import os import filelock from diffusers import FluxPipeline import torch from src.utils import makedirs from src.vision.sdxl_turbo import get_device def get_pipe_make_image(gpu_id): device = get_device(gpu_id) pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, ).to(device) return pipe def get_pipe_make_image_2(gpu_id): device = get_device(gpu_id) pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16, ).to(device) return pipe def make_image(prompt, filename=None, gpu_id='auto', pipe=None, image_guidance_scale=3.0, image_size="1024x1024", image_quality='standard', image_num_inference_steps=50, max_sequence_length=512): if pipe is None: pipe = get_pipe_make_image(gpu_id=gpu_id) if image_quality == 'manual': # listen to guidance_scale and num_inference_steps passed in pass else: if image_quality == 'quick': image_num_inference_steps = 10 image_size = "512x512" elif image_quality == 'standard': image_num_inference_steps = 20 elif image_quality == 'hd': image_num_inference_steps = 50 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, height=int(image_size.lower().split('x')[0]), width=int(image_size.lower().split('x')[1]), num_inference_steps=image_num_inference_steps, max_sequence_length=max_sequence_length, guidance_scale=image_guidance_scale).images[0] if filename: image.save(filename) return filename return image