| import torch | |
| import torch.utils.benchmark as benchmark | |
| import argparse | |
| from diffusers import DiffusionPipeline, LCMScheduler | |
| PROMPT = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux" | |
| MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0" | |
| LORA_ID = "latent-consistency/lcm-lora-sdxl" | |
| def benchmark_fn(f, *args, **kwargs): | |
| t0 = benchmark.Timer( | |
| stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f} | |
| ) | |
| return t0.blocked_autorange().mean * 1e6 | |
| def load_pipeline(standard_sdxl=False): | |
| pipe = DiffusionPipeline.from_pretrained(MODEL_ID, variant="fp16") | |
| if not standard_sdxl: | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
| pipe.load_lora_weights(LORA_ID) | |
| pipe.to(device="cuda", dtype=torch.float16) | |
| pipe.enable_model_cpu_offload() | |
| return pipe | |
| def call_pipeline(pipe, batch_size, num_inference_steps, guidance_scale): | |
| images = pipe( | |
| prompt=PROMPT, | |
| num_inference_steps=num_inference_steps, | |
| num_images_per_prompt=batch_size, | |
| guidance_scale=guidance_scale, | |
| ).images[0] | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--batch_size", type=int, default=1) | |
| parser.add_argument("--standard_sdxl", action="store_true") | |
| args = parser.parse_args() | |
| pipeline = load_pipeline(args.standard_sdxl) | |
| if args.standard_sdxl: | |
| num_inference_steps = 25 | |
| guidance_scale = 5 | |
| else: | |
| num_inference_steps = 4 | |
| guidance_scale = 1 | |
| time = benchmark_fn(call_pipeline, pipeline, args.batch_size, num_inference_steps, guidance_scale) | |
| print(f"Batch size: {args.batch_size} in {time/1e6:.3f} seconds") | |