Instructions to use ZhengmingYu/DMAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ZhengmingYu/DMAD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ZhengmingYu/DMAD") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Inference
- Notebooks
- Google Colab
- Kaggle
Model card: Updates section
Browse files
README.md
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@@ -44,6 +44,21 @@ This repository holds the DMAD students of the paper: 4-step students of **[Mini
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students of the **EDM ImageNet-64** teacher. The inference and training code is in the
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[code repository](https://github.com/Yzmblog/DMAD) (`train/h3`, `train/wan`, `train/image`).
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## MiniMax-H3 (text-to-audio-video)
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Rank-128 LoRAs on the H3 transformer that turn the 50-step teacher into a 4-step generator of 1344x768 video with
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students of the **EDM ImageNet-64** teacher. The inference and training code is in the
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[code repository](https://github.com/Yzmblog/DMAD) (`train/h3`, `train/wan`, `train/image`).
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## Updates
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* **2026/10/10:** 8 GB GPUs: a 5 s [ComfyUI workflow](#ready-to-run-workflows-8-gb-gpu-and-up) for 8 GB GPUs, and
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`--low-vram` in the [code repository](https://github.com/Yzmblog/DMAD#on-consumer-gpus) now makes 5 s videos in under
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7 GiB of GPU memory and 15 s videos in 12 GiB 🪶
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* **2026/10/08:** 16 GB GPUs: the ComfyUI workflows and `--low-vram` fit in 14 GiB of GPU memory, bit-identical to the
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larger-GPU runs 🪶
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* **2026/10/07:** Ready-to-run [ComfyUI workflows](#ready-to-run-workflows-8-gb-gpu-and-up) (4 and 8 steps, 15 s of
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video with audio) in `minimax_h3/workflows` 🎛️
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* **2026/10/06:** The DMAD weights of Wan2.1 (1.3B, 14B), SDXL and ImageNet-64, and the
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[MiniMax-H3 training data](https://huggingface.co/datasets/ZhengmingYu/DMAD-H3-data) 🤗
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* **2026/10/05:** [ComfyUI-layout LoRAs](#comfyui) of the MiniMax-H3 students, and the training code in the
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[code repository](https://github.com/Yzmblog/DMAD) 🏋️
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* **2026/10/02:** The 4-step MiniMax-H3 students (`lora_critic`, `full_critic`) released 🚀
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## MiniMax-H3 (text-to-audio-video)
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Rank-128 LoRAs on the H3 transformer that turn the 50-step teacher into a 4-step generator of 1344x768 video with
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