Created
December 7, 2023 03:21
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from diffusers import DiffusionPipeline | |
import torch | |
import time | |
# load both base & refiner | |
t1 = time.time() | |
base = DiffusionPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True | |
) | |
base.to("cuda") | |
refiner = DiffusionPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-xl-refiner-1.0", | |
text_encoder_2=base.text_encoder_2, | |
vae=base.vae, | |
torch_dtype=torch.float16, | |
use_safetensors=True, | |
variant="fp16", | |
) | |
refiner.to("cuda") | |
t2 = time.time() | |
print("Init took - ",t2-t1,"seconds") | |
# Define how many steps and what % of steps to be run on each experts (80/20) here | |
n_steps = 40 | |
high_noise_frac = 0.8 | |
prompt = "A majestic lion jumping from a big stone at night" | |
t3 = time.time() | |
for _ in range(10): | |
# run both experts | |
image = base( | |
prompt=prompt, | |
num_inference_steps=n_steps, | |
denoising_end=high_noise_frac, | |
output_type="latent", | |
).images | |
image = refiner( | |
prompt=prompt, | |
num_inference_steps=n_steps, | |
denoising_start=high_noise_frac, | |
image=image, | |
).images[0] | |
t4 = time.time() | |
print("Inference took - ",t4-t3,"seconds") | |
image.save("output.png") |
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