Diffusion model benchmark
- Dominant language
- C++
- Stars
- 933
- Forks
- 169
- PR merge metrics
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Description
We have support diffusers in https://github.com/alibaba/BladeDISC/issues/867 . This issue tracks performance of all the diffuser pipelines. For the concern of performance, we use BlaDNN to tuning models during runtime.
The following pipelines would be tested:
- [ ] StableDiffusionPipeline
- [ ] runwayml/stable-diffusion-v1-5
- [ ] stabilityai/stable-diffusion-2-1-base
- [ ] StableDiffusionImg2ImgPipeline
- [ ] runwayml/stable-diffusion-v1-5
- [ ] StableDiffusionDepth2ImgPipeline
- [ ] stabilityai/stable-diffusion-2-depth
- [ ] StableDiffusionInpaintPipeline
- [ ] runwayml/stable-diffusion-inpainting
- [ ] AltDiffusionPipeline
- [ ] BAAI/AltDiffusion
Contributor guide
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Research direction
Review the Diffusers support tracked in issue 867 and the pipeline/model checklist in this issue. Start by determining how each listed pipeline should be benchmarked with BlaDNN runtime tuning, then record performance results for Stable Diffusion and AltDiffusion variants. Done means every checklist entry has been tested and its performance is documented.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- pytorch
- Domain
- compilers, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100