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|---|---|---|---|---|---|---|---|---|---|
StableDiffusionImg2ImgPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 2.687 (-0.89%) | 3.181 (-0.34%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionImg2ImgPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 2.081 (-1.00%) | 3.191 (-0.03%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionInpaintPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 3.38 (-0.73%) | 3.184 (+0.13%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionInpaintPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 2.606 (-1.33%) | 3.188 (+0.13%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 3.352 (-1.47%) | 3.192 (0.00%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 2.539 (-0.74%) | 3.191 (0.00%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLAdapterPipeline | TencentARC/t2i-adapter-canny-sdxl-1.0 | 1 | 50 | false | false | 20.238 (-2.70%) | 10.649 (-0.02%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLAdapterPipeline | TencentARC/t2i-adapter-canny-sdxl-1.0 | 1 | 50 | false | true | 18.234 (-1.67%) | 10.652 (0.00%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionAdapterPipeline | TencentARC/t2iadapter_canny_sd14v1 | 1 | 50 | false | false | 3.277 (-0.88%) | 3.341 (-0.24%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionAdapterPipeline | TencentARC/t2iadapter_canny_sd14v1 | 1 | 50 | false | true | 3.068 (-0.58%) | 3.346 (+0.09%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLControlNetPipeline | diffusers/controlnet-canny-sdxl-1.0 | 1 | 50 | false | false | 29.262 (-0.41%) | 12.963 (+0.05%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLControlNetPipeline | diffusers/controlnet-canny-sdxl-1.0 | 1 | 50 | false | true | 25.1 (-1.22%) | 12.858 (-0.03%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
KandinskyV22CombinedPipeline | kandinsky-community/kandinsky-2-2-decoder | 1 | 50 | false | false | 4.134 (-1.62%) | 9.77 (+0.01%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
KandinskyV22CombinedPipeline | kandinsky-community/kandinsky-2-2-decoder | 1 | 50 | false | true | 3.544 (-0.92%) | 9.764 (-0.40%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLPipeline | latent-consistency/lcm-lora-sdxl | 1 | 4 | false | false | 1.641 (-1.38%) | 10.472 (+0.03%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLPipeline | latent-consistency/lcm-lora-sdxl | 1 | 4 | false | true | 1.744 (-2.08%) | 10.847 (+0.12%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionControlNetPipeline | lllyasviel/sd-controlnet-canny | 1 | 50 | false | false | 4.488 (+0.13%) | 3.897 (+0.10%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionControlNetPipeline | lllyasviel/sd-controlnet-canny | 1 | 50 | false | true | 3.419 (-0.18%) | 3.865 (-0.21%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLPipeline | segmind/SSD-1B | 1 | 50 | false | false | 12.636 (-2.44%) | 8.112 (-0.18%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLPipeline | segmind/SSD-1B | 1 | 50 | false | true | 11.046 (-1.60%) | 8.112 (+0.01%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLImg2ImgPipeline | stabilityai/sdxl-turbo | 1 | 2 | false | false | 0.423 (-2.08%) | 7.657 (+0.03%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLImg2ImgPipeline | stabilityai/sdxl-turbo | 1 | 2 | false | true | 1.482 (+1.79%) | 7.659 (+0.08%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLPipeline | stabilityai/sdxl-turbo | 1 | 1 | false | false | 0.317 (-1.86%) | 7.657 (+0.04%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLPipeline | stabilityai/sdxl-turbo | 1 | 1 | false | true | 1.357 (+1.65%) | 7.656 (+0.03%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLInpaintPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | false | 20.687 (-1.85%) | 10.468 (+0.01%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLInpaintPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | true | 18.043 (-1.97%) | 10.469 (+0.03%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | false | 20.378 (-1.86%) | 10.47 (0.00%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | true | 17.313 (-2.06%) | 10.466 (0.00%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLImg2ImgPipeline | stabilityai/stable-diffusion-xl-refiner-1.0 | 1 | 50 | false | false | 7.619 (-0.92%) | 9.613 (0.00%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
StableDiffusionXLImg2ImgPipeline | stabilityai/stable-diffusion-xl-refiner-1.0 | 1 | 50 | false | true | 6.854 (-0.04%) | 9.615 (0.00%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
WuerstchenCombinedPipeline | warp-ai/wuerstchen | 1 | 50 | false | false | 4.582 (-2.55%) | 6.072 (-0.16%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
WuerstchenCombinedPipeline | warp-ai/wuerstchen | 1 | 50 | false | true | 4.622 (-3.08%) | 6.097 (+0.44%) | 21.951 | a5f35ee4731b731d6bd8977525873b0bc480cb42 |
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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