Research collection

Image generation and editing

Text-to-image and image-editing models: diffusion transformers, autoregressive image models, and image generation foundation models.

Papers: 53
Research labs: 4
Official code: 41

51–53 of 53 papers in this collection

arXiv.org

Story2Board: A Training-Free Approach for Expressive Storyboard Generation

Story2Board is a training-free framework for generating expressive storyboards from natural language. It introduces two mechanisms: Latent Panel Anchoring (LPA), which pairs each panel with a shared reference prompt to maintain character consistency, and Reciprocal Attention Value Mixing (RAVM), which blends value vectors between semantically aligned…

David Dinkevich, Matan Levy, Omri Avrahami, Dvir Samuel, et al.

Published: Aug 2025
Citations: 12
Code: 266 stars

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ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia

Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off

Voost is a unified and scalable diffusion transformer framework that jointly learns virtual try-on and try-off using a single model. It uses a token-level concatenation of garment and person images, with a task token encoding generation direction and garment category, enabling bidirectional supervision without task-specific networks or auxiliary losses.…

Seungyong Lee, Jeong-gi Kwak

Published: Aug 2025
Citations: 12
Code: 340 stars

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Qwen

Qwen-Image Technical Report

Qwen-Image is a new image generation foundation model from the Qwen team, designed to excel in complex text rendering and precise image editing. It uses a comprehensive data pipeline and a progressive curriculum learning strategy to enhance text rendering, especially for Chinese. For editing, it employs a multi-task training paradigm with dual-encoding…

Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, et al.

Published: Aug 2025
Citations: 875
Code: 8.2K stars

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