ByteDance-Seed / ByteDance-Seed/Bagel
Inferencing for Video Generation & Evaluation
- Dominant language
- Python
- Stars
- 6.2k
- Forks
- 545
- PR merge metrics
- No merged PRs in 30d
Description
Hello, thanks for your impressive contribution.
The paper is quite interesting. I would like to test video generation capabilities of the model and evaluate if possible. Can you please share the inference & evaluation scripts for video generation / real world modeling. If not I would be happy to implement myself but I want to apply the correct logic applied in the paper. A textual description of how it was done would also be helpful.
How should I arrange the input and conditions for this purpose, and what metrics did you use (FID, etc. )?
Contributor guide
No contributing guide indexed for this repository
Research direction
No files, tests, or entry points are named. Start by locating the project's inference and evaluation entry points and compare them with the paper's video-generation setup. Done means providing the requested video inference and evaluation scripts, or documenting the input conditions and metrics used.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100