper/2310.06824/) (2023)
- [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2301.13826/) (2023)
- [Flow-GRPO: Training Flow Matching Models via Online RL](https://mlsanity.com/paper/2505.05470/) (2025)
- [What you can cram into a single vector: Probing sentence embeddings for linguistic properties](https://mlsanity.com/paper/1805.01070/) (2018)
- [What the DAAM: Interpreting Stable Diffusion Using Cross Attention](https://mlsanity.com/paper/2210.04885/) (2022)
- [Optimizing Prompts for Text-to-Image Generation](https://mlsanity.com/paper/2212.09611/) (2022)
- [In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer](https://mlsanity.com/paper/2504.20690/) (2025)
- [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://mlsanity.com/paper/2210.00939/) (2022)
- [Attentive Eraser: Unleashing Diffusion Model's Object Removal Potential via Self-Attention Redirection Guidance](https://mlsanity.com/paper/2412.12974/) (2024)
- [PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement](https://mlsanity.com/paper/2403.04014/) (2024)
- [Detecting High-Stakes Interactions with Activation Probes](https://mlsanity.com/paper/2506.10805/) (2025)
- [T2I-Copilot: A Training-Free Multi-Agent Text-to-Image System for Enhanced Prompt Interpretation and Interactive Generation](https://mlsanity.com/paper/2507.20536/) (2025)
- [CME-CAD: Heterogeneous Collaborative Multi-Expert Reinforcement Learning for CAD Code Generation](https://mlsanity.com/paper/2512.23333/) (2025)
- [Erase Diffusion: Empowering Object Removal Through Calibrating Diffusion Pathways](https://mlsanity.com/paper/2503.07026/) (2025)
- [PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLM](https://mlsanity.com/paper/2307.08985/) (2023)

## Built on by (most cited first, 14 of 14)

- [TC-Pad'e: Trajectory-Consistent Pad'e Approximation for Diffusion Acceleration](https://mlsanity.com/paper/2603.02943/) (2026)
- [simpleposter: A simple baseline for product poster generation](https://mlsanity.com/paper/2605.08784/) (2026)
- [MetaVideoAgent: Automated Video-Agent Evolution for Long-Form Video Understanding](https://mlsanity.com/paper/2608.04587/) (2026)
- [Latent Reward Registers for Diffusion Preference Alignment](https://mlsanity.com/paper/2608.03929/) (2026)
- [A Deformable Attention-Based Detection Transformer with Cross-Scale Feature Fusion for Industrial Coil Spring Inspection](https://mlsanity.com/paper/2603.13429/) (2026)
- [Multi-View Deformable Convolution Meets Visual Mamba for Coronary Artery Segmentation](https://mlsanity.com/paper/2603.21829/) (2026)
- [JND-Guided Neural Watermarking with Spatial Transformer Decoding for Screen-Capture Robustness](https://mlsanity.com/paper/2603.26766/) (2026)
- [Latent Space Probing for Adult Content Detection in Video Generative Models](https://mlsanity.com/paper/2605.00874/) (2026)
- [Yuvion VL: A Multimodal Foundation Model for Adversarial Content and AI Safety](https://mlsanity.com/paper/2606.25034/) (2026)
- [Fully Unleashing the Multimodal Attacker: Meta-Adaptive Jailbreaking of Vision-Language Models](https://mlsanity.com/paper/2608.27531/) (2026)
- [EvoHarmBench: Breaking Content Moderation with Iterative Human-Like Evasion](https://mlsanity.com/paper/2608.27844/) (2026)
- [Rank-Consistent Set Reasoning for Co-Salient Object Detection](https://mlsanity.com/paper/2609.13706/) (2026)
- [Contour-Guided Spectral Routing for Robust Real-Time Pedestrian Detection](https://mlsanity.com/paper/2609.14383/) (2026)
- [Unlocking Few-Step Diffusion for Faithful Previews](https://mlsanity.com/paper/2609.34406/) (2026)

## Abstract (arXiv)

Text-to-image (T2I) diffusion models lack an efficient mechanism for early quality assessment, leading to costly trial-and-error in multi-generation scenarios such as prompt iteration, agent-based generation, and flow-grpo. We reveal a strong correlation between early diffusion cross-attention distributions and final image quality. Based on this finding, we introduce Diffusion Probe, a framework that leverages internal cross-attention maps as predictive signals. We design a lightweight predictor that maps statistical properties of early-stage cross-attention extracted from initial denoising steps to the final image's overall quality. This enables accurate forecasting of image quality across diverse evaluation metrics long before full synthesis is complete. We validate Diffusion Probe across a wide range of settings. On multiple T2I models, across early denoising windows, resolutions, and quality metrics, it achieves strong correlation (PCC > 0.7) and high classification performance (AUC-ROC > 0.9). Its reliability translates into practical gains. By enabling early quality-aware decisions in workflows such as prompt optimization, seed selection, and accelerated RL training, the probe supports more targeted sampling and avoids computation on low-potential generations. This reduces computational overhead while improving final output quality. Diffusion Probe is model-agnostic, efficient, and broadly applicable, offering a practical solution for improving T2I generation efficiency through early quality prediction. Code: https://github.com/Alibaba-VELLDEPTH/DiffusionProbe