Abstract

Layout-to-Image generation has made substantial progress in spatial and object-level control. However, existing methods still struggle with complex scenes containing many overlapping and interacting objects. We argue that training data is a particular bottleneck: existing datasets lack examples with dense, complex object interactions. To address this gap, we introduce OverLay++, a large-scale Layout-to-Image dataset with structurally complex scenes. OverLay++ contains approximately 500K images with an average of 6.6 objects per image, exceeding existing datasets by 1.67× in annotation density. Beyond annotation density, OverLay++ provides rich semantic detail with object captions over six times longer than in current datasets. Our dataset generation pipeline is simple and produces dense, overlapping object annotations with rich per-object captions. Across multiple benchmarks, state-of-the-art Layout-to-Image methods trained on the OverLay++ dataset show consistent improvement and faster convergence, demonstrating the importance of dense, overlap-aware, and caption-rich supervision for controllable image generation.

Paper: https://arxiv.org/abs/2610.09071

Code: https://github.com/mlpc-ucsd/OverLayPP

Project Page: https://mlpc-ucsd.github.io/OverLayPP/

https://www.youtube.com/watch?v=1GfSTYlKpSo

Abstract

Variational autoencoders (VAEs) are a key part of modern text-to-image models, which generate images within their latent space. VAEs are known to disentangle the main factors of variation in the data, and color is known to be one of the most structured of these in natural images: decorrelating it yields one luminance axis and two opponent-color axes. Color should therefore be expected to emerge as a distinct factor in the VAE latent space. Yet how these latent spaces represent color remains largely unexplored. In this work, we show that the VAEs of text-to-image models share a color subspace aligned with brightness and opponent-colors. Through a linear approximation of the encoder and targeted latent steering, we find this subspace consistently across a broad range of VAEs, from SD1.5 to FLUX.2 and Z-Image. Building on this characterization, we propose three applications: ColorTuning, which achieves state-of-the-art in precise numerical color generation on the fine-grained CSS3/X11 system of GenColorBench, saturation control, to adjust the global chromatic intensity, and color transfer, to change the palette to match a reference. The code and models are publicly available at this https URL

Paper: https://arxiv.org/abs/2610.07072

Code: https://github.com/julian075/Color_Subspace

Project Page: https://julian075.github.io/Color_Subspace/

Release: https://github.com/AHEKOT/ComfyUI_VNCCS/releases#release-3.2.0

Release: https://github.com/shootthesound/Fizgig/releases/tag/v7.0.0

Release: https://github.com/invoke-ai/InvokeAI/releases/tag/v7.0.0-alpha.1#-addendum-restoring-your-v6-database

Abstract

Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce VIEScore2, a unified evaluator for image generation and editing tasks with optional conditioning images. VIEScore2 represents an image as an N x N grid and jointly predicts quality scores and defect locations in a single model pass. Its text-native grid representation provides a common interface for heterogeneous spatial supervision and enables directly verifiable post-training objectives. We train on 38K examples spanning score-only, localization-only, and joint supervision across generation and editing tasks. Starting from supervised fine-tuning, we further apply GRPO to improve defect localization using rewards that combine cell-level Dice overlap, score accuracy, and output-format validity. A parameter-free parser converts the structured predictions into readable explanations. On the primary suite, VIEScore2 achieves an overall-score SRCC of 0.601, compared with 0.491 for Gemini-3-Flash, the strongest zero-shot general-purpose VLM baseline under matched inputs. For defect localization, VIEScore2 outperforms both general-purpose VLMs and specialized spatial evaluators on three of six benchmarks in per-image grid IoU and ranks among the top three on five, including datasets beyond its training sources.

TL;DR

Most image evaluators give you a single score but don't show where the image goes wrong. VIEScore2 is one model that scores generated and edited images and marks the defective regions on a 16×16 grid in a single pass. A simple rule-based parser then turns those predictions into a readable explanation.

Because the grid is plain text, it can be trained with SFT and then GRPO using rewards checked directly against ground truth. The result agrees with human ratings better than general-purpose VLMs such as GPT and Gemini under matched inputs, and is competitive with specialized spatial evaluators at localizing defects.

Paper: https://arxiv.org/abs/2610.00994

Code: https://github.com/TIGER-AI-Lab/VIEScore2

Model: https://huggingface.co/Allenda/VIEScore2

Project Page: https://tiger-ai-lab.github.io/VIEScore2/