# Advanced Image Generation: Negative Prompt Optimization and Latent Classifier Guidance

arXiv: https://arxiv.org/abs/2607.14580 · HTML: https://mlsanity.com/paper/2607.14580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 4 of its 4 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 4 of 4)

- [InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization](https://mlsanity.com/paper/2404.04650/) (2024)
- [Controllable Generation with Text-to-Image Diffusion Models: A Survey](https://mlsanity.com/paper/2403.04279/) (2024)
- [Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation](https://mlsanity.com/paper/2403.07605/) (2024)
- [Fine-Grained Alignment and Noise Refinement for Compositional Text-to-Image Generation](https://mlsanity.com/paper/2503.06506/) (2025)

## Abstract (arXiv)

We present a novel system that integrates negative prompt optimization via a fine-tuned sequence-to-sequence LLM and latent-space classifier guidance to improve the quality of images generated by Stable Diffusion. Our approach automatically generates optimized negative prompts, and employs a CNN-RNN hybrid classifier to evaluate and guide diffusion steps, rolling back low-quality latent updates. Experimental results demonstrate that our dual-guidance framework reduces artifacts and improves semantic fidelity compared to baseline diffusion.