Jonathan Lorraine PRO
lorraine2
AI & ML interests
machine learning, computer vision, generative AI
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20 days ago
Multi-student Diffusion Distillation for Better One-step Generators
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21 days ago
🦙New NVIDIA paper: LLaMA-Mesh 🦙
We enable large language models to generate and understand 3D meshes by representing them as text and fine-tuning. This unifies the 3D and text modalities in a single model and preserves language abilities, unlocking conversational 3D creation with mesh understanding.
🔎 Project Page: https://research.nvidia.com/labs/toronto-ai/LLaMA-Mesh/
🕹️ Interactive Demo: https://huggingface.co/spaces/Zhengyi/LLaMA-Mesh (courtesy of HuggingFace and Gradio)
📖 Full Paper: https://arxiv.org/abs/2411.09595
👨💻Code: https://github.com/nv-tlabs/LLaMa-Mesh
💾 Model Checkpoint: https://huggingface.co/Zhengyi/LLaMA-Mesh
🧩 Blender Addon: https://github.com/huggingface/meshgen (courtesy of Dylan Ebert)
🎥 5-min Overview Video: https://youtu.be/eZNazN-1lPo?si=-idQa5aaceVw0Bbj (courtesy of AI Papers Academy)
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New NVIDIA paper: ⚡ Multi-student Diffusion Distillation for Better One-step Generators ⚡
Do you want to make your diffusion models (a) run in a single step, (b) run with a smaller model, and (c) have improved quality simultaneously? Check out our multi-student distillation (MSD) method, which is simple and applicable to most diffusion models! The only catch is now we have to distill (and store) a mixture-of-expert student generators.
Explore the MSD project page to learn more: https://research.nvidia.com/labs/toronto-ai/MSD/
Work led by Yanke Song along with Weili Nie, Karsten Kreis and James Lucas
Check out more work from the Toronto AI Lab here: https://research.nvidia.com/labs/toronto-ai/
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