Inference-Time Optimization of Prompt Embeddings in Diffusion Models: A Comparison of sep-CMA-ES and Adam

arXiv:2511.03913 · cs.NE, cs.AI · Submitted 2025-11-05 · Read on arXiv

cs.NE, cs.AI

Submitted: 2025-11-05

Updated: 2026-09-02

Code: https://github.com/domiciopereiraneto/eigo

Terminology

• Adam — 95× in this paper · explained in 2 episodes
• Alignment — 34× in this paper · explained in 1 episode
• Diffusion Model — 17× in this paper · explained in 8 episodes
• Diffusion Models — 15× in this paper · explained in 6 episodes
• Embeddings — 14× in this paper · explained in 2 episodes
• Generative Model — 14× in this paper · explained in 2 episodes
• Cosine Similarity — 9× in this paper · explained in 3 episodes
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Generative Models — 9× in this paper · explained in 2 episodes
• Adaptation — 8× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Transformer — 4× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Evolutionary Image Generation Optimization — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Structural Similarity Index Measure — 2× in this paper
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• IEEE Conference on Artificial Intelligence — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes

Sources

• Adam: A Method for Stochastic Optimization
• CLIPScore: A Reference-free Evaluation Metric for Image Captioning
• Conditional Generative Adversarial Nets
• Imagen 3
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Classifier-Free Diffusion Guidance
• Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
• Uncovering the Text Embedding in Text-to-Image Diffusion Models
• PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• Qwen-Image Technical Report

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