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| memory (gbs)
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| actual_gpu_memory (gbs)
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| github_sha
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value |
|---|---|---|---|---|---|---|---|---|---|
StableDiffusionImg2ImgPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 2.636 (-1.13%) | 3.192 (+0.25%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionImg2ImgPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 1.986 (-1.10%) | 3.194 (0.00%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionInpaintPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 3.302 (-0.93%) | 3.193 (0.00%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionInpaintPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 2.49 (-1.15%) | 3.194 (+0.31%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 3.212 (-1.02%) | 3.188 (+0.25%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 2.377 (-0.96%) | 3.184 (+0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLAdapterPipeline | TencentARC/t2i-adapter-canny-sdxl-1.0 | 1 | 50 | false | false | 18.619 (+0.58%) | 10.651 (+0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLAdapterPipeline | TencentARC/t2i-adapter-canny-sdxl-1.0 | 1 | 50 | false | true | 17.05 (-1.27%) | 10.647 (-0.06%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionAdapterPipeline | TencentARC/t2iadapter_canny_sd14v1 | 1 | 50 | false | false | 3.209 (-0.77%) | 3.342 (-0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionAdapterPipeline | TencentARC/t2iadapter_canny_sd14v1 | 1 | 50 | false | true | 3.004 (-0.79%) | 3.349 (+0.27%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLControlNetPipeline | diffusers/controlnet-canny-sdxl-1.0 | 1 | 50 | false | false | 27.111 (+0.58%) | 12.957 (-0.04%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLControlNetPipeline | diffusers/controlnet-canny-sdxl-1.0 | 1 | 50 | false | true | 23.136 (-0.84%) | 12.859 (0.00%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
KandinskyV22CombinedPipeline | kandinsky-community/kandinsky-2-2-decoder | 1 | 50 | false | false | 4.008 (-1.13%) | 9.761 (-0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
KandinskyV22CombinedPipeline | kandinsky-community/kandinsky-2-2-decoder | 1 | 50 | false | true | 3.403 (-1.68%) | 9.766 (-0.38%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLPipeline | latent-consistency/lcm-lora-sdxl | 1 | 4 | false | false | 1.631 (-0.49%) | 10.467 (0.00%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLPipeline | latent-consistency/lcm-lora-sdxl | 1 | 4 | false | true | 1.717 (-1.09%) | 10.837 (-0.07%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionControlNetPipeline | lllyasviel/sd-controlnet-canny | 1 | 50 | false | false | 4.42 (-1.01%) | 3.903 (-0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionControlNetPipeline | lllyasviel/sd-controlnet-canny | 1 | 50 | false | true | 3.268 (-1.27%) | 3.862 (-0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLPipeline | segmind/SSD-1B | 1 | 50 | false | false | 12.015 (-0.11%) | 8.115 (+0.06%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLPipeline | segmind/SSD-1B | 1 | 50 | false | true | 10.386 (-0.82%) | 8.11 (-0.02%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLImg2ImgPipeline | stabilityai/sdxl-turbo | 1 | 2 | false | false | 0.417 (-1.65%) | 7.652 (-0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLImg2ImgPipeline | stabilityai/sdxl-turbo | 1 | 2 | false | true | 1.656 (-2.87%) | 7.651 (-0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLPipeline | stabilityai/sdxl-turbo | 1 | 1 | false | false | 0.319 (+0.95%) | 7.652 (-0.08%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLPipeline | stabilityai/sdxl-turbo | 1 | 1 | false | true | 1.508 (+0.07%) | 7.652 (0.00%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLInpaintPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | false | 19.365 (+0.35%) | 10.468 (-0.02%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLInpaintPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | true | 16.968 (-0.49%) | 10.468 (0.00%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | false | 18.772 (+0.94%) | 10.473 (+0.03%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | true | 16.093 (-0.75%) | 10.466 (+0.01%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLImg2ImgPipeline | stabilityai/stable-diffusion-xl-refiner-1.0 | 1 | 50 | false | false | 7.223 (+0.12%) | 9.623 (+0.10%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
StableDiffusionXLImg2ImgPipeline | stabilityai/stable-diffusion-xl-refiner-1.0 | 1 | 50 | false | true | 6.4 (-0.56%) | 9.623 (+0.09%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
WuerstchenCombinedPipeline | warp-ai/wuerstchen | 1 | 50 | false | false | 4.588 (+1.66%) | 6.077 (+0.07%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
WuerstchenCombinedPipeline | warp-ai/wuerstchen | 1 | 50 | false | true | 4.502 (-1.34%) | 6.07 (-0.08%) | 21.951 | 5c94937dc7561767892d711e199f874dc35df041 |
Welcome to 🤗 Diffusers Benchmarks!
This is dataset where we keep track of the inference latency and memory information of the core pipelines in the diffusers library.
Currently, the core pipelines are the following:
- Stable Diffusion and its derivatives such as ControlNet, T2I Adapter, Image-to-Image, Inpainting
- Stable Diffusion XL and its derivatives
- SSD-1B
- Kandinsky
- Würstchen
- LCM
Note that we will continue to extend the list of core pipelines based on their API usage.
We use this GitHub Actions workflow to report the above numbers automatically. This workflow runs on a biweekly cadence.
The benchmarks are run on an A10G GPU.
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