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import os |
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import filelock |
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import torch |
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from diffusers import AutoPipelineForImage2Image, AutoPipelineForText2Image |
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from diffusers.utils import load_image |
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from src.utils import cuda_vis_check, makedirs |
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n_gpus1 = torch.cuda.device_count() if torch.cuda.is_available() else 0 |
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n_gpus1, gpu_ids = cuda_vis_check(n_gpus1) |
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def get_device(gpu_id): |
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if gpu_id == 'auto': |
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device = 'cpu' if n_gpus1 == 0 else 'cuda:0' |
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else: |
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device = 'cpu' if n_gpus1 == 0 else 'cuda:%s' % gpu_id |
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return device |
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def get_pipe_make_image(gpu_id='auto'): |
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device = get_device(gpu_id) |
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pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16").to(device) |
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return pipe |
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def make_image(prompt, filename=None, gpu_id='auto', pipe=None): |
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if pipe is None: |
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pipe = get_pipe_make_image(gpu_id=gpu_id) |
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lock_type = 'image' |
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base_path = os.path.join('locks', 'image_locks') |
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base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True) |
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lock_file = os.path.join(base_path, "%s.lock" % lock_type) |
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makedirs(os.path.dirname(lock_file)) |
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with filelock.FileLock(lock_file): |
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image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0] |
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if filename: |
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image.save(filename) |
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return filename |
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return image |
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def get_pipe_change_image(gpu_id='auto'): |
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device = get_device(gpu_id) |
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pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16").to(device) |
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return pipe |
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def change_image(prompt, init_image=None, init_file=None, filename=None, gpu_id='auto', pipe=None): |
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if pipe is None: |
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pipe = get_pipe_change_image(gpu_id) |
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if init_file: |
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init_image = load_image(init_file).resize((512, 512)) |
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image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0] |
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if filename: |
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image.save(filename) |
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return filename |
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else: |
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return image |
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