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| import tomesd | |
| import torch | |
| import torch.utils.benchmark as benchmark | |
| from diffusers import StableDiffusionPipeline | |
| def benchmark_torch_function(f, *args, **kwargs): | |
| t0 = benchmark.Timer( | |
| stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f} | |
| ) | |
| return round(t0.blocked_autorange(min_run_time=1).mean, 2) | |
| model_id = "runwayml/stable-diffusion-v1-5" | |
| prompt = "a photo of an astronaut riding a horse on mars" | |
| steps = 25 | |
| num_images_per_prompt = 1 | |
| dtype = torch.float16 | |
| resolution = 1024 | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", torch_dtype=dtype, safety_checker=None | |
| ).to("cuda") | |
| pipe.set_progress_bar_config(disable=True) | |
| # Vanilla | |
| print("Running benchmark with vanilla pipeline...") | |
| f = lambda: pipe( | |
| prompt, | |
| height=resolution, | |
| width=resolution, | |
| num_inference_steps=steps, | |
| num_images_per_prompt=num_images_per_prompt, | |
| ).images | |
| time_vanilla = benchmark_torch_function(f) | |
| # With ToMe | |
| print("Running benchmark with ToMe patched pipeline...") | |
| tomesd.apply_patch(pipe, ratio=0.5) | |
| f = lambda: pipe( | |
| prompt, | |
| height=resolution, | |
| width=resolution, | |
| num_inference_steps=steps, | |
| num_images_per_prompt=num_images_per_prompt, | |
| ).images | |
| time_tome = benchmark_torch_function(f) | |
| # With ToMe + xformers | |
| print("Running benchmark with ToMe patched + xformers enabled pipeline...") | |
| tomesd.remove_patch(pipe) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| tomesd.apply_patch(pipe, ratio=0.5) | |
| f = lambda: pipe( | |
| prompt, | |
| height=resolution, | |
| width=resolution, | |
| num_inference_steps=steps, | |
| num_images_per_prompt=num_images_per_prompt, | |
| ).images | |
| time_tome_xformers = benchmark_torch_function(f) | |
| print( | |
| f"Model: {model_id}, dtype: {dtype}, steps: {steps}, num_images_per_prompt: {num_images_per_prompt}, resolution: {resolution} x {resolution}" | |
| ) | |
| print(f"Vanilla : {time_vanilla} s") | |
| print(f"ToMe : {time_tome} s") | |
| print(f"ToMe + xformers: {time_tome_xformers} s") |
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