File size: 4,314 Bytes
45a63fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | import torch
import argparse
import glob, os
from diffusers import FluxInpaintPipeline, FluxFillPipeline
from diffusers.utils import load_image
from utils import MediapipeEngine, ImageCaptioner
# inpainting pipeline
class HandFixerPipeline:
def __init__(self,
flux_model_path="black-forest-labs/FLUX.1-dev"):
self.engine = MediapipeEngine()
self.captioner = ImageCaptioner()
self.pipe = FluxInpaintPipeline.from_pretrained(
flux_model_path,
torch_dtype=torch.bfloat16)
self.pipe.enable_model_cpu_offload()
def __call__(self, image_path,
prompt='hand',
strength=0.8,
**kwargs):
# prepare image and mask
image, mask = self.engine(load_image(image_path))
width, height = image.size
# prepare prompt
prompt = self.captioner.generate_caption(image, "")
fixed_image = self.pipe(prompt = prompt,
image = image,
mask_image = mask,
width = width,
height = height,
strength=strength, **kwargs,
).images[0]
return fixed_image
# fill pipeline
class HandFixerFillPipeline:
def __init__(self,
flux_model_path="black-forest-labs/FLUX.1-fill-dev"):
self.engine = MediapipeEngine()
self.captioner = ImageCaptioner()
self.pipe = FluxFillPipeline.from_pretrained(
flux_model_path,
torch_dtype=torch.bfloat16)
self.pipe.enable_model_cpu_offload()
def __call__(self, image_path,
prompt='hand',
strength=0.8,
**kwargs):
# prepare image and mask
image, mask = self.engine(load_image(image_path))
width, height = image.size
# prepare prompt
prompt = self.captioner.generate_caption(image, "")
fixed_image = self.pipe(prompt = prompt,
image = image,
mask_image = mask,
width = width,
height = height,
# strength=strength,
**kwargs,
).images[0]
return fixed_image
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default='black-forest-labs/FLUX.1-dev',
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pipeline",
type=str,
choices=['inpaint', 'fill'],
default='inpaint',
help="Choose the pipeline to use: 'inpaint' or 'fill'",
)
parser.add_argument(
"--input_dir",
type=str,
required=True,
default=None,
)
parser.add_argument(
"--output_dir",
type=str,
default='outputs',
)
return parser.parse_args(input_args)
if __name__ == "__main__":
args = parse_args()
input_dir = args.input_dir
# 定义常见的图片格式
image_extensions = ['*.jpg', '*.jpeg', '*.png', '*.bmp', '*.gif', '*.tiff']
# 使用列表推导式和glob来获取所有图片路径
image_paths = [
path for ext in image_extensions
for path in glob.glob(os.path.join(input_dir, ext))
]
# 打印找到的图片路径（可选）
print(f"Found {len(image_paths)} images in {input_dir}:")
# 根据参数选择pipeline
if args.pipeline == 'inpaint':
hand_fixer = HandFixerPipeline(args.pretrained_model_name_or_path)
elif args.pipeline == 'fill': # 'fill'
hand_fixer = HandFixerFillPipeline(args.pretrained_model_name_or_path)
else:
raise ValueError("Invalid pipeline choice. Must be 'inpaint' or 'fill'.")
os.makedirs(args.output_dir, exist_ok=True)
for path in image_paths:
fixed_image = hand_fixer(path, strength=0.8)
output_path = os.path.join(args.output_dir, os.path.basename(path))
# 保存图像
fixed_image.save(output_path) |