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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,
image_size="1024x1024", image_quality='standard',
image_num_inference_steps=1, image_guidance_scale=0.0):
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 = 1
image_size = "512x512"
elif image_quality == 'standard':
image_num_inference_steps = 2
elif image_quality == 'hd':
image_num_inference_steps = 3
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, # more than 1 not really helpful
guidance_scale=0.0, # disabled: https://huggingface.co/stabilityai/sdxl-turbo#diffusers
).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
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