lucidrains / lucidrains/vector-quantize-pytorch
Multi-Scale Residual Vector Quantization
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enhancement
- Dominant language
- Python
- Stars
- 4k
- Forks
- 338
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
I am wondering if there is an easy way to use ResidualVQ and apply Multi-Scale to it via your amazing library?
- Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
- SAR3D: Autoregressive 3D Object Generation and Understanding via
Multi-scale 3D VQVAE
These seem to be highly beneficial for Autoregressive modeling.
Contributor guide
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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
Start by reading the ResidualVQ entry point and the linked VAR and SAR3D references to define what multi-scale support should mean for this library. The issue names no files or tests; done would be a documented, tested way to apply multi-scale processing to ResidualVQ.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100