DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

4-step MiniMax-H3 students for joint audio-video generation

Zhengming Yu^1,2, Junkun Yuan², Haotian Yang², Gordon Guocheng Qian², Yizhi Wang², Angtian Wang², Yiding Yang², Bo Liu², Xin Li¹, Wenping Wang¹, Chongyang Ma^2
1Texas A&M University, ²ByteDance

This repository holds the 4-step DMAD students of MiniMax-H3 (33B, text-to-audio-video): rank-128 LoRAs on the H3 transformer that turn the 50-step teacher into a 4-step generator of 1344x768 video with native stereo audio. The inference code is in the code repository.

LoRA layout: Diffusers keys (<module>.lora.down.weight = A [128, in], <module>.lora.up.weight = B [out, 128]) over attn.to_q/to_k/to_v/to_out.0, ff.net.0.proj, ff.net.2 of all 50 transformer blocks and the 2 token-refiner blocks (312 modules). alpha = rank = 128. The safetensors metadata repeats this.

Usage

The inference code lives in the code repository: inference.py with the sampler the paper used (re-noise step rule) and a Diffusers-pipeline example; its README covers the environment. Sampling settings: 4 steps, time shift 12 (video) and 2 (audio), no classifier-free guidance, 124 frames at 24 fps.

git clone https://github.com/Yzmblog/DMAD.git && cd DMAD   # code + environment setup (see its README)
hf download MiniMaxAI/MiniMax-H3 --local-dir models/MiniMax-H3 --exclude "FL2VA/*" --exclude "Ref2VA/*" --exclude "transformer_ref/*"
hf download ZhengmingYu/DMAD --include "minimax_h3/*" --local-dir ckpt
python inference.py --model-dir models/MiniMax-H3 --lora ckpt/minimax_h3/dmad_minimax_h3_4step_lora_critic.safetensors \
    --prompt-file prompts/dmad_sweater.txt --seed 42 --output-dir outputs/dmad_sweater

Citation

@misc{yu2026dmad,
  title         = {DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation},
  author        = {Zhengming Yu and Junkun Yuan and Haotian Yang and Gordon Guocheng Qian and Yizhi Wang and
                   Angtian Wang and Yiding Yang and Bo Liu and Xin Li and Wenping Wang and Chongyang Ma},
  year          = {2026},
  eprint        = {2610.02188},
  archivePrefix = {arXiv}
}

License

These checkpoints are Model Derivatives of MiniMax H3 and are distributed under the MiniMax H3 Community License Agreement. The weights were modified from MiniMax-H3 by LoRA fine-tuning. Videos produced with them are AI-generated.