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|---|---|---|---|---|---|---|---|---|---|
StableDiffusionImg2ImgPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 2.666 (+0.87%) | 3.184 (+0.09%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionImg2ImgPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 2.008 (+1.06%) | 3.194 (+0.03%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionInpaintPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 3.333 (+0.91%) | 3.193 (+0.28%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionInpaintPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 2.519 (+1.08%) | 3.184 (+0.09%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionPipeline | Lykon/DreamShaper | 1 | 50 | false | false | 3.245 (+0.75%) | 3.18 (-0.41%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionPipeline | Lykon/DreamShaper | 1 | 50 | false | true | 2.4 (+0.88%) | 3.183 (+0.06%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLAdapterPipeline | TencentARC/t2i-adapter-canny-sdxl-1.0 | 1 | 50 | false | false | 18.511 (-0.31%) | 10.648 (+0.01%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLAdapterPipeline | TencentARC/t2i-adapter-canny-sdxl-1.0 | 1 | 50 | false | true | 17.269 (+0.81%) | 10.653 (+0.02%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionAdapterPipeline | TencentARC/t2iadapter_canny_sd14v1 | 1 | 50 | false | false | 3.234 (+0.94%) | 3.343 (-0.12%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionAdapterPipeline | TencentARC/t2iadapter_canny_sd14v1 | 1 | 50 | false | true | 3.028 (+0.50%) | 3.34 (-0.24%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLControlNetPipeline | diffusers/controlnet-canny-sdxl-1.0 | 1 | 50 | false | false | 26.955 (+0.39%) | 12.962 (-0.01%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLControlNetPipeline | diffusers/controlnet-canny-sdxl-1.0 | 1 | 50 | false | true | 23.332 (+0.92%) | 12.859 (+0.01%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
KandinskyV22CombinedPipeline | kandinsky-community/kandinsky-2-2-decoder | 1 | 50 | false | false | 4.054 (+0.30%) | 9.764 (-0.07%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
KandinskyV22CombinedPipeline | kandinsky-community/kandinsky-2-2-decoder | 1 | 50 | false | true | 3.461 (+1.44%) | 9.803 (+0.01%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLPipeline | latent-consistency/lcm-lora-sdxl | 1 | 4 | false | false | 1.639 (+0.61%) | 10.467 (-0.05%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLPipeline | latent-consistency/lcm-lora-sdxl | 1 | 4 | false | true | 1.736 (+1.05%) | 10.845 (-0.04%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionControlNetPipeline | lllyasviel/sd-controlnet-canny | 1 | 50 | false | false | 4.465 (+1.69%) | 3.904 (+0.08%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionControlNetPipeline | lllyasviel/sd-controlnet-canny | 1 | 50 | false | true | 3.31 (+1.91%) | 3.863 (-0.46%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLPipeline | segmind/SSD-1B | 1 | 50 | false | false | 12.028 (+0.69%) | 8.11 (-0.21%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLPipeline | segmind/SSD-1B | 1 | 50 | false | true | 10.472 (-0.01%) | 8.112 (-0.14%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLImg2ImgPipeline | stabilityai/sdxl-turbo | 1 | 2 | false | false | 0.424 (+1.19%) | 7.654 (+0.01%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLImg2ImgPipeline | stabilityai/sdxl-turbo | 1 | 2 | false | true | 1.705 (+5.12%) | 7.653 (0.00%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLPipeline | stabilityai/sdxl-turbo | 1 | 1 | false | false | 0.316 (+0.64%) | 7.658 (+0.04%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLPipeline | stabilityai/sdxl-turbo | 1 | 1 | false | true | 1.507 (-0.72%) | 7.652 (0.00%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLInpaintPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | false | 19.298 (+0.16%) | 10.47 (-0.02%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLInpaintPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | true | 17.051 (+1.08%) | 10.468 (0.00%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | false | 18.597 (-0.68%) | 10.47 (+0.03%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLPipeline | stabilityai/stable-diffusion-xl-base-1.0 | 1 | 50 | false | true | 16.215 (+0.88%) | 10.465 (-0.01%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLImg2ImgPipeline | stabilityai/stable-diffusion-xl-refiner-1.0 | 1 | 50 | false | false | 7.214 (+0.49%) | 9.613 (-0.10%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
StableDiffusionXLImg2ImgPipeline | stabilityai/stable-diffusion-xl-refiner-1.0 | 1 | 50 | false | true | 6.436 (+0.61%) | 9.614 (-0.07%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
WuerstchenCombinedPipeline | warp-ai/wuerstchen | 1 | 50 | false | false | 4.513 (-3.07%) | 6.073 (-0.16%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
WuerstchenCombinedPipeline | warp-ai/wuerstchen | 1 | 50 | false | true | 4.563 (+2.68%) | 6.075 (0.00%) | 21.951 | c75431843f3b5b4915a57fe68a3e5420dc46a280 |
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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