ipex-llm/python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion/openjourney.py
Jinhe 7e0a840f74
add optimization to openjourney (#12423)
* add optimization to openjourney

* add optimization to openjourney
2024-11-21 15:23:51 +08:00

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2.1 KiB
Python

#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Code is adapted from https://huggingface.co/prompthero/openjourney
from diffusers import StableDiffusionPipeline
import torch
from ipex_llm import optimize_model
import argparse
import time
def main(args):
pipe = StableDiffusionPipeline.from_pretrained(
args.repo_id_or_model_path,
torch_dtype=torch.float16,
use_safetensors=True)
pipe = optimize_model(pipe, low_bit=None)
pipe = pipe.to("xpu")
with torch.inference_mode():
# warmup
image = pipe(args.prompt, num_inference_steps=args.num_steps).images[0]
# start inference
st = time.time()
image = pipe(args.prompt, num_inference_steps=args.num_steps).images[0]
end = time.time()
print(f'Inference time: {end-st} s')
image.save(args.save_path)
if __name__=="__main__":
parser = argparse.ArgumentParser(description="Stable Diffusion")
parser.add_argument('--repo-id-or-model-path', type=str, default="prompthero/openjourney",
help='The huggingface repo id for the stable diffusion model checkpoint')
parser.add_argument('--prompt', type=str, default="An astronaut in the forest, detailed, 8k",
help='Prompt to infer')
parser.add_argument('--save-path',type=str,default="openjourney-gpu.png",
help="Path to save the generated figure")
parser.add_argument('--num-steps',type=int,default=20,
help="Number of inference steps")
args = parser.parse_args()
main(args)