lllyasviel / lllyasviel/stable-diffusion-webui-forge

Optimization for high-resolution generation

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Dominant language
Python
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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.

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

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