I got it installed and tested with an SD 1.5 model in Stable Diffusion Webui using a 4090. (SDXL models did not work.) I generated the same test prompt with and

I got it installed and tested with an SD 1.5 model in Stable Diffusion Webui using a 4090. (SDXL models did not work.) I generated the same test prompt with and without their TensorRT acceleration. The generated images are nearly identical, with very minor, almost imperceptible differences, which is pretty standard for techniques that optimize the attention layers. Prompt: A cat wearing pajamas Model: deliberate_v2 Size: 768x512 Batch Size: 4 Clip Skip: 2 Samler: DPM++ 2S a Karras Steps: 70 Seed: 2024828515 Generation times avg over 5 runs ================================ without TensorRT: 19.2 seconds with TensorRT: 12.3 seconds Benchmarks Batch Size 1 / 2 / 4 ======================================= without TensorRT: 31.11 / 36.23 / 42.34 with TensorRT: 55.32 / 58.06 / 62.27 It's faster, but clearly it's not 4x faster. I suppose they cherrypicked benchmarks against generation techniques not using xformers or SDP Attention. Also, it appears to be limited to a max batch size of 4.