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How to render DMAD clips in ComfyUI with ready-made workflows with DMAD

Load the ComfyUI-layout DMAD LoRA and a ready-to-run workflow to make 4-step or 8-step clips with audio.

  • Content
  • Intermediate
  • 2 hours
  • From the README
  • Any agent

Who it is forA ComfyUI user with a 16 to 24 GB GPU who wants a visual node graph instead of the command line.

The problemCommand-line inference is hard for artists who already build their video pipelines in ComfyUI.

The outcomeA 15 s video with audio rendered from a ComfyUI workflow, using the ready-to-run 4-step or 8-step graph.

What you need first

  • A CUDA GPU: 80 GB for the default path, 8 GB and up with --low-vram
  • Python 3.12 with conda and PyTorch for CUDA 12.6
  • About 170 GB of free disk space for the MiniMax-H3 base components
  • A Hugging Face account that has accepted the MiniMax H3 Community License

Steps

  1. Download the ComfyUI key layout file with hf download ZhengmingYu/DMAD --include "minimax_h3/*_comfyui.safetensors" --local-dir ckpt (inferred from the README file names). (inferred, check the README)
  2. Open the ready-to-run workflow described in comfyui/README.md for the 24 GB GPU setup.
  3. Load the file dmad_minimax_h3_4step_lora_critic_comfyui.safetensors in the LoRA loader.
  4. Set the sampler to lcm and the scheduler to simple, then run the graph.

Prompt for your agent

In the Yzmblog/DMAD repo, open the ComfyUI workflow described in comfyui/README.md and load dmad_minimax_h3_4step_lora_critic_comfyui.safetensors with the lcm sampler and simple scheduler. Expected output: a 15 s video with audio for the prompt <your prompt>. Verify that the sampler and scheduler names match the README before running.

Caveat. The ready-to-run workflows are documented for 16 to 24 GB GPUs, so an 8 GB GPU needs the separate 5 s workflow that the README links.

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