Mind-blown by this new Ref2VA acceleration LoRA for MiniMax-H3! 🤯 (6-8 steps, better quality than 40-step teacher)

A new acceleration LoRA for the MiniMax-H3 Ref2VA reference-to-video workflow, released by taobao-content-ai on Hugging Face as MiniMax-H3-PTD-RefAsymOPD-LoRAs, cuts sampling from 40 steps to 6–8 steps while reportedly improving quality and realism, according to a community test post. The LoRA uses PTD plus RefAsymOPD, an approach optimized for reference-to-video that the poster says reduces background blur and AI artifacts, and it works as a drop-in checkpoint swap in both Diffusers and ComfyUI. The poster reports roughly a 5x speedup with full-quality results maintained or improved.

Hey everyone, I’ve been testing the MiniMax-H3 Ref2VA workflow with the new acceleration LoRA PTD + RefAsymOPD , which cuts steps from 40 down to 6–8 while surprisingly improving quality and realism. You can find the model weights and custom nodes here: taobao-content-ai/MiniMax-H3-PTD-RefAsymOPD-LoRAs https://huggingface.co/taobao-content-ai/MiniMax-H3-PTD-RefAsymOPD-LoRAs Why it stands out Unlike typical distillation methods that sacrifice quality for speed, RefAsymOPD is optimized for reference-to-video, reducing background blur and AI artifacts. The sharpness and background plausibility at just 6–8 steps are incredible, feeling way less “plastic” than expected. Available Variants & Usage It is a drop-in replacement compatible with both Diffusers and ComfyUI . If you already have your MiniMax-H3 environment set up, you just need to swap the checkpoint file name. Getting a 5x speedup while maintaining or even improving full-quality results feels like pure magic. Highly recommended for open-source devs