Skip to content
K = 0.731
All news · Today0 items

#Image generation

All collected and published items, ordered by time. Picks are a selected subset.
Research

Research papers, technical reports, benchmarks and datasets.

10/10Sat
  1. Artificial Analysis · X62

    Artificial Analysis tests four image editing models across 30 consecutive edits

    Artificial Analysis tested GPT Image 2.5 Sunburst, Ideogram 4.5, FLUX 3 and Nano Banana 2.1 through 30 consecutive real estate staging edits, finding that Ideogram 4.5 preserved most of the room while the others drifted. Its leaderboard measures single edits, whereas this test fed each model its own previous output. On small edits, Ideogram 4.5 and FLUX 3 left 95% or more essentially untouched; GPT Image 2.5 Sunburst left about a fifth unchanged and drifted most visibly, while Nano Banana 2.1 edited locally but gradually darkened the image.

9/30Wed
  1. Google Research42

    Google Research introduces Diffusion Controller to unify and simplify AI image-generation control

    Google Research introduced Diffusion Controller, a framework that recasts the denoising process in diffusion models as a continuous control problem. A lightweight “steering damper” network dynamically adjusts the generation trajectory while keeping the base model frozen. On Stable Diffusion v1.4, its fully unlocked version achieved a 90% win rate against the baseline on the HPS-v2 evaluation. It also supports custom control of closed-source models whose internal weights are unavailable.

7/16Thu
1/10Sat
  1. Berkeley AI Research24

    Berkeley AI Research proposes an information-driven framework for imaging-system design in NeurIPS 2025 paper

    A Berkeley AI Research team proposes a mutual-information-based framework for evaluating and optimizing imaging systems. It estimates information directly from noisy measurements to predict system performance. Validated in color photography, radio astronomy, lensless imaging, and microscopy, optimized designs reach end-to-end-method performance with lower memory and compute needs and no task-specific decoder. The paper appeared at NeurIPS 2025.