OpenImagingLab / OpenImagingLab/FlashVSR
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- Dominant language
- Python
- Stars
- 1.9k
- Forks
- 152
- PR merge metrics
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Description
I have a question regarding the note in your README:
“⚠️ Note: This project is primarily designed and optimized for 4× video super-resolution.”
From my understanding, when performing 2× super-resolution, the pipeline seems to follow almost the same process as 4× SR, except for the difference in spatial resolution.
Does this mean that the main distinction lies in the degradation level of the input video, and that your pretrained model is more specifically optimized for ×4 scenarios—where roughly 25% of the original information is preserved and needs to be reconstructed?
Please feel free to correct me if my understanding is inaccurate.
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 README note about 4× video super-resolution and inspect the documented pipeline and pretrained-model usage. Determine whether the project distinguishes 2× and 4× operation, then clarify the README if the current note leaves that distinction unanswered.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100