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Read stable diffusion format prompts from PNGs #41

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merged 1 commit into from
Jan 6, 2024
Merged

Read stable diffusion format prompts from PNGs #41

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40 changes: 30 additions & 10 deletions demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,12 @@
import numpy as np
import cv2
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
from generate_video_tool.pano_video_generation import generate_video
from PIL import Image
from exiftool import ExifToolHelper
from datetime import datetime

torch.manual_seed(0)

def get_K_R(FOV, THETA, PHI, height, width):
Expand Down Expand Up @@ -65,26 +69,41 @@ def resize_and_center_crop(img, size):
config_file = 'configs/pano_generation.yaml'
config = yaml.load(open(config_file, 'rb'), Loader=yaml.SafeLoader)
model = PanoGenerator(config)
model.load_state_dict(torch.load('weights/pano.ckpt', map_location='cpu')[
'state_dict'], strict=True)
model.load_state_dict(torch.load('weights/pano.ckpt', map_location='cpu')['state_dict'], strict=True)
#saved_ckpt = torch.load('weights/pano.ckpt', map_location='cpu')
#model.load_state_dict(saved_ckpt, strict=False)
model=model.cuda()
img=None
else:

config_file = 'configs/pano_generation_outpaint.yaml'
config = yaml.load(open(config_file, 'rb'), Loader=yaml.SafeLoader)
model = PanoOutpaintGenerator(config)
model.load_state_dict(torch.load('weights/pano_outpaint.ckpt', map_location='cpu')[
'state_dict'], strict=True)
model.load_state_dict(torch.load('weights/pano_outpaint.ckpt', map_location='cpu')['state_dict'], strict=True)
#saved_ckpt = torch.load('weights/pano_outpaint.ckpt', map_location='cpu')
#model.load_state_dict(saved_ckpt, strict=False)
model=model.cuda()

img=cv2.imread(args.image_path)
img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img=resize_and_center_crop(img, config['dataset']['resolution'])
img=img/127.5-1

img=img/127.5-1
img=torch.tensor(img).cuda()

#read stable diffusion prompts from PNG
with ExifToolHelper() as et:
#print EXIF metadata
EXIF_dict = et.get_metadata(args.image_path)
print(f'Metadata: {EXIF_dict}')
if 'PNG:Parameters' in EXIF_dict[0]:
PNGparameters = EXIF_dict[0]['PNG:Parameters']
parsed_parameters = PNGparameters.split("\n")
positive_prompt = parsed_parameters[0]
negative_prompt = parsed_parameters[1]
print(f'Positive promts: ' + positive_prompt)
print(f'Negative promts: ' + negative_prompt)
args.text= positive_prompt

resolution=config['dataset']['resolution']
Rs=[]
Ks=[]
Expand All @@ -107,7 +126,7 @@ def resize_and_center_crop(img, size):
for i, line in enumerate(f):
prompt.append(line.strip())
if len(prompt)<8:
raise ValueError('text file should contain 8 lines')
raise ValueError('text file should contain 8 lines for each camera view')
args.text=prompt[0]
else:
prompt=[args.text]*8
Expand All @@ -121,11 +140,12 @@ def resize_and_center_crop(img, size):
'K': K
}
images_pred=model.inference(batch)
res_dir=args.text[:20]
print('save in fold: {}'.format(res_dir))
#res_dir=os.path.join('outputs/',args.text[:20])
res_dir=os.path.join('outputs/',f'results'+datetime.now().strftime('--%Y%m%d-%H%M%S'))
print('saved to the folder: {}'.format(res_dir))
os.makedirs(res_dir, exist_ok=True)
with open(os.path.join(res_dir, 'prompt.txt'), 'w') as f:
f.write(args.text)
f.write(args.text)
image_paths=[]
for i in range(8):
im = Image.fromarray(images_pred[0,i])
Expand Down