lllyasviel / lllyasviel/stable-diffusion-webui-forge
[Feature Request]: T-Stitch "Accelerating Sampling in Pre-trained Diffusion Models with Trajectory Stitching"
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 13k
- Forks
- 1.7k
- PR merge metrics
- No merged PRs in 30d
Description
They released all code can be implemented i think : https://github.com/NVlabs/T-Stitch?tab=readme-ov-file#-gradio-demo
What it does is, according to the authors claim, speed up with 0 loss of quality
They even have SDXL too
Contributor guide
No contributing guide indexed for this repository
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 linked T-Stitch repository and its README, especially the Gradio demo, to understand the released implementation and supported SDXL workflow. The issue names no files, entry points, tests, or acceptance criteria in stable-diffusion-webui-forge; completion would require defining an integration path and validating the claimed sampling speed and image-quality behavior.
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
- 20/100