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@@ -8,11 +8,11 @@ DiT-MoE as a sparse version of the diffusion Transformer, is scalable and compet
![DiT-MoE framework](visuals/framework.png)
* 🪐 A PyTorch [implementation](models.py) of DiT-MoE
* ⚡️ Pre-trained checkpoints in paper
* 🪐 A PyTorch [implementation](models.py) of DiT-MoE and pre-trained checkpoints in paper
* 🌋 **Rectified flow**-based training and sampling scripts
* 💥 A [sampling script](sample.py) for running pre-trained DiT-MoE
* 🛸 A DiT-MoE training script using PyTorch [DDP](train.py) and [deepspeed](train_deepspeed.py)
* ⚡️ A **upcycle** [scripts](https://github.com/feizc/DiT-MoE/blob/main/upcycle.py) to convert dense to MoE ckpts referring [link](https://github.com/alibaba/Pai-Megatron-Patch/blob/main/toolkits/model_checkpoints_convertor/qwen/hf2mcore_qwen1.5_dense_to_moe_convertor.sh)
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