thu-ml / thu-ml/TurboDiffusion
Generalization of the merging operation
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- Dominant language
- Python
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- Merged PRs (30d)
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Description
Hi @zhengkw18 @jt-zhang Thanks for your great efforts!
Regarding turbodiffusion/scripts/merge_models.py, could it be employed as a common practice of merging step distillation weights (e.g., DMD, discrete / continuous CD, adversarial distillation, etc.) and sparse attn weights (e.g., VSA, STA, radial attn, etc.), in order to combine their strength?
Or else, it is just a nice property of rCM together with SLA? Thanks in advance.
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 turbodiffusion/scripts/merge_models.py and the issue's references to step-distillation and sparse-attention weights. Compare how the current merge handles rCM and SLA with the listed distillation and attention methods; the issue does not define a concrete implementation or acceptance criteria, so the desired general solution needs clarification first.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100