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from schedulers.EulerA import EulerA
# Initialize the Celery app
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_openpose",
torch_dtype=torch.float16,
local_files_only=True,
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
local_files_only=True,
torch_dtype=torch.float16,
safety_checker=None,
requires_safety_checker=False,
).to('cuda')
# import one of the 2 schedulers from this repo
pipe.scheduler = EulerA.from_config(pipe.scheduler.config)
# choose from [0, 'rand_new', 'rand_init']
pipe.scheduler.history_d = 'rand_new'
# number should be between -1 and 1
pipe.scheduler.momentum = 0.95
# number should be between -1 and 1
pipe.scheduler.momentum_hist = 0.75
buffer = open('img0.png', 'rb')
buffer.seek(0)
image_bytes = buffer.read()
images = Image.open(BytesIO(image_bytes))
start_time = time.time()
generator = torch.manual_seed(2733424006)
image=pipe(
"A person standing in a field of flowers, 4k, realistic",
images,
num_inference_steps=20,
height=512,
width=512,
generator=generator
).images[0]
end_time = time.time()
execution_time = end_time - start_time
print("Execution time: {:.2f} seconds".format(execution_time))
# print(image)
image.save('img1.png', format='PNG')
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