# Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network

arXiv: https://arxiv.org/abs/1802.09178 · HTML: https://mlsanity.com/paper/1802.09178/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 30 of 64)

- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [Diffsound: Discrete Diffusion Model for Text-to-sound Generation](https://mlsanity.com/paper/2207.09983/) (2022)
- [Paint by Word](https://mlsanity.com/paper/2103.10951/) (2021)
- [DiT4Edit: Diffusion Transformer for Image Editing](https://mlsanity.com/paper/2411.03286/) (2024)
- [IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers](https://mlsanity.com/paper/2304.14400/) (2023)
- [Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation](https://mlsanity.com/paper/2304.01816/) (2023)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Improving Text-to-Image Synthesis Using Contrastive Learning](https://mlsanity.com/paper/2107.02423/) (2021)
- [Improved Vector Quantized Diffusion Models](https://mlsanity.com/paper/2205.16007/) (2022)
- [Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation](https://mlsanity.com/paper/2303.09319/) (2023)
- [Text to Image Generation with Semantic-Spatial Aware GAN](https://mlsanity.com/paper/2104.00567/) (2021)
- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Multimodal Image Synthesis and Editing: The Generative AI Era](https://mlsanity.com/paper/2112.13592/) (2021)
- [RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model](https://mlsanity.com/paper/2309.00810/) (2023)
- [SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis](https://mlsanity.com/paper/2304.14573/) (2023)
- [Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion](https://mlsanity.com/paper/2303.08767/) (2023)
- [Object-aware Inversion and Reassembly for Image Editing](https://mlsanity.com/paper/2310.12149/) (2023)
- [Gradient-Free Textual Inversion](https://mlsanity.com/paper/2304.05818/) (2023)
- [Multi-modal Machine Learning in Engineering Design: A Review and Future Directions](https://mlsanity.com/paper/2302.10909/) (2023)
- [DreamIdentity: Improved Editability for Efficient Face-identity Preserved Image Generation](https://mlsanity.com/paper/2307.00300/) (2023)
- [One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations](https://mlsanity.com/paper/2210.07883/) (2022)
- [Iterative Multi-granular Image Editing using Diffusion Models](https://mlsanity.com/paper/2309.00613/) (2023)
- [Attribute-Centric Compositional Text-to-Image Generation](https://mlsanity.com/paper/2301.01413/) (2023)
- [Avocodo: Generative Adversarial Network for Artifact-free Vocoder](https://mlsanity.com/paper/2206.13404/) (2022)
- [ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design](https://mlsanity.com/paper/2208.05621/) (2022)
- [MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models](https://mlsanity.com/paper/2303.13126/) (2023)

## Abstract (arXiv)

This paper presents a novel method to deal with the challenging task of generating photographic images conditioned on semantic image descriptions. Our method introduces accompanying hierarchical-nested adversarial objectives inside the network hierarchies, which regularize mid-level representations and assist generator training to capture the complex image statistics. We present an extensile single-stream generator architecture to better adapt the jointed discriminators and push generated images up to high resolutions. We adopt a multi-purpose adversarial loss to encourage more effective image and text information usage in order to improve the semantic consistency and image fidelity simultaneously. Furthermore, we introduce a new visual-semantic similarity measure to evaluate the semantic consistency of generated images. With extensive experimental validation on three public datasets, our method significantly improves previous state of the arts on all datasets over different evaluation metrics.