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|---|---|---|---|---|---|---|---|---|---|
StableDiffusionXLAdapterPipeline | TencentARC/t2i-adapter-canny-sdxl-1.0 | 1 | 50 | false | false | 19.118 (+27.17%) | 10.654 (0.00%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLAdapterPipeline | TencentARC/t2i-adapter-canny-sdxl-1.0 | 1 | 50 | false | true | 17.697 (+27.91%) | 10.649 (0.00%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionAdapterPipeline | TencentARC/t2iadapter_canny_sd14v1 | 1 | 50 | false | false | 3.24 (+12.11%) | 3.348 (0.00%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionAdapterPipeline | TencentARC/t2iadapter_canny_sd14v1 | 1 | 50 | false | true | 3.009 (+18.14%) | 3.34 (-0.21%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLControlNetPipeline | diffusers/controlnet-canny-sdxl-1.0 | 1 | 50 | false | false | 27.26 (+23.84%) | 12.962 (0.00%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLControlNetPipeline | diffusers/controlnet-canny-sdxl-1.0 | 1 | 50 | false | true | 24.054 (+27.49%) | 12.857 (0.00%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
KandinskyV22CombinedPipeline | kandinsky-community/kandinsky-2-2-decoder | 1 | 50 | false | false | 4.024 (+13.83%) | 9.758 (-0.44%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
KandinskyV22CombinedPipeline | kandinsky-community/kandinsky-2-2-decoder | 1 | 50 | false | true | 3.517 (+9.29%) | 9.766 (-0.07%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLPipeline | latent-consistency/lcm-lora-sdxl | 1 | 4 | false | false | 1.649 (+9.21%) | 10.458 (+0.03%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLPipeline | latent-consistency/lcm-lora-sdxl | 1 | 4 | false | true | 1.599 (+11.58%) | 10.466 (+0.04%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionControlNetPipeline | lllyasviel/sd-controlnet-canny | 1 | 50 | false | false | 4.461 (+11.16%) | 3.899 (-0.05%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionControlNetPipeline | lllyasviel/sd-controlnet-canny | 1 | 50 | false | true | 3.377 (+19.37%) | 3.881 (+0.36%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionImg2ImgPipeline | runwayml/stable-diffusion-v1-5 | 1 | 50 | false | false | 2.691 (-9.55%) | 3.18 (-27.74%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionImg2ImgPipeline | runwayml/stable-diffusion-v1-5 | 1 | 50 | false | true | 2.084 (-4.36%) | 3.183 (-27.76%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionInpaintPipeline | runwayml/stable-diffusion-v1-5 | 1 | 50 | false | false | 3.36 (+39.77%) | 3.193 (+0.41%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionInpaintPipeline | runwayml/stable-diffusion-v1-5 | 1 | 50 | false | true | 2.635 (+48.45%) | 3.184 (+0.03%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionPipeline | runwayml/stable-diffusion-v1-5 | 1 | 50 | false | false | 3.268 (+8.50%) | 3.192 (0.00%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionPipeline | runwayml/stable-diffusion-v1-5 | 1 | 50 | false | true | 2.507 (+11.92%) | 3.192 (+0.25%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLPipeline | segmind/SSD-1B | 1 | 50 | false | false | 12.024 (+316.20%) | 8.114 (+153.96%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLPipeline | segmind/SSD-1B | 1 | 50 | false | true | 10.623 (+402.74%) | 8.123 (+154.40%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLImg2ImgPipeline | stabilityai/sdxl-turbo | 1 | 2 | false | false | 0.42 (-95.83%) | 7.655 (-5.63%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLImg2ImgPipeline | stabilityai/sdxl-turbo | 1 | 2 | false | true | 1.44 (-83.31%) | 7.654 (-5.65%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLPipeline | stabilityai/sdxl-turbo | 1 | 1 | false | false | 0.311 (-23.02%) | 7.654 (+0.04%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLPipeline | stabilityai/sdxl-turbo | 1 | 1 | false | true | 1.307 (+1.71%) | 7.652 (-0.09%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLInpaintPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | false | 19.657 (+6408.94%) | 10.471 (+36.75%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLInpaintPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | true | 17.565 (+1403.85%) | 10.469 (+36.83%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | false | 19.148 (+20.99%) | 10.467 (-28.10%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | true | 16.737 (+22.40%) | 10.47 (-28.08%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLImg2ImgPipeline | stabilityai/stable-diffusion-xl-refiner-1.0 | 1 | 50 | false | false | 7.171 (-54.81%) | 9.624 (-8.05%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
StableDiffusionXLImg2ImgPipeline | stabilityai/stable-diffusion-xl-refiner-1.0 | 1 | 50 | false | true | 6.565 (-53.22%) | 9.614 (-8.18%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
WuerstchenCombinedPipeline | warp-ai/wuerstchen | 1 | 50 | false | false | 4.083 (-73.24%) | 6.073 (-41.99%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
WuerstchenCombinedPipeline | warp-ai/wuerstchen | 1 | 50 | false | true | 4.219 (-68.10%) | 6.075 (-41.95%) | 21.964 | c2a56855f1475f94e4a948b3c166796cf6e51d5a |
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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