MLSAKIIT / MLSAKIIT/stablediffusionlora
Data Augmentation
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 13
- Forks
- 14
- PR merge metrics
- No merged PRs in 30d
Description
Participants can increase the diversity of their training data (e.g., vehicles, animals, facial images, etc.) by applying transformations like cropping, flipping, or noise injection. These augmentations help make the model more robust by exposing it to variations, allowing LoRA layers to learn better representations, even with a limited dataset.
Please ensure you have read the guidelines in CONTRIBUTING.md and CODE_OF_CONDUCT.md before proceeding.
Contributor guide
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
Read CONTRIBUTING.md and CODE_OF_CONDUCT.md first. The issue names no implementation files, entry points, or tests, so locate the training-data pipeline and determine how cropping, flipping, and noise injection should be configured and evaluated; done should mean documented augmentations are integrated and their behavior is verified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100