[Training Details of dubit]
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 12/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Quiet
- Tech stack
- python
- Domain
- ai, machine-learning
Research direction
This is a question for the maintainers, not a code change, and the payload names no files, tests or entry points. It asks for the training process behind the dubbing (dubit) mode, including the mask mechanism, the order of the original and reference videos, and the use of the distillation model. A newcomer would need the maintainers to answer or publish those details before any work could start, so this is not a good first issue. Done would mean the maintainers document the training details, which is outside what a first contribution can deliver.
Written by the indexing model from the issue text.
Description
I've noticed some discrepancies between the LTX 2.3 inference dubit functionality and the original paper and repository, such as the mask mechanism, the order of the original and reference videos, and the use of the distillation model. I'd like to ask if it's possible to obtain details about the training process in the new version. Thank you so much!
- Dominant language
- Python
- Stars
- 10k
- Forks
- 1.6k
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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