# Stylecodes: Encoding Stylistic Information For Image Generation

arXiv: https://arxiv.org/abs/2411.12811 · HTML: https://mlsanity.com/paper/2411.12811/

## Key facts

- Cited by 1 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 9 of its 23 references that are in the map.
- First posted on arXiv 19 Nov 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, 9 of 9)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation](https://mlsanity.com/paper/2404.02733/) (2024)
- [Investigating Prompt Engineering in Diffusion Models](https://mlsanity.com/paper/2211.15462/) (2022)
- [Understanding the Impact of Negative Prompts: When and How Do They Take Effect?](https://mlsanity.com/paper/2406.02965/) (2024)
- [ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems](https://mlsanity.com/paper/2312.06573/) (2023)
- [IPAdapter-Instruct: Resolving Ambiguity in Image-based Conditioning using Instruct Prompts](https://mlsanity.com/paper/2408.03209/) (2024)

## Built on by (most cited first, 1 of 1)

- [Foley Control: Aligning a Frozen Latent Text-to-Audio Model to Video](https://mlsanity.com/paper/2510.21581/) (2025)

## Abstract (arXiv)

Diffusion models excel in image generation, but controlling them remains a challenge. We focus on the problem of style-conditioned image generation. Although example images work, they are cumbersome: srefs (style-reference codes) from MidJourney solve this issue by expressing a specific image style in a short numeric code. These have seen widespread adoption throughout social media due to both their ease of sharing and the fact they allow using an image for style control, without having to post the source images themselves. However, users are not able to generate srefs from their own images, nor is the underlying training procedure public. We propose StyleCodes: an open-source and open-research style encoder architecture and training procedure to express image style as a 20-symbol base64 code. Our experiments show that our encoding results in minimal loss in quality compared to traditional image-to-style techniques.