Feature Summary
Sparse MoE efficiency: 17B total capacity with only ~2B active parameters per forward pass, enabling high-quality generation at a fraction of the inference cost of dense models.
Detailed Description
https://huggingface.co/NucleusAI/Nucleus-Image
https://github.com/WithNucleusAI/Nucleus-Image
storage.googleapis.com/nucleus_image_v1/Nucleus-Image-Technical-Report.pdf
"Nucleus-Image is a text-to-image generation model built on a sparse mixture-of-experts (MoE) diffusion transformer architecture. It scales to 17B total parameters across 64 routed experts per layer while activating only ~2B parameters per forward pass, establishing a new Pareto frontier in quality-versus-efficiency. Nucleus-Image matches or exceeds leading models including Qwen-Image, GPT Image 1, Seedream 3.0, and Imagen4 on GenEval, DPG-Bench, and OneIG-Bench. This is a base model released without any post-training optimization (no DPO, no reinforcement learning, no human preference tuning). All reported results reflect pre-training performance only. We release the full model weights, training code, and dataset, making Nucleus-Image the first fully open-source MoE diffusion model at this quality tier."
Alternatives you considered
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Additional context
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Feature Summary
Sparse MoE efficiency: 17B total capacity with only ~2B active parameters per forward pass, enabling high-quality generation at a fraction of the inference cost of dense models.
Detailed Description
https://huggingface.co/NucleusAI/Nucleus-Image
https://github.com/WithNucleusAI/Nucleus-Image
storage.googleapis.com/nucleus_image_v1/Nucleus-Image-Technical-Report.pdf
"Nucleus-Image is a text-to-image generation model built on a sparse mixture-of-experts (MoE) diffusion transformer architecture. It scales to 17B total parameters across 64 routed experts per layer while activating only ~2B parameters per forward pass, establishing a new Pareto frontier in quality-versus-efficiency. Nucleus-Image matches or exceeds leading models including Qwen-Image, GPT Image 1, Seedream 3.0, and Imagen4 on GenEval, DPG-Bench, and OneIG-Bench. This is a base model released without any post-training optimization (no DPO, no reinforcement learning, no human preference tuning). All reported results reflect pre-training performance only. We release the full model weights, training code, and dataset, making Nucleus-Image the first fully open-source MoE diffusion model at this quality tier."
Alternatives you considered
No response
Additional context
No response