NVIDIA / NVIDIA/TransformerEngine
[Feature Request] Integration of DiT components into TransformerEngine.
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 3.5k
- Forks
- 831
- Avg merge
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- Merged PRs (30d)
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Description
References
Two academic papers are cited to support the request:
Scalable Diffusion Models with Transformers: https://arxiv.org/abs/2212.09748
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis: https://arxiv.org/abs/2310.00426
Current Support
TransformerEngine does not yet support DiT.
Differences Noted
Specific differences such as LN elementwise_affine=False and Transformer layer with Time step aware scale / shift are highlighted.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are named in the issue. Start by reading the cited DiT and PixArt-α papers and comparing their LayerNorm and time-step scale/shift requirements with TransformerEngine's current support; done means DiT components are integrated with those differences covered.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100