# Font Style Interpolation with Diffusion Models

arXiv: https://arxiv.org/abs/2402.14311 · HTML: https://mlsanity.com/paper/2402.14311/

## Key facts

- Cited by 3 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 30 of its 39 references that are in the map.
- First posted on arXiv 22 Feb 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, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models](https://mlsanity.com/paper/2308.07863/) (2023)
- [DG-Font: Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2104.03064/) (2021)
- [Word-As-Image for Semantic Typography](https://mlsanity.com/paper/2303.01818/) (2023)
- [Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer](https://mlsanity.com/paper/2303.08622/) (2023)
- [Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning](https://mlsanity.com/paper/1910.04987/) (2019)
- [FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning](https://mlsanity.com/paper/2312.12142/) (2023)
- [STEFANN: Scene Text Editor using Font Adaptive Neural Network](https://mlsanity.com/paper/1903.01192/) (2019)
- [DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization](https://mlsanity.com/paper/2211.10682/) (2022)
- [Diff-Font: Diffusion Model for Robust One-Shot Font Generation](https://mlsanity.com/paper/2212.05895/) (2022)
- [ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors](https://mlsanity.com/paper/2311.05463/) (2023)
- [Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator](https://mlsanity.com/paper/2205.00146/) (2022)
- [DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2309.06933/) (2023)
- [DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion](https://mlsanity.com/paper/2303.09604/) (2023)
- [CF-Font: Content Fusion for Few-shot Font Generation](https://mlsanity.com/paper/2303.14017/) (2023)
- [DeepFont: Identify Your Font from An Image](https://mlsanity.com/paper/1507.03196/) (2015)
- [Large-scale Tag-based Font Retrieval with Generative Feature Learning](https://mlsanity.com/paper/1909.02072/) (2019)
- [SGDiff: A Style Guided Diffusion Model for Fashion Synthesis](https://mlsanity.com/paper/2308.07605/) (2023)
- [XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation](https://mlsanity.com/paper/2204.05084/) (2022)
- [A Deep Factorization of Style and Structure in Fonts](https://mlsanity.com/paper/1910.00748/) (2019)
- [Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation](https://mlsanity.com/paper/2211.07751/) (2022)
- [Scalable Font Reconstruction with Dual Latent Manifolds](https://mlsanity.com/paper/2109.06627/) (2021)
- [Ambigram Generation by A Diffusion Model](https://mlsanity.com/paper/2306.12049/) (2023)
- [Analyzing Font Style Usage and Contextual Factors in Real Images](https://mlsanity.com/paper/2306.12050/) (2023)

## Built on by (most cited first, 3 of 3)

- [AID: Attention Interpolation of Text-to-Image Diffusion](https://mlsanity.com/paper/2403.17924/) (2024)
- [Calligrapher: Freestyle Text Image Customization](https://mlsanity.com/paper/2506.24123/) (2025)
- [Few-Part-Shot Font Generation](https://mlsanity.com/paper/2509.10006/) (2025)

## Abstract (arXiv)

Fonts have huge variations in their styles and give readers different impressions. Therefore, generating new fonts is worthy of giving new impressions to readers. In this paper, we employ diffusion models to generate new font styles by interpolating a pair of reference fonts with different styles. More specifically, we propose three different interpolation approaches, image-blending, condition-blending, and noise-blending, with the diffusion models. We perform qualitative and quantitative experimental analyses to understand the style generation ability of the three approaches. According to experimental results, three proposed approaches can generate not only expected font styles but also somewhat serendipitous font styles. We also compare the approaches with a state-of-the-art style-conditional Latin-font generative network model to confirm the validity of using the diffusion models for the style interpolation task.