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()