SDXL Latent Consistency Model (LCM) optimization (#18526)
Add support of LCM model
(https://huggingface.co/latent-consistency/lcm-sdxl) in SDXL demo.
Since LCM model does not need classifier-free guidance, so there is no
need to use negative prompt. The input and output shape is different
from original SDXL model: no need to double the batch dimension.
We also save metadata to image, and update image filename to include
scheduler and steps.
#### Latency (miliseconds) of generating 1024x1024 images in
A100-SXM4-80GB GPU
Engines are built with static input shape, and CUDA graph is enabled.
For dynamic shape input, the latency could be slower.
Batch Size | Pipeline | Steps | ORT_CUDA | ORT_TRT | TRT 8.6
-- | -- | -- | -- | -- | --
1 | LCM SDXL | 4 | 275 | 249 | 258
1 | LCM SDXL | 8 | 460 | 423 | 430
1 | SDXL Base | 30 | 2566 | 2535 | 2569
4 | LCM SDXL | 4 | 925 | 887 | 1032
4 | LCM SDXL | 8 | 1539 | 1493 | 1662
4 | SDXL Base | 30 | 9227 | 9408 | 9678
