def set_text(self, text):
self.text = text
textpath = self.text.replace(' ', '_')
self.textpath = textpath
self.filename = Path(f'./{textpath}.png')
self.encoded_text = tokenize(text).cuda()
def set_text(self, text):
self.text = text
textpath = self.text.replace(' ','_')[:255]
if self.save_date_time:
textpath = datetime.now().strftime("%y%m%d-%H%M%S-") + textpath
self.textpath = textpath
self.filename = Path(f'./{textpath}.png')
self.encoded_text = tokenize(text).cuda()
def set_text(self, text):
self.text = text
textpath = self.text.replace(' ','_').replace('.','_')[:30]
#textpath = datetime.now().strftime("%Y%m%d-%H%M%S-") + textpath
if exists(self.seed):
textpath = str(self.seed) + '-' + textpath
self.textpath = textpath
self.filename = Path(f'./{textpath}.png')
self.encoded_text = tokenize(text).cuda()
def __init__(
self,
text,
*,
lr = .07,
image_size = 512,
gradient_accumulate_every = 1,
save_every = 50,
epochs = 20,
iterations = 1050,
save_progress = False,
bilinear = False,
open_folder = True,
seed = None
):
super().__init__()
if exists(seed):
torch.manual_seed(seed)
self.epochs = epochs
self.iterations = iterations
model = BigSleep(
image_size = image_size,
bilinear = bilinear
).cuda()
self.model = model
self.optimizer = Adam(model.model.latents.parameters(), lr)
self.gradient_accumulate_every = gradient_accumulate_every
self.save_every = save_every
self.text = text
textpath = self.text.replace(' ','_')
self.textpath = textpath
self.filename = Path(f'./{textpath}.png')
self.save_progress = save_progress
self.encoded_text = tokenize(text).cuda()
self.open_folder = open_folder
def create_text_encoding(self, text):
tokenized_text = tokenize(text).cuda()
with torch.no_grad():
text_encoding = self.model.perceptor.encode_text(tokenized_text).detach()
return text_encoding
def encode_one_phrase(self, phrase):
return perceptor.encode_text(
tokenize(f'''{phrase}''').cuda()).detach().clone()