diffusion-models

Google has released DiffusionGemma 26B, an open-weight text generation model under Apache 2 license. The model generates over 500 tokens/second according to testing on NVIDIA's free NIM API, where it produced 2,409 tokens in 4.4 seconds.

ByteDance released Bernini-R, an open-source video generation and editing model that combines an MLLM-based semantic planner with a DiT-based renderer. The model requires Hopper-class GPUs (H100/H800/H200) for optimal performance and supports multiple tasks including text-to-video, video editing, and reference-guided generation.

NVIDIA has released Nemotron-Labs Diffusion, a family of diffusion language models at 3B, 8B, and 14B scales that generate multiple tokens in parallel rather than one at a time. The 8B model achieves 6.4× higher tokens per forward pass than autoregressive models in self-speculation mode while maintaining comparable accuracy.

Apple researchers, alongside UC San Diego, have published LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning, a framework that combines diffusion models with autoregressive generation. The system runs multiple reasoning paths in parallel during inference, each exploring different possibilities before generating a final answer.

Tencent's Hunyuan team released OmniWeaving on April 3, 2026, an open-source video generation model designed to compete with proprietary systems like Seedance-2.0. The model combines multimodal composition, reasoning-informed capabilities, and supports eight video generation tasks including text-to-video, image-to-video, video editing, and compositional generation.

Stability AI has introduced Stable Virtual Camera, a multi-view diffusion model currently in research preview that generates 3D videos from 2D images with realistic depth and perspective transformations. The model requires no complex scene reconstruction or scene-specific optimization, enabling direct camera control across multiple viewpoints.

Midjourney has released an early version of V8 for community testing, achieving roughly 5x faster image generation and introducing native 2K resolution via --hd mode. However, premium features including --hd, --q 4, style references, and mood boards cost four times as much as standard generation, with Relax mode unavailable at launch.

Segmind has released SegMoE, a mixture-of-experts (MoE) diffusion model designed to accelerate image generation while reducing computational overhead. The model applies MoE techniques traditionally used in large language models to the diffusion model architecture, enabling selective expert activation during inference.