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Scaling Diffusion Transformers with Mixture of Experts
Official PyTorch Implementation
This repo contains PyTorch model definitions, pre-trained weights and training/sampling code for our paper scaling diffusion Transformers to 16 billion parameters (DiT-MoE). DiT-MoE as a sparse version of the diffusion Transformer, is scalable and competitive with dense networks while exhibiting highly optimized inference.
- training / inference scripts
- huggingface ckpts
- experts routing analysis
- synthesized data
