DMAD

模型介绍

This repository holds the DMAD students of the paper: 4-step students of **MiniMax-H3** (33B, text-to-audio-video) and of **Wan2.1-T2V** (1.3B and 14B), 4- and 1-step students of **SDXL**, and 1-step students of the **EDM ImageNet-64** teacher. The inference and training code is in the code repository (`train/h3`, `train/wan`, `train/image`). * **2026/10/15:** The Wan2.1 training data (42,158 Wan2.1-14B teacher samples with their real-video latents), so `train/wan` can start training right away 🤗 * **2026/10/10:** 8 GB GPUs: a 5 s ComfyUI workflow for 8 GB GPUs, and `--low-vram` in the code repository now makes 5 s videos in under 7 GiB of GPU memory and 15 s videos in 12 GiB 🪶 * **2026/10/08:** 16 GB GPUs: the ComfyUI workflows and `--low-vram` fit in 14 GiB of GPU memory, bit-identical to the larger-GPU runs 🪶 * **2026/10/07:** Ready-to-run ComfyUI workflows (4 and 8 steps, 15 s of video with audio) in `minimax_h3/workflows` 🎛️ * **2026/10/06:** The DMAD weights of Wan2.1 (1.3B, 14B), SDXL and ImageNet-64, and the MiniMax-H3 training data 🤗 * **2026/10/05:** ComfyUI-layout LoRAs of the MiniMax-H3 students, and the training code in the code repository 🏋️ * **2026/10/02:** The 4-step MiniMax-H3 students (`lora_critic`, `full_critic`) released 🚀 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.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。