# A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model

arXiv: https://arxiv.org/abs/2401.10934 · HTML: https://mlsanity.com/paper/2401.10934/

## Key facts

- Cited by 27 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 28 of its 55 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## 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, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Training Diffusion Models with Reinforcement Learning](https://mlsanity.com/paper/2305.13301/) (2023)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021)
- [Projected GANs Converge Faster](https://mlsanity.com/paper/2111.01007/) (2021)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation](https://mlsanity.com/paper/2308.05095/) (2023)
- [Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs](https://mlsanity.com/paper/2205.00303/) (2022)
- [Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles](https://mlsanity.com/paper/2303.03751/) (2023)
- [Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement](https://mlsanity.com/paper/2008.07467/) (2020)
- [A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising](https://mlsanity.com/paper/2102.04033/) (2021)
- [Image Matters: Visually modeling user behaviors using Advanced Model Server](https://mlsanity.com/paper/1711.06505/) (2017)
- [CapOnImage: Context-driven Dense-Captioning on Image](https://mlsanity.com/paper/2204.12974/) (2022)
- [Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce](https://mlsanity.com/paper/1908.10139/) (2019)

## Built on by (most cited first, 27 of 27)

- [Diffusion Models for Generative Outfit Recommendation](https://mlsanity.com/paper/2402.17279/) (2024)
- [A Survey on Diffusion Models for Recommender Systems](https://mlsanity.com/paper/2409.05033/) (2024)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [BannerAgency: Advertising Banner Design with Multimodal LLM Agents](https://mlsanity.com/paper/2503.11060/) (2025)
- [DRC: Enhancing Personalized Image Generation via Disentangled Representation Composition](https://mlsanity.com/paper/2504.17349/) (2025)
- [AutoPP: Towards Automated Product Poster Generation and Optimization](https://mlsanity.com/paper/2512.21921/) (2025)
- [Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2508.03481/) (2025)
- [One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation](https://mlsanity.com/paper/2602.02033/) (2026)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [LLM-driven Constrained Copy Generation through Iterative Refinement](https://mlsanity.com/paper/2504.10391/) (2025)
- [HLLM-Creator: Hierarchical LLM-based Personalized Creative Generation](https://mlsanity.com/paper/2508.18118/) (2025)
- [ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment](https://mlsanity.com/paper/2605.27374/) (2026)
- [Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items](https://mlsanity.com/paper/2503.22182/) (2025)
- [Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization](https://mlsanity.com/paper/2508.09730/) (2025)
- [Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation](https://mlsanity.com/paper/2604.16958/) (2026)
- [Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models](https://mlsanity.com/paper/2605.12138/) (2026)
- [Patch-enhanced Mask Encoder Prompt Image Generation](https://mlsanity.com/paper/2405.19085/) (2024)
- [NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction](https://mlsanity.com/paper/2409.08703/) (2024)
- [NextAds: Towards Next-generation Personalized Video Advertising](https://mlsanity.com/paper/2603.02137/) (2026)
- [Multi-Object Advertisement Creative Generation](https://mlsanity.com/paper/2603.13745/) (2026)
- [KD-CVG: A Knowledge-Driven Approach for Creative Video Generation](https://mlsanity.com/paper/2604.21362/) (2026)
- [CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learning](https://mlsanity.com/paper/2608.27893/) (2026)
- [PinDCO: Whole-Page Aware Dynamic Creative Optimization at Scale](https://mlsanity.com/paper/2609.11943/) (2026)
- [I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery](https://mlsanity.com/paper/2609.12694/) (2026)
- [Enabling and Understanding Personalization in AI-Generated Advertising Imagery](https://mlsanity.com/paper/2609.12697/) (2026)
- [KwaiMind Technical Report](https://mlsanity.com/paper/2609.26375/) (2026)
- [Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment](https://mlsanity.com/paper/2610.13066/) (2026)

## Abstract (arXiv)

In online advertising scenario, sellers often create multiple creatives to provide comprehensive demonstrations, making it essential to present the most appealing design to maximize the Click-Through Rate (CTR). However, sellers generally struggle to consider users preferences for creative design, leading to the relatively lower aesthetics and quantities compared to Artificial Intelligence (AI)-based approaches. Traditional AI-based approaches still face the same problem of not considering user information while having limited aesthetic knowledge from designers. In fact that fusing the user information, the generated creatives can be more attractive because different users may have different preferences. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. The ranking model can predict the CTR score for each creative considering user features. However, the two above stages are regarded as two different tasks and are optimized separately. In this paper, we proposed a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage. Our contributions have 4 parts: 1) The inpainting mode in stable diffusion is firstly applied to creative generation task in online advertising scene. A self-cyclic generation pipeline is proposed to ensure the convergence of training. 2) Prompt model is designed to generate individualized creatives for different user groups, which can further improve the diversity and quality. 3) Reward model comprehensively considers the multimodal features of image and text to improve the effectiveness of creative ranking task, and it is also critical in self-cyclic pipeline. 4) The significant benefits obtained in online and offline experiments verify the significance of our proposed method.