# VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers

arXiv: https://arxiv.org/abs/2602.03210 · HTML: https://mlsanity.com/paper/2602.03210/

## 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 3 of its 41 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## 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, 3 of 3)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://mlsanity.com/paper/2210.14896/) (2022)

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

- [Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks](https://mlsanity.com/paper/2606.10967/) (2026)
- [MirrorPPR: Exemplar-Based Portrait Photo Retouching](https://mlsanity.com/paper/2606.29308/) (2026)
- [TransPhy: Visual In-Context Learning for Physically Grounded Image Editing](https://mlsanity.com/paper/2608.24119/) (2026)

## Abstract (arXiv)

Replicating In-Context Learning (ICL) in computer vision remains challenging due to task heterogeneity. We propose VIRAL, a framework that elicits visual reasoning from a pre-trained image editing model by formulating ICL as conditional generation via visual analogy (xs : xt :: xq : yq). We adapt a frozen Diffusion Transformer (DiT) using role-aware multi-image conditioning and introduce a Mixture-of-Experts LoRA to mitigate gradient interference across diverse tasks. Additionally, to bridge the gaps in current visual context datasets, we curate a large-scale dataset spanning perception, restoration, and editing. Experiments demonstrate that VIRAL outperforms existing methods, validating that a unified V-ICL paradigm can handle the majority of visual tasks, including open-domain editing. Our code is available at https://anonymous.4open.science/r/VIRAL-744A