lllyasviel / lllyasviel/stable-diffusion-webui-forge
Optimization for high-resolution generation
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- Dominant language
- Python
- Stars
- 13k
- Forks
- 1.7k
- PR merge metrics
- No merged PRs in 30d
Description
TLDR: high res generation can be optimised about 20% to 40%
I made some napkin math and wrote this post about it [High-res fix optimization](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/2234) and would guess that the basic implementation without solving for the optimal n in the optimization algorithm should be easy enough to implement since the foundation for this is the high-res fix with more steps.
Solving for the optimal n shouldn't be impossible either since it's already known how to plot the best estimate and some one have already done a time estimator from what I remember that should be good enough to get the constants needed to calculate the best n value.
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 the linked High-res fix optimization discussion and locating the existing high-resolution fix implementation. Compare its step handling with the proposed optimization, then investigate whether the project has a time estimator that can supply constants for choosing n. Done means high-resolution generation is measurably faster while retaining expected output quality.
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
- 25/100