Skip to main content
MiniMax H3 is MiniMax’s general-purpose, omni-modal generation model, now available as open weights. It jointly understands text, images, video, and audio in a single context, and generates video with native stereo audio: voice, sound effects, and music are modeled together in a single forward pass instead of being layered on afterward. Output is up to 2K resolution, 24fps, and about 15 seconds. ComfyUI natively supports MiniMax H3. The documentation is split across five pages:
  • Overview (this page): model capabilities, workflow index, output resolution, and speedups
  • Native workflows: Text-to-Video, Image-to-Video, Reference-to-Video, plus advanced native-node techniques
  • Multiframe Reference: anchor reference frames at specific points along the output timeline
  • Fun ControlNet Union: drive H3 with a control video, or run video inpainting with a mask
  • Prompt guide: official MiniMax prompt writing guides, general tips, and prompt embeddings
H3’s open weights let you run the model locally. Commercial use of locally generated outputs requires a MiniMax commercial license, available through Comfy, the only official reseller. Generations on Comfy Cloud already include commercial rights.

​
Key features

  • Native stereo audio: Dialogue, sound effects, and music are generated together with the video, synced in one MP4
  • Multimodal context: Text, images, video, and audio references can be combined in one generation
  • Reference-driven generation: Lock a character’s identity, a style, a motion, a camera move, or a voice from reference materials
  • Instruction following: Describe the relationship between references and the target shot in natural language
  • Accurate text rendering: Spelled-out text and brand elements render cleanly
  • Open weights: Run locally in ComfyUI with full control over every parameter

​
Getting started

MiniMax H3 is supported in ComfyUI with open weights. To get started:
  1. Update ComfyUI to version 0.30.0 or later
  2. Go to Template Library > Video > choose any MiniMax H3 workflow
  3. Follow the pop-up to download models and run the workflow
The model files are hosted on Hugging Face in the Comfy-Org/MiniMax-H3 repository.

​
Workflow index

The template library currently ships with five example workflows. They are example templates, not an exhaustive list: the model supports more generation modes through the native MiniMax H3 nodes, and you can build additional workflows with them.

Text to Video (T2V)

Generate videos from text prompts with native stereo audio

Image to Video (I2V)

Generate videos from an input image, with optional first/last-frame control

Reference to Video (R2V)

Lock in a character, style, motion, camera move, or voice from reference images, videos, and audio

Multiframe Reference

Anchor reference frames at specific points along the output timeline with chained Add Guide nodes

Fun ControlNet Union

Drive H3 with a Canny, Depth, HED, MLSD, or Pose control video, or run video inpainting with a mask
Underlying node modes: first/last-frame image-to-video (fl2va) via the MiniMaxH3ImageToVideo node, and reference-driven generation with images, videos, and audio (ref2va) via the MiniMaxH3ReferenceToVideo node. For prompt writing resources (official MiniMax guides, general tips, and prompt embeddings), see the prompt guide.

​
Setting the output resolution

Each workflow uses a Resolution Selector node to control the overall output size. The node computes width and height from three settings, and its outputs connect directly to the width and height inputs of the MiniMax H3 node:
  • Aspect ratio: Pick a preset such as 16:9 (Widescreen), 9:16 (Portrait Widescreen), or 1:1 (Square)
  • Megapixels: Target total pixel count for the output. Higher values give larger frames; lower values run faster
  • Multiple: The computed resolution is rounded to the nearest multiple of this number. Keep it at 32 to match H3’s resolution grid
The template ships with a fast preview size. For full-quality output at 16:9, set the Resolution Selector’s Megapixels to 0.98 for H3’s native canvas (a 768px short edge, 1344x768 at 16:9), or enter 1344 x 768 directly in the MiniMax H3 node’s width and height inputs (its default). Skip the 1.0 Megapixel step: it yields 1376x768, above the model’s 768x1344 pixel area cap.

​
Speeding up generation with Sage Attention

The example workflows use the standard attention implementation. You can roughly double the generation speed with Sage Attention, with minimal quality loss. Sage Attention is an optional dependency, so you need to install it yourself:
  1. Install the sageattention Python package. Download the wheel that matches your PyTorch and CUDA versions from the SageAttention releases page, then install it with pip install <wheel-file>.
  2. Install the KJNodes custom nodes, which provide the Patch Sage Attention KJ node. Use the ComfyUI Manager, or clone the repository into ComfyUI/custom_nodes/ and restart ComfyUI.
  3. Add a Patch Sage Attention KJ node to the workflow and connect it between the UNETLoader and the BasicGuider node: its model input receives the model from the UNETLoader, and its model output feeds the model input of the BasicGuider. Set sage_attention to auto.
  4. Run the workflow as usual. Only the guider needs the patch; the scheduler only generates the sigmas and can stay as is.
Notes:
  • Sage Attention requires float16 or bfloat16 tensors. MiniMax H3 runs some layers in other dtypes, so you may see “Input tensors must be in dtype of torch.float16 or torch.bfloat16, using pytorch attention instead” messages in the console. These are expected; the affected layers fall back to standard attention and generation still works.
  • Alternatively, you can enable Sage Attention globally by launching ComfyUI with the --use-sage-attention flag instead of adding the node.