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