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VAST AI Research and TripoAI 发布了 EdenLive 视频生成模型，支持 24+ FPS 实时渲染、480P/720P 输出，5B 参数模型可在单 GPU 上运行，并支持 2D/3D 视觉参考。
OpenMOSS 发布了 MOSS-Image 家族的新模型 MOSS-Image-SFT，这是一个基于 MOSS-Image-Base 进行监督微调的文本生成图像模型，支持自然语言提示、标签序列和 JSON 布局控制文本位置与物体排列，并提供 diffusers 和 ComfyUI 集成。
Black Forest Labs 的 FLUX.3 Image Klein 发布，支持文生图、多参考图生图以及基于边界框的布局控制，且在 Diffusers 中首日即可运行，蒸馏版在 H100 上约 2 秒生成 1024×1024 图像。
Black Forest Labs 发布 FLUX 3 Image Klein 的 ComfyUI 打包版本，这是一个 5.6B 参数的开放权重模型，统一支持文生图、单参考图编辑和多参考图合成，并支持提示词上采样器与多种量化格式。
介绍 LensErase——一个基于掩码的“擦除一切”通用基础模型，支持物体、文字、水印等多种擦除场景，并构建了含 1.01 亿样本的大规模数据集 LensErase-100M，提出 Mask-Aware RoPE 与 TraceFusion 等方法，在六个公开基准上取得显著提升。
OpenMOSS 发布了 MOSS-Image-Base 文生图基础模型，支持自然语言提示、标签序列和 JSON 布局控制文本位置与物体排列，并配套 SFT 版本、ComfyUI 插件和提示增强工具。
Qwen-Image-2.1-Turbo发布，基于7B视觉生成架构的加速检查点，仅需8个去噪步骤即可生成高质量2K图像并支持自然语言图像编辑，开放权重并支持Diffusers直接加载。
Moonworks 推出 Lunara 论文，提出一种参数量低于 10B 激活参数的 Diffusion Mixture Transformer 架构，用于图像生成中的艺术智能建模，并在 GPT-5.6 Sol 评测及人工盲评中取得最佳审美质量得分。
这篇论文提出了一种基于百分位控制的高速公路场景生成方法，通过条件扩散模型与采样时风险引导，将 corner case 的极端程度量化为其在条件风险分布中的百分位，从而实现对生成场景风险程度的精细调控。
COSMI 是一项关于多物体人机物交互合成的研究，提出了由单物体捕捉合成的 222k 序列/275 小时大规模数据集，以及一个 3000 万参数的文本到交互扩散 Transformer，可生成 1 到 5 个物体的交互并在未见物体与未见交互组合上泛化。
ThinkV2V introduces a reasoning-driven framework that activates MLLM thinking before visual generation for instruction-guided video editing, using an MLLM-to-DiT architecture with progressive curriculum training and inference-time thinking scaling. The authors also release the ThinkV2V-150K dataset and ThinkV2V-Bench, showing their 5B DiT model outperforms larger 10B baselines.
VGGT-Diff is a geometry-routed multi-view diffusion model that integrates visual geometry latents with a pretrained diffusion model to improve sparse-view novel view synthesis, achieving competitive or state-of-the-art performance.
This paper introduces a generative atmospheric super-resolution method using composable interfaces to condition a pretrained diffusion model on heterogeneous in situ observations, improving reconstruction accuracy without model retraining.
Augment Code switched its coding-agent backend to Stefano Ermon's Mercury 2.5 diffusion model, achieving 82% latency reduction and 90% cost cut in production. The article highlights the performance advantages of diffusion models and the need for independent AI benchmarking tools.
The paper proposes LLMAE, a method to repurpose pre-trained decoder-only LLMs as continuous text autoencoders using a latent bottleneck, achieving high-fidelity reconstruction and enabling downstream tasks like image captioning.
Google Gemma team has released DiffusionGemma-Jev (djev), a fork of JEV, simplifying deployment on Google Cloud Run with performance metrics of ~35-60 ms latency and batch processing at 100-123 requests per second.
Uranus is a next-generation simulation infrastructure for embodied AI that uses a joint-trajectory-conditioned autoregressive diffusion model to enable scalable, low-latency generation of robot simulations with streaming rollout and extensible control.
Supra2-IMG is a tiny 100M parameter text-to-image model that achieves state-of-the-art quality in image generation, trained from scratch in under 10 hours on a single H100 and released open-source on Hugging Face.
Qwen-Image-2.1 is a unified text-to-image generation and image editing model with 7B parameters, featuring improvements in efficiency, transparency, versatility, and realism. This GGUF quantized version from unsloth enables efficient local inference.
DSD is a diffusion-based method for discovering diverse and reusable motor skills in simulated humanoid control, improving upon prior skill discovery techniques with broader behavioral coverage.