run_benchmark.py [19:44]:
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def run_inference(pipe, args):
    _ = pipe(
        prompt=args.prompt,
        num_inference_steps=args.num_inference_steps,
        num_images_per_prompt=args.batch_size,
    )


def main(args) -> dict:
    pipeline = load_pipeline(
        ckpt=args.ckpt,
        compile_unet=args.compile_unet,
        compile_vae=args.compile_vae,
        no_sdpa=args.no_sdpa,
        no_bf16=args.no_bf16,
        upcast_vae=args.upcast_vae,
        enable_fused_projections=args.enable_fused_projections,
        do_quant=args.do_quant,
        compile_mode=args.compile_mode,
        change_comp_config=args.change_comp_config,
        device=args.device,
    )

    # Warmup.
    run_inference(pipeline, args)
    run_inference(pipeline, args)
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run_profile.py [29:54]:
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def run_inference(pipe, args):
    _ = pipe(
        prompt=args.prompt,
        num_inference_steps=args.num_inference_steps,
        num_images_per_prompt=args.batch_size,
    )


def main(args) -> dict:
    pipeline = load_pipeline(
        ckpt=args.ckpt,
        compile_unet=args.compile_unet,
        compile_vae=args.compile_vae,
        no_sdpa=args.no_sdpa,
        no_bf16=args.no_bf16,
        upcast_vae=args.upcast_vae,
        enable_fused_projections=args.enable_fused_projections,
        do_quant=args.do_quant,
        compile_mode=args.compile_mode,
        change_comp_config=args.change_comp_config,
        device=args.device,
    )

    # warmup.
    run_inference(pipeline, args)
    run_inference(pipeline, args)
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