sDMD adds adaptive Sinkhorn correction to diffusion distillation
With 2–4 steps on FLUX.1-dev, Qwen-Image-20B, and Wan2.1-14B, authors report gains over DMD2 and other baselines.
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With 2–4 steps on FLUX.1-dev, Qwen-Image-20B, and Wan2.1-14B, authors report gains over DMD2 and other baselines.
On FLUX Schnell, DICE lowers the unsafe-generation rate to 0.046, versus at least 0.161 for prior erasure methods tested.
Shifting training toward high noise raised synthetic layout adherence from 25.9% to 80.8% and COCO Average Precision from 1.0 to 57.3.

Black Forest Labs released FLUX 3 Image Klein, a 5.6B open image model that runs on 8 GB GPUs.
Hugging Face adds FLUX.3 Image Klein support to the Diffusers library.
Black Forest Labs' small FLUX image-to-image model is trending on Hugging Face with only 39 downloads.
Black Forest Labs' FLUX.3 Image Klein image-to-image model is trending on Hugging Face with 3,241 downloads.
InfoVis, a 9B slide model, nearly matches Nano-Banana-2 on a 1,527-instance benchmark.
Iris-3B shows 3B pixel-space pretraining can match latent image models, without better downstream detail.

Black Forest Labs launched Flux 3 Image with localized multi-step editing, 4K output, and licensable weights.
Latent Space recaps Pi 1.0 and Pi Durable plus Gemini 4 Argon, GPT-6.1 Sol, FLUX 3, and other AI launches.
Composing preference and rubric rewards lifts post-trained Flux2dev and Ideogram-4 on the Arena text-to-image leaderboard.
MGFlow unifies one-step visual training and beats FD-Loss on ImageNet and four-step FLUX.2.
AutoRef automatically rewrites image-generation harnesses, lifting FLUX.2 klein 4B from 5.72 to 7.37 on MultiBanana.

Black Forest Labs released FLUX 3 Action, a 7B open-weights robot model topping RoboLab-120 at 42.92%.

Black Forest Labs released FLUX 3 Action, a 7B open robotics model topping RoboLab-120.
Paint-Anything learns a shared hex-prompt interface for any-color image generation and editing on FLUX.2-4B, improving ACBench scores by 85.3% and 28.3%.
TransNormal-2 improves monocular surface-normal estimation by fixing VAE edge degradation with geometry-aware losses and refinement, matching MoGe-2 with 1.4% of annotations.
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