d2a37b6ab2
* add openjourney example * add timing * add stable diffusion to model page * 4.1 fix * small fix
67 lines
No EOL
2.6 KiB
Python
67 lines
No EOL
2.6 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Code is adapted from https://huggingface.co/docs/diffusers/main/en/using-diffusers/inference_with_lcm_lora
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import torch
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from diffusers import DiffusionPipeline, LCMScheduler
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import ipex_llm
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import argparse
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import time
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def main(args):
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pipe = DiffusionPipeline.from_pretrained(
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args.repo_id_or_model_path,
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torch_dtype=torch.bfloat16,
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).to("xpu")
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# set scheduler
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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# load LCM-LoRA
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pipe.load_lora_weights(args.lora_weights_path)
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generator = torch.manual_seed(42)
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with torch.inference_mode():
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# warmup
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image = pipe(
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prompt=args.prompt, num_inference_steps=args.num_steps, generator=generator, guidance_scale=1.0
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).images[0]
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# start inference
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st = time.time()
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image = pipe(
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prompt=args.prompt, num_inference_steps=args.num_steps, generator=generator, guidance_scale=1.0
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).images[0]
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end = time.time()
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print(f'Inference time: {end-st} s')
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image.save(args.save_path)
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if __name__=="__main__":
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parser = argparse.ArgumentParser(description="Stable Diffusion lora-lcm")
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parser.add_argument('--repo-id-or-model-path', type=str, default="stabilityai/stable-diffusion-xl-base-1.0",
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help='The huggingface repo id for the stable diffusion model checkpoint')
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parser.add_argument('--lora-weights-path',type=str,default="latent-consistency/lcm-lora-sdxl",
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help='The huggingface repo id for the lcm lora sdxl checkpoint')
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parser.add_argument('--prompt', type=str, default="A lovely dog on the table, detailed, 8k",
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help='Prompt to infer')
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parser.add_argument('--save-path',type=str,default="lcm-lora-sdxl-gpu.png",
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help="Path to save the generated figure")
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parser.add_argument('--num-steps',type=int,default=4,
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help="Number of inference steps")
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args = parser.parse_args()
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main(args) |