摘要：我们提出了一种新颖的系统，该系统将通过微调的序列到序列 LLM 进行的负提示优化与潜在空间分类器引导相结合，以改善 Stable Diffusion 生成的图像质量。我们的方法能够自动生成优化的负提示，并采用 CNN-RNN 混合分类器来评估和引导扩散步骤，回滚低质量的潜在更新。实验结果表明，与基线扩散模型相比，我们的双重引导框架减少了伪影并提高了语义保真度。

Abstract: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.