ali-vilab / ali-vilab/DiffusionOPD
Verify evals on Papers with Code
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- Dominant language
- Python
- Stars
- 181
- Forks
- 2
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Description
Hi,
Niels here from the open-source team at Hugging Face. Congratulations on your work!
I've made the paper and 1 paper-native evaluation available on Papers with Code.
The paper is part of the Image generation task page.
The DiffusionOPD result currently ranks second on GenEval.
Would it be possible to verify these results and let me know if any score, model name, benchmark protocol, or openness metadata should be corrected? The imported rows are tied to the paper or its official release artifacts; comparison-table baselines were not added.
You can also edit the task, methods, project page, and GitHub URL directly from the paper page using your Hugging Face account.
If you'd like to showcase the results in your repository README, you can copy these live leaderboard badges (or use the “Copy PwC badge” button in the Results section):
Kind regards,
Niels
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 with the linked paper and its Papers with Code result and GenEval leaderboard entry. Verify the score, model name, benchmark protocol, and openness metadata against the paper or official release artifacts; done means reporting or correcting any discrepancies on the paper page.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100