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June 17, 2024 07:47
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The script shows how to run SD3 with `torch.compile()`
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import torch | |
torch.set_float32_matmul_precision("high") | |
from diffusers import StableDiffusion3Pipeline | |
import time | |
id = "stabilityai/stable-diffusion-3-medium-diffusers" | |
pipeline = StableDiffusion3Pipeline.from_pretrained( | |
id, | |
torch_dtype=torch.float16 | |
).to("cuda") | |
pipeline.set_progress_bar_config(disable=True) | |
torch._inductor.config.conv_1x1_as_mm = True | |
torch._inductor.config.coordinate_descent_tuning = True | |
torch._inductor.config.epilogue_fusion = False | |
torch._inductor.config.coordinate_descent_check_all_directions = True | |
pipeline.transformer.to(memory_format=torch.channels_last) | |
pipeline.vae.to(memory_format=torch.channels_last) | |
pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True) | |
pipeline.vae.decode = torch.compile(pipeline.vae.decode, mode="max-autotune", fullgraph=True) | |
prompt = "a photo of a cat" | |
for _ in range(3): | |
_ = pipeline( | |
prompt=prompt, | |
num_inference_steps=50, | |
guidance_scale=5.0, | |
generator=torch.manual_seed(1), | |
) | |
start = time.time() | |
for _ in range(10): | |
_ = pipeline( | |
prompt=prompt, | |
num_inference_steps=50, | |
guidance_scale=5.0, | |
generator=torch.manual_seed(1), | |
) | |
end = time.time() | |
avg_inference_time = (end - start) / 10 | |
print(f"Average inference time: {avg_inference_time:.3f} seconds.") | |
image = pipeline( | |
prompt=prompt, | |
num_inference_steps=50, | |
guidance_scale=5.0, | |
generator=torch.manual_seed(1), | |
).images[0] | |
filename = "_".join(prompt.split(" ")) | |
image.save(f"diffusers_{filename}.png") |
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I solved the issue by installing peft:
pip install peft
I'm not sure what the main problem was exactly, but the error was caused here:
That logger caused the error when
torch.compile
was applied to the transformers.It makes 19.8% faster in 1024x1024 resolution
baseline : 12.2532 sec
compile : 9.82578 sec