lllyasviel / lllyasviel/stable-diffusion-webui-forge
Speeding up inference by batching
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- Dominant language
- Python
- Stars
- 13k
- Forks
- 1.7k
- PR merge metrics
- No merged PRs in 30d
Description
Is there any way to merge several requests in forge so that they are processed faster?
For example. I have flux working out in 24 seconds for 1 image per api. I have the ability to merge requests, but I don't understand which api request to send to forge sd. Maybe someone has encountered something like this? Can you tell me if it is possible now to speed up inference by batched requests? Or will there be no impact?
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 reviewing Forge SD's API request handling and the existing Flux inference path. Determine whether batched requests are supported and what endpoint or behavior would define completion; the issue names no files, tests, or specific implementation scope.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- api, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100