MiniMax-H3-Acc-LoRAs
Introduction
We apply Parallel Decoding Distillation (PDD) 1 to MiniMax-H3, enabling efficient video generation in only a few inference steps.
For more details, please refer to our GitHub repo.
| Name | Base Model | Hugging Face | Description |
|---|---|---|---|
| MiniMax-H3-FL2VA-Acc-8Step.safetensors | MiniMax-H3 (FL2VA) | 🤗Link | Official 8 Step Acc LoRA (rank=64 and network_alpha=64 in BF16) for MiniMax-H3 (FL2VA). |
| MiniMax-H3-Ref2VA-Acc-8Step.safetensors | MiniMax-H3 (Ref2VA) | 🤗Link | Official 8 Step Acc LoRA (rank=64 and network_alpha=64 in BF16) for MiniMax-H3 (Ref2VA). |
Demo
FL2VA (768p)
Ref2VA
[!NOTE] The above test cases are from Minimax-H3-Turbo. Videos are generated with a LoRA weight of 1.0 at both 4 and 8 NFE.
Quick Start
Set model_path and pdd_lora_path to the MiniMax-H3 model and the matching acceleration LoRA checkpoint in predict_t2v.py for FL2VA or predict_ref2v.py for Ref2VA, then run the corresponding script. Each example uses apply_pdd_lora to load the checkpoint and derive the required number of inference steps from its configuration.
[!NOTE] These scripts use Diffusers' MiniMax-H3
ModularPipelineand requirediffusers >= 0.40.0.