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LTX-Video is an open-source Python repository by Lightricks for generating and conditioning videos locally using LTX-Video models, with support for text/image inputs, multi-condition workflows, and integration with ComfyUI and Diffusers.
Nunchaku, a 4-bit diffusion inference engine based on SVDQuant, is now natively integrated into Hugging Face Diffusers, enabling fast and memory-efficient loading of quantized diffusion models with a simple from_pretrained() call.
NVIDIA NeMo Automodel integrates with Hugging Face Diffusers to enable scalable distributed fine-tuning of diffusion models for image and video generation, supporting models like FLUX.1-dev, Wan 2.1, and HunyuanVideo.
Diffusers library has been updated with new image and video pipelines including Ideogram4, MotifVideo, and the DiffusionGemma model.
Giga-World-1 is a video generation model released by GigaAI Research on Hugging Face, featuring multiple stage checkpoints and LoRA adapters for scene control.
Clark Labs released Clark Air Sana 1.6B, a ternary-quantized version of the Sana 1.6B text-to-image transformer that is 8.6× smaller than FP16 while maintaining near-FP16 quality, enabling efficient deployment.
Sayak Paul describes a project to profile and optimize Diffusers pipelines using torch.compile, and announces a tutorial series by Ari G. on the topic.