| from .base_prompter import BasePrompter | |
| from ..models.flux_text_encoder import FluxTextEncoder2 | |
| from transformers import T5TokenizerFast | |
| import os | |
| class CogPrompter(BasePrompter): | |
| def __init__( | |
| self, | |
| tokenizer_path=None | |
| ): | |
| if tokenizer_path is None: | |
| base_path = os.path.dirname(os.path.dirname(__file__)) | |
| tokenizer_path = os.path.join(base_path, "tokenizer_configs/cog/tokenizer") | |
| super().__init__() | |
| self.tokenizer = T5TokenizerFast.from_pretrained(tokenizer_path) | |
| self.text_encoder: FluxTextEncoder2 = None | |
| def fetch_models(self, text_encoder: FluxTextEncoder2 = None): | |
| self.text_encoder = text_encoder | |
| def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): | |
| input_ids = tokenizer( | |
| prompt, | |
| return_tensors="pt", | |
| padding="max_length", | |
| max_length=max_length, | |
| truncation=True, | |
| ).input_ids.to(device) | |
| prompt_emb = text_encoder(input_ids) | |
| prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) | |
| return prompt_emb | |
| def encode_prompt( | |
| self, | |
| prompt, | |
| positive=True, | |
| device="cuda" | |
| ): | |
| prompt = self.process_prompt(prompt, positive=positive) | |
| prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder, self.tokenizer, 226, device) | |
| return prompt_emb | |