Practical overview
When it fits
Use Mmdit when your work requires a reusable PyTorch architecture component for multimodal attention experimentation rather than a finished end-user generation application.
Who it is for
- PyTorch developers prototyping multimodal generative-model architectures.
- Researchers experimenting with attention across text, image, video and audio tokens.
- Builders comparing standard and learned-gating adaptive attention blocks.
Documented uses
- Construct an MMDiT block for text and image token sequences.
- Process text, video and audio tokens through a generalized multimodal interface.
- Experiment with learned gating for adaptive attention.
- Extend a research architecture toward additional modalities.
Requirements & limits
The repository is public, active and MIT-licensed. The README documents installation with pip install mmdit. No account or pricing information is provided.
- Choose the standard MMDiTBlock when the documented inputs are text and image tokens.
- Choose the generalized MMDiT interface when working with the documented text, video and audio modality example.
- The implementation is a model component, so integrating it requires a PyTorch workflow and correctly shaped tensors.
- The learned-gating adaptive-attention variant is available as a separate implementation described by the repository.
- An additional modality is marked Likely compatible, but no concrete example is supplied.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Mmdit and prepare a documented two-modality block
Set up the package and construct the text-image MMDiTBlock shown in the repository example.
Before you start
- Python environment with PyTorch available.
- A package installation workflow that can run pip install mmdit.
- Text-token tensors, an image-token tensor and a time-conditioning tensor with dimensions matching the example.
Steps
Install the package with pip install mmdit.
Import torch and MMDiTBlock.
Create an MMDiTBlock with conditioning, text and image dimensions, enabling qk_rmsnorm as shown.
Prepare the documented time-conditioning, text-token, text-mask and image-token tensors, then pass them to the block’s forward call.
Expected outcome
A configured two-modality MMDiT block with the documented text-image forward interface.
Guide limitations
- The example documents block construction and a forward call, not a complete training or generation pipeline.
- Tensor dimensions must match the configured dimensions.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 4 October 2026
- Original website title
- lucidrains/mmdit · Official homepage title · retained in its source language